diff --git a/docker/requirements/linux-amd64-py3.11-mlflow-tracking-requirements.txt b/docker/requirements/linux-amd64-py3.11-mlflow-tracking-requirements.txt index 9ed827eef..06a660b60 100644 --- a/docker/requirements/linux-amd64-py3.11-mlflow-tracking-requirements.txt +++ b/docker/requirements/linux-amd64-py3.11-mlflow-tracking-requirements.txt @@ -8,11 +8,11 @@ alembic==1.13.1 # via mlflow aniso8601==9.0.1 # via graphene -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via -r docker/pip-tools/mlflow-tracking-requirements.in -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -60,7 +60,7 @@ importlib-metadata==7.1.0 # via mlflow itsdangerous==2.2.0 # via flask -jinja2==3.1.3 +jinja2==3.1.4 # via # flask # mlflow @@ -68,7 +68,7 @@ jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn kiwisolver==1.4.5 # via matplotlib @@ -145,7 +145,7 @@ six==1.16.0 # querystring-parser smmap==5.0.1 # via gitdb -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # mlflow @@ -164,7 +164,7 @@ urllib3==2.2.1 # botocore # docker # requests -werkzeug==3.0.2 +werkzeug==3.0.3 # via flask zipp==3.18.1 # via importlib-metadata diff --git a/docker/requirements/linux-amd64-py3.11-pytorch-cpu-requirements.txt b/docker/requirements/linux-amd64-py3.11-pytorch-cpu-requirements.txt index a7103f741..e620278ee 100644 --- a/docker/requirements/linux-amd64-py3.11-pytorch-cpu-requirements.txt +++ b/docker/requirements/linux-amd64-py3.11-pytorch-cpu-requirements.txt @@ -10,6 +10,12 @@ absl-py==2.1.0 # via tensorboard adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) +aiohttp==3.9.5 + # via + # datasets + # fsspec +aiosignal==1.3.1 + # via aiohttp alembic==1.13.1 # via # dioptra (pyproject.toml) @@ -23,13 +29,14 @@ async-timeout==4.0.3 # via dioptra (pyproject.toml) attrs==23.2.0 # via + # aiohttp # jsonschema # referencing -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -44,6 +51,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # prefect # rq cloudpickle==3.0.0 @@ -52,17 +60,25 @@ cloudpickle==3.0.0 # distributed # mlflow # prefect +colorama==0.4.6 + # via pretty-errors contourpy==1.2.1 # via matplotlib croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect -distributed==2024.4.2 +datasets==2.19.1 + # via maite +dill==0.3.8 + # via + # datasets + # multiprocess +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -74,7 +90,10 @@ entrypoints==0.4 # mlflow filelock==3.14.0 # via + # datasets + # huggingface-hub # torch + # transformers # triton flask==3.0.3 # via @@ -87,7 +106,7 @@ flask==3.0.3 # mlflow flask-accepts==0.18.4 # via dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via dioptra (pyproject.toml) flask-login==0.6.3 # via dioptra (pyproject.toml) @@ -103,9 +122,15 @@ flask-sqlalchemy==3.1.1 # flask-migrate fonttools==4.51.0 # via matplotlib -fsspec==2024.3.1 +frozenlist==1.4.1 + # via + # aiohttp + # aiosignal +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub # torch gitdb==4.0.11 # via gitpython @@ -121,12 +146,21 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 - # via requests + # via + # requests + # yarl imageio==2.34.1 # via # imgaug @@ -145,7 +179,7 @@ injector==0.21.0 # via dioptra (pyproject.toml) itsdangerous==2.2.0 # via flask -jinja2==3.1.3 +jinja2==3.1.4 # via # distributed # flask @@ -155,9 +189,9 @@ jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn -jsonschema==4.21.1 +jsonschema==4.22.0 # via # dioptra (pyproject.toml) # flask-restx @@ -167,10 +201,14 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -184,7 +222,7 @@ markupsafe==2.1.5 # jinja2 # mako # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra (pyproject.toml) # flask-accepts @@ -203,31 +241,42 @@ mdurl==0.1.2 # via markdown-it-py mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) mpmath==1.3.0 # via sympy msgpack==1.0.8 # via # distributed # prefect +multidict==6.0.5 + # via + # aiohttp + # yarl multimethod==1.11.2 # via dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect networkx==3.3 # via # scikit-image # torch -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -244,7 +293,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics # torchvision + # transformers nvidia-cublas-cu12==12.1.3.1 # via # nvidia-cudnn-cu12 @@ -285,20 +336,26 @@ opencv-python==4.9.0.80 packaging==24.0 # via # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow # prefect # scikit-image + # torchmetrics + # transformers pandas==2.2.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via dioptra (pyproject.toml) @@ -316,6 +373,8 @@ pillow==10.3.0 # torchvision prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics protobuf==5.26.1 # via # mlflow @@ -326,15 +385,19 @@ psycopg2-binary==2.9.9 # via dioptra (pyproject.toml) pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk -pygments==2.17.2 +pygments==2.18.0 # via rich pyparsing==3.1.2 # via matplotlib @@ -364,37 +427,52 @@ pytz==2024.1 pyyaml==6.0.1 # via # dask + # datasets # dioptra (pyproject.toml) # distributed + # huggingface-hub # mlflow # prefect + # timm + # transformers querystring-parser==1.2.4 # via mlflow redis==5.0.4 # via # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications +regex==2024.4.28 + # via transformers requests==2.31.0 # via + # datasets # dioptra (pyproject.toml) # docker + # huggingface-hub # mlflow # prefect # smqtk-dataprovider + # transformers rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -455,7 +533,7 @@ smqtk-image-io==0.17.1 # smqtk-detection sortedcontainers==2.4.0 # via distributed -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra (pyproject.toml) @@ -475,16 +553,24 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask @@ -493,26 +579,46 @@ toolz==0.12.1 torch==2.2.2 # via # -r requirements-dev-pytorch.in + # maite + # timm # torchaudio + # torchmetrics # torchvision torchaudio==2.2.2 # via -r requirements-dev-pytorch.in +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite torchvision==0.17.2 - # via -r requirements-dev-pytorch.in + # via + # -r requirements-dev-pytorch.in + # maite + # timm tornado==6.4 # via distributed -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub + # maite # nrtk + # transformers +transformers==4.40.2 + # via maite triton==2.2.0 # via torch typing-extensions==4.11.0 # via # alembic # dioptra (pyproject.toml) + # huggingface-hub + # lightning-utilities + # maite # sqlalchemy # torch + # torcheval tzdata==2024.1 # via # pandas @@ -524,7 +630,7 @@ urllib3==2.2.1 # docker # prefect # requests -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra (pyproject.toml) # flask @@ -532,6 +638,10 @@ werkzeug==3.0.2 # flask-login # flask-restx # tensorboard +xxhash==3.4.1 + # via datasets +yarl==1.9.4 + # via aiohttp zict==3.0.0 # via distributed zipp==3.18.1 diff --git a/docker/requirements/linux-amd64-py3.11-pytorch-gpu-requirements.txt b/docker/requirements/linux-amd64-py3.11-pytorch-gpu-requirements.txt index 94a084a72..9e59ba701 100644 --- a/docker/requirements/linux-amd64-py3.11-pytorch-gpu-requirements.txt +++ b/docker/requirements/linux-amd64-py3.11-pytorch-gpu-requirements.txt @@ -10,6 +10,12 @@ absl-py==2.1.0 # via tensorboard adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) +aiohttp==3.9.5 + # via + # datasets + # fsspec +aiosignal==1.3.1 + # via aiohttp alembic==1.13.1 # via # dioptra (pyproject.toml) @@ -23,13 +29,14 @@ async-timeout==4.0.3 # via dioptra (pyproject.toml) attrs==23.2.0 # via + # aiohttp # jsonschema # referencing -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -44,6 +51,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # prefect # rq cloudpickle==3.0.0 @@ -52,17 +60,25 @@ cloudpickle==3.0.0 # distributed # mlflow # prefect +colorama==0.4.6 + # via pretty-errors contourpy==1.2.1 # via matplotlib croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect -distributed==2024.4.2 +datasets==2.19.1 + # via maite +dill==0.3.8 + # via + # datasets + # multiprocess +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -74,7 +90,10 @@ entrypoints==0.4 # mlflow filelock==3.14.0 # via + # datasets + # huggingface-hub # torch + # transformers # triton flask==3.0.3 # via @@ -87,7 +106,7 @@ flask==3.0.3 # mlflow flask-accepts==0.18.4 # via dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via dioptra (pyproject.toml) flask-login==0.6.3 # via dioptra (pyproject.toml) @@ -103,9 +122,15 @@ flask-sqlalchemy==3.1.1 # flask-migrate fonttools==4.51.0 # via matplotlib -fsspec==2024.3.1 +frozenlist==1.4.1 + # via + # aiohttp + # aiosignal +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub # torch gitdb==4.0.11 # via gitpython @@ -121,12 +146,21 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 - # via requests + # via + # requests + # yarl imageio==2.34.1 # via # imgaug @@ -145,7 +179,7 @@ injector==0.21.0 # via dioptra (pyproject.toml) itsdangerous==2.2.0 # via flask -jinja2==3.1.3 +jinja2==3.1.4 # via # distributed # flask @@ -155,9 +189,9 @@ jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn -jsonschema==4.21.1 +jsonschema==4.22.0 # via # dioptra (pyproject.toml) # flask-restx @@ -167,10 +201,14 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -184,7 +222,7 @@ markupsafe==2.1.5 # jinja2 # mako # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra (pyproject.toml) # flask-accepts @@ -203,31 +241,42 @@ mdurl==0.1.2 # via markdown-it-py mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) mpmath==1.3.0 # via sympy msgpack==1.0.8 # via # distributed # prefect +multidict==6.0.5 + # via + # aiohttp + # yarl multimethod==1.11.2 # via dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect networkx==3.3 # via # scikit-image # torch -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -244,7 +293,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics # torchvision + # transformers nvidia-cublas-cu12==12.1.3.1 # via # nvidia-cudnn-cu12 @@ -285,20 +336,26 @@ opencv-python==4.9.0.80 packaging==24.0 # via # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow # prefect # scikit-image + # torchmetrics + # transformers pandas==2.2.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via dioptra (pyproject.toml) @@ -316,6 +373,8 @@ pillow==10.3.0 # torchvision prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics protobuf==5.26.1 # via # mlflow @@ -326,15 +385,19 @@ psycopg2-binary==2.9.9 # via dioptra (pyproject.toml) pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk -pygments==2.17.2 +pygments==2.18.0 # via rich pyparsing==3.1.2 # via matplotlib @@ -364,37 +427,52 @@ pytz==2024.1 pyyaml==6.0.1 # via # dask + # datasets # dioptra (pyproject.toml) # distributed + # huggingface-hub # mlflow # prefect + # timm + # transformers querystring-parser==1.2.4 # via mlflow redis==5.0.4 # via # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications +regex==2024.4.28 + # via transformers requests==2.31.0 # via + # datasets # dioptra (pyproject.toml) # docker + # huggingface-hub # mlflow # prefect # smqtk-dataprovider + # transformers rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -455,7 +533,7 @@ smqtk-image-io==0.17.1 # smqtk-detection sortedcontainers==2.4.0 # via distributed -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra (pyproject.toml) @@ -475,16 +553,24 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask @@ -493,26 +579,46 @@ toolz==0.12.1 torch==2.2.2 # via # -r requirements-dev-pytorch-gpu.in + # maite + # timm # torchaudio + # torchmetrics # torchvision torchaudio==2.2.2 # via -r requirements-dev-pytorch-gpu.in +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite torchvision==0.17.2 - # via -r requirements-dev-pytorch-gpu.in + # via + # -r requirements-dev-pytorch-gpu.in + # maite + # timm tornado==6.4 # via distributed -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub + # maite # nrtk + # transformers +transformers==4.40.2 + # via maite triton==2.2.0 # via torch typing-extensions==4.11.0 # via # alembic # dioptra (pyproject.toml) + # huggingface-hub + # lightning-utilities + # maite # sqlalchemy # torch + # torcheval tzdata==2024.1 # via # pandas @@ -524,7 +630,7 @@ urllib3==2.2.1 # docker # prefect # requests -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra (pyproject.toml) # flask @@ -532,6 +638,10 @@ werkzeug==3.0.2 # flask-login # flask-restx # tensorboard +xxhash==3.4.1 + # via datasets +yarl==1.9.4 + # via aiohttp zict==3.0.0 # via distributed zipp==3.18.1 diff --git a/docker/requirements/linux-amd64-py3.11-restapi-requirements.txt b/docker/requirements/linux-amd64-py3.11-restapi-requirements.txt index 4cccd2dc6..63f5c5f57 100644 --- a/docker/requirements/linux-amd64-py3.11-restapi-requirements.txt +++ b/docker/requirements/linux-amd64-py3.11-restapi-requirements.txt @@ -16,11 +16,11 @@ attrs==23.2.0 # via # jsonschema # referencing -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -50,7 +50,7 @@ flask==3.0.3 # flask-sqlalchemy flask-accepts==0.18.4 # via dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via dioptra (pyproject.toml) flask-login==0.6.3 # via dioptra (pyproject.toml) @@ -82,13 +82,13 @@ injector==0.21.0 # via dioptra (pyproject.toml) itsdangerous==2.2.0 # via flask -jinja2==3.1.3 +jinja2==3.1.4 # via flask jmespath==1.0.1 # via # boto3 # botocore -jsonschema==4.21.1 +jsonschema==4.22.0 # via # dioptra (pyproject.toml) # flask-restx @@ -101,7 +101,7 @@ markupsafe==2.1.5 # jinja2 # mako # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra (pyproject.toml) # flask-accepts @@ -149,7 +149,7 @@ redis==5.0.4 # via # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications @@ -157,11 +157,11 @@ requests==2.31.0 # via # dioptra (pyproject.toml) # mlflow-skinny -rpds-py==0.18.0 +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 @@ -173,7 +173,7 @@ six==1.16.0 # via python-dateutil smmap==5.0.1 # via gitdb -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra (pyproject.toml) @@ -193,7 +193,7 @@ urllib3==2.2.1 # via # botocore # requests -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra (pyproject.toml) # flask diff --git a/docker/requirements/linux-amd64-py3.11-tensorflow2-cpu-requirements.txt b/docker/requirements/linux-amd64-py3.11-tensorflow2-cpu-requirements.txt index d920ddff8..f110b79a7 100644 --- a/docker/requirements/linux-amd64-py3.11-tensorflow2-cpu-requirements.txt +++ b/docker/requirements/linux-amd64-py3.11-tensorflow2-cpu-requirements.txt @@ -11,6 +11,12 @@ absl-py==2.1.0 # tensorflow adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) +aiohttp==3.9.5 + # via + # datasets + # fsspec +aiosignal==1.3.1 + # via aiohttp alembic==1.13.1 # via # dioptra (pyproject.toml) @@ -26,13 +32,14 @@ async-timeout==4.0.3 # via dioptra (pyproject.toml) attrs==23.2.0 # via + # aiohttp # jsonschema # referencing -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -47,6 +54,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # prefect # rq cloudpickle==3.0.0 @@ -55,17 +63,25 @@ cloudpickle==3.0.0 # distributed # mlflow # prefect +colorama==0.4.6 + # via pretty-errors contourpy==1.2.1 # via matplotlib croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect -distributed==2024.4.2 +datasets==2.19.1 + # via maite +dill==0.3.8 + # via + # datasets + # multiprocess +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -75,6 +91,13 @@ entrypoints==0.4 # via # dioptra (pyproject.toml) # mlflow +filelock==3.14.0 + # via + # datasets + # huggingface-hub + # torch + # transformers + # triton flask==3.0.3 # via # dioptra (pyproject.toml) @@ -86,7 +109,7 @@ flask==3.0.3 # mlflow flask-accepts==0.18.4 # via dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via dioptra (pyproject.toml) flask-login==0.6.3 # via dioptra (pyproject.toml) @@ -104,8 +127,16 @@ flatbuffers==24.3.25 # via tensorflow fonttools==4.51.0 # via matplotlib -fsspec==2024.3.1 - # via dask +frozenlist==1.4.1 + # via + # aiohttp + # aiosignal +fsspec[http]==2024.3.1 + # via + # dask + # datasets + # huggingface-hub + # torch gast==0.5.4 # via tensorflow gitdb==4.0.11 @@ -124,7 +155,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via # tensorboard # tensorflow @@ -134,8 +165,17 @@ h5py==3.11.0 # via # keras # tensorflow +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 - # via requests + # via + # requests + # yarl imageio==2.34.1 # via # imgaug @@ -154,18 +194,19 @@ injector==0.21.0 # via dioptra (pyproject.toml) itsdangerous==2.2.0 # via flask -jinja2==3.1.3 +jinja2==3.1.4 # via # distributed # flask # mlflow + # torch jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn -jsonschema==4.21.1 +jsonschema==4.22.0 # via # dioptra (pyproject.toml) # flask-restx @@ -179,10 +220,14 @@ lazy-loader==0.4 # via scikit-image libclang==18.1.1 # via tensorflow +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -196,7 +241,7 @@ markupsafe==2.1.5 # jinja2 # mako # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra (pyproject.toml) # flask-accepts @@ -219,32 +264,47 @@ ml-dtypes==0.3.2 # tensorflow mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed # prefect +multidict==6.0.5 + # via + # aiohttp + # yarl multimethod==1.11.2 # via dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect namex==0.0.8 # via keras networkx==3.3 - # via scikit-image -nrtk==0.3.1 + # via + # scikit-image + # torch +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra (pyproject.toml) # h5py # imageio # imgaug # keras + # maite # matplotlib # ml-dtypes # mlflow + # modelscan # nrtk # opencv-python # opt-einsum @@ -263,6 +323,40 @@ numpy==1.26.4 # tensorboard # tensorflow # tifffile + # torchmetrics + # torchvision + # transformers +nvidia-cublas-cu12==12.1.3.1 + # via + # nvidia-cudnn-cu12 + # nvidia-cusolver-cu12 + # torch +nvidia-cuda-cupti-cu12==12.1.105 + # via torch +nvidia-cuda-nvrtc-cu12==12.1.105 + # via torch +nvidia-cuda-runtime-cu12==12.1.105 + # via torch +nvidia-cudnn-cu12==8.9.2.26 + # via torch +nvidia-cufft-cu12==11.0.2.54 + # via torch +nvidia-curand-cu12==10.3.2.106 + # via torch +nvidia-cusolver-cu12==11.4.5.107 + # via torch +nvidia-cusparse-cu12==12.1.0.106 + # via + # nvidia-cusolver-cu12 + # torch +nvidia-nccl-cu12==2.20.5 + # via torch +nvidia-nvjitlink-cu12==12.4.127 + # via + # nvidia-cusolver-cu12 + # nvidia-cusparse-cu12 +nvidia-nvtx-cu12==12.1.105 + # via torch opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -276,21 +370,27 @@ optree==0.11.0 packaging==24.0 # via # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow # prefect # scikit-image # tensorflow + # torchmetrics + # transformers pandas==2.2.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via dioptra (pyproject.toml) @@ -305,8 +405,11 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics protobuf==4.25.3 # via # mlflow @@ -318,15 +421,19 @@ psycopg2-binary==2.9.9 # via dioptra (pyproject.toml) pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk -pygments==2.17.2 +pygments==2.18.0 # via rich pyparsing==3.1.2 # via matplotlib @@ -356,40 +463,54 @@ pytz==2024.1 pyyaml==6.0.1 # via # dask + # datasets # dioptra (pyproject.toml) # distributed + # huggingface-hub # mlflow # prefect + # timm + # transformers querystring-parser==1.2.4 # via mlflow redis==5.0.4 # via # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications +regex==2024.4.28 + # via transformers requests==2.31.0 # via + # datasets # dioptra (pyproject.toml) # docker + # huggingface-hub # mlflow # prefect # smqtk-dataprovider # tensorflow + # transformers rich==13.7.1 # via # dioptra (pyproject.toml) # keras -rpds-py==0.18.0 + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -453,7 +574,7 @@ smqtk-image-io==0.17.1 # smqtk-detection sortedcontainers==2.4.0 # via distributed -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra (pyproject.toml) @@ -463,6 +584,8 @@ sqlparse==0.5.0 # via mlflow structlog==24.1.0 # via dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -475,38 +598,73 @@ tensorboard-data-server==0.7.2 # via tensorboard tensorflow==2.16.1 # via -r requirements-dev-tensorflow.in -tensorflow-io-gcs-filesystem==0.36.0 - # via tensorflow +tensorflow-io-gcs-filesystem==0.34.0 + # via + # modelscan + # tensorflow termcolor==2.4.0 # via tensorflow text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.3.0 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.18.0 + # via + # maite + # timm tornado==6.4 # via distributed -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub + # maite # nrtk + # transformers +transformers==4.40.2 + # via maite +triton==2.3.0 + # via torch typing-extensions==4.11.0 # via # alembic # dioptra (pyproject.toml) + # huggingface-hub + # lightning-utilities + # maite # optree # sqlalchemy # tensorflow + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -518,7 +676,7 @@ urllib3==2.2.1 # docker # prefect # requests -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra (pyproject.toml) # flask @@ -530,6 +688,10 @@ wheel==0.43.0 # via astunparse wrapt==1.16.0 # via tensorflow +xxhash==3.4.1 + # via datasets +yarl==1.9.4 + # via aiohttp zict==3.0.0 # via distributed zipp==3.18.1 diff --git a/docker/requirements/linux-amd64-py3.11-tensorflow2-gpu-requirements.txt b/docker/requirements/linux-amd64-py3.11-tensorflow2-gpu-requirements.txt index 16a0759fa..f53cb5d31 100644 --- a/docker/requirements/linux-amd64-py3.11-tensorflow2-gpu-requirements.txt +++ b/docker/requirements/linux-amd64-py3.11-tensorflow2-gpu-requirements.txt @@ -11,6 +11,12 @@ absl-py==2.1.0 # tensorflow adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) +aiohttp==3.9.5 + # via + # datasets + # fsspec +aiosignal==1.3.1 + # via aiohttp alembic==1.13.1 # via # dioptra (pyproject.toml) @@ -26,13 +32,14 @@ async-timeout==4.0.3 # via dioptra (pyproject.toml) attrs==23.2.0 # via + # aiohttp # jsonschema # referencing -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -47,6 +54,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # prefect # rq cloudpickle==3.0.0 @@ -55,17 +63,27 @@ cloudpickle==3.0.0 # distributed # mlflow # prefect +cmake==3.29.2 + # via triton +colorama==0.4.6 + # via pretty-errors contourpy==1.2.1 # via matplotlib croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect -distributed==2024.4.2 +datasets==2.19.1 + # via maite +dill==0.3.8 + # via + # datasets + # multiprocess +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -75,6 +93,13 @@ entrypoints==0.4 # via # dioptra (pyproject.toml) # mlflow +filelock==3.14.0 + # via + # datasets + # huggingface-hub + # torch + # transformers + # triton flask==3.0.3 # via # dioptra (pyproject.toml) @@ -86,7 +111,7 @@ flask==3.0.3 # mlflow flask-accepts==0.18.4 # via dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via dioptra (pyproject.toml) flask-login==0.6.3 # via dioptra (pyproject.toml) @@ -104,8 +129,15 @@ flatbuffers==24.3.25 # via tensorflow fonttools==4.51.0 # via matplotlib -fsspec==2024.3.1 - # via dask +frozenlist==1.4.1 + # via + # aiohttp + # aiosignal +fsspec[http]==2024.3.1 + # via + # dask + # datasets + # huggingface-hub gast==0.5.4 # via tensorflow gitdb==4.0.11 @@ -124,7 +156,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via # tensorboard # tensorflow @@ -134,8 +166,17 @@ h5py==3.11.0 # via # keras # tensorflow +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 - # via requests + # via + # requests + # yarl imageio==2.34.1 # via # imgaug @@ -154,18 +195,19 @@ injector==0.21.0 # via dioptra (pyproject.toml) itsdangerous==2.2.0 # via flask -jinja2==3.1.3 +jinja2==3.1.4 # via # distributed # flask # mlflow + # torch jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn -jsonschema==4.21.1 +jsonschema==4.22.0 # via # dioptra (pyproject.toml) # flask-restx @@ -179,10 +221,16 @@ lazy-loader==0.4 # via scikit-image libclang==18.1.1 # via tensorflow +lightning-utilities==0.11.2 + # via torchmetrics +lit==18.1.4 + # via triton locket==1.0.0 # via # distributed # partd +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -196,7 +244,7 @@ markupsafe==2.1.5 # jinja2 # mako # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra (pyproject.toml) # flask-accepts @@ -219,32 +267,47 @@ ml-dtypes==0.3.2 # tensorflow mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed # prefect +multidict==6.0.5 + # via + # aiohttp + # yarl multimethod==1.11.2 # via dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect namex==0.0.8 # via keras networkx==3.3 - # via scikit-image -nrtk==0.3.1 + # via + # scikit-image + # torch +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra (pyproject.toml) # h5py # imageio # imgaug # keras + # maite # matplotlib # ml-dtypes # mlflow + # modelscan # nrtk # opencv-python # opt-einsum @@ -263,33 +326,59 @@ numpy==1.26.4 # tensorboard # tensorflow # tifffile + # torchmetrics + # torchvision + # transformers +nvidia-cublas-cu11==11.10.3.66 + # via + # nvidia-cudnn-cu11 + # nvidia-cusolver-cu11 + # torch nvidia-cublas-cu12==12.3.4.1 # via # nvidia-cudnn-cu12 # nvidia-cusolver-cu12 # tensorflow +nvidia-cuda-cupti-cu11==11.7.101 + # via torch nvidia-cuda-cupti-cu12==12.3.101 # via tensorflow nvidia-cuda-nvcc-cu12==12.3.107 # via tensorflow +nvidia-cuda-nvrtc-cu11==11.7.99 + # via torch nvidia-cuda-nvrtc-cu12==12.3.107 # via # nvidia-cudnn-cu12 # tensorflow +nvidia-cuda-runtime-cu11==11.7.99 + # via torch nvidia-cuda-runtime-cu12==12.3.101 # via tensorflow +nvidia-cudnn-cu11==8.5.0.96 + # via torch nvidia-cudnn-cu12==8.9.7.29 # via tensorflow +nvidia-cufft-cu11==10.9.0.58 + # via torch nvidia-cufft-cu12==11.0.12.1 # via tensorflow +nvidia-curand-cu11==10.2.10.91 + # via torch nvidia-curand-cu12==10.3.4.107 # via tensorflow +nvidia-cusolver-cu11==11.4.0.1 + # via torch nvidia-cusolver-cu12==11.5.4.101 # via tensorflow +nvidia-cusparse-cu11==11.7.4.91 + # via torch nvidia-cusparse-cu12==12.2.0.103 # via # nvidia-cusolver-cu12 # tensorflow +nvidia-nccl-cu11==2.14.3 + # via torch nvidia-nccl-cu12==2.19.3 # via tensorflow nvidia-nvjitlink-cu12==12.3.101 @@ -297,6 +386,8 @@ nvidia-nvjitlink-cu12==12.3.101 # nvidia-cusolver-cu12 # nvidia-cusparse-cu12 # tensorflow +nvidia-nvtx-cu11==11.7.91 + # via torch opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -310,21 +401,27 @@ optree==0.11.0 packaging==24.0 # via # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow # prefect # scikit-image # tensorflow + # torchmetrics + # transformers pandas==2.2.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via dioptra (pyproject.toml) @@ -339,8 +436,11 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics protobuf==4.25.3 # via # mlflow @@ -352,15 +452,19 @@ psycopg2-binary==2.9.9 # via dioptra (pyproject.toml) pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk -pygments==2.17.2 +pygments==2.18.0 # via rich pyparsing==3.1.2 # via matplotlib @@ -390,40 +494,55 @@ pytz==2024.1 pyyaml==6.0.1 # via # dask + # datasets # dioptra (pyproject.toml) # distributed + # huggingface-hub # mlflow # prefect + # timm + # transformers querystring-parser==1.2.4 # via mlflow redis==5.0.4 # via # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications +regex==2024.4.28 + # via transformers requests==2.31.0 # via + # datasets # dioptra (pyproject.toml) # docker + # huggingface-hub # mlflow # prefect # smqtk-dataprovider # tensorflow + # torchvision + # transformers rich==13.7.1 # via # dioptra (pyproject.toml) # keras -rpds-py==0.18.0 + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -487,7 +606,7 @@ smqtk-image-io==0.17.1 # smqtk-detection sortedcontainers==2.4.0 # via distributed -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra (pyproject.toml) @@ -497,6 +616,8 @@ sqlparse==0.5.0 # via mlflow structlog==24.1.0 # via dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -509,38 +630,74 @@ tensorboard-data-server==0.7.2 # via tensorboard tensorflow[and-cuda]==2.16.1 ; sys_platform == "linux" and (platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64") # via -r requirements-dev-tensorflow-gpu.in -tensorflow-io-gcs-filesystem==0.36.0 - # via tensorflow +tensorflow-io-gcs-filesystem==0.34.0 + # via + # modelscan + # tensorflow termcolor==2.4.0 # via tensorflow text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.0.1 + # via + # maite + # timm + # torchmetrics + # torchvision + # triton +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.15.2 + # via + # maite + # timm tornado==6.4 # via distributed -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub + # maite # nrtk + # transformers +transformers==4.40.2 + # via maite +triton==2.0.0 + # via torch typing-extensions==4.11.0 # via # alembic # dioptra (pyproject.toml) + # huggingface-hub + # lightning-utilities + # maite # optree # sqlalchemy # tensorflow + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -552,7 +709,7 @@ urllib3==2.2.1 # docker # prefect # requests -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra (pyproject.toml) # flask @@ -561,9 +718,20 @@ werkzeug==3.0.2 # flask-restx # tensorboard wheel==0.43.0 - # via astunparse + # via + # astunparse + # nvidia-cublas-cu11 + # nvidia-cuda-cupti-cu11 + # nvidia-cuda-runtime-cu11 + # nvidia-curand-cu11 + # nvidia-cusparse-cu11 + # nvidia-nvtx-cu11 wrapt==1.16.0 # via tensorflow +xxhash==3.4.1 + # via datasets +yarl==1.9.4 + # via aiohttp zict==3.0.0 # via distributed zipp==3.18.1 diff --git a/docker/requirements/linux-arm64-py3.11-mlflow-tracking-requirements.txt b/docker/requirements/linux-arm64-py3.11-mlflow-tracking-requirements.txt index 1a7872050..a57a19e6c 100644 --- a/docker/requirements/linux-arm64-py3.11-mlflow-tracking-requirements.txt +++ b/docker/requirements/linux-arm64-py3.11-mlflow-tracking-requirements.txt @@ -8,11 +8,11 @@ alembic==1.13.1 # via mlflow aniso8601==9.0.1 # via graphene -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via -r docker/pip-tools/mlflow-tracking-requirements.in -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -60,7 +60,7 @@ importlib-metadata==7.1.0 # via mlflow itsdangerous==2.2.0 # via flask -jinja2==3.1.3 +jinja2==3.1.4 # via # flask # mlflow @@ -68,7 +68,7 @@ jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn kiwisolver==1.4.5 # via matplotlib @@ -145,7 +145,7 @@ six==1.16.0 # querystring-parser smmap==5.0.1 # via gitdb -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # mlflow @@ -164,7 +164,7 @@ urllib3==2.2.1 # botocore # docker # requests -werkzeug==3.0.2 +werkzeug==3.0.3 # via flask zipp==3.18.1 # via importlib-metadata diff --git a/docker/requirements/linux-arm64-py3.11-pytorch-cpu-requirements.txt b/docker/requirements/linux-arm64-py3.11-pytorch-cpu-requirements.txt index ef470ab31..39158eb36 100644 --- a/docker/requirements/linux-arm64-py3.11-pytorch-cpu-requirements.txt +++ b/docker/requirements/linux-arm64-py3.11-pytorch-cpu-requirements.txt @@ -10,6 +10,12 @@ absl-py==2.1.0 # via tensorboard adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) +aiohttp==3.9.5 + # via + # datasets + # fsspec +aiosignal==1.3.1 + # via aiohttp alembic==1.13.1 # via # dioptra (pyproject.toml) @@ -23,13 +29,14 @@ async-timeout==4.0.3 # via dioptra (pyproject.toml) attrs==23.2.0 # via + # aiohttp # jsonschema # referencing -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -44,6 +51,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # prefect # rq cloudpickle==3.0.0 @@ -52,17 +60,25 @@ cloudpickle==3.0.0 # distributed # mlflow # prefect +colorama==0.4.6 + # via pretty-errors contourpy==1.2.1 # via matplotlib croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect -distributed==2024.4.2 +datasets==2.19.1 + # via maite +dill==0.3.8 + # via + # datasets + # multiprocess +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -73,7 +89,11 @@ entrypoints==0.4 # dioptra (pyproject.toml) # mlflow filelock==3.14.0 - # via torch + # via + # datasets + # huggingface-hub + # torch + # transformers flask==3.0.3 # via # dioptra (pyproject.toml) @@ -85,7 +105,7 @@ flask==3.0.3 # mlflow flask-accepts==0.18.4 # via dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via dioptra (pyproject.toml) flask-login==0.6.3 # via dioptra (pyproject.toml) @@ -101,9 +121,15 @@ flask-sqlalchemy==3.1.1 # flask-migrate fonttools==4.51.0 # via matplotlib -fsspec==2024.3.1 +frozenlist==1.4.1 + # via + # aiohttp + # aiosignal +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub # torch gitdb==4.0.11 # via gitpython @@ -119,12 +145,21 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 - # via requests + # via + # requests + # yarl imageio==2.34.1 # via # imgaug @@ -143,7 +178,7 @@ injector==0.21.0 # via dioptra (pyproject.toml) itsdangerous==2.2.0 # via flask -jinja2==3.1.3 +jinja2==3.1.4 # via # distributed # flask @@ -153,9 +188,9 @@ jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn -jsonschema==4.21.1 +jsonschema==4.22.0 # via # dioptra (pyproject.toml) # flask-restx @@ -165,10 +200,14 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -182,7 +221,7 @@ markupsafe==2.1.5 # jinja2 # mako # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra (pyproject.toml) # flask-accepts @@ -201,31 +240,42 @@ mdurl==0.1.2 # via markdown-it-py mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) mpmath==1.3.0 # via sympy msgpack==1.0.8 # via # distributed # prefect +multidict==6.0.5 + # via + # aiohttp + # yarl multimethod==1.11.2 # via dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect networkx==3.3 # via # scikit-image # torch -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -242,7 +292,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -252,20 +304,26 @@ opencv-python==4.9.0.80 packaging==24.0 # via # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow # prefect # scikit-image + # torchmetrics + # transformers pandas==2.2.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via dioptra (pyproject.toml) @@ -283,6 +341,8 @@ pillow==10.3.0 # torchvision prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics protobuf==5.26.1 # via # mlflow @@ -293,15 +353,19 @@ psycopg2-binary==2.9.9 # via dioptra (pyproject.toml) pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk -pygments==2.17.2 +pygments==2.18.0 # via rich pyparsing==3.1.2 # via matplotlib @@ -331,37 +395,52 @@ pytz==2024.1 pyyaml==6.0.1 # via # dask + # datasets # dioptra (pyproject.toml) # distributed + # huggingface-hub # mlflow # prefect + # timm + # transformers querystring-parser==1.2.4 # via mlflow redis==5.0.4 # via # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications +regex==2024.4.28 + # via transformers requests==2.31.0 # via + # datasets # dioptra (pyproject.toml) # docker + # huggingface-hub # mlflow # prefect # smqtk-dataprovider + # transformers rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -422,7 +501,7 @@ smqtk-image-io==0.17.1 # smqtk-detection sortedcontainers==2.4.0 # via distributed -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra (pyproject.toml) @@ -442,16 +521,24 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask @@ -460,24 +547,44 @@ toolz==0.12.1 torch==2.2.2 # via # -r requirements-dev-pytorch.in + # maite + # timm # torchaudio + # torchmetrics # torchvision torchaudio==2.2.2 # via -r requirements-dev-pytorch.in +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite torchvision==0.17.2 - # via -r requirements-dev-pytorch.in + # via + # -r requirements-dev-pytorch.in + # maite + # timm tornado==6.4 # via distributed -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub + # maite # nrtk + # transformers +transformers==4.40.2 + # via maite typing-extensions==4.11.0 # via # alembic # dioptra (pyproject.toml) + # huggingface-hub + # lightning-utilities + # maite # sqlalchemy # torch + # torcheval tzdata==2024.1 # via # pandas @@ -489,7 +596,7 @@ urllib3==2.2.1 # docker # prefect # requests -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra (pyproject.toml) # flask @@ -497,6 +604,10 @@ werkzeug==3.0.2 # flask-login # flask-restx # tensorboard +xxhash==3.4.1 + # via datasets +yarl==1.9.4 + # via aiohttp zict==3.0.0 # via distributed zipp==3.18.1 diff --git a/docker/requirements/linux-arm64-py3.11-restapi-requirements.txt b/docker/requirements/linux-arm64-py3.11-restapi-requirements.txt index 40bcc822d..561aacc08 100644 --- a/docker/requirements/linux-arm64-py3.11-restapi-requirements.txt +++ b/docker/requirements/linux-arm64-py3.11-restapi-requirements.txt @@ -16,11 +16,11 @@ attrs==23.2.0 # via # jsonschema # referencing -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -50,7 +50,7 @@ flask==3.0.3 # flask-sqlalchemy flask-accepts==0.18.4 # via dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via dioptra (pyproject.toml) flask-login==0.6.3 # via dioptra (pyproject.toml) @@ -82,13 +82,13 @@ injector==0.21.0 # via dioptra (pyproject.toml) itsdangerous==2.2.0 # via flask -jinja2==3.1.3 +jinja2==3.1.4 # via flask jmespath==1.0.1 # via # boto3 # botocore -jsonschema==4.21.1 +jsonschema==4.22.0 # via # dioptra (pyproject.toml) # flask-restx @@ -101,7 +101,7 @@ markupsafe==2.1.5 # jinja2 # mako # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra (pyproject.toml) # flask-accepts @@ -149,7 +149,7 @@ redis==5.0.4 # via # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications @@ -157,11 +157,11 @@ requests==2.31.0 # via # dioptra (pyproject.toml) # mlflow-skinny -rpds-py==0.18.0 +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 @@ -173,7 +173,7 @@ six==1.16.0 # via python-dateutil smmap==5.0.1 # via gitdb -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra (pyproject.toml) @@ -193,7 +193,7 @@ urllib3==2.2.1 # via # botocore # requests -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra (pyproject.toml) # flask diff --git a/docker/requirements/linux-arm64-py3.11-tensorflow2-cpu-requirements.txt b/docker/requirements/linux-arm64-py3.11-tensorflow2-cpu-requirements.txt index cc28d6269..c9ba043b6 100644 --- a/docker/requirements/linux-arm64-py3.11-tensorflow2-cpu-requirements.txt +++ b/docker/requirements/linux-arm64-py3.11-tensorflow2-cpu-requirements.txt @@ -11,6 +11,12 @@ absl-py==2.1.0 # tensorflow adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) +aiohttp==3.9.5 + # via + # datasets + # fsspec +aiosignal==1.3.1 + # via aiohttp alembic==1.13.1 # via # dioptra (pyproject.toml) @@ -26,13 +32,14 @@ async-timeout==4.0.3 # via dioptra (pyproject.toml) attrs==23.2.0 # via + # aiohttp # jsonschema # referencing -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -47,6 +54,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # prefect # rq cloudpickle==3.0.0 @@ -55,17 +63,25 @@ cloudpickle==3.0.0 # distributed # mlflow # prefect +colorama==0.4.6 + # via pretty-errors contourpy==1.2.1 # via matplotlib croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect -distributed==2024.4.2 +datasets==2.19.1 + # via maite +dill==0.3.8 + # via + # datasets + # multiprocess +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -75,6 +91,12 @@ entrypoints==0.4 # via # dioptra (pyproject.toml) # mlflow +filelock==3.14.0 + # via + # datasets + # huggingface-hub + # torch + # transformers flask==3.0.3 # via # dioptra (pyproject.toml) @@ -86,7 +108,7 @@ flask==3.0.3 # mlflow flask-accepts==0.18.4 # via dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via dioptra (pyproject.toml) flask-login==0.6.3 # via dioptra (pyproject.toml) @@ -104,8 +126,16 @@ flatbuffers==24.3.25 # via tensorflow fonttools==4.51.0 # via matplotlib -fsspec==2024.3.1 - # via dask +frozenlist==1.4.1 + # via + # aiohttp + # aiosignal +fsspec[http]==2024.3.1 + # via + # dask + # datasets + # huggingface-hub + # torch gast==0.5.4 # via tensorflow gitdb==4.0.11 @@ -124,7 +154,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via # tensorboard # tensorflow @@ -134,8 +164,17 @@ h5py==3.11.0 # via # keras # tensorflow +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 - # via requests + # via + # requests + # yarl imageio==2.34.1 # via # imgaug @@ -154,18 +193,19 @@ injector==0.21.0 # via dioptra (pyproject.toml) itsdangerous==2.2.0 # via flask -jinja2==3.1.3 +jinja2==3.1.4 # via # distributed # flask # mlflow + # torch jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn -jsonschema==4.21.1 +jsonschema==4.22.0 # via # dioptra (pyproject.toml) # flask-restx @@ -179,10 +219,14 @@ lazy-loader==0.4 # via scikit-image libclang==18.1.1 # via tensorflow +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -196,7 +240,7 @@ markupsafe==2.1.5 # jinja2 # mako # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra (pyproject.toml) # flask-accepts @@ -219,32 +263,47 @@ ml-dtypes==0.3.2 # tensorflow mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed # prefect +multidict==6.0.5 + # via + # aiohttp + # yarl multimethod==1.11.2 # via dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect namex==0.0.8 # via keras networkx==3.3 - # via scikit-image -nrtk==0.3.1 + # via + # scikit-image + # torch +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra (pyproject.toml) # h5py # imageio # imgaug # keras + # maite # matplotlib # ml-dtypes # mlflow + # modelscan # nrtk # opencv-python # opt-einsum @@ -263,6 +322,9 @@ numpy==1.26.4 # tensorboard # tensorflow # tifffile + # torchmetrics + # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -276,21 +338,27 @@ optree==0.11.0 packaging==24.0 # via # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow # prefect # scikit-image # tensorflow + # torchmetrics + # transformers pandas==2.2.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via dioptra (pyproject.toml) @@ -305,8 +373,11 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics protobuf==4.25.3 # via # mlflow @@ -318,15 +389,19 @@ psycopg2-binary==2.9.9 # via dioptra (pyproject.toml) pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk -pygments==2.17.2 +pygments==2.18.0 # via rich pyparsing==3.1.2 # via matplotlib @@ -356,40 +431,54 @@ pytz==2024.1 pyyaml==6.0.1 # via # dask + # datasets # dioptra (pyproject.toml) # distributed + # huggingface-hub # mlflow # prefect + # timm + # transformers querystring-parser==1.2.4 # via mlflow redis==5.0.4 # via # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications +regex==2024.4.28 + # via transformers requests==2.31.0 # via + # datasets # dioptra (pyproject.toml) # docker + # huggingface-hub # mlflow # prefect # smqtk-dataprovider # tensorflow + # transformers rich==13.7.1 # via # dioptra (pyproject.toml) # keras -rpds-py==0.18.0 + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -453,7 +542,7 @@ smqtk-image-io==0.17.1 # smqtk-detection sortedcontainers==2.4.0 # via distributed -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra (pyproject.toml) @@ -463,6 +552,8 @@ sqlparse==0.5.0 # via mlflow structlog==24.1.0 # via dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -475,38 +566,71 @@ tensorboard-data-server==0.7.2 # via tensorboard tensorflow==2.16.1 # via -r requirements-dev-tensorflow.in -tensorflow-io-gcs-filesystem==0.36.0 - # via tensorflow +tensorflow-io-gcs-filesystem==0.34.0 + # via + # modelscan + # tensorflow termcolor==2.4.0 # via tensorflow text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.3.0 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.18.0 + # via + # maite + # timm tornado==6.4 # via distributed -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub + # maite # nrtk + # transformers +transformers==4.40.2 + # via maite typing-extensions==4.11.0 # via # alembic # dioptra (pyproject.toml) + # huggingface-hub + # lightning-utilities + # maite # optree # sqlalchemy # tensorflow + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -518,7 +642,7 @@ urllib3==2.2.1 # docker # prefect # requests -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra (pyproject.toml) # flask @@ -530,6 +654,10 @@ wheel==0.43.0 # via astunparse wrapt==1.16.0 # via tensorflow +xxhash==3.4.1 + # via datasets +yarl==1.9.4 + # via aiohttp zict==3.0.0 # via distributed zipp==3.18.1 diff --git a/examples/pytorch-maite-nrtk/README.md b/examples/pytorch-maite-nrtk/README.md new file mode 100644 index 000000000..d59196c05 --- /dev/null +++ b/examples/pytorch-maite-nrtk/README.md @@ -0,0 +1,21 @@ +# PyTorch MAITE Demo + +The demo provided in the Jupyter notebook `demo.ipynb` uses Dioptra to run experiments that demonstrate compatibility with the MAITE framework to run an attack on datasets and models downloaded from the huggingface repository. + +## Running the example + +To prepare your environment for running this example, follow the linked instructions below: + +1. [Create and activate a Python virtual environment and install the necessary dependencies](../README.md#creating-a-virtual-environment) +2. [Download the MNIST dataset using the download_data.py script.](../README.md#downloading-datasets) +3. [Follow the links in these User Setup instructions](../../README.md#user-setup) to do the following: + - Build the containers + - Use the cookiecutter template to generate the scripts, configuration files, and Docker Compose files you will need to run Dioptra +4. [Edit the docker-compose.yml file to mount the data folder in the worker containers](../README.md#mounting-the-data-folder-in-the-worker-containers) +5. [Initialize and start Dioptra](https://pages.nist.gov/dioptra/getting-started/running-dioptra.html#initializing-the-deployment) +6. [Register the custom task plugins for Dioptra's examples and demos](../README.md#registering-custom-task-plugins) +7. [Register the queues for Dioptra's examples and demos](../README.md#registering-queues) +8. [Start JupyterLab and open `demo.ipynb`](../README.md#starting-jupyter-lab) + +Steps 1–4 and 6–7 only need to be run once. +**Returning users only need to repeat Steps 5 (if you stopped Dioptra using `docker compose down`) and 8 (if you stopped the `jupyter lab` process)**. diff --git a/examples/pytorch-maite-nrtk/maite_nrtk_demo.ipynb b/examples/pytorch-maite-nrtk/maite_nrtk_demo.ipynb new file mode 100644 index 000000000..9c392d867 --- /dev/null +++ b/examples/pytorch-maite-nrtk/maite_nrtk_demo.ipynb @@ -0,0 +1,606 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Natural Robustness Toolkit (NRTK) demo" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook contains an end-to-end demostration of Dioptra that can be run on any modern laptop." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we import the necessary Python modules and ensure the proper environment variables are set so that all the code blocks will work as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import packages from the Python standard library\n", + "import importlib.util\n", + "import os\n", + "import sys\n", + "import pprint\n", + "import time\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "\n", + "def register_python_source_file(module_name: str, filepath: Path) -> None:\n", + " \"\"\"Import a source file directly.\n", + "\n", + " Args:\n", + " module_name: The module name to associate with the imported source file.\n", + " filepath: The path to the source file.\n", + "\n", + " Notes:\n", + " Adapted from the following implementation in the Python documentation:\n", + " https://docs.python.org/3/library/importlib.html#importing-a-source-file-directly\n", + " \"\"\"\n", + " spec = importlib.util.spec_from_file_location(module_name, str(filepath))\n", + " module = importlib.util.module_from_spec(spec)\n", + " sys.modules[module_name] = module\n", + " spec.loader.exec_module(module)\n", + "\n", + "\n", + "# Filter out warning messages\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "# Experiment name\n", + "EXPERIMENT_NAME = \"pytorch_maite_nrtk\"\n", + "\n", + "# Default address for accessing the RESTful API service\n", + "RESTAPI_ADDRESS = \"http://localhost:80\"\n", + "\n", + "# Set DIOPTRA_RESTAPI_URI variable if not defined, used to connect to RESTful API service\n", + "os.environ[\"DIOPTRA_RESTAPI_URI\"] = RESTAPI_ADDRESS\n", + "\n", + "# Default address for accessing the MLFlow Tracking server\n", + "MLFLOW_TRACKING_URI = \"http://localhost:35000\"\n", + "\n", + "# Set MLFLOW_TRACKING_URI variable, used to connect to MLFlow Tracking service\n", + "if os.getenv(\"MLFLOW_TRACKING_URI\") is None:\n", + " os.environ[\"MLFLOW_TRACKING_URI\"] = MLFLOW_TRACKING_URI\n", + "\n", + "# Path to workflows archive\n", + "WORKFLOWS_TAR_GZ = Path(\"workflows.tar.gz\")\n", + "\n", + "# Register the examples/scripts directory as a Python module\n", + "register_python_source_file(\"scripts\", Path(\"..\", \"scripts\", \"__init__.py\"))\n", + "\n", + "from scripts.client import DioptraClient\n", + "from scripts.utils import make_tar\n", + "\n", + "# Import third-party Python packages\n", + "import numpy as np\n", + "from mlflow.tracking import MlflowClient\n", + "\n", + "# Create random number generator\n", + "rng = np.random.default_rng(54399264723942495723666216079516778448)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submit and run jobs" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The entrypoints that we will be running in this example are implemented in the Python source files under `src/` and the `src/MLproject` file.\n", + "To run these entrypoints within Dioptra's architecture, we need to package those files up into an archive and submit it to the Dioptra RESTful API to create a new job.\n", + "For convenience, we provide the `make_tar` helper function defined in `examples/scripts/utils.py`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def mlflow_run_id_is_not_known(response_nrtk):\n", + " return response_nrtk[\"mlflowRunId\"] is None and response_nrtk[\"status\"] not in [\n", + " \"failed\",\n", + " \"finished\",\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('/Users/bhodges/Desktop/Dioptra branches/bjpatrick-dioptra-nrtk/examples/pytorch-obj-detect-nrtk/workflows.tar.gz')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "make_tar([\"src\"], WORKFLOWS_TAR_GZ)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To connect with the endpoint, we will use a client class defined in the `examples/scripts/client.py` file that is able to connect with the Dioptra RESTful API using the HTTP protocol.\n", + "We connect using the client below.\n", + "The client uses the environment variable `DIOPTRA_RESTAPI_URI`, which we configured at the top of the notebook, to figure out how to connect to the Dioptra RESTful API." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "restapi_client = DioptraClient()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to register an experiment under which to collect our job runs.\n", + "The code below checks if the relevant experiment exists.\n", + "If it does, then it just returns info about the experiment, if it doesn't, it then registers the new experiment." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;36m╭─────────────────────────────────────────────────╮\u001b[0m\n", + "\u001b[1;36m│\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36mDioptra Examples - Register Custom Task Plugins\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36m│\u001b[0m\n", + "\u001b[1;36m╰─────────────────────────────────────────────────╯\u001b[0m\n", + " ‣ \u001b[1mplugins_dir:\u001b[0m ..\u001b[35m/\u001b[0m\u001b[95mtask-plugins\u001b[0m\n", + " ‣ \u001b[1mapi_url:\u001b[0m \u001b[4;39mhttp://localhost:80\u001b[0m\n", + " ‣ \u001b[1mforce:\u001b[0m \u001b[3;92mTrue\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\u001b[39m'pytorch_mi'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\n", + "\u001b[39m'model_inversion'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\u001b[39m'modelscan'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\n", + "\u001b[39m'custom_poisoning_plugins'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\n", + "\u001b[39m'custom_fgm_plugins'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\n", + "\u001b[39m'pixel_threshold'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\n", + "\u001b[39m'custom_patch_plugins'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\u001b[39m'maite'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\u001b[39m'pytorch_d2'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\u001b[39m'evaluation'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\n", + "\u001b[39m'feature_squeezing'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m \u001b[1;33mOverwritten.\u001b[0m \u001b[39mRemoved and re-registered the custom task plugin \u001b[0m\u001b[39m'nrtk'\u001b[0m\u001b[39m.\u001b[0m\n", + " \u001b[1;92m✔\u001b[0m Custom task plugin registration is complete.\n" + ] + } + ], + "source": [ + "!python ../scripts/register_task_plugins.py --force --plugins-dir ../task-plugins --api-url http://localhost:80" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'experimentId': 1,\n", + " 'createdOn': '2024-07-13T18:27:57.486712',\n", + " 'lastModified': '2024-07-13T18:27:57.486712',\n", + " 'name': 'pytorch_maite_nrtk'}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response_experiment = restapi_client.get_experiment_by_name(name=EXPERIMENT_NAME)\n", + "\n", + "if response_experiment is None or \"Not Found\" in response_experiment.get(\"message\", []):\n", + " response_experiment = restapi_client.register_experiment(name=EXPERIMENT_NAME)\n", + "\n", + "response_experiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `full_workflow` entry point tests basic MAITE functionality: load a dataset from huggingface, load a model from huggingface, load a metric from torchvision and run that metric on that model/dataset. It also saves the model into MLFlow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response_test_metrics = restapi_client.submit_job(\n", + " workflows_file=WORKFLOWS_TAR_GZ,\n", + " experiment_name=EXPERIMENT_NAME,\n", + " entry_point=\"full_workflow\",\n", + " entry_point_kwargs=\" \".join([\n", + " \"-P dataset_provider_name=torchvision\",\n", + " \"-P dataset_name=voc\",\n", + " \"-P dataset_task=object-detection\",\n", + " \"-P split=val\",\n", + " \"-P model_provider_name=torchvision\",\n", + " \"-P model_name=fasterrcnn_resnet50_fpn\",\n", + " \"-P model_task=object-detection\",\n", + " \"-P metric_provider_name=torchmetrics\",\n", + " \"-P metric_name=MeanAveragePrecision\",\n", + " \"-P metric_task=detection\",\n", + " \"-P classes=46\",\n", + " \"-P batch_size=4\",\n", + " \"-P shape=[800,800]\"\n", + " ]),\n", + " queue=\"pytorch_cpu\",\n", + " timeout=\"1h\",\n", + ")\n", + "pprint.pprint(response_test_metrics)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `save_model` entry point loads a model from huggingface and saves it to MLFlow. In this example, we are pulling this object detection model from huggingface: https://huggingface.co/spaces/lkeab/transfiner/tree/main " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'createdOn': '2024-07-16T20:41:05.733304',\n", + " 'dependsOn': None,\n", + " 'entryPoint': 'save_model',\n", + " 'entryPointKwargs': '-P model_provider_name=torchvision -P '\n", + " 'model_name=fasterrcnn_resnet50_fpn -P '\n", + " 'model_task=object-detection',\n", + " 'experimentId': 1,\n", + " 'jobId': 'd77bda2f-a610-4bf5-a6c1-4788205eb880',\n", + " 'lastModified': '2024-07-16T20:41:05.733304',\n", + " 'mlflowRunId': None,\n", + " 'queueId': 3,\n", + " 'status': 'queued',\n", + " 'timeout': '1h',\n", + " 'workflowUri': 's3://workflow/4340a0e2bd5e4901adeccd9de152bc5b/workflows.tar.gz'}\n" + ] + } + ], + "source": [ + "response_model = restapi_client.submit_job(\n", + " workflows_file=WORKFLOWS_TAR_GZ,\n", + " experiment_name=EXPERIMENT_NAME,\n", + " entry_point=\"save_model\",\n", + " entry_point_kwargs=\" \".join([\n", + " \"-P model_provider_name=torchvision\",\n", + " \"-P model_name=fasterrcnn_resnet50_fpn\",\n", + " \"-P model_task=object-detection\"\n", + " ]),\n", + " queue=\"pytorch_cpu\",\n", + " timeout=\"1h\",\n", + ")\n", + "pprint.pprint(response_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `scan_model` entrypoint loads the previously saved model from MLFlow and uses modelscan to evaluate the model files for insecure or malicious code.\n", + "\n", + "It is important to note that modelscan searches for publicly known vulnerable coding practices, which the tool labels as a CRITICAL vulnerability. If the tool is used to scan models on opensource platforms like HuggingFace, there is a possibility for the tool to report a critical finding. Use caution when engaging with models that produce critical scan reports and ensure the model is published by a trusted source. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'createdOn': '2024-07-16T20:44:08.019447',\n", + " 'dependsOn': 'd77bda2f-a610-4bf5-a6c1-4788205eb880',\n", + " 'entryPoint': 'scan_model',\n", + " 'entryPointKwargs': '-P mlflow_run_id=fe5b0db1fee14cb294e8c1c87acf7720',\n", + " 'experimentId': 1,\n", + " 'jobId': 'f4b45e7f-f60e-4004-8a72-a95d119d66db',\n", + " 'lastModified': '2024-07-16T20:44:08.019447',\n", + " 'mlflowRunId': None,\n", + " 'queueId': 3,\n", + " 'status': 'queued',\n", + " 'timeout': '1h',\n", + " 'workflowUri': 's3://workflow/1d40de8c18b649ddb230a2d88ebf9a99/workflows.tar.gz'}\n" + ] + } + ], + "source": [ + "while mlflow_run_id_is_not_known(response_model):\n", + " time.sleep(1)\n", + " response_model = restapi_client.get_job_by_id(response_model[\"jobId\"])\n", + "\n", + "response_scan_model = restapi_client.submit_job(\n", + " workflows_file=WORKFLOWS_TAR_GZ,\n", + " experiment_name=EXPERIMENT_NAME,\n", + " entry_point=\"scan_model\",\n", + " entry_point_kwargs=\" \".join([\n", + " f\"-P mlflow_run_id={response_model['mlflowRunId']}\",\n", + " ]),\n", + " queue=\"pytorch_cpu\",\n", + " timeout=\"1h\",\n", + " depends_on=response_model[\"jobId\"],\n", + ")\n", + "pprint.pprint(response_scan_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `test_model` entrypoint loads the previously saved model from MLFlow into a MAITE-readable format, and then uses maite to test metrics and a dataset on it." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "while mlflow_run_id_is_not_known(response_model):\n", + " time.sleep(1)\n", + " response_model = restapi_client.get_job_by_id(response_model[\"jobId\"])\n", + "\n", + "response_use_model = restapi_client.submit_job(\n", + " workflows_file=WORKFLOWS_TAR_GZ,\n", + " experiment_name=EXPERIMENT_NAME,\n", + " entry_point=\"test_model\",\n", + " entry_point_kwargs=\" \".join([\n", + " \"-P model_name=loaded_model\",\n", + " \"-P model_version=1\",\n", + " \"-P model_task=object-detection\", \n", + " \"-P dataset_provider_name=huggingface\",\n", + " \"-P dataset_name=detection-datasets/fashionpedia\",\n", + " \"-P dataset_task=object-detection\",\n", + " \"-P split=val\",\n", + " \"-P metric_provider_name=torchmetrics\",\n", + " \"-P metric_name=MeanAveragePrecision\", \n", + " \"-P metric_task=detection\",\n", + " \"-P classes=80\",\n", + " \"-P batch_size=4\",\n", + " \"-P shape=[800,800]\"\n", + " ]),\n", + " queue=\"pytorch_cpu\",\n", + " timeout=\"1h\",\n", + ")\n", + "#HuggingFace datasets:\n", + "#detection-datasets/fashionpedia; classes=46; TEST STATUS=Success\n", + "#detection-datasets/coco; classes=80; TEST STATUS=Success" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `load_dataset` entrypoint loads a dataset from disk, puts it into maite format, then loads a model and metric using maite and runs it on that dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response_load_dataset = restapi_client.submit_job(\n", + " workflows_file=WORKFLOWS_TAR_GZ,\n", + " experiment_name=EXPERIMENT_NAME,\n", + " entry_point=\"load_dataset\",\n", + " entry_point_kwargs=\" \".join([\n", + " \"-P subset=400\"\n", + " ]),\n", + " queue=\"pytorch_cpu\",\n", + " timeout=\"1h\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `gen_nrtk` entrypoint loads a dataset using MAITE, applies a NRTK perturbations on it, creates a new `perturbed_dataset` and registers it to MLFlow as an artifact. \n", + "\n", + "Currently, Dioptra's NRTK custom plugin is configured to apply skimage and PIL perturbations to object detection datasets. For more insight into NRTK's perturbations, please refer to their documentation. Parameters for this example were derived from NRTK's perturbation jupyter notebook example here: https://github.com/Kitware/nrtk/blob/main/examples/perturbers.ipynb." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "response_gen_nrtk = restapi_client.submit_job(\n", + " workflows_file=WORKFLOWS_TAR_GZ,\n", + " experiment_name=EXPERIMENT_NAME,\n", + " entry_point=\"gen_nrtk\",\n", + " entry_point_kwargs=\" \".join([\n", + " \"-P dataset_provider_name=huggingface\",\n", + " \"-P dataset_name=detection-datasets/fashionpedia\",\n", + " \"-P dataset_task=object-detection\",\n", + " \"-P split=val\",\n", + " \"-P perturbation=SaltNoisePerturber\",\n", + " \"-P seed=42\",\n", + " \"-P amount=0.25\"\n", + " ]),\n", + " queue=\"pytorch_cpu\",\n", + " timeout=\"1h\",\n", + ")\n", + "#Tested datasets\n", + "#dataset_provider_name=huggingface; dataset_name=detection-datasets/coco\n", + "#dataset_provider_name=huggingface; dataset_name=detection-datasets/fashionpedia" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `infer_nrtk` entrypoint takes the previously generated `perturbed_dataset` results and runs it against a given model and metric. It is included here as a function and tested against 4 models on huggingface from different authors. Note that not all targeted models on huggingface are compatible for various reasons - missing `config.json`, different requirements for data formatting, etc. The examples included below worked at the time of testing.\n", + "\n", + "Although MAITE supports torchvision as a provider as well, torchvision does not seem to provide pretrained CIFAR10 models. An ImageNET example may be more suited to cross-testing torchvision and huggingface models." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def test_nrtk_dataset(provider, model):\n", + " global response_gen_nrtk\n", + " while mlflow_run_id_is_not_known(response_gen_nrtk):\n", + " time.sleep(1)\n", + " response_gen_nrtk = restapi_client.get_job_by_id(response_gen_nrtk[\"jobId\"])\n", + " response_infer_nrtk = restapi_client.submit_job(\n", + " workflows_file=WORKFLOWS_TAR_GZ,\n", + " experiment_name=EXPERIMENT_NAME,\n", + " entry_point=\"infer_nrtk\",\n", + " entry_point_kwargs=\" \".join([\n", + " f\"-P run_id={response_gen_nrtk['mlflowRunId']}\",\n", + " f\"-P model_provider_name={provider}\",\n", + " f\"-P model_name={model}\",\n", + " f\"-P model_task=image-classification\"\n", + " ]),\n", + " queue=\"pytorch_cpu\",\n", + " timeout=\"1h\",\n", + " depends_on=response_gen_nrtk[\"jobId\"],\n", + " )\n", + " return response_infer_nrtk" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'jobId': '005bdf2e-ec0f-4947-a3eb-c13d8d4a82ed',\n", + " 'mlflowRunId': None,\n", + " 'experimentId': 1,\n", + " 'queueId': 3,\n", + " 'createdOn': '2024-07-16T22:13:32.531231',\n", + " 'lastModified': '2024-07-16T22:13:32.531231',\n", + " 'timeout': '1h',\n", + " 'workflowUri': 's3://workflow/cfb8540537e84152a3f8d1189ed92c6c/workflows.tar.gz',\n", + " 'entryPoint': 'infer_nrtk',\n", + " 'entryPointKwargs': '-P run_id=171fdfd812a848278e5d35bab2960e72 -P model_provider_name=torchvision -P model_name=fasterrcnn_resnet50_fpn -P model_task=object-detection',\n", + " 'dependsOn': 'a32b9497-2f92-4112-9f2c-da54ff4d7521',\n", + " 'status': 'queued'}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_perturbed_dataset_nrtk(\"torchvision\",\"fasterrcnn_resnet50_fpn\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_perturbed_dataset_nrtk(\"huggingface\",\"facebook/detr-resnet-50\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "edee40310913f16e2ca02c1d37887bcb7f07f00399ca119bb7e27de7d632ea99" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/pytorch-maite-nrtk/src/MLproject b/examples/pytorch-maite-nrtk/src/MLproject new file mode 100644 index 000000000..401908850 --- /dev/null +++ b/examples/pytorch-maite-nrtk/src/MLproject @@ -0,0 +1,187 @@ +# This Software (Dioptra) is being made available as a public service by the +# National Institute of Standards and Technology (NIST), an Agency of the United +# States Department of Commerce. This software was developed in part by employees of +# NIST and in part by NIST contractors. Copyright in portions of this software that +# were developed by NIST contractors has been licensed or assigned to NIST. Pursuant +# to Title 17 United States Code Section 105, works of NIST employees are not +# subject to copyright protection in the United States. However, NIST may hold +# international copyright in software created by its employees and domestic +# copyright (or licensing rights) in portions of software that were assigned or +# licensed to NIST. To the extent that NIST holds copyright in this software, it is +# being made available under the Creative Commons Attribution 4.0 International +# license (CC BY 4.0). The disclaimers of the CC BY 4.0 license apply to all parts +# of the software developed or licensed by NIST. +# +# ACCESS THE FULL CC BY 4.0 LICENSE HERE: +# https://creativecommons.org/licenses/by/4.0/legalcode +name: pytorch-obj-detect-nrtk + +entry_points: + full_workflow: + parameters: + dataset_provider_name: { type: string, default: "huggingface" } + dataset_name: { type: string, default: "detection-datasets/coco" } + dataset_task: { type: string, default: "object-detection" } + split: { type: string, default: "test" } + model_provider_name: { type: string, default: "torchvision" } + model_name: { type: string, default: "fasterrcnn_resnet50_fpn" } + model_task: { type: string, default: "object-detection" } + metric_provider_name: { type: string, default: "torchmetrics" } + metric_name: { type: string, default: "Accuracy" } + metric_task: { type: string, default: "multiclass" } + classes: { type: int, default: 80 } + batch_size: { type: int, default: 32 } + shape: { type: string, default: "[800,800]" } + subset: { type: int, default: 0 } + command: > + PYTHONPATH=$DIOPTRA_PLUGIN_DIR validate-experiment full_workflow.yml && PYTHONPATH=$DIOPTRA_PLUGIN_DIR run-experiment full_workflow.yml + -P dataset_provider_name={dataset_provider_name} + -P dataset_name={dataset_name} + -P dataset_task={dataset_task} + -P split={split} + -P model_provider_name={model_provider_name} + -P model_name={model_name} + -P model_task={model_task} + -P metric_provider_name={metric_provider_name} + -P metric_name={metric_name} + -P metric_task={metric_task} + -P classes={classes} + -P batch_size={batch_size} + -P shape={shape} + -P subset={subset} + save_model: + parameters: + model_provider_name: { type: string, default: "huggingface" } + model_name: { type: string, default: "fasterrcnn_resnet50_fpn"} + model_task: { type: string, default: "object-detection" } + register_model: { type: string, default: "loaded_model" } + command: > + PYTHONPATH=$DIOPTRA_PLUGIN_DIR validate-experiment save_model.yml && PYTHONPATH=$DIOPTRA_PLUGIN_DIR run-experiment save_model.yml + -P model_provider_name={model_provider_name} + -P model_name={model_name} + -P model_task={model_task} + -P register_model={register_model} + scan_model: + parameters: + mlflow_run_id: { type: string} + command: > + PYTHONPATH=$DIOPTRA_PLUGIN_DIR validate-experiment scan_model.yml && PYTHONPATH=$DIOPTRA_PLUGIN_DIR run-experiment scan_model.yml + -P mlflow_run_id={mlflow_run_id} + test_model: + parameters: + dataset_provider_name: { type: string, default: "huggingface" } + dataset_name: { type: string, default: "detection-datasets/coco" } + dataset_task: { type: string, default: "object-detection" } + split: { type: string, default: "test" } + model_name: { type: string, default: "loaded_model" } + model_version: { type: int, default: 1 } + model_task: { type: string, default: "object-detection" } + metric_provider_name: { type: string, default: "torchmetrics" } + metric_name: { type: string, default: "MeanAveragePrecision" } + metric_task: { type: string, default: "detection" } + classes: { type: int, default: 80 } + batch_size: { type: int, default: 8 } + shape: { type: string, default: "[800,800]" } + subset: { type: int, default: 0 } + command: > + PYTHONPATH=$DIOPTRA_PLUGIN_DIR validate-experiment test_model.yml && PYTHONPATH=$DIOPTRA_PLUGIN_DIR run-experiment test_model.yml + -P dataset_provider_name={dataset_provider_name} + -P dataset_name={dataset_name} + -P dataset_task={dataset_task} + -P split={split} + -P model_name={model_name} + -P model_version={model_version} + -P model_task={model_task} + -P metric_provider_name={metric_provider_name} + -P metric_name={metric_name} + -P metric_task={metric_task} + -P classes={classes} + -P batch_size={batch_size} + -P shape={shape} + -P subset={subset} + load_dataset: + parameters: + testing_dir: { type: string, default: "/dioptra/data/Mnist/testing" } + validation_split: { type: int, default: 0.3 } + image_size: { type: string, default: "[28,28,3]" } + new_size: { type: integer, default: 224 } + model_provider_name: { type: string, default: "huggingface" } + model_name: { type: string, default: "farleyknight-org-username/vit-base-mnist" } + model_task: { type: string, default: "object-detection" } + metric_provider_name: { type: string, default: "torchmetrics" } + metric_name: { type: string, default: "Accuracy" } + metric_task: { type: string, default: "multiclass" } + classes: { type: int, default: 10 } + batch_size: { type: int, default: 32 } + shape: { type: string, default: "[3,224,224]" } + subset: { type: int, default: 0 } + command: > + PYTHONPATH=$DIOPTRA_PLUGIN_DIR validate-experiment load_dataset.yml && PYTHONPATH=$DIOPTRA_PLUGIN_DIR run-experiment load_dataset.yml + -P testing_dir={testing_dir} + -P validation_split={validation_split} + -P image_size={image_size} + -P new_size={new_size} + -P model_provider_name={model_provider_name} + -P model_name={model_name} + -P model_task={model_task} + -P metric_provider_name={metric_provider_name} + -P metric_name={metric_name} + -P metric_task={metric_task} + -P classes={classes} + -P batch_size={batch_size} + -P shape={shape} + -P subset={subset} + gen_nrtk: + parameters: + dataset_provider_name: { type: string, default: "huggingface" } + dataset_name: { type: string, default: "detection-datasets/coco" } + dataset_task: { type: string, default: "object-detection" } + split: { type: string, default: "test" } + perturbation: { type: string, default: "SaltNoisePerturber" } + seed: { type: int, default: 42 } + amount: { type: float, default: 0.25 } + salt_vs_pepper: { type: float, default: 0.5 } + var: { type: float, default: 0.05 } + mean: { type: int, default: 0 } + ksize: { type: int, default: 7 } + factor: { type: float, default: 0.25 } + command: > + PYTHONPATH=$DIOPTRA_PLUGIN_DIR validate-experiment gen_nrtk.yml && PYTHONPATH=$DIOPTRA_PLUGIN_DIR run-experiment gen_nrtk.yml + -P split={split} + -P dataset_provider_name={dataset_provider_name} + -P dataset_name={dataset_name} + -P dataset_task={dataset_task} + -P perturbation={perturbation} + -P seed={seed} + -P amount={amount} + -P salt_vs_pepper={salt_vs_pepper} + -P var={var} + -P mean={mean} + -P ksize={ksize} + -P factor={amount} + infer_nrtk: + parameters: + mlflow_run_id: { type: string } + model_provider_name: { type: string, default: "torchvision" } + model_name: { type: string, default: "fasterrcnn_resnet50_fpn"} + model_task: { type: string, default: "object-detection" } + metric_provider_name: { type: string, default: "torchmetrics" } + metric_name: { type: string, default: "MeanAveragePrecision" } + metric_task: { type: string, default: "detection" } + classes: { type: int, default: 46 } + batch_size: { type: int, default: 4 } + shape: { type: string, default: "[800,800]" } + subset: { type: int, default: 0 } + command: > + PYTHONPATH=$DIOPTRA_PLUGIN_DIR validate-experiment infer_nrtk.yml && PYTHONPATH=$DIOPTRA_PLUGIN_DIR run-experiment infer_nrtk.yml + -P mlflow_run_id={mlflow_run_id} + -P model_provider_name={model_provider_name} + -P model_name={model_name} + -P model_task={model_task} + -P metric_provider_name={metric_provider_name} + -P metric_name={metric_name} + -P metric_task={metric_task} + -P classes={classes} + -P batch_size={batch_size} + -P shape={shape} + -P subset={subset} diff --git a/examples/pytorch-maite-nrtk/src/full_workflow.yml b/examples/pytorch-maite-nrtk/src/full_workflow.yml new file mode 100644 index 000000000..b7632634d --- /dev/null +++ b/examples/pytorch-maite-nrtk/src/full_workflow.yml @@ -0,0 +1,157 @@ +types: + callback: + dataset: + directoryiterator: + functiontype: + kerasclassifier: + metric: + mlflowrun: + tuple: + model: + modelversion: + np.ndarray: + optimizer: + path: + dataframe: + rngenerator: + sequential: + tarfile: + list_tuple_string_any: + list: tuple_string_any + list_union_metric_functiontype: + list: union_metric_functiontype + list_union_string_path: + list: union_string_path + list_mapping_string_string: + list: mapping_string_string + mapping_string_any: + mapping: [string, any] + mapping_string_number: + mapping: [string, number] + mapping_string_string: + mapping: [string, string] + tuple_integer_integer: + tuple: [integer, integer] + tuple_integer_integer_integer: + tuple: [integer, integer, integer] + tuple_string_any: + tuple: [string, any] + union_integer_number_string: + union: [integer, number, string] + union_integer_any: + union: [integer, any] + union_metric_functiontype: + union: [metric, functiontype] + union_null_list_tuple_string_any: + union: [list_tuple_string_any , "null"] + union_null_mapping_string_any: + union: [mapping_string_any, "null"] + union_null_modelversion: + union: [modelversion, "null"] + union_null_number: + union: [number, "null"] + union_null_string: + union: [string, "null"] + union_null_union_integer_any: + union: [union_integer_any, "null"] + union_string_path: + union: [string, path] + union_null_union_string_path: + union: [union_string_path, "null"] +parameters: + dataset_provider_name: huggingface + dataset_name: cifar10 + dataset_task: image-classification + split: test + model_provider_name: huggingface + model_name: aaraki/vit-base-patch16-224-in21k-finetuned-cifar10 + model_task: image-classification + metric_provider_name: torchmetrics + metric_name: Accuracy + metric_task: multiclass + classes: 10 + batch_size: 32 + shape: [224, 224] + subset: 0 +tasks: + get_dataset: + plugin: dioptra_custom.maite.maite.get_dataset + inputs: + - provider_name: string + - dataset_name: string + - task: string + - split: string + outputs: + dataset: dataset + get_model: + plugin: dioptra_custom.maite.maite.get_model + inputs: + - provider_name: string + - model_name: string + - task: string + outputs: + model: model + get_metric: + plugin: dioptra_custom.maite.maite.get_metric + inputs: + - provider_name: string + - metric_name: string + - task: string + - classes: integer + outputs: + metric: metric + transform_tensor: + plugin: dioptra_custom.maite.maite.transform_tensor + inputs: + - dataset: dataset + - shape: any + - name: totensor + type: boolean + required: false + - name: subset + type: integer + required: false + outputs: + dataset: dataset + compute_metric: + plugin: dioptra_custom.maite.maite.compute_metric + inputs: + - dataset: dataset + - model: model + - metric: metric + - task: string + - batch_size: integer + outputs: + computed: mapping_string_number +graph: + dataset: + get_dataset: + provider_name: $dataset_provider_name + dataset_name: $dataset_name + task: $dataset_task + split: $split + model: + get_model: + provider_name: $model_provider_name + model_name: $model_name + task: $model_task + metric: + get_metric: + provider_name: $metric_provider_name + metric_name: $metric_name + task: $metric_task + classes: $classes + transformed_dataset: + transform_tensor: + dataset: $dataset + shape: $shape + subset: $subset + totensor: true + metric_results: + compute_metric: + dataset: $transformed_dataset.dataset + model: $model + metric: $metric + task: $model_task + batch_size: $batch_size + dependencies: [transformed_dataset, model, metric] diff --git a/examples/pytorch-maite-nrtk/src/gen_nrtk.yml b/examples/pytorch-maite-nrtk/src/gen_nrtk.yml new file mode 100644 index 000000000..0b52ba6cc --- /dev/null +++ b/examples/pytorch-maite-nrtk/src/gen_nrtk.yml @@ -0,0 +1,115 @@ +types: + callback: + dataset: + directoryiterator: + functiontype: + kerasclassifier: + metric: + mlflowrun: + tuple: + model: + modelversion: + np.ndarray: + optimizer: + path: + dataframe: + rngenerator: + sequential: + tarfile: + list_tuple_string_any: + list: tuple_string_any + list_union_metric_functiontype: + list: union_metric_functiontype + list_union_string_path: + list: union_string_path + list_mapping_string_string: + list: mapping_string_string + mapping_string_any: + mapping: [string, any] + mapping_string_number: + mapping: [string, number] + mapping_string_string: + mapping: [string, string] + tuple_integer_integer: + tuple: [integer, integer] + tuple_integer_integer_integer: + tuple: [integer, integer, integer] + tuple_string_any: + tuple: [string, any] + union_integer_number_string: + union: [integer, number, string] + union_integer_any: + union: [integer, any] + union_metric_functiontype: + union: [metric, functiontype] + union_null_list_tuple_string_any: + union: [list_tuple_string_any , "null"] + union_null_mapping_string_any: + union: [mapping_string_any, "null"] + union_null_modelversion: + union: [modelversion, "null"] + union_null_number: + union: [number, "null"] + union_null_string: + union: [string, "null"] + union_null_union_integer_any: + union: [union_integer_any, "null"] + union_string_path: + union: [string, path] + union_null_union_string_path: + union: [union_string_path, "null"] +parameters: + dataset_provider_name: huggingface + dataset_name: detection-datasets/coco + dataset_task: object-detection + split: val + perturbation: SaltNoisePerturber + seed: 42 + amount: 0.25 + salt_vs_pepper: 0.5 + var: 0.05 + mean: 0 + ksize: 7 + factor: 0.25 +tasks: + get_dataset: + plugin: dioptra_custom.maite.maite.get_dataset + inputs: + - provider_name: string + - dataset_name: string + - task: string + - split: string + outputs: + dataset: dataset + perturb_images: + plugin: dioptra_custom.nrtk.nrtk.perturb_images + inputs: + - dataset: dataset + - perturbation: string + - seed: integer + - amount: number + - salt_vs_pepper: number + - var: number + - mean: integer + - ksize: integer + - factor: number + outputs: + dataset: dataset +graph: + dataset: + get_dataset: + provider_name: $dataset_provider_name + dataset_name: $dataset_name + task: $dataset_task + split: $split + create_nrtk_dataset_from_hf_dataset: + perturb_images: + dataset: $dataset + perturbation: $perturbation + seed: $seed + amount: $amount + salt_vs_pepper: $salt_vs_pepper + var: $var + mean: $mean + ksize: $ksize + factor: $factor \ No newline at end of file diff --git a/examples/pytorch-maite-nrtk/src/infer_nrtk.yml b/examples/pytorch-maite-nrtk/src/infer_nrtk.yml new file mode 100644 index 000000000..905a9c28c --- /dev/null +++ b/examples/pytorch-maite-nrtk/src/infer_nrtk.yml @@ -0,0 +1,148 @@ +types: + callback: + dataset: + directoryiterator: + functiontype: + kerasclassifier: + metric: + mlflowrun: + tuple: + model: + modelversion: + np.ndarray: + optimizer: + path: + dataframe: + rngenerator: + sequential: + tarfile: + list_tuple_string_any: + list: tuple_string_any + list_union_metric_functiontype: + list: union_metric_functiontype + list_union_string_path: + list: union_string_path + list_mapping_string_string: + list: mapping_string_string + mapping_string_any: + mapping: [string, any] + mapping_string_number: + mapping: [string, number] + mapping_string_string: + mapping: [string, string] + tuple_integer_integer: + tuple: [integer, integer] + tuple_integer_integer_integer: + tuple: [integer, integer, integer] + tuple_string_any: + tuple: [string, any] + union_integer_number_string: + union: [integer, number, string] + union_integer_any: + union: [integer, any] + union_metric_functiontype: + union: [metric, functiontype] + union_null_list_tuple_string_any: + union: [list_tuple_string_any , "null"] + union_null_mapping_string_any: + union: [mapping_string_any, "null"] + union_null_modelversion: + union: [modelversion, "null"] + union_null_number: + union: [number, "null"] + union_null_string: + union: [string, "null"] + union_null_union_integer_any: + union: [union_integer_any, "null"] + union_string_path: + union: [string, path] + union_null_union_string_path: + union: [union_string_path, "null"] +parameters: + mlflow_run_id: string + model_provider_name: torchvision + model_name: fasterrcnn_resnet50_fpn + model_task: object-detection + metric_provider_name: torchmetrics + metric_name: MeanAveragePrecision + metric_task: detection + classes: 80 + batch_size: 32 + shape: [800,800] + subset: 0 +tasks: + get_dataset: + plugin: dioptra_custom.nrtk.nrtk.get_perturbed_dataset + inputs: + - mlflow_run_id: string + outputs: + dataset: dataset + get_model: + plugin: dioptra_custom.maite.maite.get_model + inputs: + - provider_name: string + - model_name: string + - task: string + outputs: + model: model + get_metric: + plugin: dioptra_custom.maite.maite.get_metric + inputs: + - provider_name: string + - metric_name: string + - task: string + - classes: integer + outputs: + metric: metric + transform_tensor: + plugin: dioptra_custom.maite.maite.transform_tensor + inputs: + - dataset: dataset + - shape: any + - name: totensor + type: boolean + required: false + - name: subset + type: integer + required: false + outputs: + dataset: dataset + compute_metric: + plugin: dioptra_custom.maite.maite.compute_metric + inputs: + - dataset: dataset + - model: model + - metric: metric + - task: string + - batch_size: integer + outputs: + computed: mapping_string_number +graph: + dataset: + get_dataset: + mlflow_run_id: $mlflow_run_id + model: + get_model: + provider_name: $model_provider_name + model_name: $model_name + task: $model_task + metric: + get_metric: + provider_name: $metric_provider_name + metric_name: $metric_name + task: $metric_task + classes: $classes + transformed_dataset: + transform_tensor: + dataset: $dataset + shape: $shape + subset: $subset + totensor: true + metric_results: + compute_metric: + dataset: $transformed_dataset.dataset + model: $model + metric: $metric + task: $model_task + batch_size: $batch_size + dependencies: [transformed_dataset, model, metric] \ No newline at end of file diff --git a/examples/pytorch-maite-nrtk/src/load_dataset.yml b/examples/pytorch-maite-nrtk/src/load_dataset.yml new file mode 100644 index 000000000..2cb4469a5 --- /dev/null +++ b/examples/pytorch-maite-nrtk/src/load_dataset.yml @@ -0,0 +1,177 @@ +types: + callback: + dataset: + directoryiterator: + functiontype: + kerasclassifier: + metric: + mlflowrun: + tuple: + model: + modelversion: + np.ndarray: + optimizer: + path: + dataframe: + rngenerator: + sequential: + tarfile: + list_tuple_string_any: + list: tuple_string_any + list_union_metric_functiontype: + list: union_metric_functiontype + list_union_string_path: + list: union_string_path + list_mapping_string_string: + list: mapping_string_string + mapping_string_any: + mapping: [string, any] + mapping_string_number: + mapping: [string, number] + mapping_string_string: + mapping: [string, string] + tuple_integer_integer: + tuple: [integer, integer] + tuple_integer_integer_integer: + tuple: [integer, integer, integer] + tuple_string_any: + tuple: [string, any] + union_integer_number_string: + union: [integer, number, string] + union_integer_any: + union: [integer, any] + union_metric_functiontype: + union: [metric, functiontype] + union_null_list_tuple_string_any: + union: [list_tuple_string_any , "null"] + union_null_mapping_string_any: + union: [mapping_string_any, "null"] + union_null_modelversion: + union: [modelversion, "null"] + union_null_number: + union: [number, "null"] + union_null_string: + union: [string, "null"] + union_null_union_integer_any: + union: [union_integer_any, "null"] + union_string_path: + union: [string, path] + union_null_union_string_path: + union: [union_string_path, "null"] +parameters: + testing_dir: /dioptra/data/Mnist/testing + validation_split: 0.3 + image_size: [28,28,3] + new_size: 224 + model_provider_name: huggingface + model_name: road-signs-6ih4y + model_task: object-detection + metric_provider_name: torchmetrics + metric_name: mAP + metric_task: detection + classes: 22 + batch_size: 8 + shape: [640,640] + subset: 0 +tasks: + create_image_dataset: + plugin: dioptra_custom.maite.maite.create_image_dataset + inputs: + - data_dir: string + - image_size: tuple_integer_integer_integer + - name: new_size + type: integer + required: false + - name: validation_split + type: union_null_number + required: false + - name: batch_size + type: integer + required: false + - name: label_mode + type: string + required: false + outputs: + - dataset_a: dataset + - dataset_b: dataset + get_dataset: + plugin: dioptra_custom.maite.maite.get_dataset + inputs: + - provider_name: string + - dataset_name: string + - task: string + - split: string + outputs: + dataset: dataset + get_model: + plugin: dioptra_custom.maite.maite.get_model + inputs: + - provider_name: string + - model_name: string + - task: string + outputs: + model: model + get_metric: + plugin: dioptra_custom.maite.maite.get_metric + inputs: + - provider_name: string + - metric_name: string + - task: string + - classes: integer + outputs: + metric: metric + transform_tensor: + plugin: dioptra_custom.maite.maite.transform_tensor + inputs: + - dataset: dataset + - shape: any + - name: totensor + type: boolean + required: false + - name: subset + type: integer + required: false + outputs: + dataset: dataset + compute_metric: + plugin: dioptra_custom.maite.maite.compute_metric + inputs: + - dataset: dataset + - model: model + - metric: metric + - task: string + - batch_size: integer + outputs: + computed: mapping_string_number +graph: + dataset: + create_image_dataset: + data_dir: $testing_dir + validation_split: $validation_split + batch_size: $batch_size + image_size: $image_size + new_size: $new_size + model: + get_model: + provider_name: $model_provider_name + model_name: $model_name + task: $model_task + metric: + get_metric: + provider_name: $metric_provider_name + metric_name: $metric_name + task: $metric_task + classes: $classes + transformed_dataset: + transform_tensor: + dataset: $dataset.dataset_a + shape: $shape + subset: $subset + metric_results: + compute_metric: + dataset: $transformed_dataset.dataset + model: $model + metric: $metric + task: $model_task + batch_size: $batch_size + dependencies: [transformed_dataset, model, metric] diff --git a/examples/pytorch-maite-nrtk/src/save_model.yml b/examples/pytorch-maite-nrtk/src/save_model.yml new file mode 100644 index 000000000..3daf3df20 --- /dev/null +++ b/examples/pytorch-maite-nrtk/src/save_model.yml @@ -0,0 +1,82 @@ +types: + callback: + dataset: + directoryiterator: + functiontype: + kerasclassifier: + metric: + mlflowrun: + tuple: + model: + modelversion: + np.ndarray: + optimizer: + path: + dataframe: + rngenerator: + sequential: + tarfile: + list_tuple_string_any: + list: tuple_string_any + list_union_metric_functiontype: + list: union_metric_functiontype + list_union_string_path: + list: union_string_path + list_mapping_string_string: + list: mapping_string_string + mapping_string_any: + mapping: [string, any] + mapping_string_number: + mapping: [string, number] + mapping_string_string: + mapping: [string, string] + tuple_integer_integer: + tuple: [integer, integer] + tuple_integer_integer_integer: + tuple: [integer, integer, integer] + tuple_string_any: + tuple: [string, any] + union_integer_number_string: + union: [integer, number, string] + union_integer_any: + union: [integer, any] + union_metric_functiontype: + union: [metric, functiontype] + union_null_list_tuple_string_any: + union: [list_tuple_string_any , "null"] + union_null_mapping_string_any: + union: [mapping_string_any, "null"] + union_null_modelversion: + union: [modelversion, "null"] + union_null_number: + union: [number, "null"] + union_null_string: + union: [string, "null"] + union_null_union_integer_any: + union: [union_integer_any, "null"] + union_string_path: + union: [string, path] + union_null_union_string_path: + union: [union_string_path, "null"] +parameters: + model_provider_name: torchvision + model_name: fasterrcnn_resnet50_fpn + model_task: object-detection + register_model: loaded_model +tasks: + get_model: + plugin: dioptra_custom.maite.maite.get_model + inputs: + - provider_name: string + - model_name: string + - task: string + - register_model_name: string + outputs: + model: model +graph: + model: + get_model: + provider_name: $model_provider_name + model_name: $model_name + task: $model_task + register_model_name: $register_model diff --git a/examples/pytorch-maite-nrtk/src/scan_model.yml b/examples/pytorch-maite-nrtk/src/scan_model.yml new file mode 100644 index 000000000..8233ac7e3 --- /dev/null +++ b/examples/pytorch-maite-nrtk/src/scan_model.yml @@ -0,0 +1,73 @@ +types: + callback: + dataset: + directoryiterator: + functiontype: + kerasclassifier: + metric: + mlflowrun: + tuple: + model: + modelversion: + np.ndarray: + optimizer: + path: + dataframe: + rngenerator: + sequential: + tarfile: + list_tuple_string_any: + list: tuple_string_any + list_union_metric_functiontype: + list: union_metric_functiontype + list_union_string_path: + list: union_string_path + list_mapping_string_string: + list: mapping_string_string + mapping_string_any: + mapping: [string, any] + mapping_string_number: + mapping: [string, number] + mapping_string_string: + mapping: [string, string] + tuple_integer_integer: + tuple: [integer, integer] + tuple_integer_integer_integer: + tuple: [integer, integer, integer] + tuple_string_any: + tuple: [string, any] + union_integer_number_string: + union: [integer, number, string] + union_integer_any: + union: [integer, any] + union_metric_functiontype: + union: [metric, functiontype] + union_null_list_tuple_string_any: + union: [list_tuple_string_any , "null"] + union_null_mapping_string_any: + union: [mapping_string_any, "null"] + union_null_modelversion: + union: [modelversion, "null"] + union_null_number: + union: [number, "null"] + union_null_string: + union: [string, "null"] + union_null_union_integer_any: + union: [union_integer_any, "null"] + union_string_path: + union: [string, path] + union_null_union_string_path: + union: [union_string_path, "null"] +parameters: + mlflow_run_id: string +tasks: + scan_model: + plugin: dioptra_custom.modelscan.modelscan.scan_model + inputs: + - mlflow_run_id: string + outputs: + scan_results: string +graph: + model: + scan_model: + mlflow_run_id: $mlflow_run_id diff --git a/examples/pytorch-maite-nrtk/src/test_model.yml b/examples/pytorch-maite-nrtk/src/test_model.yml new file mode 100644 index 000000000..7ed129130 --- /dev/null +++ b/examples/pytorch-maite-nrtk/src/test_model.yml @@ -0,0 +1,157 @@ +types: + callback: + dataset: + directoryiterator: + functiontype: + kerasclassifier: + metric: + mlflowrun: + tuple: + model: + modelversion: + np.ndarray: + optimizer: + path: + dataframe: + rngenerator: + sequential: + tarfile: + list_tuple_string_any: + list: tuple_string_any + list_union_metric_functiontype: + list: union_metric_functiontype + list_union_string_path: + list: union_string_path + list_mapping_string_string: + list: mapping_string_string + mapping_string_any: + mapping: [string, any] + mapping_string_number: + mapping: [string, number] + mapping_string_string: + mapping: [string, string] + tuple_integer_integer: + tuple: [integer, integer] + tuple_integer_integer_integer: + tuple: [integer, integer, integer] + tuple_string_any: + tuple: [string, any] + union_integer_number_string: + union: [integer, number, string] + union_integer_any: + union: [integer, any] + union_metric_functiontype: + union: [metric, functiontype] + union_null_list_tuple_string_any: + union: [list_tuple_string_any , "null"] + union_null_mapping_string_any: + union: [mapping_string_any, "null"] + union_null_modelversion: + union: [modelversion, "null"] + union_null_number: + union: [number, "null"] + union_null_string: + union: [string, "null"] + union_null_union_integer_any: + union: [union_integer_any, "null"] + union_string_path: + union: [string, path] + union_null_union_string_path: + union: [union_string_path, "null"] +parameters: + dataset_provider_name: huggingface + dataset_name: cifar10 + dataset_task: image-classification + split: test + model_name: loaded_model + model_version: 1 + model_task: object-detection + metric_provider_name: torchmetrics + metric_name: MeanAveragePrecision + metric_task: detection + classes: 80 + batch_size: 32 + shape: [800, 800] + subset: 0 +tasks: + get_dataset: + plugin: dioptra_custom.maite.maite.get_dataset + inputs: + - provider_name: string + - dataset_name: string + - task: string + - split: string + outputs: + dataset: dataset + get_metric: + plugin: dioptra_custom.maite.maite.get_metric + inputs: + - provider_name: string + - metric_name: string + - task: string + - classes: integer + outputs: + metric: metric + transform_tensor: + plugin: dioptra_custom.maite.maite.transform_tensor + inputs: + - dataset: dataset + - shape: any + - name: totensor + type: boolean + required: false + - name: subset + type: integer + required: false + outputs: + dataset: dataset + compute_metric: + plugin: dioptra_custom.maite.maite.compute_metric + inputs: + - dataset: dataset + - model: model + - metric: metric + - task: string + - batch_size: integer + outputs: + computed: mapping_string_number + load_pytorch_classifier: + inputs: + - name: name + type: string + required: true + - version: integer + outputs: + ret: model + plugin: dioptra_custom.pytorch_d2.registry_mlflow_detectron2.load_pytorch_classifier +graph: + model: + load_pytorch_classifier: + name: $model_name + version: $model_version + dataset: + get_dataset: + provider_name: $dataset_provider_name + dataset_name: $dataset_name + task: $dataset_task + split: $split + metric: + get_metric: + provider_name: $metric_provider_name + metric_name: $metric_name + task: $metric_task + classes: $classes + transformed_dataset: + transform_tensor: + dataset: $dataset + shape: $shape + subset: $subset + totensor: true + metric_results: + compute_metric: + dataset: $transformed_dataset.dataset + model: $model + metric: $metric + task: $model_task + batch_size: $batch_size + dependencies: [transformed_dataset, model, metric] diff --git a/pyproject.toml b/pyproject.toml index 5c8852d47..daad096eb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -128,7 +128,9 @@ worker = [ "async_timeout", "adversarial-robustness-toolbox>=1.9.0", "imgaug>=0.4.0", + "maite[all_interop]==0.4.0", "matplotlib", + "modelscan>=0.1.1", "nrtk>=0.3.0", "opencv-python", "Pillow>=9.2.0", diff --git a/requirements/linux-amd64-py3.11-requirements-dev-pytorch.txt b/requirements/linux-amd64-py3.11-requirements-dev-pytorch.txt index 47b93ffa4..957261282 100644 --- a/requirements/linux-amd64-py3.11-requirements-dev-pytorch.txt +++ b/requirements/linux-amd64-py3.11-requirements-dev-pytorch.txt @@ -13,7 +13,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -56,7 +59,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -70,13 +73,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -113,6 +116,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -123,7 +127,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -138,19 +144,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -171,8 +183,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub # torch # tox + # transformers # triton # virtualenv flake8==7.0.0 @@ -195,7 +210,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -227,9 +242,11 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub # torch # universal-pathlib gitdb==4.0.11 @@ -246,7 +263,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow @@ -256,6 +273,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -306,7 +330,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -325,13 +349,13 @@ jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -394,12 +418,16 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -414,7 +442,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -442,6 +470,8 @@ mistune==3.0.2 # via nbconvert mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) mpmath==1.3.0 # via sympy msgpack==1.0.8 @@ -456,6 +486,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect nbclient==0.10.0 @@ -482,18 +514,21 @@ notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -510,7 +545,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics # torchvision + # transformers nvidia-cublas-cu12==12.1.3.1 # via # nvidia-cudnn-cu12 @@ -554,14 +591,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -573,9 +613,12 @@ packaging==24.0 # qtpy # scikit-image # sphinx + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -583,7 +626,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -617,6 +660,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -641,13 +686,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -662,7 +711,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -725,20 +774,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -749,23 +802,28 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow # prefect # smqtk-dataprovider # sphinx + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -775,17 +833,23 @@ rfc3986-validator==0.1.1 # jsonschema # jupyter-events rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -879,7 +943,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -904,6 +968,8 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan terminado==0.18.1 # via # jupyter-server @@ -912,16 +978,22 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask @@ -930,12 +1002,22 @@ toolz==0.12.1 torch==2.2.2 # via # -r requirements-dev-pytorch.in + # maite + # timm # torchaudio + # torchmetrics # torchvision torchaudio==2.2.2 # via -r requirements-dev-pytorch.in +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite torchvision==0.17.2 - # via -r requirements-dev-pytorch.in + # via + # -r requirements-dev-pytorch.in + # maite + # timm tornado==6.4 # via # distributed @@ -947,11 +1029,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -969,6 +1055,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite triton==2.2.0 # via torch types-python-dateutil==2.9.0.20240316 @@ -978,9 +1066,13 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # sqlalchemy # torch + # torcheval tzdata==2024.1 # via # pandas @@ -1009,7 +1101,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -1024,6 +1116,8 @@ wheel==0.43.0 # pip-tools widgetsnbextension==4.0.10 # via ipywidgets +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/linux-amd64-py3.11-requirements-dev-tensorflow.txt b/requirements/linux-amd64-py3.11-requirements-dev-tensorflow.txt index 3f1912094..566942aef 100644 --- a/requirements/linux-amd64-py3.11-requirements-dev-tensorflow.txt +++ b/requirements/linux-amd64-py3.11-requirements-dev-tensorflow.txt @@ -14,7 +14,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -59,7 +62,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -73,13 +76,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -116,6 +119,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -126,7 +130,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -141,19 +147,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -174,7 +186,12 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub + # torch # tox + # transformers + # triton # virtualenv flake8==7.0.0 # via @@ -196,7 +213,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -230,9 +247,12 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub + # torch # universal-pathlib gast==0.5.4 # via tensorflow @@ -252,7 +272,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via # tensorboard # tensorflow @@ -268,6 +288,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -318,7 +345,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -330,19 +357,20 @@ jinja2==3.1.3 # mlflow # nbconvert # sphinx + # torch jinja2-time==0.2.0 # via cookiecutter jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -409,12 +437,16 @@ lazy-loader==0.4 # via scikit-image libclang==18.1.1 # via tensorflow +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -429,7 +461,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -461,6 +493,10 @@ ml-dtypes==0.3.2 # tensorflow mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed @@ -473,6 +509,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect namex==0.0.8 @@ -492,28 +530,33 @@ nbformat==5.10.4 nest-asyncio==1.6.0 # via ipykernel networkx==3.3 - # via scikit-image + # via + # scikit-image + # torch notebook==7.1.3 # via jupyter notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # h5py # imageio # imgaug # keras + # maite # matplotlib # ml-dtypes # mlflow + # modelscan # nrtk # opencv-python # opt-einsum @@ -532,6 +575,40 @@ numpy==1.26.4 # tensorboard # tensorflow # tifffile + # torchmetrics + # torchvision + # transformers +nvidia-cublas-cu12==12.1.3.1 + # via + # nvidia-cudnn-cu12 + # nvidia-cusolver-cu12 + # torch +nvidia-cuda-cupti-cu12==12.1.105 + # via torch +nvidia-cuda-nvrtc-cu12==12.1.105 + # via torch +nvidia-cuda-runtime-cu12==12.1.105 + # via torch +nvidia-cudnn-cu12==8.9.2.26 + # via torch +nvidia-cufft-cu12==11.0.2.54 + # via torch +nvidia-curand-cu12==10.3.2.106 + # via torch +nvidia-cusolver-cu12==11.4.5.107 + # via torch +nvidia-cusparse-cu12==12.1.0.106 + # via + # nvidia-cusolver-cu12 + # torch +nvidia-nccl-cu12==2.20.5 + # via torch +nvidia-nvjitlink-cu12==12.4.127 + # via + # nvidia-cusolver-cu12 + # nvidia-cusparse-cu12 +nvidia-nvtx-cu12==12.1.105 + # via torch opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -548,14 +625,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -568,9 +648,12 @@ packaging==24.0 # scikit-image # sphinx # tensorflow + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -578,7 +661,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -597,6 +680,7 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision pip-tools==7.4.1 # via dioptra (pyproject.toml) platformdirs==4.2.1 @@ -611,6 +695,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -636,13 +722,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -657,7 +747,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -720,20 +810,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -744,17 +838,21 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow @@ -762,6 +860,7 @@ requests==2.31.0 # smqtk-dataprovider # sphinx # tensorflow + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -774,16 +873,21 @@ rich==13.7.1 # via # dioptra (pyproject.toml) # keras -rpds-py==0.18.0 + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -880,7 +984,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -895,6 +999,8 @@ structlog==24.1.0 # via # dioptra # dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -907,8 +1013,10 @@ tensorboard-data-server==0.7.2 # via tensorboard tensorflow==2.16.1 # via -r requirements-dev-tensorflow.in -tensorflow-io-gcs-filesystem==0.36.0 - # via tensorflow +tensorflow-io-gcs-filesystem==0.34.0 + # via + # modelscan + # tensorflow termcolor==2.4.0 # via tensorflow terminado==0.18.1 @@ -919,21 +1027,41 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.3.0 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.18.0 + # via + # maite + # timm tornado==6.4 # via # distributed @@ -945,11 +1073,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -967,6 +1099,10 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite +triton==2.3.0 + # via torch types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -974,10 +1110,15 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # optree # sqlalchemy # tensorflow + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -1006,7 +1147,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -1024,6 +1165,8 @@ widgetsnbextension==4.0.10 # via ipywidgets wrapt==1.16.0 # via tensorflow +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/linux-amd64-py3.11-requirements-dev.txt b/requirements/linux-amd64-py3.11-requirements-dev.txt index 1f1fb3345..e79a13dc5 100644 --- a/requirements/linux-amd64-py3.11-requirements-dev.txt +++ b/requirements/linux-amd64-py3.11-requirements-dev.txt @@ -11,7 +11,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -54,7 +57,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -68,13 +71,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -111,6 +114,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -121,7 +125,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -136,19 +142,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -169,7 +181,12 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub + # torch # tox + # transformers + # triton # virtualenv flake8==7.0.0 # via @@ -191,7 +208,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -223,9 +240,12 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub + # torch # universal-pathlib gitdb==4.0.11 # via gitpython @@ -241,7 +261,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow @@ -251,6 +271,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -301,7 +328,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -313,19 +340,20 @@ jinja2==3.1.3 # mlflow # nbconvert # sphinx + # torch jinja2-time==0.2.0 # via cookiecutter jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -388,12 +416,16 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -408,7 +440,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -436,6 +468,10 @@ mistune==3.0.2 # via nbconvert mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed @@ -448,6 +484,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect nbclient==0.10.0 @@ -465,25 +503,30 @@ nbformat==5.10.4 nest-asyncio==1.6.0 # via ipykernel networkx==3.3 - # via scikit-image + # via + # scikit-image + # torch notebook==7.1.3 # via jupyter notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -500,6 +543,40 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics + # torchvision + # transformers +nvidia-cublas-cu12==12.1.3.1 + # via + # nvidia-cudnn-cu12 + # nvidia-cusolver-cu12 + # torch +nvidia-cuda-cupti-cu12==12.1.105 + # via torch +nvidia-cuda-nvrtc-cu12==12.1.105 + # via torch +nvidia-cuda-runtime-cu12==12.1.105 + # via torch +nvidia-cudnn-cu12==8.9.2.26 + # via torch +nvidia-cufft-cu12==11.0.2.54 + # via torch +nvidia-curand-cu12==10.3.2.106 + # via torch +nvidia-cusolver-cu12==11.4.5.107 + # via torch +nvidia-cusparse-cu12==12.1.0.106 + # via + # nvidia-cusolver-cu12 + # torch +nvidia-nccl-cu12==2.20.5 + # via torch +nvidia-nvjitlink-cu12==12.4.127 + # via + # nvidia-cusolver-cu12 + # nvidia-cusparse-cu12 +nvidia-nvtx-cu12==12.1.105 + # via torch opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -512,14 +589,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -531,9 +611,12 @@ packaging==24.0 # qtpy # scikit-image # sphinx + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -541,7 +624,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -560,6 +643,7 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision pip-tools==7.4.1 # via dioptra (pyproject.toml) platformdirs==4.2.1 @@ -574,6 +658,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -598,13 +684,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -619,7 +709,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -682,20 +772,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -706,23 +800,28 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow # prefect # smqtk-dataprovider # sphinx + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -732,17 +831,23 @@ rfc3986-validator==0.1.1 # jsonschema # jupyter-events rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -836,7 +941,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -851,6 +956,8 @@ structlog==24.1.0 # via # dioptra # dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -859,6 +966,8 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan terminado==0.18.1 # via # jupyter-server @@ -867,21 +976,41 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.3.0 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.18.0 + # via + # maite + # timm tornado==6.4 # via # distributed @@ -893,11 +1022,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -915,6 +1048,10 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite +triton==2.3.0 + # via torch types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -922,8 +1059,13 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # sqlalchemy + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -952,7 +1094,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -967,6 +1109,8 @@ wheel==0.43.0 # pip-tools widgetsnbextension==4.0.10 # via ipywidgets +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/linux-arm64-py3.11-requirements-dev-pytorch.txt b/requirements/linux-arm64-py3.11-requirements-dev-pytorch.txt index a7aa49681..95a020b9b 100644 --- a/requirements/linux-arm64-py3.11-requirements-dev-pytorch.txt +++ b/requirements/linux-arm64-py3.11-requirements-dev-pytorch.txt @@ -13,7 +13,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -56,7 +59,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -70,13 +73,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -113,6 +116,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -123,7 +127,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -138,19 +144,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -171,8 +183,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -194,7 +209,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -226,9 +241,11 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub # torch # universal-pathlib gitdb==4.0.11 @@ -245,7 +262,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow @@ -255,6 +272,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -305,7 +329,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -324,13 +348,13 @@ jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -393,12 +417,16 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -413,7 +441,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -441,6 +469,8 @@ mistune==3.0.2 # via nbconvert mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) mpmath==1.3.0 # via sympy msgpack==1.0.8 @@ -455,6 +485,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect nbclient==0.10.0 @@ -481,18 +513,21 @@ notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -509,7 +544,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -522,14 +559,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -541,9 +581,12 @@ packaging==24.0 # qtpy # scikit-image # sphinx + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -551,7 +594,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -585,6 +628,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -609,13 +654,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -630,7 +679,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -693,20 +742,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -717,23 +770,28 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow # prefect # smqtk-dataprovider # sphinx + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -743,17 +801,23 @@ rfc3986-validator==0.1.1 # jsonschema # jupyter-events rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -847,7 +911,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -872,6 +936,8 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan terminado==0.18.1 # via # jupyter-server @@ -880,16 +946,22 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask @@ -898,12 +970,22 @@ toolz==0.12.1 torch==2.2.2 # via # -r requirements-dev-pytorch.in + # maite + # timm # torchaudio + # torchmetrics # torchvision torchaudio==2.2.2 # via -r requirements-dev-pytorch.in +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite torchvision==0.17.2 - # via -r requirements-dev-pytorch.in + # via + # -r requirements-dev-pytorch.in + # maite + # timm tornado==6.4 # via # distributed @@ -915,11 +997,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -937,6 +1023,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -944,9 +1032,13 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # sqlalchemy # torch + # torcheval tzdata==2024.1 # via # pandas @@ -975,7 +1067,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -990,6 +1082,8 @@ wheel==0.43.0 # pip-tools widgetsnbextension==4.0.10 # via ipywidgets +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/linux-arm64-py3.11-requirements-dev-tensorflow.txt b/requirements/linux-arm64-py3.11-requirements-dev-tensorflow.txt index 1d928e30a..50ac80469 100644 --- a/requirements/linux-arm64-py3.11-requirements-dev-tensorflow.txt +++ b/requirements/linux-arm64-py3.11-requirements-dev-tensorflow.txt @@ -14,7 +14,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -59,7 +62,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -73,13 +76,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -116,6 +119,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -126,7 +130,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -141,19 +147,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -174,7 +186,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub + # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -196,7 +212,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -230,9 +246,12 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub + # torch # universal-pathlib gast==0.5.4 # via tensorflow @@ -252,7 +271,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via # tensorboard # tensorflow @@ -268,6 +287,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -318,7 +344,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -330,19 +356,20 @@ jinja2==3.1.3 # mlflow # nbconvert # sphinx + # torch jinja2-time==0.2.0 # via cookiecutter jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -409,12 +436,16 @@ lazy-loader==0.4 # via scikit-image libclang==18.1.1 # via tensorflow +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -429,7 +460,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -461,6 +492,10 @@ ml-dtypes==0.3.2 # tensorflow mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed @@ -473,6 +508,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect namex==0.0.8 @@ -492,28 +529,33 @@ nbformat==5.10.4 nest-asyncio==1.6.0 # via ipykernel networkx==3.3 - # via scikit-image + # via + # scikit-image + # torch notebook==7.1.3 # via jupyter notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # h5py # imageio # imgaug # keras + # maite # matplotlib # ml-dtypes # mlflow + # modelscan # nrtk # opencv-python # opt-einsum @@ -532,6 +574,9 @@ numpy==1.26.4 # tensorboard # tensorflow # tifffile + # torchmetrics + # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -548,14 +593,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -568,9 +616,12 @@ packaging==24.0 # scikit-image # sphinx # tensorflow + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -578,7 +629,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -597,6 +648,7 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision pip-tools==7.4.1 # via dioptra (pyproject.toml) platformdirs==4.2.1 @@ -611,6 +663,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -636,13 +690,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -657,7 +715,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -720,20 +778,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -744,17 +806,21 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow @@ -762,6 +828,7 @@ requests==2.31.0 # smqtk-dataprovider # sphinx # tensorflow + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -774,16 +841,21 @@ rich==13.7.1 # via # dioptra (pyproject.toml) # keras -rpds-py==0.18.0 + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -880,7 +952,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -895,6 +967,8 @@ structlog==24.1.0 # via # dioptra # dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -907,8 +981,10 @@ tensorboard-data-server==0.7.2 # via tensorboard tensorflow==2.16.1 # via -r requirements-dev-tensorflow.in -tensorflow-io-gcs-filesystem==0.36.0 - # via tensorflow +tensorflow-io-gcs-filesystem==0.34.0 + # via + # modelscan + # tensorflow termcolor==2.4.0 # via tensorflow terminado==0.18.1 @@ -919,21 +995,41 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.3.0 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.18.0 + # via + # maite + # timm tornado==6.4 # via # distributed @@ -945,11 +1041,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -967,6 +1067,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -974,10 +1076,15 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # optree # sqlalchemy # tensorflow + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -1006,7 +1113,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -1024,6 +1131,8 @@ widgetsnbextension==4.0.10 # via ipywidgets wrapt==1.16.0 # via tensorflow +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/linux-arm64-py3.11-requirements-dev.txt b/requirements/linux-arm64-py3.11-requirements-dev.txt index dd3b03150..4a3754553 100644 --- a/requirements/linux-arm64-py3.11-requirements-dev.txt +++ b/requirements/linux-arm64-py3.11-requirements-dev.txt @@ -11,7 +11,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -54,7 +57,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -68,13 +71,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -111,6 +114,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -121,7 +125,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -136,19 +142,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -169,7 +181,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub + # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -191,7 +207,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -223,9 +239,12 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub + # torch # universal-pathlib gitdb==4.0.11 # via gitpython @@ -241,7 +260,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow @@ -251,6 +270,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -301,7 +327,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -313,19 +339,20 @@ jinja2==3.1.3 # mlflow # nbconvert # sphinx + # torch jinja2-time==0.2.0 # via cookiecutter jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -388,12 +415,16 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -408,7 +439,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -436,6 +467,10 @@ mistune==3.0.2 # via nbconvert mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed @@ -448,6 +483,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect nbclient==0.10.0 @@ -465,25 +502,30 @@ nbformat==5.10.4 nest-asyncio==1.6.0 # via ipykernel networkx==3.3 - # via scikit-image + # via + # scikit-image + # torch notebook==7.1.3 # via jupyter notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -500,6 +542,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics + # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -512,14 +557,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -531,9 +579,12 @@ packaging==24.0 # qtpy # scikit-image # sphinx + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -541,7 +592,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -560,6 +611,7 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision pip-tools==7.4.1 # via dioptra (pyproject.toml) platformdirs==4.2.1 @@ -574,6 +626,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -598,13 +652,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -619,7 +677,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -682,20 +740,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -706,23 +768,28 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow # prefect # smqtk-dataprovider # sphinx + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -732,17 +799,23 @@ rfc3986-validator==0.1.1 # jsonschema # jupyter-events rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -836,7 +909,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -851,6 +924,8 @@ structlog==24.1.0 # via # dioptra # dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -859,6 +934,8 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan terminado==0.18.1 # via # jupyter-server @@ -867,21 +944,41 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.3.0 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.18.0 + # via + # maite + # timm tornado==6.4 # via # distributed @@ -893,11 +990,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -915,6 +1016,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -922,8 +1025,13 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # sqlalchemy + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -952,7 +1060,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -967,6 +1075,8 @@ wheel==0.43.0 # pip-tools widgetsnbextension==4.0.10 # via ipywidgets +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/macos-amd64-py3.11-requirements-dev-pytorch.txt b/requirements/macos-amd64-py3.11-requirements-dev-pytorch.txt index 6d4802007..dc83474cb 100644 --- a/requirements/macos-amd64-py3.11-requirements-dev-pytorch.txt +++ b/requirements/macos-amd64-py3.11-requirements-dev-pytorch.txt @@ -13,7 +13,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -58,7 +61,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -72,13 +75,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -115,6 +118,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -125,7 +129,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -140,19 +146,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -173,8 +185,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -196,7 +211,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -228,9 +243,11 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub # torch # universal-pathlib gitdb==4.0.11 @@ -247,7 +264,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow @@ -257,6 +274,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -307,7 +331,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -326,13 +350,13 @@ jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -395,12 +419,16 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -415,7 +443,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -443,6 +471,8 @@ mistune==3.0.2 # via nbconvert mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) mpmath==1.3.0 # via sympy msgpack==1.0.8 @@ -457,6 +487,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect nbclient==0.10.0 @@ -483,18 +515,21 @@ notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -511,7 +546,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -524,14 +561,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -543,9 +583,12 @@ packaging==24.0 # qtpy # scikit-image # sphinx + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -553,7 +596,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -587,6 +630,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -611,13 +656,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -632,7 +681,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -695,20 +744,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -719,23 +772,28 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow # prefect # smqtk-dataprovider # sphinx + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -745,17 +803,23 @@ rfc3986-validator==0.1.1 # jsonschema # jupyter-events rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -849,7 +913,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -874,6 +938,8 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan terminado==0.18.1 # via # jupyter-server @@ -882,16 +948,22 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask @@ -900,12 +972,22 @@ toolz==0.12.1 torch==2.2.2 # via # -r requirements-dev-pytorch.in + # maite + # timm # torchaudio + # torchmetrics # torchvision torchaudio==2.2.2 # via -r requirements-dev-pytorch.in +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite torchvision==0.17.2 - # via -r requirements-dev-pytorch.in + # via + # -r requirements-dev-pytorch.in + # maite + # timm tornado==6.4 # via # distributed @@ -917,11 +999,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -939,6 +1025,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -946,9 +1034,13 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # sqlalchemy # torch + # torcheval tzdata==2024.1 # via # pandas @@ -977,7 +1069,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -992,6 +1084,8 @@ wheel==0.43.0 # pip-tools widgetsnbextension==4.0.10 # via ipywidgets +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/macos-amd64-py3.11-requirements-dev-tensorflow.txt b/requirements/macos-amd64-py3.11-requirements-dev-tensorflow.txt index 11dc21f93..1c00c0167 100644 --- a/requirements/macos-amd64-py3.11-requirements-dev-tensorflow.txt +++ b/requirements/macos-amd64-py3.11-requirements-dev-tensorflow.txt @@ -14,7 +14,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -61,7 +64,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -75,13 +78,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -118,6 +121,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -128,7 +132,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -143,19 +149,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -176,7 +188,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub + # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -198,7 +214,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -232,9 +248,12 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub + # torch # universal-pathlib gast==0.5.4 # via tensorflow @@ -254,7 +273,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via # tensorboard # tensorflow @@ -270,6 +289,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -320,7 +346,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -332,19 +358,20 @@ jinja2==3.1.3 # mlflow # nbconvert # sphinx + # torch jinja2-time==0.2.0 # via cookiecutter jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -411,12 +438,16 @@ lazy-loader==0.4 # via scikit-image libclang==18.1.1 # via tensorflow +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -431,7 +462,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -463,6 +494,10 @@ ml-dtypes==0.3.2 # tensorflow mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed @@ -475,6 +510,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect namex==0.0.8 @@ -494,28 +531,33 @@ nbformat==5.10.4 nest-asyncio==1.6.0 # via ipykernel networkx==3.3 - # via scikit-image + # via + # scikit-image + # torch notebook==7.1.3 # via jupyter notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # h5py # imageio # imgaug # keras + # maite # matplotlib # ml-dtypes # mlflow + # modelscan # nrtk # opencv-python # opt-einsum @@ -534,6 +576,9 @@ numpy==1.26.4 # tensorboard # tensorflow # tifffile + # torchmetrics + # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -550,14 +595,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -570,9 +618,12 @@ packaging==24.0 # scikit-image # sphinx # tensorflow + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -580,7 +631,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -599,6 +650,7 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision pip-tools==7.4.1 # via dioptra (pyproject.toml) platformdirs==4.2.1 @@ -613,6 +665,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -638,13 +692,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -659,7 +717,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -722,20 +780,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -746,17 +808,21 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow @@ -764,6 +830,7 @@ requests==2.31.0 # smqtk-dataprovider # sphinx # tensorflow + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -776,16 +843,21 @@ rich==13.7.1 # via # dioptra (pyproject.toml) # keras -rpds-py==0.18.0 + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -882,7 +954,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -897,6 +969,8 @@ structlog==24.1.0 # via # dioptra # dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -909,8 +983,10 @@ tensorboard-data-server==0.7.2 # via tensorboard tensorflow==2.16.1 # via -r requirements-dev-tensorflow.in -tensorflow-io-gcs-filesystem==0.36.0 - # via tensorflow +tensorflow-io-gcs-filesystem==0.34.0 + # via + # modelscan + # tensorflow termcolor==2.4.0 # via tensorflow terminado==0.18.1 @@ -921,21 +997,41 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.2.2 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.17.2 + # via + # maite + # timm tornado==6.4 # via # distributed @@ -947,11 +1043,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -969,6 +1069,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -976,10 +1078,15 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # optree # sqlalchemy # tensorflow + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -1008,7 +1115,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -1026,6 +1133,8 @@ widgetsnbextension==4.0.10 # via ipywidgets wrapt==1.16.0 # via tensorflow +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/macos-amd64-py3.11-requirements-dev.txt b/requirements/macos-amd64-py3.11-requirements-dev.txt index d2c419178..b4ebd0efe 100644 --- a/requirements/macos-amd64-py3.11-requirements-dev.txt +++ b/requirements/macos-amd64-py3.11-requirements-dev.txt @@ -11,7 +11,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -56,7 +59,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -70,13 +73,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -113,6 +116,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -123,7 +127,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -138,19 +144,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -171,7 +183,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub + # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -193,7 +209,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -225,9 +241,12 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub + # torch # universal-pathlib gitdb==4.0.11 # via gitpython @@ -243,7 +262,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow @@ -253,6 +272,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -303,7 +329,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -315,19 +341,20 @@ jinja2==3.1.3 # mlflow # nbconvert # sphinx + # torch jinja2-time==0.2.0 # via cookiecutter jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -390,12 +417,16 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -410,7 +441,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -438,6 +469,10 @@ mistune==3.0.2 # via nbconvert mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed @@ -450,6 +485,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect nbclient==0.10.0 @@ -467,25 +504,30 @@ nbformat==5.10.4 nest-asyncio==1.6.0 # via ipykernel networkx==3.3 - # via scikit-image + # via + # scikit-image + # torch notebook==7.1.3 # via jupyter notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -502,6 +544,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics + # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -514,14 +559,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -533,9 +581,12 @@ packaging==24.0 # qtpy # scikit-image # sphinx + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -543,7 +594,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -562,6 +613,7 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision pip-tools==7.4.1 # via dioptra (pyproject.toml) platformdirs==4.2.1 @@ -576,6 +628,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -600,13 +654,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -621,7 +679,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -684,20 +742,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -708,23 +770,28 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow # prefect # smqtk-dataprovider # sphinx + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -734,17 +801,23 @@ rfc3986-validator==0.1.1 # jsonschema # jupyter-events rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -838,7 +911,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -853,6 +926,8 @@ structlog==24.1.0 # via # dioptra # dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -861,6 +936,8 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan terminado==0.18.1 # via # jupyter-server @@ -869,21 +946,41 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.2.2 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.17.2 + # via + # maite + # timm tornado==6.4 # via # distributed @@ -895,11 +992,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -917,6 +1018,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -924,8 +1027,13 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # sqlalchemy + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -954,7 +1062,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -969,6 +1077,8 @@ wheel==0.43.0 # pip-tools widgetsnbextension==4.0.10 # via ipywidgets +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/macos-arm64-py3.11-requirements-dev-pytorch.txt b/requirements/macos-arm64-py3.11-requirements-dev-pytorch.txt index 3b5df101c..d68b87813 100644 --- a/requirements/macos-arm64-py3.11-requirements-dev-pytorch.txt +++ b/requirements/macos-arm64-py3.11-requirements-dev-pytorch.txt @@ -13,7 +13,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -58,7 +61,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -72,13 +75,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -115,6 +118,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -125,7 +129,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -140,19 +146,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -173,8 +185,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -196,7 +211,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -228,9 +243,11 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub # torch # universal-pathlib gitdb==4.0.11 @@ -245,7 +262,7 @@ graphql-core==3.2.3 # graphql-relay graphql-relay==3.2.0 # via graphene -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow @@ -255,6 +272,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -305,7 +329,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -324,13 +348,13 @@ jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -393,12 +417,16 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -413,7 +441,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -441,6 +469,8 @@ mistune==3.0.2 # via nbconvert mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) mpmath==1.3.0 # via sympy msgpack==1.0.8 @@ -455,6 +485,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect nbclient==0.10.0 @@ -481,18 +513,21 @@ notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -509,7 +544,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -522,14 +559,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -541,9 +581,12 @@ packaging==24.0 # qtpy # scikit-image # sphinx + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -551,7 +594,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -585,6 +628,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -609,13 +654,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -630,7 +679,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -693,20 +742,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -717,23 +770,28 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow # prefect # smqtk-dataprovider # sphinx + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -743,17 +801,23 @@ rfc3986-validator==0.1.1 # jsonschema # jupyter-events rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -847,7 +911,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -872,6 +936,8 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan terminado==0.18.1 # via # jupyter-server @@ -880,16 +946,22 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask @@ -898,12 +970,22 @@ toolz==0.12.1 torch==2.2.2 # via # -r requirements-dev-pytorch.in + # maite + # timm # torchaudio + # torchmetrics # torchvision torchaudio==2.2.2 # via -r requirements-dev-pytorch.in +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite torchvision==0.17.2 - # via -r requirements-dev-pytorch.in + # via + # -r requirements-dev-pytorch.in + # maite + # timm tornado==6.4 # via # distributed @@ -915,11 +997,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -937,6 +1023,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -944,9 +1032,13 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # sqlalchemy # torch + # torcheval tzdata==2024.1 # via # pandas @@ -975,7 +1067,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -990,6 +1082,8 @@ wheel==0.43.0 # pip-tools widgetsnbextension==4.0.10 # via ipywidgets +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/macos-arm64-py3.11-requirements-dev-tensorflow.txt b/requirements/macos-arm64-py3.11-requirements-dev-tensorflow.txt index 0270bc2da..d644e4cae 100644 --- a/requirements/macos-arm64-py3.11-requirements-dev-tensorflow.txt +++ b/requirements/macos-arm64-py3.11-requirements-dev-tensorflow.txt @@ -14,7 +14,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -61,7 +64,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -75,13 +78,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -118,6 +121,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -128,7 +132,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -143,19 +149,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -176,7 +188,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub + # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -198,7 +214,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -232,9 +248,12 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub + # torch # universal-pathlib gast==0.5.4 # via tensorflow @@ -252,7 +271,7 @@ graphql-core==3.2.3 # graphql-relay graphql-relay==3.2.0 # via graphene -grpcio==1.62.2 +grpcio==1.63.0 # via # tensorboard # tensorflow @@ -268,6 +287,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -318,7 +344,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -330,19 +356,20 @@ jinja2==3.1.3 # mlflow # nbconvert # sphinx + # torch jinja2-time==0.2.0 # via cookiecutter jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -409,12 +436,16 @@ lazy-loader==0.4 # via scikit-image libclang==18.1.1 # via tensorflow +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -429,7 +460,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -461,6 +492,10 @@ ml-dtypes==0.3.2 # tensorflow mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed @@ -473,6 +508,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect namex==0.0.8 @@ -492,28 +529,33 @@ nbformat==5.10.4 nest-asyncio==1.6.0 # via ipykernel networkx==3.3 - # via scikit-image + # via + # scikit-image + # torch notebook==7.1.3 # via jupyter notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # h5py # imageio # imgaug # keras + # maite # matplotlib # ml-dtypes # mlflow + # modelscan # nrtk # opencv-python # opt-einsum @@ -532,6 +574,9 @@ numpy==1.26.4 # tensorboard # tensorflow # tifffile + # torchmetrics + # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -548,14 +593,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -568,9 +616,12 @@ packaging==24.0 # scikit-image # sphinx # tensorflow + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -578,7 +629,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -597,6 +648,7 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision pip-tools==7.4.1 # via dioptra (pyproject.toml) platformdirs==4.2.1 @@ -611,6 +663,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -636,13 +690,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -657,7 +715,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -720,20 +778,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -744,17 +806,21 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow @@ -762,6 +828,7 @@ requests==2.31.0 # smqtk-dataprovider # sphinx # tensorflow + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -774,16 +841,21 @@ rich==13.7.1 # via # dioptra (pyproject.toml) # keras -rpds-py==0.18.0 + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -881,7 +953,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -896,6 +968,8 @@ structlog==24.1.0 # via # dioptra # dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -908,8 +982,10 @@ tensorboard-data-server==0.7.2 # via tensorboard tensorflow==2.16.1 # via -r requirements-dev-tensorflow.in -tensorflow-io-gcs-filesystem==0.36.0 - # via tensorflow +tensorflow-io-gcs-filesystem==0.34.0 + # via + # modelscan + # tensorflow tensorflow-metal==1.1.0 ; sys_platform == "darwin" and (platform_machine == "aarch64" or platform_machine == "arm64") # via -r requirements-dev-tensorflow.in termcolor==2.4.0 @@ -922,21 +998,41 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.3.0 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.18.0 + # via + # maite + # timm tornado==6.4 # via # distributed @@ -948,11 +1044,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -970,6 +1070,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -977,10 +1079,15 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # optree # sqlalchemy # tensorflow + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -1009,7 +1116,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -1028,6 +1135,8 @@ widgetsnbextension==4.0.10 # via ipywidgets wrapt==1.16.0 # via tensorflow +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/macos-arm64-py3.11-requirements-dev.txt b/requirements/macos-arm64-py3.11-requirements-dev.txt index 7984bbc64..5b4954fb3 100644 --- a/requirements/macos-arm64-py3.11-requirements-dev.txt +++ b/requirements/macos-arm64-py3.11-requirements-dev.txt @@ -11,7 +11,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -56,7 +59,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -70,13 +73,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -113,6 +116,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -123,7 +127,9 @@ cloudpickle==3.0.0 # mlflow # prefect colorama==0.4.6 - # via tox + # via + # pretty-errors + # tox comm==0.2.2 # via # ipykernel @@ -138,19 +144,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -171,7 +183,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub + # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -193,7 +209,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -225,9 +241,12 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub + # torch # universal-pathlib gitdb==4.0.11 # via gitpython @@ -241,7 +260,7 @@ graphql-core==3.2.3 # graphql-relay graphql-relay==3.2.0 # via graphene -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard gunicorn==21.2.0 # via mlflow @@ -251,6 +270,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -301,7 +327,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -313,19 +339,20 @@ jinja2==3.1.3 # mlflow # nbconvert # sphinx + # torch jinja2-time==0.2.0 # via cookiecutter jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -388,12 +415,16 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -408,7 +439,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -436,6 +467,10 @@ mistune==3.0.2 # via nbconvert mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed @@ -448,6 +483,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect nbclient==0.10.0 @@ -465,25 +502,30 @@ nbformat==5.10.4 nest-asyncio==1.6.0 # via ipykernel networkx==3.3 - # via scikit-image + # via + # scikit-image + # torch notebook==7.1.3 # via jupyter notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -500,6 +542,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics + # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -512,14 +557,17 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker # gunicorn + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -531,9 +579,12 @@ packaging==24.0 # qtpy # scikit-image # sphinx + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -541,7 +592,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -560,6 +611,7 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision pip-tools==7.4.1 # via dioptra (pyproject.toml) platformdirs==4.2.1 @@ -574,6 +626,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -598,13 +652,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -619,7 +677,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -682,20 +740,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -706,23 +768,28 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow # prefect # smqtk-dataprovider # sphinx + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -732,17 +799,23 @@ rfc3986-validator==0.1.1 # jsonschema # jupyter-events rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -836,7 +909,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -851,6 +924,8 @@ structlog==24.1.0 # via # dioptra # dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect tblib==3.0.0 @@ -859,6 +934,8 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.34.0 + # via modelscan terminado==0.18.1 # via # jupyter-server @@ -867,21 +944,41 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.3.0 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.18.0 + # via + # maite + # timm tornado==6.4 # via # distributed @@ -893,11 +990,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -915,6 +1016,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -922,8 +1025,13 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # sqlalchemy + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -952,7 +1060,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -967,6 +1075,8 @@ wheel==0.43.0 # pip-tools widgetsnbextension==4.0.10 # via ipywidgets +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/win-amd64-py3.11-requirements-dev-pytorch.txt b/requirements/win-amd64-py3.11-requirements-dev-pytorch.txt index 79599995c..aa4f93704 100644 --- a/requirements/win-amd64-py3.11-requirements-dev-pytorch.txt +++ b/requirements/win-amd64-py3.11-requirements-dev-pytorch.txt @@ -13,7 +13,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -56,7 +59,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -70,13 +73,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -113,6 +116,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -127,6 +131,7 @@ colorama==0.4.6 # build # click # ipython + # pretty-errors # pytest # sphinx # tox @@ -145,19 +150,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -178,8 +189,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -201,7 +215,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -233,9 +247,11 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub # torch # universal-pathlib gitdb==4.0.11 @@ -252,7 +268,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard h11==0.14.0 # via httpcore @@ -260,6 +276,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -310,7 +333,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -329,13 +352,13 @@ jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -398,12 +421,16 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -418,7 +445,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -446,6 +473,8 @@ mistune==3.0.2 # via nbconvert mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) mpmath==1.3.0 # via sympy msgpack==1.0.8 @@ -460,6 +489,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect nbclient==0.10.0 @@ -486,18 +517,21 @@ notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -514,7 +548,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -527,13 +563,16 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -545,9 +584,12 @@ packaging==24.0 # qtpy # scikit-image # sphinx + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -555,7 +597,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -587,6 +629,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -607,13 +651,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -628,7 +676,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -700,20 +748,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -724,23 +776,28 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow # prefect # smqtk-dataprovider # sphinx + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -750,17 +807,23 @@ rfc3986-validator==0.1.1 # jsonschema # jupyter-events rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -854,7 +917,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -879,6 +942,8 @@ tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.31.0 + # via modelscan terminado==0.18.1 # via # jupyter-server @@ -887,16 +952,22 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask @@ -905,12 +976,22 @@ toolz==0.12.1 torch==2.2.2 # via # -r requirements-dev-pytorch.in + # maite + # timm # torchaudio + # torchmetrics # torchvision torchaudio==2.2.2 # via -r requirements-dev-pytorch.in +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite torchvision==0.17.2 - # via -r requirements-dev-pytorch.in + # via + # -r requirements-dev-pytorch.in + # maite + # timm tornado==6.4 # via # distributed @@ -922,11 +1003,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -944,6 +1029,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -951,9 +1038,13 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # sqlalchemy # torch + # torcheval tzdata==2024.1 # via # pandas @@ -984,7 +1075,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -999,6 +1090,8 @@ wheel==0.43.0 # pip-tools widgetsnbextension==4.0.10 # via ipywidgets +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/win-amd64-py3.11-requirements-dev-tensorflow.txt b/requirements/win-amd64-py3.11-requirements-dev-tensorflow.txt index a573d0ea1..319e820d2 100644 --- a/requirements/win-amd64-py3.11-requirements-dev-tensorflow.txt +++ b/requirements/win-amd64-py3.11-requirements-dev-tensorflow.txt @@ -14,7 +14,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -59,7 +62,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -73,13 +76,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -116,6 +119,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -130,6 +134,7 @@ colorama==0.4.6 # build # click # ipython + # pretty-errors # pytest # sphinx # tox @@ -148,19 +153,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -181,7 +192,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub + # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -203,7 +218,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -237,9 +252,12 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub + # torch # universal-pathlib gast==0.5.4 # via tensorflow-intel @@ -259,7 +277,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via # tensorboard # tensorflow-intel @@ -273,6 +291,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -302,6 +327,8 @@ injector==0.21.0 # via # dioptra # dioptra (pyproject.toml) +intel-openmp==2021.4.0 + # via mkl ipykernel==6.29.4 # via # dioptra (pyproject.toml) @@ -323,7 +350,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -335,19 +362,20 @@ jinja2==3.1.3 # mlflow # nbconvert # sphinx + # torch jinja2-time==0.2.0 # via cookiecutter jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -414,12 +442,16 @@ lazy-loader==0.4 # via scikit-image libclang==18.1.1 # via tensorflow-intel +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -434,7 +466,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -460,12 +492,18 @@ mdurl==0.1.2 # via markdown-it-py mistune==3.0.2 # via nbconvert +mkl==2021.4.0 + # via torch ml-dtypes==0.3.2 # via # keras # tensorflow-intel mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed @@ -478,6 +516,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect namex==0.0.8 @@ -497,28 +537,33 @@ nbformat==5.10.4 nest-asyncio==1.6.0 # via ipykernel networkx==3.3 - # via scikit-image + # via + # scikit-image + # torch notebook==7.1.3 # via jupyter notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # h5py # imageio # imgaug # keras + # maite # matplotlib # ml-dtypes # mlflow + # modelscan # nrtk # opencv-python # opt-einsum @@ -537,6 +582,9 @@ numpy==1.26.4 # tensorboard # tensorflow-intel # tifffile + # torchmetrics + # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -553,13 +601,16 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -572,9 +623,12 @@ packaging==24.0 # scikit-image # sphinx # tensorflow-intel + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -582,7 +636,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -599,6 +653,7 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision pip-tools==7.4.1 # via dioptra (pyproject.toml) platformdirs==4.2.1 @@ -613,6 +668,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -634,13 +691,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -655,7 +716,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -727,20 +788,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -751,17 +816,21 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow @@ -769,6 +838,7 @@ requests==2.31.0 # smqtk-dataprovider # sphinx # tensorflow-intel + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -781,16 +851,21 @@ rich==13.7.1 # via # dioptra (pyproject.toml) # keras -rpds-py==0.18.0 + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -887,7 +962,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -902,8 +977,12 @@ structlog==24.1.0 # via # dioptra # dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect +tbb==2021.12.0 + # via mkl tblib==3.0.0 # via distributed tensorboard==2.16.2 @@ -917,7 +996,9 @@ tensorflow==2.16.1 tensorflow-intel==2.16.1 # via tensorflow tensorflow-io-gcs-filesystem==0.31.0 - # via tensorflow-intel + # via + # modelscan + # tensorflow-intel termcolor==2.4.0 # via tensorflow-intel terminado==0.18.1 @@ -928,21 +1009,41 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.3.0 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.18.0 + # via + # maite + # timm tornado==6.4 # via # distributed @@ -954,11 +1055,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -976,6 +1081,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -983,10 +1090,15 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # optree # sqlalchemy # tensorflow-intel + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -1017,7 +1129,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -1035,6 +1147,8 @@ widgetsnbextension==4.0.10 # via ipywidgets wrapt==1.16.0 # via tensorflow-intel +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/requirements/win-amd64-py3.11-requirements-dev.txt b/requirements/win-amd64-py3.11-requirements-dev.txt index 69db1e0e2..0ef7676ba 100644 --- a/requirements/win-amd64-py3.11-requirements-dev.txt +++ b/requirements/win-amd64-py3.11-requirements-dev.txt @@ -11,7 +11,10 @@ absl-py==2.1.0 adversarial-robustness-toolbox==1.17.1 # via dioptra (pyproject.toml) aiohttp==3.9.5 - # via dioptra (pyproject.toml) + # via + # datasets + # dioptra (pyproject.toml) + # fsspec aiosignal==1.3.1 # via aiohttp alabaster==0.7.16 @@ -54,7 +57,7 @@ attrs==23.2.0 # referencing autopep8==2.1.0 # via dioptra (pyproject.toml) -babel==2.14.0 +babel==2.15.0 # via # jupyterlab-server # sphinx @@ -68,13 +71,13 @@ bleach==6.1.0 # via # kaggle # nbconvert -blinker==1.8.1 +blinker==1.8.2 # via flask -boto3==1.34.93 +boto3==1.34.99 # via # dioptra # dioptra (pyproject.toml) -botocore==1.34.93 +botocore==1.34.99 # via # boto3 # s3transfer @@ -111,6 +114,7 @@ click==8.1.7 # distributed # flask # mlflow + # modelscan # pip-tools # prefect # rq @@ -125,6 +129,7 @@ colorama==0.4.6 # build # click # ipython + # pretty-errors # pytest # sphinx # tox @@ -143,19 +148,25 @@ croniter==2.0.5 # via prefect cycler==0.12.1 # via matplotlib -dask==2024.4.2 +dask==2024.5.0 # via # distributed # prefect +datasets==2.19.1 + # via maite debugpy==1.8.1 # via ipykernel decorator==5.1.1 # via ipython defusedxml==0.7.1 # via nbconvert +dill==0.3.8 + # via + # datasets + # multiprocess distlib==0.3.8 # via virtualenv -distributed==2024.4.2 +distributed==2024.5.0 # via prefect docker==7.0.0 # via @@ -176,7 +187,11 @@ fastjsonschema==2.19.1 # via nbformat filelock==3.14.0 # via + # datasets + # huggingface-hub + # torch # tox + # transformers # virtualenv flake8==7.0.0 # via @@ -198,7 +213,7 @@ flask-accepts==0.18.4 # via # dioptra # dioptra (pyproject.toml) -flask-cors==4.0.0 +flask-cors==4.0.1 # via # dioptra # dioptra (pyproject.toml) @@ -230,9 +245,12 @@ frozenlist==1.4.1 # via # aiohttp # aiosignal -fsspec==2024.3.1 +fsspec[http]==2024.3.1 # via # dask + # datasets + # huggingface-hub + # torch # universal-pathlib gitdb==4.0.11 # via gitpython @@ -248,7 +266,7 @@ graphql-relay==3.2.0 # via graphene greenlet==3.0.3 # via sqlalchemy -grpcio==1.62.2 +grpcio==1.63.0 # via tensorboard h11==0.14.0 # via httpcore @@ -256,6 +274,13 @@ httpcore==1.0.5 # via httpx httpx==0.27.0 # via jupyterlab +huggingface-hub==0.23.0 + # via + # datasets + # maite + # timm + # tokenizers + # transformers idna==3.7 # via # anyio @@ -285,6 +310,8 @@ injector==0.21.0 # via # dioptra # dioptra (pyproject.toml) +intel-openmp==2021.4.0 + # via mkl ipykernel==6.29.4 # via # dioptra (pyproject.toml) @@ -306,7 +333,7 @@ itsdangerous==2.2.0 # via flask jedi==0.19.1 # via ipython -jinja2==3.1.3 +jinja2==3.1.4 # via # cookiecutter # distributed @@ -318,19 +345,20 @@ jinja2==3.1.3 # mlflow # nbconvert # sphinx + # torch jinja2-time==0.2.0 # via cookiecutter jmespath==1.0.1 # via # boto3 # botocore -joblib==1.4.0 +joblib==1.4.2 # via scikit-learn json5==0.9.25 # via jupyterlab-server jsonpointer==2.4 # via jsonschema -jsonschema[format-nongpl]==4.21.1 +jsonschema[format-nongpl]==4.22.0 # via # dioptra # dioptra (pyproject.toml) @@ -393,12 +421,16 @@ kiwisolver==1.4.5 # via matplotlib lazy-loader==0.4 # via scikit-image +lightning-utilities==0.11.2 + # via torchmetrics locket==1.0.0 # via # distributed # partd lsprotocol==2023.0.1 # via pygls +maite[all-interop]==0.4.0 + # via dioptra (pyproject.toml) mako==1.3.3 # via alembic markdown==3.6 @@ -413,7 +445,7 @@ markupsafe==2.1.5 # mako # nbconvert # werkzeug -marshmallow==3.21.1 +marshmallow==3.21.2 # via # dioptra # dioptra (pyproject.toml) @@ -439,8 +471,14 @@ mdurl==0.1.2 # via markdown-it-py mistune==3.0.2 # via nbconvert +mkl==2021.4.0 + # via torch mlflow==2.12.1 # via dioptra (pyproject.toml) +modelscan==0.7.3 + # via dioptra (pyproject.toml) +mpmath==1.3.0 + # via sympy msgpack==1.0.8 # via # distributed @@ -453,6 +491,8 @@ multimethod==1.11.2 # via # dioptra # dioptra (pyproject.toml) +multiprocess==0.70.16 + # via datasets mypy-extensions==1.0.0 # via prefect nbclient==0.10.0 @@ -470,25 +510,30 @@ nbformat==5.10.4 nest-asyncio==1.6.0 # via ipykernel networkx==3.3 - # via scikit-image + # via + # scikit-image + # torch notebook==7.1.3 # via jupyter notebook-shim==0.2.4 # via # jupyterlab # notebook -nrtk==0.3.1 +nrtk==0.4.0 # via dioptra (pyproject.toml) -numpy==1.26.4 +numpy==1.24.0 # via # adversarial-robustness-toolbox # contourpy + # datasets # dioptra # dioptra (pyproject.toml) # imageio # imgaug + # maite # matplotlib # mlflow + # modelscan # nrtk # opencv-python # pandas @@ -505,6 +550,9 @@ numpy==1.26.4 # smqtk-image-io # tensorboard # tifffile + # torchmetrics + # torchvision + # transformers opencv-python==4.9.0.80 # via # dioptra (pyproject.toml) @@ -517,13 +565,16 @@ packaging==24.0 # via # build # dask + # datasets # distributed # docker + # huggingface-hub # ipykernel # jupyter-server # jupyterlab # jupyterlab-server # lazy-loader + # lightning-utilities # marshmallow # matplotlib # mlflow @@ -535,9 +586,12 @@ packaging==24.0 # qtpy # scikit-image # sphinx + # torchmetrics # tox + # transformers pandas==2.2.2 # via + # datasets # dioptra # dioptra (pyproject.toml) # mlflow @@ -545,7 +599,7 @@ pandocfilters==1.5.1 # via nbconvert parso==0.8.4 # via jedi -partd==1.4.1 +partd==1.4.2 # via dask passlib==1.7.4 # via @@ -562,6 +616,7 @@ pillow==10.3.0 # nrtk # scikit-image # smqtk-image-io + # torchvision pip-tools==7.4.1 # via dioptra (pyproject.toml) platformdirs==4.2.1 @@ -576,6 +631,8 @@ pluggy==1.5.0 # tox prefect==1.4.1 # via dioptra (pyproject.toml) +pretty-errors==1.2.25 + # via torchmetrics prometheus-client==0.20.0 # via jupyter-server prompt-toolkit==3.0.43 @@ -596,13 +653,17 @@ pure-eval==0.2.2 # via stack-data pyarrow==15.0.2 # via + # datasets # dioptra (pyproject.toml) # mlflow -pybsm==0.1.1 +pyarrow-hotfix==0.6 + # via datasets +pybsm==0.2.0 # via nrtk pycocotools==2.0.7 # via # dioptra (pyproject.toml) + # maite # nrtk pycodestyle==2.11.1 # via @@ -617,7 +678,7 @@ pyflakes==3.2.0 # via flake8 pygls==1.3.1 # via esbonio -pygments==2.17.2 +pygments==2.18.0 # via # ipython # jupyter-console @@ -689,20 +750,24 @@ pyyaml==6.0.1 # via # cookiecutter # dask + # datasets # dioptra # dioptra (pyproject.toml) # distributed + # huggingface-hub # jupyter-events # mlflow # prefect -pyzmq==26.0.2 + # timm + # transformers +pyzmq==26.0.3 # via # ipykernel # jupyter-client # jupyter-console # jupyter-server # qtconsole -qtconsole==5.5.1 +qtconsole==5.5.2 # via jupyter qtpy==2.4.1 # via qtconsole @@ -713,23 +778,28 @@ redis==5.0.4 # dioptra # dioptra (pyproject.toml) # rq -referencing==0.35.0 +referencing==0.35.1 # via # jsonschema # jsonschema-specifications # jupyter-events +regex==2024.4.28 + # via transformers requests==2.31.0 # via # cookiecutter + # datasets # dioptra # dioptra (pyproject.toml) # docker + # huggingface-hub # jupyterlab-server # kaggle # mlflow # prefect # smqtk-dataprovider # sphinx + # transformers rfc3339-validator==0.1.4 # via # jsonschema @@ -739,17 +809,23 @@ rfc3986-validator==0.1.1 # jsonschema # jupyter-events rich==13.7.1 - # via dioptra (pyproject.toml) -rpds-py==0.18.0 + # via + # dioptra (pyproject.toml) + # modelscan +rpds-py==0.18.1 # via # jsonschema # referencing -rq==1.16.1 +rq==1.16.2 # via # dioptra # dioptra (pyproject.toml) s3transfer==0.10.1 # via boto3 +safetensors==0.4.3 + # via + # timm + # transformers scikit-image==0.23.2 # via # imgaug @@ -843,7 +919,7 @@ sphinxcontrib-qthelp==1.0.7 # via sphinx sphinxcontrib-serializinghtml==1.1.10 # via sphinx -sqlalchemy==2.0.29 +sqlalchemy==2.0.30 # via # alembic # dioptra @@ -858,14 +934,20 @@ structlog==24.1.0 # via # dioptra # dioptra (pyproject.toml) +sympy==1.12 + # via torch tabulate==0.9.0 # via prefect +tbb==2021.12.0 + # via mkl tblib==3.0.0 # via distributed tensorboard==2.16.2 # via dioptra (pyproject.toml) tensorboard-data-server==0.7.2 # via tensorboard +tensorflow-io-gcs-filesystem==0.31.0 + # via modelscan terminado==0.18.1 # via # jupyter-server @@ -874,21 +956,41 @@ text-unidecode==1.3 # via python-slugify threadpoolctl==3.5.0 # via scikit-learn -tifffile==2024.4.24 +tifffile==2024.5.3 # via scikit-image time-machine==2.14.1 # via pendulum +timm==0.9.16 + # via maite tinycss2==1.3.0 # via nbconvert +tokenizers==0.19.1 + # via transformers toml==0.10.2 # via prefect tomli==2.0.1 # via dioptra (pyproject.toml) +tomlkit==0.12.4 + # via modelscan toolz==0.12.1 # via # dask # distributed # partd +torch==2.3.0 + # via + # maite + # timm + # torchmetrics + # torchvision +torcheval==0.0.7 + # via maite +torchmetrics==1.4.0 + # via maite +torchvision==0.18.0 + # via + # maite + # timm tornado==6.4 # via # distributed @@ -900,11 +1002,15 @@ tornado==6.4 # terminado tox==4.15.0 # via dioptra (pyproject.toml) -tqdm==4.66.2 +tqdm==4.66.4 # via # adversarial-robustness-toolbox + # datasets + # huggingface-hub # kaggle + # maite # nrtk + # transformers traitlets==5.14.3 # via # comm @@ -922,6 +1028,8 @@ traitlets==5.14.3 # nbconvert # nbformat # qtconsole +transformers==4.40.2 + # via maite types-python-dateutil==2.9.0.20240316 # via arrow typing-extensions==4.11.0 @@ -929,8 +1037,13 @@ typing-extensions==4.11.0 # alembic # dioptra # dioptra (pyproject.toml) + # huggingface-hub # ipython + # lightning-utilities + # maite # sqlalchemy + # torch + # torcheval tzdata==2024.1 # via # pandas @@ -961,7 +1074,7 @@ webencodings==0.5.1 # tinycss2 websocket-client==1.8.0 # via jupyter-server -werkzeug==3.0.2 +werkzeug==3.0.3 # via # dioptra # dioptra (pyproject.toml) @@ -976,6 +1089,8 @@ wheel==0.43.0 # pip-tools widgetsnbextension==4.0.10 # via ipywidgets +xxhash==3.4.1 + # via datasets yarl==1.9.4 # via aiohttp zict==3.0.0 diff --git a/task-plugins/dioptra_builtins/modelscan/modelscan.py b/task-plugins/dioptra_builtins/modelscan/modelscan.py new file mode 100644 index 000000000..2b47ddf7b --- /dev/null +++ b/task-plugins/dioptra_builtins/modelscan/modelscan.py @@ -0,0 +1,110 @@ +# This Software (Dioptra) is being made available as a public service by the +# National Institute of Standards and Technology (NIST), an Agency of the United +# States Department of Commerce. This software was developed in part by employees of +# NIST and in part by NIST contractors. Copyright in portions of this software that +# were developed by NIST contractors has been licensed or assigned to NIST. Pursuant +# to Title 17 United States Code Section 105, works of NIST employees are not +# subject to copyright protection in the United States. However, NIST may hold +# international copyright in software created by its employees and domestic +# copyright (or licensing rights) in portions of software that were assigned or +# licensed to NIST. To the extent that NIST holds copyright in this software, it is +# being made available under the Creative Commons Attribution 4.0 International +# license (CC BY 4.0). The disclaimers of the CC BY 4.0 license apply to all parts +# of the software developed or licensed by NIST. +# +# ACCESS THE FULL CC BY 4.0 LICENSE HERE: +# https://creativecommons.org/licenses/by/4.0/legalcode +from __future__ import annotations + +from types import FunctionType +from typing import Any, Dict, List, Union + +import mlflow +import modelscan +import os +import re +import structlog +import subprocess +import tempfile +from structlog.stdlib import BoundLogger + +from dioptra import pyplugs +from dioptra.sdk.utilities.decorators import require_package +from mlflow.tracking import MlflowClient + +LOGGER: BoundLogger = structlog.stdlib.get_logger() + +def trim_key(key): + return key.replace('-', '').strip() +def convert_to_int(value): + try: + value = int(value) + return value + except ValueError: + return value +@pyplugs.register +def scan_model(mlflow_run_id: str) -> Any: + """ + Run the modelscan library on a model stored in MlFlow. + + Parameters: + mlflow_run_id (str): The run_id of the job that stored a model in MlFlow. + + Returns: + result (dict): A dictionary storing the modelscan results: + output (str): The standard output from the modelscan command. The output will be logged in MlFlow. + error (str): The standard error from the modelscan command. + return_code (int): The return code of the modelscan command. + """ + + #get the artifact_path for the huggingface model just stored in mlflow + client = MlflowClient() + run_id = mlflow_run_id + artifact_path = "model/data" + artifact_uri = client.get_run(mlflow_run_id).info.artifact_uri + model_artifact_path = f"{artifact_uri}/{artifact_path}" + print(model_artifact_path) + + #download the model file to a localized temp file + with tempfile.TemporaryDirectory() as tmpdir: + local_path = mlflow.artifacts.download_artifacts(run_id=run_id, artifact_path=artifact_path, dst_path=tmpdir) + model_file_path = os.path.join(local_path, "model.pth") + + if os.path.exists(model_file_path): + scan_command = ["modelscan", "--path", model_file_path, "--show-skipped"] + + try: + result = subprocess.run(scan_command, capture_output=True, text=True) + output = result.stdout + error = result.stderr + return_code = result.returncode + + #record scan results as metrics and artifacts in mlflow + with open("scan_output.txt", "w") as f: + f.write(output) + + mlflow.log_artifact("scan_output.txt") + result = {} + + for line in output.split('\n'): + if ': ' in line: + key, value = line.split(': ') + result[key.strip(' .')] = value.strip() + + for key, value in result.items(): + trimmed_key = trim_key(key) + result[key] = convert_to_int(value) + if isinstance(result[key], int) == True: + mlflow.log_metric(trimmed_key, value) + + total_skipped = int(re.search(r"Total skipped:\s+(\d+)", output).group(1)) + mlflow.log_metric("total_skipped", total_skipped) + mlflow.log_metric("return_code", return_code) + + except Exception as e: + raise Exception(f"An error occurred while running modelscan: {str(e)}") + + else: + print("Error: Model file path does not exist.") + + return result diff --git a/task-plugins/dioptra_builtins/nrtk/nrtk.py b/task-plugins/dioptra_builtins/nrtk/nrtk.py new file mode 100644 index 000000000..54caec290 --- /dev/null +++ b/task-plugins/dioptra_builtins/nrtk/nrtk.py @@ -0,0 +1,249 @@ +# This Software (Dioptra) is being made available as a public service by the +# National Institute of Standards and Technology (NIST), an Agency of the United +# States Department of Commerce. This software was developed in part by employees of +# NIST and in part by NIST contractors. Copyright in portions of this software that +# were developed by NIST contractors has been licensed or assigned to NIST. Pursuant +# to Title 17 United States Code Section 105, works of NIST employees are not +# subject to copyright protection in the United States. However, NIST may hold +# international copyright in software created by its employees and domestic +# copyright (or licensing rights) in portions of software that were assigned or +# licensed to NIST. To the extent that NIST holds copyright in this software, it is +# being made available under the Creative Commons Attribution 4.0 International +# license (CC BY 4.0). The disclaimers of the CC BY 4.0 license apply to all parts +# of the software developed or licensed by NIST. +# +# ACCESS THE FULL CC BY 4.0 LICENSE HERE: +# https://creativecommons.org/licenses/by/4.0/legalcode +from __future__ import annotations + +from pathlib import Path +from typing import Any +import numpy as np +from PIL import Image +import os +import json +from io import BytesIO +import base64 +import shutil +import random + +import mlflow +import tempfile +from mlflow.entities import Run as MlflowRun +from mlflow.tracking import MlflowClient + +import torch +from torchvision.transforms import functional as F +from torch.utils.data import Dataset + +import nrtk +from nrtk.impls.perturb_image.generic.skimage.random_noise import ( + SaltNoisePerturber, + PepperNoisePerturber, + SaltAndPepperNoisePerturber, + GaussianNoisePerturber, + SpeckleNoisePerturber +) +#from nrtk.impls.perturb_image.generic.cv2.blur import ( +# AverageBlurPerturber, +# GaussianBlurPerturber, +# MedianBlurPerturber +#) +from nrtk.impls.perturb_image.generic.PIL.enhance import ( + BrightnessPerturber, + ColorPerturber, + ContrastPerturber, + SharpnessPerturber +) + +from dioptra import pyplugs + +############################################################ +# CREATE PERTURBED DATASET # +############################################################ + +def image_to_base64(image: Image.Image) -> str: + buffered = BytesIO() + image.save(buffered, format="PNG") + return base64.b64encode(buffered.getvalue()).decode("utf-8") + +def image_to_numpy(image: Image.Image) -> np.array: + return np.array(image) + +def numpy_to_image(array: np.ndarray) -> Image.Image: + return Image.fromarray(array) + +def get_perturber(perturbation: str, seed: int, amount: float, salt_vs_pepper: float, var: float, mean: int, ksize: int, factor: float): + perturber_mapping = { + "SaltNoisePerturber": SaltNoisePerturber(rng=seed, amount=amount), + "PepperNoisePerturber": PepperNoisePerturber(rng=seed, amount=amount), + "SaltAndPepperNoisePerturber": SaltAndPepperNoisePerturber(rng=seed, amount=amount, salt_vs_pepper=salt_vs_pepper), + "GaussianNoisePerturber": GaussianNoisePerturber(rng=seed, mean=mean, var=var), + "SpeckleNoisePerturber": SpeckleNoisePerturber(rng=seed, mean=mean, var=var), + #"AverageBlurPerturber": AverageBlurPerturber(ksize=ksize), + #"GaussianBlurPerturber": GaussianBlurPerturber(ksize=ksize), + #"MedianBlurPerturber": MedianBlurPerturber(ksize=ksize), + "BrightnessPerturber": BrightnessPerturber(factor=factor), + "ColorPerturber": ColorPerturber(factor=factor), + "ContrastPerturber": ContrastPerturber(factor=factor), + "SharpnessPerturber": SharpnessPerturber(factor=factor), + } + return perturber_mapping.get(perturbation) + +def serialize_metadata(metadata): + if isinstance(metadata, dict): + return metadata + elif hasattr(metadata, '__dict__'): + return metadata.__dict__ + else: + return str(metadata) + +@pyplugs.register +def perturb_images(dataset: Any, perturbation: str, seed: int, amount: float, salt_vs_pepper: float, var: float, mean: int, ksize: int, factor: float) -> Any: + + perturber = get_perturber(perturbation, seed, amount, salt_vs_pepper, var, mean, ksize, factor) + + if perturber is None: + raise ValueError(f"Unknown perturbation type: {perturbation}") + + def apply_perturbation(original_dataset): + original_image = image_to_numpy(original_dataset['image']) + perturbed_image = perturber(original_image) + original_dataset['image'] = numpy_to_image(perturbed_image) + return original_dataset + + perturbed_dataset = [apply_perturbation(img_data) for img_data in dataset] + + with tempfile.TemporaryDirectory() as tmp_dir: + annotations = [] + for i, perturbed_image in enumerate(perturbed_dataset): + image_path = os.path.join(tmp_dir, f"perturbed_image_{i}.png") + perturbed_image['image'].save(image_path) + + metadata_serializable = {key: serialize_metadata(value) for key, value in perturbed_image.items() if key != 'image'} + metadata_serializable['image_path'] = image_path + annotations.append(metadata_serializable) + + annotations_path = os.path.join(tmp_dir, 'annotations.json') + with open(annotations_path, 'w') as f: + json.dump(annotations, f) + + mlflow.log_artifacts(tmp_dir, artifact_path="perturbed_dataset") + + return perturbed_dataset + +############################################################ +# CREATE OBJECT DETECTION DATASET CLASS # +############################################################ + +class ObjectDetectionDataset(Dataset): + def __init__(self, annotations, images, transform=None): + self.annotations = annotations + self.images = images + self.transform = transform + + def __len__(self): + return len(self.annotations) + + def __getitem__(self, idx): + annotation = self.annotations[idx] + image_filename = os.path.basename(annotation['image_path']) + image = self.images[image_filename] + + objects = annotation['objects'] + boxes = objects['boxes'] + labels = objects['labels'] + bbox_id = objects.get('bbox_id', []) + area = objects.get('area', []) + + data = { + "image": image, + "objects": { + "boxes": boxes, + "labels": labels, + "bbox_id": bbox_id, + "area": area + } + } + + if self.transform: + data = self.transform(data) + + return data + + def set_transform(self, transform): + self.transform = transform + +def to_tensor(image): + return F.to_tensor(image) + +def transform_function(data, shape, totensor=False): + image = data['image'].convert("RGB") + if totensor: + image = to_tensor(image.resize(shape)) + else: + image = image.resize(shape) + + data["image"] = image + data["objects"]["boxes"] = torch.tensor(data["objects"]["boxes"], dtype=torch.float32) if totensor else data["objects"]["boxes"] + return data + +def set_transform(dataset, shape, totensor=False): + def transform(data): + return transform_function(data, shape, totensor) + dataset.set_transform(transform) + +############################################################ +# PULL PERTURBED DATASET ARTIFACT # +############################################################ + +@pyplugs.register +def get_perturbed_dataset(mlflow_run_id: str) -> Any: + """Pulls a perturbed image dataset from mlflow and converts it into a MAITE readable + object detection dataset. + + Args: + mlflow_run_id: A string representing the run_id from the job that perturbed the original + dataset and registered the perturbed images into mlflow. + + Returns: + One ObjectDetectionDataset populated with NRTK perturbed images. + """ + client = MlflowClient() + run_id = mlflow_run_id + artifact_path = "perturbed_dataset" + artifact_uri = client.get_run(run_id).info.artifact_uri + dataset_artifact_path = f"{artifact_uri}/{artifact_path}" + + with tempfile.TemporaryDirectory() as tmpdir: + data_annotation_path = mlflow.artifacts.download_artifacts(run_id=run_id, artifact_path=artifact_path, dst_path=tmpdir) + + images_dir = os.path.join(tmpdir, 'images') + if not os.path.exists(images_dir): + os.makedirs(images_dir) + + annotations_file = None + + for filename in os.listdir(data_annotation_path): + file_path = os.path.join(data_annotation_path, filename) + if filename == 'annotations.json': + annotations_file = file_path + elif filename.endswith('.png') or filename.endswith('.jpg') or filename.endswith('.jpeg'): + shutil.move(file_path, images_dir) + + if annotations_file is None: + raise FileNotFoundError("Annotations file not found in the artifact path.") + + with open(annotations_file, 'r') as f: + annotations = json.load(f) + + images = {} + for annotation in annotations: + image_filename = annotation['image_path'].split('/')[-1] + image_path = os.path.join(images_dir, image_filename) + image = Image.open(image_path) + images[image_filename] = image + + perturbed_dataset = ObjectDetectionDataset(annotations, images) + + return perturbed_dataset