Move to ml-energy

.github/workflows/push_docker.yaml

@@ -40,7 +40,9 @@ jobs:
         id: meta
         uses: docker/metadata-action@v4
         with:
-          images: symbioticlab/zeus
+          images: |
+            symbioticlab/zeus
+            mlenergy/zeus
           tags: |
             type=ref,event=branch
             type=ref,event=tag
@@ -55,6 +57,6 @@ jobs:
           push: true
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=registry,ref=symbioticlab/zeus:buildcache
-          cache-to: type=registry,ref=symbioticlab/zeus:buildcache,mode=max
+          cache-from: type=registry,ref=mlenergy/zeus:buildcache
+          cache-to: type=registry,ref=mlenergy/zeus:buildcache,mode=max
           build-args: TARGETARCH=amd64

Dockerfile

@@ -15,7 +15,7 @@
 # Build instructions
 #   If you're building this image locally, make sure you specify `TARGETARCH`.
 #   Currently, this image supports `amd64` and `arm64`. For instance:
-#     docker build -t symbioticlab/zeus:master --build-arg TARGETARCH=amd64 .
+#     docker build -t mlenergy/zeus:master --build-arg TARGETARCH=amd64 .
 
 FROM nvidia/cuda:11.8.0-base-ubuntu22.04
 

README.md

@@ -8,10 +8,10 @@
 </div>
 
 [![NSDI23 paper](https://custom-icon-badges.herokuapp.com/badge/NSDI'23-paper-b31b1b.svg)](https://www.usenix.org/conference/nsdi23/presentation/you)
-[![Docker Hub](https://badgen.net/docker/pulls/symbioticlab/zeus?icon=docker&label=Docker%20pulls)](https://hub.docker.com/r/symbioticlab/zeus)
+[![Docker Hub](https://badgen.net/docker/pulls/symbioticlab/zeus?icon=docker&label=Docker%20pulls)](https://hub.docker.com/r/mlenergy/zeus)
 [![Slack workspace](https://badgen.net/badge/icon/Join%20workspace/611f69?icon=slack&label=Slack)](https://join.slack.com/t/zeus-ml/shared_invite/zt-1najba5mb-WExy7zoNTyaZZfTlUWoLLg)
-[![Homepage build](https://github.com/SymbioticLab/Zeus/actions/workflows/deploy_homepage.yaml/badge.svg)](https://github.com/SymbioticLab/Zeus/actions/workflows/deploy_homepage.yaml)
-[![Apache-2.0 License](https://custom-icon-badges.herokuapp.com/github/license/SymbioticLab/Zeus?logo=law)](/LICENSE)
+[![Homepage build](https://github.com/ml-energy/zeus/actions/workflows/deploy_homepage.yaml/badge.svg)](https://github.com/ml-energy/zeus/actions/workflows/deploy_homepage.yaml)
+[![Apache-2.0 License](https://custom-icon-badges.herokuapp.com/github/license/ml-energy/zeus?logo=law)](/LICENSE)
 
 ---
 **Project News** ⚡ 
@@ -134,7 +134,7 @@ docker run -it \
     --gpus all                  `# Mount all GPUs` \
     --cap-add SYS_ADMIN         `# Needed to change the power limit of the GPU` \
     --ipc host                  `# PyTorch DataLoader workers need enough shm` \
-    symbioticlab/zeus:latest \
+    mlenergy/zeus:latest \
     bash
 ```
 
@@ -154,7 +154,7 @@ Refer to [Extending Zeus](https://ml.energy/zeus/extend/) for details.
 
 ## Carbon-Aware Zeus
 
-The use of GPUs for training DNNs results in high carbon emissions and energy consumption. Building on top of Zeus, we introduce *Chase* -- a carbon-aware solution. *Chase* dynamically controls the energy consumption of GPUs; adapts to shifts in carbon intensity during DNN training, reducing carbon footprint with minimal compromises on training performance. To proactively adapt to shifting carbon intensity, a lightweight machine learning algorithm is used to forecast the carbon intensity of the upcoming time frame. For more details on Chase, please refer to our [paper](https://symbioticlab.org/publications/files/chase:ccai23/chase-ccai23.pdf) and the [chase branch](https://github.com/SymbioticLab/Zeus/tree/chase). 
+The use of GPUs for training DNNs results in high carbon emissions and energy consumption. Building on top of Zeus, we introduce *Chase* -- a carbon-aware solution. *Chase* dynamically controls the energy consumption of GPUs; adapts to shifts in carbon intensity during DNN training, reducing carbon footprint with minimal compromises on training performance. To proactively adapt to shifting carbon intensity, a lightweight machine learning algorithm is used to forecast the carbon intensity of the upcoming time frame. For more details on Chase, please refer to our [paper](https://symbioticlab.org/publications/files/chase:ccai23/chase-ccai23.pdf) and the [chase branch](https://github.com/ml-energy/zeus/tree/chase). 
 
 
 ## Citation

docs/extend.md

@@ -18,7 +18,7 @@ There are two ways to run Zeus: trace-driven and end-to-end.
 ### Trace-driven Zeus
 
 The Zeus simulator ([`Simulator`][zeus.simulate.Simulator]) accepts one [`BatchSizeOptimizer`][zeus.policy.BatchSizeOptimizer] and [`PowerLimitOptimizer`][zeus.policy.PowerLimitOptimizer] in its constructor.
-A full-example can be found in [`examples/trace_driven`](https://github.com/SymbioticLab/Zeus/tree/master/examples/trace_driven/).
+A full-example can be found in [`examples/trace_driven`](https://github.com/ml-energy/zeus/tree/master/examples/trace_driven/).
 
 ### End-to-end Zeus
 

docs/getting_started/environment.md

@@ -11,7 +11,7 @@ We encourage users to do everything inside a Docker container spawned with our p
 
 ## Zeus Docker image
 
-We provide a pre-built Docker image in [Docker Hub](https://hub.docker.com/r/symbioticlab/zeus){.external}.
+We provide a pre-built Docker image in [Docker Hub](https://hub.docker.com/r/mlenergy/zeus){.external}.
 On top of the `nvidia/cuda:11.8.0-devel-ubuntu22.04` image, the following are added:
 
 1. Miniconda3 23.3.1, PyTorch 2.0.1, torchvision 0.15.2
@@ -26,7 +26,7 @@ On top of the `nvidia/cuda:11.8.0-devel-ubuntu22.04` image, the following are ad
 !!! Tip
     If you want to build our Docker image locally, you should specify `TARGETARCH` to be one of `amd64` or `arm64` based on your environment's architecture:
     ```sh
-    docker build -t symbioticlab/zeus:master --build-arg TARGETARCH=amd64 .
+    docker build -t mlenergy/zeus:master --build-arg TARGETARCH=amd64 .
     ```
 
 
@@ -44,7 +44,7 @@ docker run -it \
     --gpus all \                      # (1)!
     --cap-add SYS_ADMIN \           # (2)!
     --ipc host \                  # (3)!
-    symbioticlab/zeus:latest \
+    mlenergy/zeus:latest \
     bash
 ```
 
@@ -64,7 +64,7 @@ docker run -it \
     --ipc host \                           # (3)!
     -v $(pwd):/workspace/zeus \          # (4)!
     -v /data/imagenet:/data/imagenet:ro \
-    symbioticlab/zeus:latest \
+    mlenergy/zeus:latest \
     bash
 ```
 

docs/getting_started/index.md

@@ -73,7 +73,7 @@ We provide ready-to-go [Docker images](environment.md).
 After going through the prerequisites, [`GlobalPowerLimitOptimizer`][zeus.optimizer.power_limit.GlobalPowerLimitOptimizer] into your training script.
 
 Refer to
-[our integration example with ImageNet](https://github.com/SymbioticLab/Zeus/tree/master/examples/imagenet/)
+[our integration example with ImageNet](https://github.com/ml-energy/zeus/tree/master/examples/imagenet/)
 for complete running examples for single-GPU and multi-GPU data parallel training.
 
 ```python
@@ -110,7 +110,7 @@ for epoch in range(100):
 
 !!! Info
     We plan to integrate [`ZeusMaster`][zeus.run.ZeusMaster] with an MLOps platform like [KubeFlow](https://www.kubeflow.org/).
-    Let us know about your preferences, use cases, and expectations by [posting an issue](https://github.com/SymbioticLab/Zeus/issues/new?assignees=&labels=&template=feature_request.md&title=Regarding%20Integration%20with%20MLOps%20Platroms)!
+    Let us know about your preferences, use cases, and expectations by [posting an issue](https://github.com/ml-energy/zeus/issues/new?assignees=&labels=&template=feature_request.md&title=Regarding%20Integration%20with%20MLOps%20Platroms)!
 
 The cost-optimal batch size is located *across* multiple job runs using a Multi-Armed Bandit algorithm.
 First, go through the steps for non-recurring jobs. 
@@ -119,4 +119,4 @@ Then, you can use [`ZeusMaster`][zeus.run.ZeusMaster] to drive recurring jobs an
 
 This example will come in handy:
 
-- [Running trace-driven simulation on single recurring jobs and the Alibaba GPU cluster trace](https://github.com/SymbioticLab/Zeus/tree/master/examples/trace_driven){.external}
+- [Running trace-driven simulation on single recurring jobs and the Alibaba GPU cluster trace](https://github.com/ml-energy/zeus/tree/master/examples/trace_driven){.external}

docs/getting_started/installing_and_building.md

@@ -5,7 +5,7 @@ This document explains how to install the [`zeus`][zeus] Python package.
 !!! Tip
     We encourage users to utilize our Docker image. Please refer to [Environment setup](./environment.md). Quick command:
     ```bash
-    docker run -it --gpus all --cap-add SYS_ADMIN --shm-size 64G symbioticlab/zeus:latest bash
+    docker run -it --gpus all --cap-add SYS_ADMIN --ipc host mlenergy/zeus:latest bash
     ```
 
 
@@ -33,15 +33,15 @@ pip install zeus-ml
 If you would like to follow the `HEAD`:
 
 ```bash
-git clone https://github.com/SymbioticLab/Zeus.git zeus
+git clone https://github.com/ml-energy/zeus.git zeus
 cd zeus
 pip install .
 ```
 
 For those would like to make changes to the source code and run them, we suggest an editable installation:
 
 ```bash
-git clone https://github.com/SymbioticLab/Zeus.git zeus
+git clone https://github.com/ml-energy/zeus.git zeus
 cd zeus
 pip install -e .
 ```
@@ -62,7 +62,7 @@ All dependencies are pre-installed if you're using our Docker image.
 
 ### Configuration
 
-You can change the `#define`s in lines 28 to 31 in [`zemo/zemo.hpp`](https://github.com/SymbioticLab/Zeus/tree/master/zeus_monitor/zemo/zemo.hpp) to configure what information from the GPU is polled.
+You can change the `#define`s in lines 28 to 31 in [`zemo/zemo.hpp`](https://github.com/ml-energy/zeus/tree/master/zeus_monitor/zemo/zemo.hpp) to configure what information from the GPU is polled.
 By default, only the momentary power draw of the GPU will be collected.
 
 ### Building the power monitor

docs/index.md

@@ -11,7 +11,7 @@ hide:
 </div>
 
 !!! Success "Join the Zeus Slack workspace!"
-    Zeus exceeded 100k+ pulls on [Docker Hub](https://hub.docker.com/r/symbioticlab/zeus)!
+    Zeus exceeded 100k+ pulls on [Docker Hub](https://hub.docker.com/r/mlenergy/zeus)!
     The Zeus team is always happy to chat with Zeus users and help out.
     Reach out to us in the [Zeus Slack workspace](https://join.slack.com/t/zeus-ml/shared_invite/zt-1najba5mb-WExy7zoNTyaZZfTlUWoLLg).
 
@@ -100,7 +100,7 @@ Zeus is part of [The ML.ENERGY Initiative](https://ml.energy).
 ## Getting Started
 
 Refer to [Getting Started](getting_started/index.md) for instructions on environment setup, installation, and integration.
-We also provide integration [examples](https://github.com/SymbioticLab/Zeus/tree/master/examples) in our GitHub repository.
+We also provide integration [examples](https://github.com/ml-energy/zeus/tree/master/examples) in our GitHub repository.
 
 ## Extending Zeus
 

mkdocs.yml

@@ -6,8 +6,8 @@ site_description: Deep learning energy measurement and optimization
 edit_uri: ""
 
 # Repository
-repo_name: SymbioticLab/Zeus
-repo_url: https://github.com/SymbioticLab/Zeus
+repo_name: ml-energy/zeus
+repo_url: https://github.com/ml-energy/zeus
 
 # Copyright
 copyright: Copyright © 2023 Jae-Won Chung
@@ -122,7 +122,7 @@ extra:
   social:
     - name: Zeus GitHub repository
       icon: fontawesome/brands/github
-      link: https://github.com/SymbioticLab/Zeus
+      link: https://github.com/ml-energy/zeus
     - name: Zeus Docker Hub registry
       icon: fontawesome/brands/docker
-      link: https://hub.docker.com/r/symbioticlab/zeus
+      link: https://hub.docker.com/r/mlenergy/zeus

pyproject.toml

@@ -31,7 +31,7 @@ dependencies = [
 dynamic = ["version"]
 
 [project.urls]
-Repository = "https://github.com/SymbioticLab/Zeus"
+Repository = "https://github.com/ml-energy/zeus"
 Homepage = "https://ml.energy/zeus"
 Documentation = "https://ml.energy/zeus"
 

zeus/run/dataloader.py

@@ -80,7 +80,7 @@ class ZeusDataLoader(DataLoader):
     with five specific steps, which are noted below in the comments.
 
     Please refer to
-    [our integration example with ImageNet](https://github.com/SymbioticLab/Zeus/tree/master/examples/imagenet/train.py)
+    [our integration example with ImageNet](https://github.com/ml-energy/zeus/tree/master/examples/imagenet/train.py)
     for a complete example.
 
     ```python