Open davidjurado opened 8 months ago
MLCube™ GitHub repository. MLCube™ wiki.
An important requirement is that you must have Docker installed.
# Create Python environment and install MLCube Docker runner virtualenv -p python3 ./env && source ./env/bin/activate && pip install pip==24.0 pip install mlcube-docker # Fetch the implementation from GitHub git clone https://github.com/mlcommons/training && cd ./training git fetch origin pull/696/head:feature/mlcube_sd && git checkout feature/mlcube_sd cd ./stable_diffusion/mlcube
Inside the mlcube directory run the following command to check implemented tasks.
mlcube describe
Download dataset.
mlcube run --task=download_data
Download models.
mlcube run --task=download_models
Train.
mlcube run --task=train
Here is a video explaining the demo steps:
Download demo dataset.
mlcube run --task=download_demo
Train demo.
mlcube run --task=demo
You can execute the complete pipeline with one single command.
mlcube run --task=download_data,download_models,train
Tested in an Nvidia A100 (40G)
mlcube run --task=download_demo,download_models,demo
Note: To rebuild the image use the flag: -Pdocker.build_strategy=always during the mlcube run command.
-Pdocker.build_strategy=always
mlcube run
MLCommons CLA bot All contributors have signed the MLCommons CLA ✍️ ✅
MLCube for Stable Diffusion
MLCube™ GitHub repository. MLCube™ wiki.
Project setup
An important requirement is that you must have Docker installed.
Inside the mlcube directory run the following command to check implemented tasks.
MLCube tasks
Download dataset.
Download models.
Train.
Here is a video explaining the demo steps:
Download demo dataset.
Download models.
Train demo.
Execute the complete pipeline
You can execute the complete pipeline with one single command.
Tested in an Nvidia A100 (40G)
Note: To rebuild the image use the flag:
-Pdocker.build_strategy=always
during themlcube run
command.