Open davidjurado opened 9 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/695/head:feature/mlcube_3d_unet && git checkout feature/mlcube_3d_unet cd ./image_segmentation/pytorch/mlcube
Inside the mlcube directory run the following command to check implemented tasks.
mlcube describe
Download dataset.
mlcube run --task=download_data -Pdocker.build_strategy=always
Process dataset.
mlcube run --task=process_data -Pdocker.build_strategy=always
Train SSD.
mlcube run --task=train -Pdocker.build_strategy=always
You can execute the complete pipeline with one single command.
mlcube run --task=download_data,process_data,train -Pdocker.build_strategy=always
You can run a quick demo that first downloads a tiny dataset and then executes a short training workload.
mlcube run --task=download_demo,demo -Pdocker.build_strategy=always
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MLCube for 3D Unet
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.
Process dataset.
Train SSD.
Execute the complete pipeline
You can execute the complete pipeline with one single command.
Run a quick demo
You can run a quick demo that first downloads a tiny dataset and then executes a short training workload.