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  • NVIDIA
    Merlin PyTorch Training
    Container
    NVIDIA
    Merlin PyTorch Training

    This container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with PyTorch.

  • LayerLabelCreated
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4RUN
    /bin/bash -c #(nop) CMD ["/bin/bash"]
    		03/09/2021 6:49 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4RUN
    /bin/bash -c #(nop) ENTRYPOINT []
    		03/09/2021 6:49 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4RUN
    /bin/bash -c #(nop) HEALTHCHECK &{["NONE"] "0s" "0s" "0s" '\x00'}
    		03/09/2021 6:49 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4RUN
    CC=8 CONDA_ENV=rapids CXX=8 NVTAB_VER=v0.4.0 RAPIDS_VER=0.18 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c echo $(du -h --max-depth=1 /)
    		03/09/2021 6:49 AM UTC
    sha256:f49a1615757ba8eaccd96e0c567138e3331e1e3a289d08a558c02f916af4504bRUN
    CC=8 CONDA_ENV=rapids CXX=8 NVTAB_VER=v0.4.0 RAPIDS_VER=0.18 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; conda clean --all -y
    		03/09/2021 6:49 AM UTC
    sha256:784807f9e26dda959ffb333bd1a1cb1c37b33a184acb625e68ba4b8794902fe8RUN
    CC=8 CONDA_ENV=rapids CXX=8 NVTAB_VER=v0.4.0 RAPIDS_VER=0.18 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; conda env config vars set PYTHONPATH=$PYTHONPATH:/opt/conda/lib/python3.8/site-packages/
    		03/09/2021 6:48 AM UTC
    sha256:e96bc8f6a825278d5de49c4342a6841193af5f2ab3d52cf23426d6c6520cafb5RUN
    CC=8 CONDA_ENV=rapids CXX=8 NVTAB_VER=v0.4.0 RAPIDS_VER=0.18 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; apt update; apt install -y graphviz ;
    		03/09/2021 6:48 AM UTC
    sha256:5786243b5ebad2e862d2a326eef367c78500b2aa61ce29547f0e99193baf146bRUN
    CC=8 CONDA_ENV=rapids CXX=8 NVTAB_VER=v0.4.0 RAPIDS_VER=0.18 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; conda install -c rapidsai asvdb
    		03/09/2021 6:48 AM UTC
    sha256:0bff4a6344620d3f761a88281f42ece2f80d7d970ce08777cfbbeaffe4537a63RUN
    CC=8 CONDA_ENV=rapids CXX=8 NVTAB_VER=v0.4.0 RAPIDS_VER=0.18 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; pip install --no-deps fastai fastcore fastprogress
    		03/09/2021 6:48 AM UTC
    sha256:23012fff78579bd9dab77313831e525317dc31966f757e0564602b878e31af94RUN
    CC=8 CONDA_ENV=rapids CXX=8 NVTAB_VER=v0.4.0 RAPIDS_VER=0.18 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; pip install pynvml pytest spacy graphviz sklearn scipy matplotlib
    		03/09/2021 6:48 AM UTC

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