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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"]
    		04/13/2021 2:13 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4RUN
    /bin/bash -c #(nop) ENTRYPOINT []
    		04/13/2021 2:13 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4RUN
    /bin/bash -c #(nop) HEALTHCHECK &{["NONE"] "0s" "0s" "0s" '\x00'}
    		04/13/2021 2:13 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4RUN
    CC=8 CONDA_ENV=merlin CXX=8 NVTAB_VER=v0.5.0 RAPIDS_VER=0.18.0 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c echo $(du -h --max-depth=1 /)
    		04/13/2021 2:13 AM UTC
    sha256:b13617e58dcd9f9c21fbdffcbe12a442755dc62763e82aff0d690b033c35d271RUN
    CC=8 CONDA_ENV=merlin CXX=8 NVTAB_VER=v0.5.0 RAPIDS_VER=0.18.0 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; conda clean --all -y
    		04/13/2021 2:13 AM UTC
    sha256:37b2d2bd1bd1492d222f61768bf6a45b99c3468f36297c35ef4eb6f49ebd1e6bRUN
    CC=8 CONDA_ENV=merlin CXX=8 NVTAB_VER=v0.5.0 RAPIDS_VER=0.18.0 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; apt update; apt install -y graphviz ;
    		04/13/2021 2:13 AM UTC
    sha256:6a692e23019a8b03d31f6876873a7592d4e82e34879cfb4cd8fc10b9fa9edd95RUN
    CC=8 CONDA_ENV=merlin CXX=8 NVTAB_VER=v0.5.0 RAPIDS_VER=0.18.0 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; conda install -c rapidsai asvdb
    		04/13/2021 2:12 AM UTC
    sha256:219fa402b58e89b5089d857e4b5d1e1e656a10cbc216f3e5629cef09b5d91b66RUN
    CC=8 CONDA_ENV=merlin CXX=8 NVTAB_VER=v0.5.0 RAPIDS_VER=0.18.0 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; pip install --no-deps fastai==2.1.9 fastcore fastprogress
    		04/13/2021 2:12 AM UTC
    sha256:abcf66eebec94182a98f70cde209b85aba91490ffed6ea4b32aaab2cb3bcdbe3RUN
    CC=8 CONDA_ENV=merlin CXX=8 NVTAB_VER=v0.5.0 RAPIDS_VER=0.18.0 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; pip install pynvml pytest spacy graphviz sklearn scipy matplotlib
    		04/13/2021 2:12 AM UTC
    sha256:865131cedbbf3592a39b7d3fb1faca14f025d6b27b9f3ae725deab66dc72aeb6RUN
    CC=8 CONDA_ENV=merlin CXX=8 NVTAB_VER=v0.5.0 RAPIDS_VER=0.18.0 RAP_CHAN=rapidsai RELEASE=true /bin/bash -c source activate ${CONDA_ENV}; git clone https://github.com/NVIDIA/NVTabular.git /nvtabular/; cd /nvtabular/; if [[ "$RELEASE" == "true" ]] ; then git fetch --all --tags &&
  git checkout tags/${NVTAB_VER}; else git checkout main; fi; pip install -e .;
    		04/13/2021 2:11 AM UTC