mark-e-deyoung/afit_mlperf_training:recommendation

$ singularity pull shub://mark-e-deyoung/afit_mlperf_training:recommendation

Singularity Recipe

Bootstrap: docker
# https://hub.docker.com/r/nvidia/cuda
From: nvidia/cuda:9.1-cudnn7-devel

%environment
	PATH="/usr/local/anaconda/bin:$PATH"
	MLPERF_DATA_DIR="/data"

%post

    # install debian packages
    apt-get update
    apt-get install -y eatmydata
    eatmydata apt-get install -y wget bzip2 \
      ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 \
      git git-annex uuid-runtime unzip
    apt-get clean

    # install anaconda
    if [ ! -d /usr/local/anaconda ]; then
         wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
            -O ~/anaconda.sh && \
         bash ~/anaconda.sh -b -p /usr/local/anaconda && \
         rm ~/anaconda.sh
    fi
    # set anaconda path
    export PATH="/usr/local/anaconda/bin:$PATH"

    # install required packages
    conda install pip
    conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
    pip install mlperf-compliance numpy pyyaml mkl mkl-include setuptools cmake cffi typing pandas tqdm scipy
    conda clean --tarballs

    # make /data and /code for mounts to external directories
    if [ ! -d /data ]; then mkdir /data; fi
    if [ ! -d /code ]; then mkdir /code; fi

% runscript
	echo "Singularity: PyTorch"
	exec /bin/bash

Collection


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Metrics

key value
id /containers/mark-e-deyoung-afit_mlperf_training-recommendation
collection name mark-e-deyoung/afit_mlperf_training
branch master
tag recommendation
commit 78b08ded39c7b245f00375b0c022a81f1eb99b84
version (container hash) 4cbebf25cf7329bf5605abcea6b8d84b
build date 2020-05-06T17:22:39.702Z
size (MB) 6224
size (bytes) 3342721055
SIF Download URL (please use pull with shub://)
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