mark-e-deyoung/afit_mlperf_training:rnn_translator_cuda8cudnn7
$ singularity pull shub://mark-e-deyoung/afit_mlperf_training:rnn_translator_cuda8cudnn7
Singularity Recipe
Bootstrap: docker
# https://hub.docker.com/r/nvidia/cuda
From: nvidia/cuda:8.0-cudnn7-devel
%environment
PATH="/usr/local/anaconda/bin:$PATH"
MLPERF_DATA_DIR="/data"
%post
# install debian packages
apt-get update
apt-get install -y eatmydata
eatmydata apt-get install -y wget bzip2 \
ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 \
git git-annex uuid-runtime
apt-get clean
# install anaconda
if [ ! -d /usr/local/anaconda ]; then
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
-O ~/anaconda.sh && \
bash ~/anaconda.sh -b -p /usr/local/anaconda && \
rm ~/anaconda.sh
fi
# set anaconda path
export PATH="/usr/local/anaconda/bin:$PATH"
# install required packages
conda install pip
conda install pytorch torchvision cudatoolkit=8.0 -c pytorch
pip install sacrebleu numpy mlperf-compliance
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 (CUDA 8.0 cuDNN 7"
exec /bin/bash
Collection
- Name: mark-e-deyoung/afit_mlperf_training
- License: Apache License 2.0
View on Datalad
Metrics
key | value |
---|---|
id | /containers/mark-e-deyoung-afit_mlperf_training-rnn_translator_cuda8cudnn7 |
collection name | mark-e-deyoung/afit_mlperf_training |
branch | master |
tag | rnn_translator_cuda8cudnn7 |
commit | 93714630801207478eacd3210cff7f77b7a6c84a |
version (container hash) | c3188fa2af0cbb79f7aae6712038a427 |
build date | 2020-05-06T17:44:25.538Z |
size (MB) | 4293 |
size (bytes) | 2299346975 |
SIF | Download URL (please use pull with shub://) |
Datalad URL | View on Datalad |
Singularity Recipe | Singularity Recipe on Datalad |
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