37 lines
1.4 KiB
Bash
Executable File
37 lines
1.4 KiB
Bash
Executable File
#!/bin/bash
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set -e
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script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
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# 1. Init occlum workspace
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[ -d occlum_instance ] || occlum new occlum_instance
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# 2. Install python and dependencies to specified position
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[ -f Miniconda3-latest-Linux-x86_64.sh ] || wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
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[ -d miniconda ] || bash ./Miniconda3-latest-Linux-x86_64.sh -b -p $script_dir/miniconda
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$script_dir/miniconda/bin/conda create --prefix $script_dir/python-occlum -y matplotlib numpy python=3.8.10 paddlepaddle==2.4.2 -c paddle
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CORE_PY=$script_dir/python-occlum/lib/python3.8/site-packages/paddle/fluid/core.py
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IMAGE_PY=$script_dir/python-occlum/lib/python3.8/site-packages/paddle/dataset/image.py
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# Adjust the source code to run in Occlum
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sed -i "186 i \ elif sysstr == 'occlum':\n return True" $CORE_PY
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sed -ie "37,64d" $IMAGE_PY
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sed -i "37 i \try:\n import cv2\nexcept ImportError:\n cv2 = None" $IMAGE_PY
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# Download the dataset
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DATASET=$script_dir/mnist
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[ -d $DATASET ] && exit 0
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TRAIN_IMAGE=train-images-idx3-ubyte.gz
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TRAIN_LABEL=train-labels-idx1-ubyte.gz
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TEST_IMAGE=t10k-images-idx3-ubyte.gz
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TEST_LABEL=t10k-labels-idx1-ubyte.gz
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URL=http://yann.lecun.com/exdb/mnist
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mkdir $DATASET
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wget $URL/$TRAIN_IMAGE -P $DATASET
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wget $URL/$TRAIN_LABEL -P $DATASET
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wget $URL/$TEST_IMAGE -P $DATASET
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wget $URL/$TEST_LABEL -P $DATASET
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