3_image_test.sh

#!/bin/bash
TARGET_DIR=gpu-base-image-test
# 检查是否提供了输入参数
if [ -z "$1" ]; then
  echo "please set input image"
  exit 1
fi

if [[ "$1" == *"pytorch"* ]]; then 
  docker run --rm --platform=linux/amd64 --gpus all -v ./$TARGET_DIR:/workspace --workdir /workspace/pytorch/stable-diffusion-v1-4 $1 python infer.py; fi

if [[ "$1" == *"tensorflow"* ]]; then
  tensorflow_version=$(echo "$1" | cut -d: -f2 | cut -d- -f1)
  # 当tensorflow版本为2.16.1时，不添加环境变量找不到cuda，所以需要这样执行验证。在正常交互式启动容器时，会默认激活/etc/bash.bashrc，可以正常找到cuda
  if [[ "$tensorflow_version" == "2.16.1" ]]; then
    python_version=$(echo $1 | awk -F'[-:]' '{for(i=3;i<=NF;i++) if($i ~ /^py[0-9]+\.[0-9]+$/) {gsub(/^py/,"",$i); print $i; exit}}')
    docker run --rm --platform=linux/amd64 --gpus all \
    -e CUDNN_PATH="/opt/conda/lib/python$python_version/site-packages/nvidia/cudnn" \
    -e LD_LIBRARY_PATH="/opt/conda/lib/python$python_version/site-packages/nvidia/cudnn/lib:/usr/local/cuda/lib64" \
    -v ./$TARGET_DIR:/workspace --workdir /workspace/tensorflow/mnist $1 python train.py
  else
    docker run --rm --platform=linux/amd64 --gpus all -v ./$TARGET_DIR:/workspace --workdir /workspace/tensorflow/mnist $1 python train.py; fi; fi 

if [[ "$1" == *"paddle"* ]]; then 
  TARGET_DIR=gpu-base-image-test/paddletest 
  docker run --rm --platform=linux/amd64 --gpus all -v ./$TARGET_DIR:/workspace --workdir /workspace $1 python image.py; fi