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Deploy CMPE257 ML LD Docker Container

1. Create proj/ and go to Lesion Detection folder:

# Create proj/ folder
mkdir -p $HOME/Documents/GitHub/
cd $HOME/Documents/GitHub/

# Clone cmpe257 Lesion Detection project repo to GitHub folder
git clone [email protected]:shreyahunur/Lesion-Detection.git
cd Lesion-Detection

Approach 1: Build a Docker GPU Container for LD

2. Build GPU Docker image from Dockerfile:

# copy over GPU Dockerfile
cp Docker_GPU/Dockerfile .

# Build cmpe257_ld docker image with Anaconda3, TensorFlow Jupyter Notebook, 
# if on linux, prepend sudo
docker build -t cmpe257_ld_gpu:dev .

Deploy a Docker GPU Container for LD

2. Deploy cmpe257 Lesion Detection GPU Docker container:

# volume mount created from dev host curr proj $PWD to /sjsu/cmpe257 in container
# if on linux, prepend sudo
docker run --name cmpe257_ld_dev --gpus all -p 8888:8888 -it -v $PWD:/sjsu/cmpe257_ld cmpe257_ld:dev

Approach 2: Build a Docker CPU Container for LD

3. Build CPU Docker image from Dockerfile:

# copy over CPU Dockerfile
cp Docker_CPU/Dockerfile .

# Build cmpe257_ld docker image with Anaconda3, TensorFlow Jupyter Notebook, 
# if on linux, prepend sudo
docker build -t cmpe257_ld_cpu:dev .

Deploy a Docker CPU Container for LD

2. Deploy cmpe257 Lesion Detection CPU Docker container:

# volume mount created from dev host curr proj $PWD to /sjsu/cmpe257 in container
# if on linux, prepend sudo
docker run --name cmpe257_ld_dev -p 8888:8888 -it -v $PWD:/sjsu/cmpe257_ld cmpe257_ld:dev