enabling development on docker (#291)

docker/Dockerfile

@@ -13,6 +13,8 @@ ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
 ENV PATH /opt/conda/bin:$PATH
 SHELL ["/bin/bash", "-c"] 
 
+WORKDIR /home/username
+
 # Install Anaconda and download the seismic-deeplearning repo
 RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
     /bin/bash ~/miniconda.sh -b -p /opt/conda && \
@@ -23,17 +25,17 @@ RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86
     wget --quiet https://github.com/microsoft/seismic-deeplearning/archive/staging.zip -O staging.zip && \
     unzip staging.zip && rm staging.zip
 
-WORKDIR seismic-deeplearning-staging
-
-RUN conda env create -n seismic-interpretation --file environment/anaconda/local/environment.yml && \
+RUN cd seismic-deeplearning-staging && \
+    conda env create -n seismic-interpretation --file environment/anaconda/local/environment.yml && \
     source activate seismic-interpretation && \
     python -m ipykernel install --user --name seismic-interpretation && \
     pip install -e interpretation && \
     pip install -e cv_lib 
 
 # TODO: add back in later when Penobscot notebook is available
 # Download Penobscot dataset:
-# RUN data_dir="/home/username/data/penobscot" && \
+# RUN cd seismic-deeplearning-staging && \
+#    data_dir="/home/username/data/penobscot" && \
 #    mkdir -p "$data_dir" && \
 #    ./scripts/download_penobscot.sh "$data_dir"  && \
 #    cd scripts  && \
@@ -42,7 +44,8 @@ RUN conda env create -n seismic-interpretation --file environment/anaconda/local
 #    cd ..
 
 # Download F3 dataset:
-RUN data_dir="/home/username/data/dutch" && \
+RUN cd seismic-deeplearning-staging && \
+    data_dir="/home/username/data/dutch" && \
     mkdir -p "$data_dir" && \
     ./scripts/download_dutch_f3.sh "$data_dir" && \
     cd scripts && \
@@ -57,4 +60,4 @@ EXPOSE 9000/tcp
 EXPOSE 9001/tcp
 
 CMD source activate seismic-interpretation && \
-    jupyter notebook --allow-root --ip 0.0.0.0 --port 9000
+    jupyter lab --allow-root --ip 0.0.0.0 --port 9000

docker/README.md

@@ -22,7 +22,7 @@ This process will take a few minutes to complete.
 # Run the Docker image:
 Once the Docker image is built, you can run it anytime using the following command:
 ```bash
-sudo docker run --rm -it -p 9000:9000 -p 9001:9001 --gpus=all --shm-size 11G --mount type=bind,source=$PWD/hrnetv2_w48_imagenet_pretrained.pth,target=/home/username/models/hrnetv2_w48_imagenet_pretrained.pth seismic-deeplearning
+sudo docker run --rm -it -p 9000:9000 -p 9001:9001 --gpus=all --shm-size 11G --mount type=bind,source=$PWD/hrnetv2_w48_imagenet_pretrained.pth,target=/home/models/hrnetv2_w48_imagenet_pretrained.pth -v ~/:/home/username seismic-deeplearning
 ```
-If you have saved the pretrained model in a different directory, make sure you replace `$PWD/hrnetv2_w48_imagenet_pretrained.pth` with the **absolute** path to the pretrained HRNet model. The command above will run a jupyter notebook server that you can access by clicking on the link in your terminal. You can then navigate to the notebook that you would like to run.  
+If you have saved the pretrained model in a different directory, make sure you replace `$PWD/hrnetv2_w48_imagenet_pretrained.pth` with the **absolute** path to the pretrained HRNet model. The command above will run a Jupyter Lab instance that you can access by clicking on the link in your terminal. You can then navigate to the notebook that you would like to run. By default, running the command above would mount your home directory to the Docker container, allowing you to access your files and data from within Jupyter Lab. 
 

environment/anaconda/local/environment.yml

@@ -18,6 +18,7 @@ dependencies:
   - itkwidgets==0.23.1
   - pytest
   - papermill>=1.0.1
+  - jupyterlab
   - pip:
     - segyio==1.8.8
     - pytorch-ignite==0.3.0