README.md

This Docker image allows the user to run the notebooks in this repository on any Unix based operating system without having to setup the environment or install anything other than the Docker engine. We recommend using Azure Data Science Virtual Machine (DSVM) for Linux (Ubuntu) as outlined here. For instructions on how to install the Docker engine, click here.

Build the Docker image:

In the docker directory, run the following command to build the Docker image and tag it as seismic-deeplearning:

sudo docker build -t seismic-deeplearning . 

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:

sudo docker run --rm -it -p 9000:9000 -p 9001:9001 --gpus=all --shm-size 11G seismic-deeplearning

The command above will run a JupyterLab instance that you can access by clicking on the link in your terminal. You can then navigate to the notebook or script that you would like to run.

We recommend using Google Chrome web browser for any visualizations shown in the notebook.

You can alternatively use Jupyter notebook instead of Jupyter Lab by changing the last line in the Dockerfile from

jupyter lab --allow-root --ip 0.0.0.0 --port 9000

to

jupyter notebook --allow-root --ip 0.0.0.0 --port 9000

and rebuilding the Docker image.

Run TensorBoard:

To run Tensorboard to visualize the logged metrics and results, open a terminal in Jupyter Lab, navigate to the parent of the output directory of your model, and run the following command:

tensorboard --logdir output/ --port 9001 --bind_all

Make sure your VM has the port 9001 allowed in the networking rules, and then you can open TensorBoard by navigating to http://<vm_ip_address>:9001/ on your browser where <vm_ip_address> is your public VM IP address (or private VM IP address if you are using a VPN).

Experimental

We also offer the ability so use a semantic segmentation HRNet model with the repository from Microsoft Research. Its use is currently experimental.

Download the HRNet model:

To run the Dutch_F3_patch_model_training_and_evaluation.ipynb, you will need to manually download the HRNet-W48-C pretrained model. You can follow the instructions here.

If you are using an Azure Virtual Machine to run this code, you can download the HRNet model to your local machine, and then copy it to your Azure VM through the command below. Please make sure you update the <azureuser> and <azurehost> feilds.

scp hrnetv2_w48_imagenet_pretrained.pth <azureuser>@<azurehost>:/home/<azureuser>/seismic-deeplearning/docker/hrnetv2_w48_imagenet_pretrained.pth

Run the Docker image:

Once you have the model downloaded (ideally under the docker directory), you can proceed to build the Docker image: go to the Build the Docker image section above to do so.

Once the Docker image is built, you can run it anytime using the following command:

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/seismic-deeplearning/docker/hrnetv2_w48_imagenet_pretrained.pth 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 Lab instance that you can access by clicking on the link in your terminal. You can then navigate to the notebook or script that you would like to run.