Kaggle Challenge to Classify Covid Pneumonia Xray
To start the docker container execute the following command
$ ./bin/docker [-n <string>] [-t <tag-name>] [--sudo] [--build] [-d] [-c <command>]- latest The latest release of TensorFlow CPU binary image. Default.
- nightly Nightly builds of the TensorFlow image. (unstable) version Specify the version of the TensorFlow binary image, for example: 2.1.0
- devel Nightly builds of a TensorFlow master development environment. Includes TensorFlow source code.
Each base tag has variants that add or change functionality:
- <tag>-gpu The specified tag release with GPU support. (See below)
- <tag>-py3 The specified tag release with Python 3 support.
- <tag>-jupyter The specified tag release with Jupyter (includes TensorFlow tutorial notebooks)
You can use multiple variants at once. For example, the following downloads TensorFlow release images to your machine. For example:
$ ./bin/docker -n myContainer --build # latest stable release
$ ./bin/docker -n myContainer --build -t devel-gpu # nightly dev release w/ GPU support
$ ./bin/docker -n myContainer --build -t latest-gpu-jupyter # latest release w/ GPU support and JupyterOnce the docker container is running it will execute the contents of the /bin/execute file.
You can execute
$ docker exec -it <container-id> /bin/sh -c "[ -e /bin/bash ] && /bin/bash || /bin/sh"to access the running container's shell.
You will need the other datasets to use localted at data/<dataset_name>/data in order to generate the new dataset. Then, you can just run the following command in order to generate the new dataset.
$ pip install -e .
$ python src/datasets/datagen/cli.py \
--dataset aa2020 \
--splits_dir ./data/aa2020/splits \
--split v1 \
--train_size 0.80 \
--test_size 0.20 \
--seed 40 \
--gs \
-w