Deep Learning

Project for the Deep Learning class

Public page: https://deepfrench.gitlab.io/deep-learning-project
Repo page: https://gitlab.com/DeepFrench/deep-learning-project

Training

There are two ways to train the model: either use the training script, or use Google Cloud ML-Engine AI Platform.

Downloading and running the training script

From a fresh AWS/Google Cloud Compute instance, run the following to install everything:

bash <(curl -s https://deepfrench.gitlab.io/deep-learning-project/install.sh)

Then, just run the following:

conda activate python3-dl
python trainer.py

Feel free to modify at your convenience the training configuration located in trainer.py.

Training on Google Cloud AI Platform

Just edit and run the train-on-google-cloud.sh script. You'll need to update PROJECT_ID and BUCKET_ID.

Make sure the bucket exists before running the script!

The script will create a Docker container with the code, push it to Google Cloud Container Registry, and submit a training task on AI Platform to tune hyperparameters according to the configuration file you select in google_cloud_jobs/.

Documentation:

• Using Hyperparameter Tuning
• HyperParameter Spec Documentation
• Hyperparameter tuning in Cloud Machine Learning Engine using Bayesian Optimization (Blog post)

Installing

Cloning

Install git lfs (on macOS: brew install git-lfs), and then run:

git lfs install

If you installed git-lfs after cloning the repo, you can use the following command to download LFS files:

git lfs fetch
git lfs pull

Setting up an environment (Optional)

If you set up a virtual environment and store it in the root folder, make sure not to add it to git to name it like one of those options in the .gitignore:

env/
venv/
ENV/
env.bak/
venv.bak/

Installing packages

pip install -r requirements.txt

Running tests

To run all tests with test discovery:

python -m unittest