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Setup

1. Check out the repo

You should already have the repo in your home directory. Go into it and make sure you have the latest.

cd fsdl-text-recognizer-project
git pull origin master

If not, open a shell in your JupyterLab instance and run

git clone https://github.com/full-stack-deep-learning/fsdl-text-recognizer-project.git
cd fsdl-text-recognizer-project

2. Set up the Python environment

If on GCP AI Platform Notebooks instance

Simply run pip install -r requirements.txt -r requirements-dev.txt.

Also, run export PYTHONPATH=. before executing any commands later on, or you will get errors like ModuleNotFoundError: No module named 'text_recognizer'.

In order to not have to set PYTHONPATH in every terminal you open, just add that line as the last line of the ~/.bashrc file using a text editor of your choice (e.g. nano ~/.bashrc)

If on own machine

Run conda env create to create an environment called fsdl-text-recognizer, as defined in environment.yml. This environment will provide us with the right Python version as well as the CUDA and CUDNN libraries. We will install Python libraries using pip-sync, however, which will let us do three nice things:

  1. Separate out dev from production dependencies (requirements-dev.in vs requirements.in).
  2. Have a lockfile of exact versions for all dependencies (the auto-generated requirements-dev.txt and requirements.txt).
  3. Allow us to easily deploy to targets that may not support the conda environment.

So, after running conda env create, activate the new environment and install the requirements:

conda activate fsdl-text-recognizer
pip-sync requirements.txt requirements-dev.txt

If you add, remove, or need to update versions of some requirements, edit the .in files, then run

pip-compile requirements.in && pip-compile requirements-dev.in

Now, every time you work in this directory, make sure to start your session with conda activate fsdl-text-recognizer.

3. Kick off a command

Before we get started, please run a command that will take a little bit of time to execute.

cd lab1/
python text_recognizer/datasets/emnist_dataset.py
cd ..

Ready

Now you should be setup for the labs. The instructions for each lab are in readme files in their folders.

You will notice that there are solutions for all the labs right here in the repo, too. If you get stuck, you are welcome to take a look!