Quickstart#

TL;DR#

# given that project_name is `Quicksetup-ai` via cookiecutter installation step
cd Quicksetup-ai/
conda create -n quicksetup_ai_env python=3.9
conda activate quicksetup_ai_env
pip install -e .
python scripts/train.py

Create the pipeline environment#

  • First, create a virtual environment (for the sake of the example, we’ll call it quicksetup_ai_env).

You can either do it with conda (preferred) or venv. Using conda:

conda create -n quicksetup_ai_env python=3.9

  • Then, activate the environment

conda activate quicksetup_ai_env

Install the quicksetup-ai package#

Before using the template, one needs to install the project as a package. Run:

pip install -e .

Run the MNIST example#

This pipeline comes with a toy example (MNIST dataset with a simple feedforward neural network). To run the training (resp. testing) pipeline, simply run:

python scripts/train.py
# or python scripts/test.py

Or, if you want to submit the training job to a submit (resp. interactive) cluster node via slurm, run:

sbatch job_submission.sbatch
# or sbatch job_submission_interactive.sbatch

  • The experiments, evaluations, etc., are stored under the logs directory.

  • The default experiments tracking system is mlflow. The mlruns directory is contained in logs. To view a user friendly view of the experiments, run:

# make sure you are inside logs (where mlruns is located)
mlflow ui --host 0000

  • When evaluating (running test.py), make sure you give the correct checkpoint path in configs/test.yaml