Create a conda environment for the project via
make venv # will create a cpu environment
# NOTE: This will simply call
# conda env create --prefix=./venv -f requirements/env.yml
# For a gpu environment call
# make name=venv_gpu sys=gpu venv
# conda activate ./venv_gpu
# For a Mac m1 environment call
# make name=venv sys=m1 venv
# conda activate ./venv
# To activate the environment, use:
conda activate ./venv
Set environemnt variables for you system in a .env
file at the project directory
(same as this readme.)
To log to wandb, you will first need to log in. To do so, simply install wandb via pip
with pip install wandb
and call wandb login
from the commandline.
If you are already logged in and need to relogin for some reason, use wandb login --relogin
.
To run a model simply use
python src/train.py run_name=<YOUR_RUN_NAME>
To use parameters that are different from the default parameters in src/configs/config.yaml
you can simply provide them in the command line call. For example:
python src/train.py run_name=<YOUR_RUN_NAME> epochs=100
To configure extra things such as logging, use
# LOGGING
# For running at DEBUG logging level:
# (c.f. https://hydra.cc/docs/tutorials/basic/running_your_app/logging/ )
## Activating debug log level only for loggers named `__main__` and `hydra`
python src/train.py 'hydra.verbose=[__main__, hydra]'
## Activating debug log level for all loggers
python src/train.py hydra.verbose=true
# PRINTING CONFIG ONLY
## Print only the job config, then return without running
python src/train.py --cfg job
# GET HELP
python src/train.py --help