This repo depends on having a conda environment with Pytorch and WandB installed. Make sure an environment containing both packages is activated prior to running.
Navigate to the cpp directory and run
python setup.py install
This will build the extensions needed for the sparse Resnets.
From your terminal navigate to the root of the project directory and run
python train/train.py
This will start the training script. If the command is not run from the root of the project directory, you will face relative import errors.
WandB sweeps are used for hyperparameter tuning and automates the process
of launching and logging multiple runs with slightly different configs. In order
to initiate a sweep, feed in a config file. These are located in the config
directory. As an example:
wandb sweep config/Resnet18_sweeps.yml
This command will return a sweep id (eg. tejalapeno/ampere_acc_test/2d5pl0gc). The sweep id is given to the WandB
agent after being generated from the config. This is what you send to srun.
srun -p bowser --gpus=1 wandb agent --count=1 tejalapeno/ampere_acc_test/2d5pl0gc
I recommend ssh'ing into owens and then using srun to parcel out jobs from each sweep ID. In time, I'll figure out how to automate this with a bash script.