docker pull nvcr.io/nvidia/pytorch:20.11-py3

docker run --gpus=all --rm -it -d -p 8888:8888 -p 6006:6006 -v /home/das/srishtiy/viewmaker:/viewmaker --ipc=host nvcr.io/nvidia/pytorch:20.11-py3 /bin/bash

docker ps
docker exec -it <change as required, e.g. beautiful_merkel> /bin/bash

jupyter-lab --allow-root --ip='0.0.0.0' --NotebookApp.token='' & 

You can then open your code on http://localhost:8888/

1) Install Dependencies

Install other dependencies:
```console
pip install -r requirements.txt

2) Running experiments

Start by running

source init_env.sh

Note: Our experiements will logged in wandb. Hence, before runnig the experiments;

1. Create a wandb account
2. Create a username when prompted.
3. Update run_image.py at line 67 with your wandb-username

    wandb.init(project='image', entity='<wandb-username>', name=config.exp_name, config=config, sync_tensorboard=True)

Example:

    wandb.init(project='image', entity='sulphur', name=config.exp_name, config=config, sync_tensorboard=True)

4. Save the file run_image.py

Now, you can run experiments for the different modalities (here image) as follows:

python scripts/run_image.py config/image/pretrain_viewmaker_cifar10_simclr.json --gpu-device 0

(If you see an error about wandb directory not created, create a directory mkdir wandb at the base path ad re-run the code)

The scripts directory holds:

• run_image.py: for pretraining and running linear evaluation on CIFAR-10
• run_meta_transfer.py: for running linear evaluation on a range of transfer datasets, including many from MetaDataset
• run_audio.py: for pretraining on LibriSpeech and running linear evaluation on a range of transfer datasets
• run_sensor.py: for pretraining on Pamap2 and running transfer, supervised, and semi-supervised learning on different splits of Pamap2
• eval_cifar10_c.py: for evaluating a linear evaluation model on the CIFAR-10-C dataset for assessing robustness to common corruptions

The config directory holds configuration files for the different experiments, specifying the hyperparameters from each experiment. The first field in every config file is exp_base which specifies the base directory to save experiment outputs, which you should change for your own setup.

You are responsible for downloading the datasets. Update the paths in src/datasets/root_paths.py.

Training curves and other metrics are logged using wandb.ai