YAML Configuration system¶

VISSL uses Hydra for configuration management. Hydra provides flexible yet powerful configuration system composed of simple YAML files.

Users can create configs for only a specific component of their training (for example: using a different datasets) and overwrite a main configuration setting for that specific component. This way, Hydra allows reusability of configs.
Hydra also allows users to modify the configuration values from command line.

For example:

python <binary-name>.py config=<yaml_config path>/<yaml_config_file_name>

VISSL Settings: You can see all the parameters and settings VISSL supports in VISSL defaults.yaml file. Tip: This is a great place to look for documentation on the various parameter settings.

Detecting new configuration directories in Hydra¶

VISSL provides configuration files here and uses the Hydra Plugin VisslPlugin to automatically search for the configs folder in VISSL.

If users want to create their own configuration directories and not use the configs directory provided by VISSL, then users must add their own Plugin following the VisslPlugin.

Note

For any new folder containing configuration files, Hydra requires creating an empty __init__.py file. Hence, if users create a new configuration directory, they must create an empty __init__.py file.

How to use VISSL provided config files¶

For example, to train SwAV model on 8-nodes (32-gpu) with VISSL:

python tools/run_distributed_engines.py config=pretrain/swav/swav_8node_resnet

where swav_8node_resnet.yaml is a main configuration file for SwAV training and exists at vissl/configs/config/pretrain/swav/swav_8node_resnet.yaml.

How to add configuration files for new SSL approaches¶

Let’s say you have a new self-supervision approach that you implemented in VISSL and want to create config files for training. You can simply create a new folder and config file for your approach.

For example:

python tools/run_distributed_engines.py \
  config=pretrain/my_new_approach/my_approach_config_file.yaml

In the above case, we are simply creating the my_new_approach folder under pretrain/ path and creating a file my_approach_config_file.yaml with the path pretrain/my_new_approach/my_approach_config_file.yaml.

How to override a training component with config files¶

To replace one training component with another, like replacing the training dataset, one can simply create a new yaml file for the dataset and use that during training.

For example:

python tools/run_distributed_engines.py \
  config=pretrain/swav/swav_8node_resnet \
  +config/pretrain/swav/optimization=my_new_optimization \
  +config/pretrain/swav/my_new_dataset=my_new_dataset_file_name \

In the above case, we are overriding optimization and data settings for the SwAV training. To override, we simply create the my_new_dataset sub-folder under pretrain/swav path and create a file my_new_dataset_file_name.yaml with the path pretrain/swav/my_new_dataset_file_name.yaml

How to override single values in config files¶

If you want to override single value of an existing key in the config, you can achieve that via the command-line by setting: my_key=my_new_value

For example:

python tools/run_distributed_engines.py \
    config=pretrain/swav/swav_8node_resnet \
    config.MODEL.WEIGHTS_INIT.PARAMS_FILE=<my_weights_path.torch>

How to add new keys to the dictionary in config files¶

If you want to add a single key to a dictionary in the config, you can achieve that with +my_new_key_name=my_value. Note the use of +.

For example:

python tools/run_distributed_engines.py \
    config=pretrain/swav/swav_8node_resnet \
    +config.MY_NEW_KEY=MY_VALUE \
    +config.LOSS.simclr_info_nce_loss.MY_NEW_KEY=MY_VALUE