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.
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¶
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
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
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
swav_8node_resnet.yaml is a main configuration file for SwAV training and exists at
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.
python tools/run_distributed_engines.py \ config=pretrain/my_new_approach/my_approach_config_file.yaml
In the above case, we are simply
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.
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
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:
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
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