Installation & Setup

Lab

pip install labml

Create .labml.yaml file

check_repo_dirty: true
data_path: 'data'
experiments_path: 'logs'
analytics_path: 'analytics'

You need to create a .labml.yaml file at the root of your project. The values will default to above so an empty file should work for most of the use cases.

check_repo_dirty: If true, before running an experiment it checks and aborts if there are any uncommitted changes

data_path: The location of data files. this can be accessed via labml.lab.get_data_path().

experiments_path: This is where all the experiment details such as logs, configs and checkpoints are saved. This can be accessed via labml.lab.get_experiments_path().

analytics_path: ⚠️ This is where Jupyter Notebooks for custom analytics will be saved. This is still experimental.

Note

You don’t need the .labml.yaml file if you only use labml.logger.

Dashboard

pip install labml-dashboard

Navigate to the path of the project and run the following command to start the server.

labml dashboard