Installation & Setup¶
pip install labml
check_repo_dirty: true data_path: 'data' experiments_path: 'logs' analytics_path: 'analytics' web_api: 'TOKEN from web.lab-ml.com' web_api_frequency: 60 web_api_verify_connection: true web_api_open_browser: true indicators: - class_name: Scalar is_print: True name: '*'
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.
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
experiments_path: This is where all the experiment details such as logs, configs and checkpoints are saved. This can be accessed via
analytics_path: ⚠️ This is where Jupyter Notebooks for custom analytics will be saved. This is still experimental.
web_api: The token from web.lab-ml.com <https://web.lab-ml.com>_ or a url to a hosted LabML App.
web_api_frequency: Interval in seconds to push stats to LabML App <https://web.lab-ml.com>_.
web_api_verify_connection: Whether to verify SSL certificate of the app. You might want to set this to false if you self host and use an unverified SSL certificate.
web_api_open_browser:` Whether to open the monitoring url in the browser automatically.
indicators`: Use this to specify types of indicators for tracker.
class_nameis the type of the indicator.
is_printis whether to output the statistic to console and LabML App.
namecan be a wildcard selector for indicator names. You can set these individually with the tracker API.
You can set these configurations with
configure() as well.
pip install labml-dashboard
Navigate to the path of the project and run the following command to start the server.