Monitor deep learning model training and hardware usage from mobile.

• Monitor running experiments from mobile phone or laptop
• Monitor hardware usage on any computer with a single command
• Integrate with just 2 lines of code (see examples below)
• Keeps track of experiments including infomation like git commit, configurations and hyper-parameters
• API for custom visualizations
• Pretty logs of training progress
• Open source!

To install MongoDB, refer to the official documentation here.

Install the package using pip:

pip install labml-app

# Start the server on the default port (5005) labml app-server # To start the server on a different port, use the following command labml app-server --port PORT

Optional: to setup and configure Nginx in your server, please refer to this.

You can access the user interface either by visiting http://localhost:{port} or, if configured on a separate machine, by navigating to http://{server-ip}:{port}.

1. Install the package using pip.

pip install labml

2. Create a file named .labml.yaml at the top level of your project folder, and add the following line to the file:

app_url: http://localhost:{port}/api/v1/default # If you are setting up the project on a different machine, include the following line instead, app_url: http://{server-ip}:{port}/api/v1/default

from labml import tracker, experiment with experiment.record(name='sample', exp_conf=conf): for i in range(50): loss, accuracy = train() tracker.save(i, {'loss': loss, 'accuracy': accuracy})

from labml import tracker, experiment uuid = experiment.generate_uuid() # make sure to sync this in every machine experiment.create(uuid=uuid, name='distributed training sample', distributed_rank=0, distributed_world_size=8, ) with experiment.start(): for i in range(50): loss, accuracy = train() tracker.save(i, {'loss': loss, 'accuracy': accuracy})

• Python API Reference
• Samples

• API to create experiments
• Track training metrics
• Monitored training loop and other iterators
• API for custom visualizations
• Configurations management API
• Logger for stylized logging

# Install packages and dependencies pip install labml psutil py3nvml # Start monitoring labml monitor

If you use LabML for academic research, please cite the library using the following BibTeX entry.

@misc{labml, author = {Varuna Jayasiri, Nipun Wijerathne, Adithya Narasinghe, Lakshith Nishshanke}, title = {labml.ai: A library to organize machine learning experiments}, year = {2020}, url = {https://labml.ai/}, }