labml - Track Deep Learning Models and Hardware Usage Remotely

Introduction to LabML

LabML is a powerful tool designed for monitoring deep learning model training and hardware usage directly from your mobile device or laptop. This open-source project caters to researchers and developers, providing an easy-to-use interface and essential functionalities that make tracking experiments and hardware resources more efficient and straightforward.

Key Features

Mobile and Laptop Monitoring: LabML allows users to track running experiments on a mobile phone or laptop. This mobility enhances user flexibility, making it easier to keep an eye on progress from anywhere.
Hardware Usage Monitoring: With a simple command, users can also keep track of hardware performance on any computer. This is crucial for optimizing resource use during complex computations.
Easy Integration: LabML is incredibly simple to integrate into projects. Just two lines of code are required to set it up, making it an accessible tool even for beginners.
Comprehensive Experiment Tracking: The tool keeps detailed records of experiment configurations, including git commits, hyper-parameters, and other crucial details, helping maintain detailed logs for future reference.
Custom Visualizations API: LabML offers an API for creating custom visualizations, which can be particularly beneficial for interpreting experiment results effectively.
Attractive Logs: Logs are displayed in a user-friendly manner, making it easy to follow the training progress.
Open Source: Being open source means that LabML is free to use and modify, encouraging community involvement and contributions.

Setting Up LabML

To start using LabML, users need to host an experiment server. Installation prerequisites include setting up MongoDB. Detailed installation steps involve simple commands using pip, a Python package manager, making the setup process straightforward:

pip install labml-app

To start the server:

labml app-server

Users can access the server interface by navigating to the appropriate localhost or server IP address, depending on their configuration.

Monitoring Experiments

Installation for monitoring involves another simple pip command:

pip install labml

Users need to configure LabML by creating a .labml.yaml file in the project folder. This file will define the app URL, directing LabML to the local or remote server where the experiments run.

PyTorch Integration Example

LabML can be easily used with popular frameworks like PyTorch. Here’s a simple PyTorch example demonstrating how to record an experiment:

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})

Distributed Training

LabML supports distributed training, enabling users to scale their experiments across multiple machines:

from labml import tracker, experiment

uuid = experiment.generate_uuid() 
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})

Comprehensive Documentation

LabML provides extensive documentation, including a Python API reference and numerous samples and guides, which are crucial for understanding how to fully utilize the tool's capabilities.

Visualizations and Analytics

LabML includes options for custom visualizations, offering insights into Tensorboard logs and analyses, helping users to better interpret data and outcomes.

Monitoring Hardware Usage

LabML's capability to monitor hardware usage is a flexible feature aimed at optimizing resource management. Installation is straightforward:

pip install labml psutil py3nvml

To start monitoring:

labml monitor

Conclusion

LabML stands out as a convenient, open-source option for those wanting to keep tabs on deep learning experiments and hardware usage efficiently. It combines seamless integration, comprehensive tracking, and user-friendly interfaces to create a powerful tool for modern machine learning workflows.