Skip to main content

Train, test, debug and optimize PyTorch models

Project description Documentation Status


The aim of this library is to simplify the process of building, optimizing, testing and debugging deep learning models using PyTorch as well as providing implementations of some of the latest research papers. Extensibility is kept in mind so that it is easy to customize the framework for your particular needs.

Some key features of the framework:

  • Easy to use, flexible and extensible API to build simple & complex models
  • Model debugging (e.g., activation statistics of each layer, gradient norm for each layer, embedding visualization)
  • Model understanding and result analysis (e.g., attention maps, confusion matrix, ROC curves, model comparisons, errors)
  • Support hyper-parameter optimization (random search, hyperband) and analysis
  • Architecture learning (DARTS & evolutionary algorithms)
  • Keep track of the results for retrospective analysis and model selection


  • Linux/Windows
  • Python >= 3.6
  • PyTorch >= 1.0

Installation / Usage

To install use pip:

$ pip install trw

Or clone the repo:

$ git clone

$ python install


The documentation can be found at ReadTheDocs.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for trw, version 0.1.0
Filename, size File type Python version Upload date Hashes
Filename, size trw-0.1.0-py2.py3-none-any.whl (172.2 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size trw-0.1.0.tar.gz (152.8 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page