matches

KO | EN

gitlite — search

#javascript #hacktoberfest #react #golang #android #rust #macos #cli #kubernetes #vue #security #ios #openai #open-source

(★ 18)

Take control back!

matches Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Matches. Next-generation pytorch train-loop
Take control back!

## Philosophy & Design

* Minimum inversion of control. Your code calls framework code, not vice versa.
  This approach provides almost same flexibility as pure PyTorch, but allows solving
  common problems like metrics, checkpoints, progress.

* Callbacks **only** for misc functionality (checkpoints, tensorboard, 
  benchmarking, debug etc).
  
* No oversimplification of most common cases like supervised learning.
  Other libraries out there do this, and as a result they have complex messy 
  internals and low flexibility for not-so-common usecase, like multiple optimizers 
  or multiple backward passes for single batch.

## Features
* Flexible metrics saving to tensorboard (batch/epoch/whatever)
* DDP
* Best model checkpoint by metric
* Automatic logdir naming
* Callback for checking that repo has no uncommitted changes
* Development mode to check pipeline fast
* Nice TQDM progress-bar that correctly handles printing  
* Simple and clear API. Also, it's quite future-proof and will have minimum 
  breakage over future development
* Comprehensive internals. Library code is clean and easy to understand, which
  prevents bugs and simplifies debugging if bug still happened
  
#### Planned
* DeepSpeed support
* Logging abstraction to seamlessly work with multiple sinks (eg tensorboard and WandB)
* Training stages/phases
* Rich configuration API with CLI support
* Train resuming

## Installation
```shell
pip install git+https://github.com/hexfaker/matches
```

## Examples
Examples are located in `examples/` dir. 
* [DCGAN example](https://github.com/hexfaker/matches/tree/master/examples/dcgan)
  has complex batch handling, image saving to tensorboard and configs
* [CIFAR example](https://github.com/hexfaker/matches/blob/master/examples/ddp_cifar/ddp_cifar.py) is 
well-commented and has OneCycle scheduler.

matches

matches Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

matches

matches Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README