Watch the short presentation video to learn more

This framework is intended to quickly bootstrap a PyTorch project with all the necessary boilerplate that one typically writes in every project. We give you all the bells and whistles, so you can focus on what matters.

Yes, we love PyTorch Lightning too, but we feel that it's not flexible enough for research code and fast iteration. Too much opacity and abstraction sometimes kills productivity.

Matchbox comes with 3 killer features.

A useful Text User Interface (TUI) for instant access to training loss curves. No more loading heavy web apps and waiting for synchronization; you don't need to leave the terminal (even through SSH sessions)!

Of course, Weights & Biases is still integrated in Matchbox ;)

No more failed experiments due to stale dataset caches, no more spending hours recomputing a dataset because implementing multiprocessing would require a massive refactoring, and no more countless scrolls to find the parameters of your model after training.

Matchbox gives you a framework that you can jump right in. It boils down to:

• A minimal PyTorch project template.
• A bootstrapping boilerplate for datasets, models, training & testing logic with powerful configuration via hydra-zen.
• Dataset composable mixins for pragmatic dataset implementation:
  • Safeguards for automatic and smart caching of data pre- and post-processing. Did you change a parameter? Modify a line of code? No worries, Matchbox will catch that and flush your cache.
  • Dataset pre-processing boilerplate: never write the multiprocessing code ever again, just write processing code per-sample!
• A set of utility functions for a broad range of deep learning projects.

Matchbox fully erases the most painful part of writing deep learning research code: no more relaunching the whole code to fix some tensor shapes that occur at runtime. Matchbox will hold the expensive “cold code” in memory and let you work on the quick-to-reload “hot code” via hot reloading.

This builder feature allows PyTorch developers to:

• Freeze entire components (dataset, model, training logic, etc.) in memory to work on the next feature with no time-waisting edit-reload-wait cycle!
• Experiment very quickly with hot code reloading!
• Catch exceptions and interactively debug tensor operations on their actual data!

And all of this graphically :)

• Keep it DRY (Don't Repeat Yourself) and repeatable with Hydra-Zen.
• Raw PyTorch for maximum flexibility and transparency.
• Minimal abstraction and opacity but focus on extensibility.
• The sweet spot between a template and a framework.
  • The bare minimum boilerplate is taken care of but not hidden away.
  • You are free to do whatever you want, everything is transparent: no pip package!
  • Provides base classes for datasets, trainer, etc. to make it easier to get started and provide good structure for DRY code and easy debugging.
  • Provides a set of defaults for the most common use cases.
  • Provides a set of tools to make it easier to debug and visualize.
  • Provides all the bells and whistles to make your programming experience fun!

While the lightning-hydra-template is clean, mature, highly functional and comes with all the features you could ever want, we find that because of all the abstraction it actually becomes a hindrance when writing research code. Unless you know the template on the tip of your fingers, it's too much hassle to look for the right file, the right method where you have to put your code. And PyTorch Lightning? Great for building stuff, not so much for researching new training pipelines or working with custom data loading.

• DRY configuration with Hydra-Zen: painlessly configure experiments and swap whole groups with the CLI, simpler and DRYier than Hydra!
• Run isolation and experiment reproducibility: Hydra isolates each of your run and saves a YAML file of your config so you can always backtrack in your ML experiments.
• Gorgeous terminal UI: no more waiting for the slow Weights&Biases UI to load and sync, the curves are in your terminal (thanks to Plotext and Textual)! An informative and good-looking TUI lets you know just what you need to know.
• Weights & Biases integration: of course, take advantage of W&B when your code is ready to be launched into orbit.
• Best-n model saver: automatically deletes obsolete models from earlier epochs and only keeps the N best validation models on disk!
• Automatic loading of the best model from a run name: no need to look for that good model file, just pass the run name that was generated for you. They have cool and colorful names :)

Have a look at our documentation. If you are still struggling, open a discussion thread on this repo and we'll help you ASAP :)

my-pytorch-project/
    bootstrap/
        factories.py <-- Factory functions for instantiating models, optimizers, etc.
        launch_experiment.py <-- Bootstraps the experiment and launches the training/testing loop
    conf/
        experiment.py <-- experiment-level configurations
        project.py <-- project-level constants
    data/
        . <-- your dataset files and cache/preprocessing output go here
    dataset/
        base/
            __init__.py <-- base dataset implementation
            image.py <- base image dataset implementation
        . <-- your dataset implementation goes here
    model/
        . <-- your model implementation goes here
    scripts/
        resize_image_dataset.py
        . <-- your utility scripts go here
    src/
        losses/
            . <-- Custom losses go here
        metrics/
            . <-- Custom metrics go here
        base_tester.py <-- the core of the testing logic
        base_trainer.py <-- the core of the training logic
    utils/
        __init__.py <-- low-level utilities
        helpers.py <-- high-level utilities
        training.py <-- training-related utilities
    vendor/
        . <-- third-party code goes here (github submodules, etc.)
    train.py <-- training entry point (calls bootstrap/launch_experiment)
    test.py <-- testing entry point (calls bootstrap/launch_experiment)

1. Set up a virtual environment and activate it.
2. Install PyTorch for your system.
3. Run pip install -r requirements.txt.
4. Run pre-commit install to setup the pre-commit hooks. These will run Black, Isort, Autoflake and others to clean up your code before each commit.

A typical way of using this template is to follow these steps:

1. Implement your dataset loader (look at dataset/example.py)
2. Configure it (look at the dataset section in conf/experiment.py)
3. Implement your model (look at model/example.py)
4. Configure it (look at the model section in conf/experiment.py)
5. Configure your entire experiment(s) in conf/experiment.py.
6. Implement _train_val_iteration() in src/base_trainer.py, or derive your own trainer for more complex use cases.

To run an experiment, use ./train.py +experiment=my_experiment.

You may experiment on the fly with ./train.py +experiment=my_experiment dataset=my_dataset data_loader.batch_size=32 model.latent_dim=128 run.epochs=30 run.viz_every=5.

To evaluate a model, run test.py +experiment=my_experiment run.load_from_run=<run_name_from_previous_training>.

You can always look at what's available in your config with ./train.py --help or ./test.py --help!

This template comes with an example dataset and model that are pre-configured in conf/experiment.py using hydra-zen. To learn how to adapt it to your project, you may refer to their documentation and tutorials.

In addition, you can configure the behaviour of the training logic as well as other settings by setting project-level constants in conf/project.py, such as the name of your project (i.e. for wandb.ai).

$ PLOT_ENABLED=false REPRODUCIBLE=0 ./train.py +experiment=my_experiment

Each of your run creates a folder with its name in runs/. You can find there the used YAML config, the checkpoints and any other file you wish to save.

• Torchmetrics: this takes care of batching the loss and averaging it
• Saving top 3 best val models (easily configure metric)
• Training + evaluation loop
• Wandb integration with predefined logging metrics
• Automatic instantiation for the optimizer, scheduler, model
• The best progress display I can ever get!! (kinda like torchlightning template? But I want colour (as in PIP), I want to see my hydra conf, and I want to see a little graph in a curses style in real time (look into Rich, Textual, etc.).
• Interception of SIGKILL, SIGTERM to stop training but save everything: two behaviours (1 will be the default ofc) -- a) wait for epoch end and validation, b) abort epoch.
• Add git hooks for linting, formatting, etc.
• Training logic
• Generate random run name (human readble from words)
• Automatic model loading from run name
• Test logic
• requirements.txt
• Feedback & improvements (continuous so don't expect this to ever be checked!)
• Refactor what is necessary (UI stuff, training & testing)
• Tests?
• Streamline the configuration (make it more DRY with either conf gen or runtime conf inference)
• Make datasets highly reproducible to the max (masterplan):
  • Hash the dataset post-instantiation (iterate and hash) and log to wandb.
  • Log the date of creation of all files (log anomalies like one date sticking out)
  • ???