https://github.com/scitator/kittylyst

A tiny Catalyst-like experiment runner framework on top of micrograd.
https://github.com/scitator/kittylyst

Last synced: 4 days ago
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A tiny Catalyst-like experiment runner framework on top of micrograd.

• Host: GitHub
• URL: https://github.com/scitator/kittylyst
• Owner: Scitator
• License: mit
• Created: 2020-09-26T16:02:15.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2021-01-18T16:01:58.000Z (almost 4 years ago)
• Last Synced: 2024-11-02T02:33:41.579Z (11 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 555 KB
• Stars: 52
• Watchers: 6
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Kittylyst

![kitty](assets/kitty.jpg)

A tiny [Catalyst](https://github.com/catalyst-team/catalyst)-like 

experiment runner framework on top of 

[micrograd](https://github.com/karpathy/micrograd).

Implements `Experiment`, `Runner` and `Callback` Catalyst-core abstractions 

and has extra [PyTorch](https://github.com/pytorch)-like micrograd modules - 

`MicroLoader`, `MicroCriterion`, `MicroOptimizer` and `MicroScheduler`.

Every module is tiny, with about 100 lines of code (even this readme).

However, this is enough to make `Kittylyst` easily extendable 

for any number of data sources and support multi-stage experiments,

as the demo notebook shows.

Potentially useful for educational purposes.

### Installation

```bash

pip install kittylyst

```

### Example usage

```python

from micrograd.nn import MLP

import kittylyst as kt

loaders = {"train": kt.MicroLoader(...), "valid": kt.MicroLoader(...)}

model = MLP(2, [16, 16, 1])

criterion = kt.MicroCriterion()

optimizer = kt.MicroOptimizer(model)

scheduler = kt.MicroScheduler(optimizer, num_epochs=10)

experiment = kt.Experiment(

    model=model,

    criterion=criterion,

    optimizer=optimizer,

    scheduler=scheduler,

    loaders=loaders,

    num_epochs=10,

    callbacks=[

        kt.CriterionCallback(),

        kt.AccuracyCallback(),

        kt.OptimizerCallback(),

        kt.SchedulerCallback(),

        kt.LoggerCallback(),

    ],

    verbose=True,

)

kt.SupervisedRunner().run_experiment(experiment)

```

### Running an experiment

The notebook `demo.ipynb` provides a full demo of

running an `Experiment` with `SupervisedRunner`

for  binary classification task.

This is achieved by training `MLP` from `micrograd` module

with a simple svm "max-margin" binary classification loss (`MicroCriterion`)

and SGD (`MicroOptimizer`) with learning rate decay (`MicroScheduler`).

As shown in the notebook, 

using a 2-layer neural net with two 16-node hidden layers 

we achieve the following decision boundary on the moon dataset:

![2d neuron](assets/moon_mlp.png)

### Running codestyle

To run the codestyle check you will have to install 

[catalyst-codestyle](https://github.com/catalyst-team/codestyle). Then simply:

```bash

catalyst-make-codestyle

```

### License

MIT