Metadata-Version: 2.1
Name: flow-toolkit
Version: 0.0.1
Summary: CLI toolkit for Flow language, and integration with python.
Author-email: StealthyPanda <shaikm259@gmail.com>
License: MIT License
        
        Copyright (c) [2024] [StealthyPanda]
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Keywords: flow,ML,DL
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: codexregius
Requires-Dist: argparse
Requires-Dist: ply
Requires-Dist: dulwich

# Flow toolkit
---

A simple development toolkit for streamlining ML and DL workflows.

## Getting started

### Installation

The base compiler can be installed via `pip`:
```bash
pip install flow-toolkit
```

Next, install a plugin for your intended output. For example:

```bash
flow -i https://github.com/StealthyPanda/
```


Any github repository can be used as a valid flow plugin, as long as it contains a `plugin.py` in its root directory, and contains a `main` function.


### Quick Start

A simple flow for a dense neural network would be:

```
// example.fl

flow linear(x) [weights, biases] {
    return
        (weights @ x) + biases;
}

flow NeuralNetwork (x) {
    let linear l1;
    let linear l2;

    y = l1(x);
    y = l2(y);

    return y;
}

build NeuralNetwork simple {
    x => 784;
    output => 10;
}
```

Build the flow to a pytorch model with:
```bash
flow -f example.fl -o example
```




