semvec/pypi-packages huggingface dataset:
sloth-paste 1.0.2
A simple file content sharing app.
License: MIT

# Sloth
A simple file content sharing app.

## Installation
```bash
$ pip install sloth-paste
```

## Usage
```bash
$ sloth -h
usage: sloth [-h] [-p POSTER] [--syntax SYNTAX] [--exp {day,week,year}] [-u]
             file [file ...]

Code sharing app for lazy people.

positional arguments:
  file                  File to share content

optional arguments:
  -h, --help            show this help message and exit
  -p POSTER, --poster POSTER
                        Author name
  --syntax SYNTAX       Desired syntax (py, js, cpp...)
  --exp {day,week,year}
                        Expiration time
  -u, --update-db       Update suported langs from paste.ubuntu.com
```

```bash
sloth setup.py test.py -p bufgix
[i] 2 files entered...
[i] Poster: bufgix...
==============================
[i] File: setup.py
[i] Detected language python3
[+] Paste link: https://paste.ubuntu.com/p/4Jm6gVNbQ4/

==============================

[i] File: test.py
[i] Detected language python3
[+] Paste link: https://paste.ubuntu.com/p/JV4yPXMDc6/

==============================

Copied last link: https://paste.ubuntu.com/p/JV4yPXMDc6/
```

## LICENSE
[MIT](https://github.com/bufgix/slothpaste/blob/master/LICENSE)
tfpyth 1.0.1
Putting TensorFlow back in PyTorch, back in Tensorflow (differentiable TensorFlow PyTorch adapters).
License: MIT
Keywords: ml machine learning

# TfPyTh

[![Build Status](https://travis-ci.com/BlackHC/tfpyth.svg?branch=master)](https://travis-ci.com/BlackHC/tfpyth) [![codecov](https://codecov.io/gh/BlackHC/tfpyth/branch/master/graph/badge.svg)](https://codecov.io/gh/BlackHC/tfpyth)

Putting TensorFlow back in PyTorch, back in TensorFlow (differentiable TensorFlow PyTorch adapters).

> A light-weight differentiable adapter library to make TensorFlow and PyTorch interact.

## Install

```
pip install tfpyth
```

### Example

```python
import tensorflow as tf
import torch as th
import numpy as np
import tfpyth

session = tf.Session()

def get_torch_function():
    a = tf.placeholder(tf.float32, name='a')
    b = tf.placeholder(tf.float32, name='b')
    c = 3 * a + 4 * b * b

    f = tfpyth.torch_from_tensorflow(session, [a, b], c).apply
    return f

f = get_torch_function()
a = th.tensor(1, dtype=th.float32, requires_grad=True)
b = th.tensor(3, dtype=th.float32, requires_grad=True)
x = f(a, b)

assert x == 39.

x.backward()

assert np.allclose((a.grad, b.grad), (3., 24.))
```

## What it's got

### `torch_from_tensorflow`

Creates a PyTorch function that is differentiable by evaluating a TensorFlow output tensor given input placeholders.

### `eager_tensorflow_from_torch`

Creates an eager Tensorflow function from a PyTorch function.

### `tensorflow_from_torch`

Creates a TensorFlow op/tensor from a PyTorch function.

## Future work

- [ ] support JAX
- [ ] support higher-order derivatives