semvec/pypi-packages huggingface dataset:
fast-hadamard-transform 1.0.4.post1
Fast Hadamard Transform in CUDA, with a PyTorch interface
Requires Python: >=3.7

# Fast Hadamard Transform in CUDA, with a PyTorch interface

Features:
- Support fp32, fp16, bf16, for dimension up to 32768.
- Implicitly pad with zeros if dimension is not a power of 2.

## How to use

```
from fast_hadamard_transform import hadamard_transform
```

```
def hadamard_transform(x, scale=1.0):
    """
    Arguments:
        x: (..., dim)
        scale: float. Multiply the output by this number.
    Returns:
        out: (..., dim)

    Multiply each row of x by the Hadamard transform matrix.
    Equivalent to F.linear(x, torch.tensor(scipy.linalg.hadamard(dim))) * scale.
    If dim is not a power of 2, we implicitly pad x with zero so that dim is the next power of 2.
    """
```

## Speed

Benchmarked on A100, for not too small batch size, compared to memcpy
(torch.clone), which is a lower bound for the time taken as we'd need to read
inputs from GPU memory and write output to GPU memory anyway.

| Data type |  Dimension | Time taken vs memcpy |
| --------- | ---------- | -------------------- |
| fp16/bf16 |     <= 512 |                 1.0x |
|           | 512 - 8192 |              <= 1.2x |
|           |      16384 |                 1.3x |
|           |      32768 |                 1.8x |
| fp32      |    <= 8192 |                 1.0x |
|           |      16384 |                 1.1x |
|           |      32768 |                 1.2x |
axlearn 0.0.1.post20240213000026
AXLearn
Requires Python: >=3.9

# The AXLearn Library for Deep Learning

**This library is under active development and the API is subject to change.**

## Table of Contents

| Section | Description |
| - | - |
| [Introduction](#introduction) | What is AXLearn? |
| [Getting Started](docs/01-start.md) | Getting up and running with AXLearn. |
| [Concepts](docs/02-concepts.md) | Core concepts and design principles. |
| [CLI User Guide](docs/03-cli.md) | How to use the CLI. |
| [Infrastructure](docs/04-infrastructure.md) | Core infrastructure components. |

## Introduction

AXLearn is a library built on top of [JAX](https://jax.readthedocs.io/) and
[XLA](https://www.tensorflow.org/xla) to support the development of large-scale deep learning models.

AXLearn takes an object-oriented approach to the software engineering challenges that arise from
building, iterating, and maintaining models.
The configuration system of the library lets users compose models from reusable building blocks and
integrate with other libraries such as [Flax](https://flax.readthedocs.io/) and
[Hugging Face transformers](https://github.com/huggingface/transformers).

AXLearn is built to scale.
It supports the training of models with up to hundreds of billions of parameters across thousands of
accelerators at high utilization.
It is also designed to run on public clouds and provides tools to deploy and manage jobs and data.
Built on top of [GSPMD](https://arxiv.org/abs/2105.04663), AXLearn adopts a global computation
paradigm to allow users to describe computation on a virtual global computer rather than on a
per-accelerator basis.

AXLearn supports a wide range of applications, including natural language processing, computer
vision, and speech recognition and contains baseline configurations for training state-of-the-art
models.

Please see [Concepts](docs/02-concepts.md) for more details on the core components and design of AXLearn, or [Getting Started](docs/01-start.md) if you want to get your hands dirty.
cart-flask-ngrok 0.0.1
An offshoot of flask-ngrok for demo Flask apps using ngrok.
Keywords: flask ngrok CART

# flask-ngrok-CART
An offshoot of [flask-ngrok](https://github.com/gstaff/flask-ngrok) (see also [flask-ngrok2](https://github.com/MohamedAliRashad/flask-ngrok2), [flask-ngrok3](https://github.com/Partycode/flask-ngrok3)) for making demo Flask apps from personal machine.

## Installation

```bash
pip3 install flask-ngrok-CART
```
flask-ngrok-CART 0.0.0
An offshoot of flask-ngrok for demo Flask apps using ngrok.
Keywords: flask ngrok CART

# flask-ngrok-CART
An offshoot of [flask-ngrok](https://github.com/gstaff/flask-ngrok) (see also [flask-ngrok2](https://github.com/MohamedAliRashad/flask-ngrok2), [flask-ngrok3](https://github.com/Partycode/flask-ngrok3)) for making demo Flask apps from personal machine.

## Installation

```bash
pip3 install flask-ngrok-CART
```
flask-ngrok2-CART 0.0.0
An offshoot of flask-ngrok for demo Flask apps using ngrok.
Keywords: flask ngrok CART

# flask-ngrok-CART
An offshoot of [flask-ngrok](https://github.com/gstaff/flask-ngrok) (see also [flask-ngrok2](https://github.com/MohamedAliRashad/flask-ngrok2), [flask-ngrok3](https://github.com/Partycode/flask-ngrok3)) for making demo Flask apps from personal machine.

## Installation

```bash
pip3 install flask-ngrok-CART
```