MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models

A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

A cross-platform, OpenGL terminal emulator.

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

deepspeed

Welcome to fastai
================



![CI](https://github.com/fastai/fastai/workflows/CI/badge.svg)
[![PyPI](https://img.shields.io/pypi/v/fastai?color=blue&label=pypi%20version.png)](https://pypi.org/project/fastai/#description)
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only)](https://img.shields.io/conda/vn/fastai/fastai?color=seagreen&label=conda%20version.png)](https://anaconda.org/fastai/fastai)
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images](https://github.com/fastai/docker-containers/workflows/Build%20fastai%20images/badge.svg)](https://github.com/fastai/docker-containers)
![docs](https://github.com/fastai/fastai/workflows/docs/badge.svg)

## Installing

You can use fastai without any installation by using [Google
Colab](https://colab.research.google.com/). In fact, every page of this
documentation is also available as an interactive notebook - click “Open
in colab” at the top of any page to open it (be sure to change the Colab
runtime to “GPU” to have it run fast!) See the fast.ai documentation on
[Using Colab](https://course.fast.ai/start_colab) for more information.

You can install fastai on your own machines with conda (highly
recommended), as long as you’re running Linux or Windows (NB: Mac is not
supported). For Windows, please see the “Running on Windows” for
important notes.

If you’re using
[miniconda](https://docs.conda.io/en/latest/miniconda.html)
(recommended) then run (note that if you replace `conda` with
[mamba](https://github.com/mamba-org/mamba) the install process will be
much faster and more reliable):

``` bash
conda install -c fastchan fastai
```

…or if you’re using
[Anaconda](https://www.anaconda.com/products/individual) then run:

``` bash
conda install -c fastchan fastai anaconda
```

To install with pip, use: `pip install fastai`. If you install with pip,
you should install PyTorch first by following the PyTorch [installation
instructions](https://pytorch.org/get-started/locally/).

If you plan to develop fastai yourself, or want to be on the cutting
edge, you can use an editable install (if you do this, you should also
use an editable install of
[fastcore](https://github.com/fastai/fastcore) to go with it.) First
install PyTorch, and then:

    git clone https://github.com/fastai/fastai
    pip install -e "fastai[dev]"

## Learning fastai

The best way to get started with fastai (and deep learning) is to read
[the
book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527),
and complete [the free course](https://course.fast.ai).

To see what’s possible with fastai, take a look at the [Quick
Start](https://docs.fast.ai/quick_start.html), which shows how to use
around 5 lines of code to build an image classifier, an image
segmentation model, a text sentiment model, a recommendation system, and
a tabular model. For each of the applications, the code is much the
same.

Read through the [Tutorials](https://docs.fast.ai/tutorial.html) to
learn how to train your own models on your own datasets. Use the
navigation sidebar to look through the fastai documentation. Every
class, function, and method is documented here.

To learn about the design and motivation of the library, read the [peer
reviewed paper](https://www.mdpi.com/2078-2489/11/2/108/htm).

## About fastai

fastai is a deep learning library which provides practitioners with
high-level components that can quickly and easily provide
state-of-the-art results in standard deep learning domains, and provides
researchers with low-level components that can be mixed and matched to
build new approaches. It aims to do both things without substantial
compromises in ease of use, flexibility, or performance. This is
possible thanks to a carefully layered architecture, which expresses
common underlying patterns of many deep learning and data processing
techniques in terms of decoupled abstractions. These abstractions can be
expressed concisely and clearly by leveraging the dynamism of the
underlying Python language and the flexibility of the PyTorch library.
fastai includes:

- A new type dispatch system for Python along with a semantic type
  hierarchy for tensors
- A GPU-optimized computer vision library which can be extended in pure
  Python
- An optimizer which refactors out the common functionality of modern
  optimizers into two basic pieces, allowing optimization algorithms to
  be implemented in 4–5 lines of code
- A novel 2-way callback system that can access any part of the data,
  model, or optimizer and change it at any point during training
- A new data block API
- And much more…

fastai is organized around two main design goals: to be approachable and
rapidly productive, while also being deeply hackable and configurable.
It is built on top of a hierarchy of lower-level APIs which provide
composable building blocks. This way, a user wanting to rewrite part of
the high-level API or add particular behavior to suit their needs does
not have to learn how to use the lowest level.

<img alt="Layered API" src="https://raw.githubusercontent.com/fastai/fastai/master/images/layered.png" width="345">

## Migrating from other libraries

It’s very easy to migrate from plain PyTorch, Ignite, or any other
PyTorch-based library, or even to use fastai in conjunction with other
libraries. Generally, you’ll be able to use all your existing data
processing code, but will be able to reduce the amount of code you
require for training, and more easily take advantage of modern best
practices. Here are migration guides from some popular libraries to help
you on your way:

- [Plain PyTorch](https://docs.fast.ai/examples/migrating_pytorch.html)
- [Ignite](https://docs.fast.ai/examples/migrating_ignite.html)
- [Lightning](https://docs.fast.ai/examples/migrating_lightning.html)
- [Catalyst](https://docs.fast.ai/examples/migrating_catalyst.html)

## Windows Support

When installing with `mamba` or `conda` replace `-c fastchan` in the
installation with `-c pytorch -c nvidia -c fastai`, since fastchan is
not currently supported on Windows.

Due to python multiprocessing issues on Jupyter and Windows,
`num_workers` of `Dataloader` is reset to 0 automatically to avoid
Jupyter hanging. This makes tasks such as computer vision in Jupyter on
Windows many times slower than on Linux. This limitation doesn’t exist
if you use fastai from a script.

See [this
example](https://github.com/fastai/fastai/blob/master/nbs/examples/dataloader_spawn.py)
to fully leverage the fastai API on Windows.

## Tests

To run the tests in parallel, launch:

`nbdev_test`

For all the tests to pass, you’ll need to install the dependencies
specified as part of dev_requirements in settings.ini

`pip install -e .[dev]`

Tests are written using `nbdev`, for example see the documentation for
`test_eq`.

## Contributing

After you clone this repository, make sure you have run
`nbdev_install_hooks` in your terminal. This install Jupyter and git
hooks to automatically clean, trust, and fix merge conflicts in
notebooks.

After making changes in the repo, you should run `nbdev_prepare` and
make additional and necessary changes in order to pass all the tests.

## Docker Containers

For those interested in official docker containers for this project,
they can be found
[here](https://github.com/fastai/docker-containers#fastai).

fastai

Productive & portable high-performance programming in Python.

<div align="center">
  <img width="500px" src="https://github.com/taichi-dev/taichi/raw/master/misc/logo.png"/>
</div>

---
[![Latest Release](https://img.shields.io/github/v/release/taichi-dev/taichi?color=blue&label=Latest%20Release)](https://github.com/taichi-dev/taichi/releases/latest)
[![downloads](https://pepy.tech/badge/taichi)](https://pepy.tech/project/taichi)
[![CI](https://github.com/taichi-dev/taichi/actions/workflows/testing.yml/badge.svg)](https://github.com/taichi-dev/taichi/actions/workflows/testing.yml)
[![Nightly Release](https://github.com/taichi-dev/taichi/actions/workflows/release.yml/badge.svg)](https://github.com/taichi-dev/taichi/actions/workflows/release.yml)
<a href="https://discord.gg/f25GRdXRfg"><img alt="discord invitation link" src="https://dcbadge.vercel.app/api/server/f25GRdXRfg?style=flat"></a>

```shell
pip install taichi  # Install Taichi Lang
ti gallery          # Launch demo gallery
```

## What is Taichi Lang?

Taichi Lang is an open-source, imperative, parallel programming language for high-performance numerical computation. It is embedded in Python and uses just-in-time (JIT) compiler frameworks, for example LLVM, to offload the compute-intensive Python code to the native GPU or CPU instructions.

<a href="https://github.com/taichi-dev/taichi/blob/master/python/taichi/examples/simulation/fractal.py#L1-L31"> <img src="https://github.com/taichi-dev/public_files/raw/master/taichi/fractal_code.png" height="270px"></a>  <img src="https://raw.githubusercontent.com/taichi-dev/public_files/master/taichi/fractal_small.gif" height="270px">

The language has broad applications spanning real-time physical simulation, numerical computation, augmented reality, artificial intelligence, vision and robotics, visual effects in films and games, general-purpose computing, and much more.

<a href="https://github.com/taichi-dev/taichi/blob/master/python/taichi/examples/simulation/mpm128.py"><img src="https://github.com/taichi-dev/public_files/raw/master/taichi/mpm128.gif" height="192px"></a>
<a href="https://github.com/taichi-dev/quantaichi"> <img src="https://raw.githubusercontent.com/taichi-dev/public_files/master/taichi/smoke_3d.gif" height="192px"></a>
<a href="https://github.com/taichi-dev/taichi/blob/master/python/taichi/examples/rendering/sdf_renderer.py"><img src="https://github.com/taichi-dev/public_files/raw/master/taichi/sdf_renderer.jpg" height="192px"></a>
<a href="https://github.com/taichi-dev/taichi/blob/master/python/taichi/examples/simulation/euler.py"><img src="https://github.com/taichi-dev/public_files/raw/master/taichi/euler.gif" height="192px"></a>

<a href="https://github.com/taichi-dev/quantaichi"><img src="https://raw.githubusercontent.com/taichi-dev/public_files/master/taichi/elastic_letters.gif" height="213px"></a>
<a href="https://github.com/taichi-dev/quantaichi"><img src="https://raw.githubusercontent.com/taichi-dev/public_files/master/taichi/fluid_with_bunnies.gif" height="213px"></a>

[...More](#demos)

## Why Taichi Lang?

- Built around Python: Taichi Lang shares almost the same syntax with Python, allowing you to write algorithms with minimal language barrier. It is also well integrated into the Python ecosystem, including NumPy and PyTorch.
- Flexibility: Taichi Lang provides a set of generic data containers known as *SNode* (/ˈsnoʊd/), an effective mechanism for composing hierarchical, multi-dimensional fields. This can cover many use patterns in numerical simulation (e.g. [spatially sparse computing](https://docs.taichi-lang.org/docs/sparse)).
- Performance: With the `@ti.kernel` decorator, Taichi Lang's JIT compiler automatically compiles your Python functions into efficient GPU or CPU machine code for parallel execution.
- Portability: Write your code once and run it everywhere. Currently, Taichi Lang supports most mainstream GPU APIs, such as CUDA and Vulkan.
- ... and many more features! A cross-platform, Vulkan-based 3D visualizer, [differentiable programming](https://docs.taichi-lang.org/docs/differentiable_programming),  [quantized computation](https://github.com/taichi-dev/quantaichi) (experimental), etc.

## Getting Started

### Installation

<details>
  <summary>Prerequisites</summary>



- Operating systems
  - Windows
  - Linux
  - macOS
- Python: 3.6 ~ 3.10 (64-bit only)
- Compute backends
  - x64/ARM CPUs
  - CUDA
  - Vulkan
  - OpenGL (4.3+)
  - Apple Metal
  - WebAssembly (experiemental)
 </details>

Use Python's package installer **pip** to install Taichi Lang:

```bash
pip install --upgrade taichi
```

*We also provide a nightly package. Note that nightly packages may crash because they are not fully tested.  We cannot guarantee their validity, and you are at your own risk trying out our latest, untested features. The nightly packages can be installed from our self-hosted PyPI (Using self-hosted PyPI allows us to provide more frequent releases over a longer period of time)*

```bash
pip install -i https://pypi.taichi.graphics/simple/ taichi-nightly
```

### Run your "Hello, world!"

Here is how you can program a 2D fractal in Taichi:

```py
# python/taichi/examples/simulation/fractal.py

import taichi as ti

ti.init(arch=ti.gpu)

n = 320
pixels = ti.field(dtype=float, shape=(n * 2, n))


@ti.func
def complex_sqr(z):
    return ti.Vector([z[0]**2 - z[1]**2, z[1] * z[0] * 2])


@ti.kernel
def paint(t: float):
    for i, j in pixels:  # Parallelized over all pixels
        c = ti.Vector([-0.8, ti.cos(t) * 0.2])
        z = ti.Vector([i / n - 1, j / n - 0.5]) * 2
        iterations = 0
        while z.norm() < 20 and iterations < 50:
            z = complex_sqr(z) + c
            iterations += 1
        pixels[i, j] = 1 - iterations * 0.02


gui = ti.GUI("Julia Set", res=(n * 2, n))

for i in range(1000000):
    paint(i * 0.03)
    gui.set_image(pixels)
    gui.show()
```

*If Taichi Lang is properly installed, you should get the animation below 🎉:*

<a href="https://github.com/taichi-dev/taichi/blob/master/python/taichi/examples/simulation/fractal.py#L1-L31"> </a><img src="https://raw.githubusercontent.com/taichi-dev/public_files/master/taichi/fractal_small.gif" height="270px">

See [Get started](https://docs.taichi-lang.org) for more information.

### Build from source

If you wish to try our our experimental features or build Taichi Lang for your own environments, see [Developer installation](https://docs.taichi-lang.org/docs/dev_install).

## Documentation

- [Technical documents](https://docs.taichi-lang.org/)
- [API Reference](https://docs.taichi-lang.org/api/)
- [Blog](https://docs.taichi-lang.org/blog)

## Community activity [![Time period](https://images.repography.com/32602247/taichi-dev/taichi/recent-activity/RlhQybvihwEjfE7ngXyQR9tudBDYAvl27v-NVNMxUrg_badge.svg)](https://repography.com)
[![Timeline graph](https://images.repography.com/32602247/taichi-dev/taichi/recent-activity/RlhQybvihwEjfE7ngXyQR9tudBDYAvl27v-NVNMxUrg_timeline.svg)](https://github.com/taichi-dev/taichi/commits)
[![Issue status graph](https://images.repography.com/32602247/taichi-dev/taichi/recent-activity/RlhQybvihwEjfE7ngXyQR9tudBDYAvl27v-NVNMxUrg_issues.svg)](https://github.com/taichi-dev/taichi/issues)
[![Pull request status graph](https://images.repography.com/32602247/taichi-dev/taichi/recent-activity/RlhQybvihwEjfE7ngXyQR9tudBDYAvl27v-NVNMxUrg_prs.svg)](https://github.com/taichi-dev/taichi/pulls)
[![Trending topics](https://images.repography.com/32602247/taichi-dev/taichi/recent-activity/RlhQybvihwEjfE7ngXyQR9tudBDYAvl27v-NVNMxUrg_words.svg)](https://github.com/taichi-dev/taichi/commits)

## Contributing

Kudos to all of our amazing contributors! Taichi Lang thrives through open-source. In that spirit, we welcome all kinds of contributions from the community. If you would like to participate, check out the [Contribution Guidelines](CONTRIBUTING.md) first.

<a href="https://github.com/taichi-dev/taichi/graphs/contributors"><img src="https://raw.githubusercontent.com/taichi-dev/public_files/master/taichi/contributors_taichi-dev_taichi_18.png" width="800px"></a>

*Contributor avatars are randomly shuffled.*

## License

Taichi Lang is distributed under the terms of Apache License (Version 2.0).

See [Apache License](https://github.com/taichi-dev/taichi/blob/master/LICENSE) for details.

## Community

For more information about the events or community, please refer to [this page](https://github.com/taichi-dev/community)


### Join our discussions

- [Discord](https://discord.gg/f25GRdXRfg)
- [GitHub Discussions](https://github.com/taichi-dev/taichi/discussions)
- [太极编程语言中文论坛](https://forum.taichi.graphics/)

### Report an issue

- If you spot an technical or documentation issue, file an issue at [GitHub Issues](https://github.com/taichi-dev/taichi/issues)
- If you spot any security issue, mail directly to <a href = "mailto:security@taichi.graphics?subject = Taichi Security Problem">security@taichi.graphics</a>.

### Contact us

- [Discord](https://discord.gg/f25GRdXRfg)
- [WeChat](https://forum.taichi-lang.cn/t/topic/2884)

## Reference

### Demos

- [Nerf with Taichi](https://github.com/taichi-dev/taichi-nerfs)
- [Taichi Lang examples](https://github.com/taichi-dev/taichi/tree/master/python/taichi/examples)
- [Advanced Taichi Lang examples](https://github.com/taichi-dev/advanced_examples)
- [Awesome Taichi](https://github.com/taichi-dev/awesome-taichi)
- [DiffTaichi](https://github.com/taichi-dev/difftaichi)
- [Taichi elements](https://github.com/taichi-dev/taichi_elements)
- [Taichi Houdini](https://github.com/taichi-dev/taichi_houdini)
- [More...](misc/links.md)


### AOT deployment

- [Taichi AOT demos & tutorial](https://github.com/taichi-dev/taichi-aot-demo/)


### Lectures & talks

- SIGGRAPH 2020 course on Taichi basics: [YouTube](https://youtu.be/Y0-76n3aZFA), [Bilibili](https://www.bilibili.com/video/BV1kA411n7jk/), [slides (pdf)](https://yuanming.taichi.graphics/publication/2020-taichi-tutorial/taichi-tutorial.pdf).
- Chinagraph 2020 用太极编写物理引擎: [哔哩哔哩](https://www.bilibili.com/video/BV1gA411j7H5)
- GAMES 201 高级物理引擎实战指南 2020: [课件](https://github.com/taichi-dev/games201)
- 太极图形课第一季：[课件](https://github.com/taichiCourse01)
- [TaichiCon](https://github.com/taichi-dev/taichicon): Taichi Developer Conferences
- More to come...

### Citations

If you use Taichi Lang in your research, please cite the corresponding papers:

- [**(SIGGRAPH Asia 2019) Taichi: High-Performance Computation on Sparse Data Structures**](https://yuanming.taichi.graphics/publication/2019-taichi/taichi-lang.pdf) [[Video]](https://youtu.be/wKw8LMF3Djo) [[BibTex]](https://raw.githubusercontent.com/taichi-dev/taichi/master/misc/taichi_bibtex.txt) [[Code]](https://github.com/taichi-dev/taichi)
- [**(ICLR 2020) DiffTaichi: Differentiable Programming for Physical Simulation**](https://arxiv.org/abs/1910.00935) [[Video]](https://www.youtube.com/watch?v=Z1xvAZve9aE) [[BibTex]](https://raw.githubusercontent.com/taichi-dev/taichi/master/misc/difftaichi_bibtex.txt) [[Code]](https://github.com/yuanming-hu/difftaichi)
- [**(SIGGRAPH 2021) QuanTaichi: A Compiler for Quantized Simulations**](https://yuanming.taichi.graphics/publication/2021-quantaichi/quantaichi.pdf) [[Video]](https://www.youtube.com/watch?v=0jdrAQOxJlY) [[BibTex]](https://raw.githubusercontent.com/taichi-dev/taichi/master/misc/quantaichi_bibtex.txt) [[Code]](https://github.com/taichi-dev/quantaichi)

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