# tvm

**Repository Path**: cokeom/tvm

## Basic Information

- **Project Name**: tvm
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-15
- **Last Updated**: 2023-12-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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<img src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/> Open Deep Learning Compiler Stack
==============================================
[Documentation](https://tvm.apache.org/docs) |
[Contributors](CONTRIBUTORS.md) |
[Community](https://tvm.apache.org/community) |
[Release Notes](NEWS.md)

[![Build Status](https://ci.tlcpack.ai/buildStatus/icon?job=tvm/main)](https://ci.tlcpack.ai/job/tvm/job/main/)
[![WinMacBuild](https://github.com/apache/tvm/workflows/WinMacBuild/badge.svg)](https://github.com/apache/tvm/actions?query=workflow%3AWinMacBuild)

Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the
productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends.
TVM works with deep learning frameworks to provide end to end compilation to different backends.

License
-------
TVM is licensed under the [Apache-2.0](LICENSE) license.

Getting Started
---------------
Check out the [TVM Documentation](https://tvm.apache.org/docs/) site for installation instructions, tutorials, examples, and more.
The [Getting Started with TVM](https://tvm.apache.org/docs/tutorial/introduction.html) tutorial is a great
place to start.

Contribute to TVM
-----------------
TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community.
Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/).

Acknowledgement
---------------
We learned a lot from the following projects when building TVM.
- [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module
  originates from Halide. We also learned and adapted some part of lowering pipeline from Halide.
- [Loopy](https://github.com/inducer/loopy): use of integer set analysis and its loop transformation primitives.
- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence.