Tvm
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Onnxruntime | 10,484 |  | 8 | 59 | 11 hours ago | 34 | June 16, 2023 | 2,015 | mit | C++ |
| ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator | ||||||||||
| Tvm | 10,360 | | 3 | 3 hours ago | 1 | February 23, 2021 | 767 | apache-2.0 | Python | |
| Open deep learning compiler stack for cpu, gpu and specialized accelerators | ||||||||||
| Arrayfire | 4,227 | 12 days ago | 308 | bsd-3-clause | C++ | |||||
| ArrayFire: a general purpose GPU library. | ||||||||||
| Spark Rapids | 580 | 3 hours ago | 10 | April 14, 2022 | 1,182 | apache-2.0 | Scala | |||
| Spark RAPIDS plugin - accelerate Apache Spark with GPUs | ||||||||||
| Aparapi | 370 | 17 | 8 | 2 years ago | 19 | July 12, 2021 | 47 | apache-2.0 | Java | |
| The New Official Aparapi: a framework for executing native Java and Scala code on the GPU. | ||||||||||
| Alpaka | 291 | 6 days ago | 179 | mpl-2.0 | C++ | |||||
| Abstraction Library for Parallel Kernel Acceleration :llama: | ||||||||||
| Autodock Gpu | 282 | a month ago | 51 | gpl-2.0 | C++ | |||||
| AutoDock for GPUs and other accelerators | ||||||||||
| Oneapi.jl | 157 | 6 hours ago | 23 | other | Julia | |||||
| Julia support for the oneAPI programming toolkit. | ||||||||||
| Dcompute | 116 | 2 years ago | 2 | October 15, 2017 | 18 | bsl-1.0 | D | |||
| DCompute: Native execution of D on GPUs and other Accelerators | ||||||||||
| Ml Testing Accelerators | 55 | 4 days ago | 28 | apache-2.0 | Jsonnet | |||||
| Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU) | ||||||||||
Open Deep Learning Compiler Stack
Documentation | Contributors | Community | Release Notes
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.
Getting Started
Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM 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.
Acknowledgement
We learned a lot from the following projects when building TVM.
- 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: use of integer set analysis and its loop transformation primitives.
- Theano: the design inspiration of symbolic scan operator for recurrence.
