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<!--- Licensed to the Apache Software Foundation (ASF) under one --> <!--- or more contributor license agreements. See the NOTICE file --> <!--- distributed with this work for additional information --> <!--- regarding copyright ownership. The ASF licenses this file --> <!--- to you under the Apache License, Version 2.0 (the --> <!--- "License"); you may not use this file except in compliance --> <!--- with the License. You may obtain a copy of the License at --> <!--- http://www.apache.org/licenses/LICENSE-2.0 --> <!--- Unless required by applicable law or agreed to in writing, --> <!--- software distributed under the License is distributed on an --> <!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --> <!--- KIND, either express or implied. See the License for the --> <!--- specific language governing permissions and limitations --> <!--- under the License. --><img src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/> 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.
Neo-AI/TVM is a downstream branch of TVM that includes vendor- and product-specific features on top of the upstream codebase.
Branches
- dev
- This is the development branch with most update to date source code.
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.