Open deep learning compiler stack for cpu, gpu and specialized accelerators
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雾雨魔理沙 df88c411f5 save (#3033)
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please the ci god

Update src/relay/pass/partial_eval.cc

Co-Authored-By: Wei Chen <ipondering.weic@gmail.com>

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2019-06-15 15:08:46 -07:00
.github
3rdparty Make the behavior of data nullptr check of pooling layer same as others. (#3322) 2019-06-08 09:17:29 -07:00
apps Fixed a typo (#3218) 2019-05-24 17:48:58 -07:00
cmake [VTA] [Hardware] Chisel implementation (#3258) 2019-06-05 10:17:11 -07:00
conda More fixes and tweaks to the cuda conda packages (#3281) 2019-06-05 10:14:12 -07:00
docker Update tflite schema version to 1.13 (#3356) 2019-06-13 08:52:25 -07:00
docs [DOC] minor grammatical improvements (#3341) 2019-06-11 21:22:22 -07:00
golang
include/tvm save (#3033) 2019-06-15 15:08:46 -07:00
jvm
nnvm Fix typo in word explicitly (#3376) 2019-06-14 21:34:37 -07:00
python save (#3033) 2019-06-15 15:08:46 -07:00
rust [Rust] Static syslib (#3274) 2019-06-08 20:56:58 -07:00
src save (#3033) 2019-06-15 15:08:46 -07:00
tests save (#3033) 2019-06-15 15:08:46 -07:00
topi [TEST][FLAKY] Fix flaky test on topk and quantize pass (#3362) 2019-06-13 17:48:17 -07:00
tutorials [DOC] minor grammatical improvements (#3341) 2019-06-11 21:22:22 -07:00
vta fix hardware-makefile for osx, bugfix chisel-RegFile, and rename driver (#3371) 2019-06-14 01:01:00 -07:00
web
.clang-format
.gitignore More fixes and tweaks to the cuda conda packages (#3281) 2019-06-05 10:14:12 -07:00
.gitmodules
CMakeLists.txt [Relay][VM] Add AllocTensor instruction and better instruction printer (#3306) 2019-06-14 15:18:14 -07:00
CONTRIBUTORS.md [Team] Jian Weng -> Committer (#3359) 2019-06-13 08:51:58 -07:00
Jenkinsfile [BUILD] Enable more visible symbols by default (#3365) 2019-06-14 10:30:46 -07:00
LICENSE
Makefile
NEWS.md
NOTICE
README.md
version.py

README.md

Open Deep Learning Compiler Stack

GitHub license Build Status

Documentation | Contributors | Community | Release Notes

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. Checkout the tvm stack homepage for more information.

License

© Contributors Licensed under an Apache-2.0 license.

Contribute to TVM

TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Checkout the Contributor Guide

Acknowledgement

We learnt a lot from the following projects when building TVM.

  • Halide: TVM uses HalideIR as data structure for arithmetic simplification and low level lowering. We also learnt 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.