Open deep learning compiler stack for cpu, gpu and specialized accelerators
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Clouds d1cdb623c3 Update setup.py (#803)
fix errors when running `python3 setup.py sdist bdist_wheel`
2018-01-22 10:00:08 -08:00
HalideIR@aadbf02d6b Halide -> HalideIR (#698) 2017-12-18 07:52:48 +08:00
apps [APP] fix gradle build for Android build (#685) 2017-11-30 14:19:49 -08:00
cmake use auto source_group (#146) 2017-05-18 09:17:47 -07:00
dlpack@10892ac964 [DLPack] Upgrade dlpack to 0.2 (#609) 2017-11-03 15:55:17 +08:00
dmlc-core@c0871823b5 [TOPI] add schedule for ARM Mali GPU (#786) 2018-01-16 09:14:01 -08:00
docs small fixes on docs (#769) 2018-01-09 15:39:35 -08:00
include/tvm [WIP] WebGL Backend (#672) 2018-01-20 13:51:34 -08:00
jvm [DLPack] Upgrade dlpack to 0.2 (#609) 2017-11-03 15:55:17 +08:00
make [CONTRIB] rocBLAS integration (#751) 2018-01-03 13:38:24 -08:00
python Update setup.py (#803) 2018-01-22 10:00:08 -08:00
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tests temporarily disable opengl in test_runtime_ndarray.py (#804) 2018-01-22 09:58:48 -08:00
topi simplify expr in get_const_tuple (#795) 2018-01-19 10:31:51 -08:00
tutorials small fixes on docs (#769) 2018-01-09 15:39:35 -08:00
verilog [CONTRIB/BLAS] Add CBLAS Example to contrib (#120) 2017-05-05 10:55:34 -07:00
web [WIP] WebGL Backend (#672) 2018-01-20 13:51:34 -08:00
.gitignore Fix dependency problem of reducer condition (#712) (#721) 2017-12-23 15:20:16 +08:00
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README.md

TVM: Tensor IR Stack for Deep Learning Systems

GitHub license Build Status

Installation | Documentation | Tutorials | Operator Inventory | FAQ | Contributors | Release Notes

TVM is a Tensor intermediate representation(IR) 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 our announcement for more details.

License

© Contributors, 2017. 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.

Acknowledgement

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

  • Halide: TVM uses HalideIR as data structure for arithematic 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.