jp6/cu122/: tvm-0.16.0 metadata and description
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TVM: An End to End Tensor IR/DSL Stack for Deep Learning Systems
author |
Apache TVM |
classifiers |
- License :: OSI Approved :: Apache Software License
- Development Status :: 4 - Beta
- Intended Audience :: Developers
- Intended Audience :: Education
- Intended Audience :: Science/Research
|
description_content_type |
text/markdown |
download_url |
https://github.com/apache/tvm/tags |
keywords |
machine learning |
license |
Apache |
requires_dist |
- attrs
- cloudpickle
- decorator
- ml-dtypes
- numpy
- psutil
- scipy
- tornado
- typing-extensions
- attrs ; extra == 'all-prod'
- cloudpickle ; extra == 'all-prod'
- coremltools ; extra == 'all-prod'
- decorator ; extra == 'all-prod'
- ethos-u-vela ==3.8.0 ; extra == 'all-prod'
- future ; extra == 'all-prod'
- h5py ==2.10.0 ; extra == 'all-prod'
- ml-dtypes ; extra == 'all-prod'
- numpy ; extra == 'all-prod'
- onnx ; extra == 'all-prod'
- onnxoptimizer ; extra == 'all-prod'
- onnxruntime ; extra == 'all-prod'
- opencv-python ; extra == 'all-prod'
- paddlepaddle ; extra == 'all-prod'
- progressbar ; extra == 'all-prod'
- protobuf ; extra == 'all-prod'
- psutil ; extra == 'all-prod'
- scikit-image ; extra == 'all-prod'
- scipy ; extra == 'all-prod'
- six ; extra == 'all-prod'
- tensorflow ; extra == 'all-prod'
- tensorflow-estimator ; extra == 'all-prod'
- tflite ; extra == 'all-prod'
- torch ; extra == 'all-prod'
- torchvision ; extra == 'all-prod'
- tornado ; extra == 'all-prod'
- typing-extensions ; extra == 'all-prod'
- xgboost >=1.1.0 ; extra == 'all-prod'
- astroid ; extra == 'dev'
- autodocsumm ; extra == 'dev'
- black ==20.8b1 ; extra == 'dev'
- commonmark >=0.7.3 ; extra == 'dev'
- cpplint ; extra == 'dev'
- docutils <0.17 ; extra == 'dev'
- image ; extra == 'dev'
- matplotlib ; extra == 'dev'
- pillow ; extra == 'dev'
- pylint ; extra == 'dev'
- sphinx ; extra == 'dev'
- sphinx-autodoc-annotation ; extra == 'dev'
- sphinx-gallery ; extra == 'dev'
- sphinx-rtd-theme ; extra == 'dev'
- types-psutil ; extra == 'dev'
- ethos-u-vela ==3.8.0 ; extra == 'ethosu'
- numpy ; extra == 'importer-caffe'
- protobuf ; extra == 'importer-caffe'
- scikit-image ; extra == 'importer-caffe'
- six ; extra == 'importer-caffe'
- future ; extra == 'importer-caffe2'
- torch ; extra == 'importer-caffe2'
- coremltools ; extra == 'importer-coreml'
- opencv-python ; extra == 'importer-darknet'
- tensorflow ; extra == 'importer-keras'
- tensorflow-estimator ; extra == 'importer-keras'
- future ; extra == 'importer-onnx'
- onnx ; extra == 'importer-onnx'
- onnxoptimizer ; extra == 'importer-onnx'
- onnxruntime ; extra == 'importer-onnx'
- torch ; extra == 'importer-onnx'
- torchvision ; extra == 'importer-onnx'
- paddlepaddle ; extra == 'importer-paddle'
- future ; extra == 'importer-pytorch'
- torch ; extra == 'importer-pytorch'
- torchvision ; extra == 'importer-pytorch'
- tensorflow ; extra == 'importer-tensorflow'
- tensorflow-estimator ; extra == 'importer-tensorflow'
- tensorflow ; extra == 'importer-tflite'
- tensorflow-estimator ; extra == 'importer-tflite'
- tflite ; extra == 'importer-tflite'
- ethos-u-vela ==3.8.0 ; extra == 'tvmc'
- future ; extra == 'tvmc'
- onnx ; extra == 'tvmc'
- onnxoptimizer ; extra == 'tvmc'
- onnxruntime ; extra == 'tvmc'
- paddlepaddle ; extra == 'tvmc'
- tensorflow ; extra == 'tvmc'
- tflite ; extra == 'tvmc'
- torch ; extra == 'tvmc'
- torchvision ; extra == 'tvmc'
- xgboost >=1.1.0 ; extra == 'tvmc'
- h5py ==2.10.0 ; extra == 'vitis-ai'
- progressbar ; extra == 'vitis-ai'
- future ; extra == 'xgboost'
- torch ; extra == 'xgboost'
- xgboost >=1.1.0 ; extra == 'xgboost'
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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.