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* Fix PR #5550 reverted in #5911 (performance improvment for operator Transpose) (#5916) * Improves implementation of transpose operator * Fix issue mentioned in #5911 * adding unit test for function DoTransposeImpl * Make operator TreeEnsemble 5x faster for batches of size 100.000 (#5965) * improves processing time by 10 * extend coverage unit test coverage * better implementation for the multi regression case * better comment, keep parallelization by trees when not enough trees * Initialize a structure in operator ReduceSum (#6005) * fix initialisation issue * Fuse MatMulIntegerToFloat only when scales are scalar (#6008) MatMulIntegerToFloat fusion fuses per-row and per-column MatMulInteger, which is not supported by the MatMulIntegerToFloat kernel now. Limit the fusion to per-matrix only before we supporting the per-channel fully. * Disable Python 3.9 for training Python packaging build. (#6012) Disable Python 3.9 for training Python packaging build. Python 3.9 is not supported by the PyTorch dependency. * Fix bugs for 1: Calibrator should check model inputs; 2: (#6017) quantize_inupts forgot to use parameter initializer_use_weight_qtyp. * Bump highlight.js from 10.2.1 to 10.4.1 in /nodejs Bumps [highlight.js](https://github.com/highlightjs/highlight.js) from 10.2.1 to 10.4.1. - [Release notes](https://github.com/highlightjs/highlight.js/releases) - [Changelog](https://github.com/highlightjs/highlight.js/blob/master/CHANGES.md) - [Commits](https://github.com/highlightjs/highlight.js/compare/10.2.1...10.4.1) Signed-off-by: dependabot[bot] <support@github.com> * work around of the build break in mac (#6069) * Fix the build break in macos release * revert android change * Bump up API version for 1.6 release (#6076) * Update version to 1.6.0 (#6041) * Update version to 1.6.0 * Add v 1.5.3 info * Updating WindowsAI and ONNX version Co-authored-by: Du Li <duli@OrtTrainingDev0.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> * Rsevert "Fuse MatMulIntegerToFloat only when scales are scalar (#6008)" This reverts commit beb950eb66308eeaa8c60e4db9a006948e2ba7bb. Co-authored-by: Xavier Dupré <xadupre@users.noreply.github.com> Co-authored-by: Yufeng Li <liyufeng1987@gmail.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Zhang Lei <zhang.huanning@hotmail.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Pranav Sharma <prs@microsoft.com> Co-authored-by: Du Li <duli@OrtTrainingDev0.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> |
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ONNX Runtime Node.js API
ONNX Runtime Node.js binding enables Node.js applications to run ONNX model inference.
Usage
Install the latest stable version:
npm install onnxruntime
Install the latest dev version:
npm install onnxruntime@dev
Refer to Node.js samples for samples and tutorials.
Requirements
ONNXRuntime works on Node.js v12.x+ or Electron v5.x+.
Following platforms are supported with pre-built binaries:
- Windows x64 CPU NAPI_v3
- Linux x64 CPU NAPI_v3
- MacOS x64 CPU NAPI_v3
To use on platforms without pre-built binaries, you can build Node.js binding from source and consume it by npm install <onnxruntime_repo_root>/nodejs/. See also BUILD.MD for building ONNX Runtime Node.js binding locally.
License
License information can be found here.