Announcements

This release
- Building onnxruntime from source now requires a C++ compiler with full C++14 support.
- Builds with OpenMP are no longer published. They can still be built from source if needed. The default threadpool option should provide optimal performance for the majority of models.
- New dependency for Python package: flatbuffers
Next release (v1.9)
- Builds will require C++ 17 compiler
- GPU build will be updated to CUDA 11.1

General

ONNX opset 14 support - new and updated operators from the ONNX 1.9 release
Dynamically loadable CUDA execution provider
- Allows a single build to work for both CPU and GPU (excludes Python packages)
Profiler tool now includes information on threadpool usage
- multi-threading preparation time
- multi-threading run time
- multi-threading wait time
[Experimental] onnxruntime-extensions package
- Crowd-sourced library of common/shareable custom operator implementations that can be loaded and run with ONNX Runtime; community contributions are welcome! - microsoft/onnxruntime-extensions
- Currently includes mostly ops and tokenizers for string operations (full list here)
- Tutorials to export and load custom ops from onnxruntime-extensions: TensorFlow, PyTorch

torch-ort package released as the ONNX Runtime backend in PyTorch
onnxruntime-training-gpu and onnxruntime-training-rocm packages now available for distributed training on NVIDIA and AMD GPUs

Official package now available
- Pre-built Android and iOS packages with support for selected operators and data types
Objective-C API for iOS in preview
Expanded operators supported by NNAPI (Android) and CoreML (iOS) execution providers
All operators in the ai.onnx domain now support type reduction
- Create ORT format model with --enable_type_reduction flag, and perform minimal build --enable_reduced_operator_type_support flag

New ONNX Runtime Javascript API
ONNX Runtime Web package
- Support WebAssembly and WebGL for CPU and GPU
- Support Web Worker based multi-threaded WebAssembly backend
- Supports ORT model format
- Improved WebGL performance

Memory footprint reduction through shared pre-packed weights for shared initializers
- Pre-packing refers to weights that are pre-processed at model load time
- Allows pre-packed weights of shared initializers to also be shared between sessions, preserving memory savings from using shared initializers
Memory footprint reduction through arena shrinkage
- By default, the memory arena doesn't shrink and it holds onto any allocated memory forever. This feature exposes a RunOption that scans the arena and potentially returns unused memory back to the system after the end of a Run. This feature is particularly useful while running a dynamic shape model that may occasionally process an outlier inference request that requires a large amount of memory. If the shrinkage option if invoked as part of these Runs, the memory that was required for that Run is not held on forever by the memory arena.
Quantization
- Native support of Quantize-Dequantize (QDQ) format for CPU
- Support for Concat, Transpose, GlobalAveragePool, AveragePool, Resize, Squeeze
- Improved performance on high-end ARM devices by leveraging dot-product instructions
- Improved performance for batched quant GEMM with optimized multi-threading logic
- Per-column quantization for MatMul
Transformers
- GPT-2 and beam search integration (example)

WinML
- New native WinML API SetIntraOpThreadSpinning for toggling Intra Op thread spin behavior. When enabled, and when there is no current workload, IntraOp threads will continue to spin for some additional time as it waits for any additional work. This can result in better performance for the current workload but may impact performance of other unrelated workloads. This toggle is enabled by default.
ORT Inferencing
- The following APIs have been added to this release. Please check the API documentation for information.
  - KernelInfoGetAttributeArray_float
  - KernelInfoGetAttributeArray_int64
  - CreateArenaCfgV2
  - AddRunConfigEntry
  - CreatePrepackedWeightsContainer
  - PrepackedWeightsContainer
  - CreateSessionWithPrepackedWeightsContainer
  - CreateSessionFromArrayWithPrepackedWeightsContainer

TensorRT
- Added support for TensorRT EP configuration using session options instead of environment variables.
- Added support for DLA on Jetson Xavier (AGX, NX)
- General bug fixes and quality improvements.
OpenVINO
- Added support for OpenVINO 2021.3
- Removed support for OpenVINO 2020.4
- Added support for Loading/Saving of Blobs on MyriadX devices to avoid expensive model blob compilation at runtime.
DirectML
• Supports ARM/ARM64 architectures now in WinML and ONNX RunTime NuGet packages.
• Support for 8-dimensional tensors to: BatchNormalization, Cast, Join, LpNormalization, MeanVarianceNormalization, Padding, Tile, TopK.
• Substantial performance improvements for several operators.
• Resize nearest_mode “floor” and “round_prefer_ceil”.
• Fusion activations for: Conv, ConvTranspose, BatchNormalization, MeanVarianceNormalization, Gemm, MatMul.
• Decomposes unsupported QLinearSigmoid operation.
• Removes strided 64-bit emulation in Cast.
• Allows empty shapes on constant CPU inputs.

This release has an issue that may result in segmentation faults when deployed on Intel 12th Gen processors with hybrid architecture capabilities with Performance and Efficient-cores (P-core and E-core). This has been fixed in ORT 1.9.
The CUDA build of this release has a regression in that the memory utilization increases significantly compared to the previous releases. A fix for this will be released shortly as part of 1.8.1 patch. Here is an incomplete list of issues where this was reported - 8287, 8171, 8147.
GPU part of source code is not compatible with
- Visual Studio 2019 16.10.0 ( which was just released on May 25, 2021). 16.9.x is fine.
- clang 12
CPU part of source code is not compatible with
- VS 2017 (#7936). Before we fix it please use VS 2019 instead.
- GCC 11. See #7918
C# OpenVino EP is broken. #7951
Python and Windows only: if your CUDNN DLLs are not in CUDA's installation dir, then you need to set manually "CUDNN_HOME" variable. Just putting them in %PATH% is not enough. #7965
onnxruntime-win-gpu-x64-1.8.0.zip on this page misses important DLLs, please don't use it.

Contributors to ONNX Runtime include members across teams at Microsoft, along with our community members: