From 8bc4e3195df7c4e28d2b663656072f08db1ab7f1 Mon Sep 17 00:00:00 2001 From: Faith Xu Date: Mon, 16 Mar 2020 18:19:07 -0700 Subject: [PATCH] Updates to roadmap (#3155) * Updates to roadmap * remove redundant directML * Add JS to future investments --- docs/Roadmap.md | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/docs/Roadmap.md b/docs/Roadmap.md index a181fed085..bf133095e1 100644 --- a/docs/Roadmap.md +++ b/docs/Roadmap.md @@ -57,18 +57,20 @@ Additionally, we understand that lightweight devices and local applications may #### Platforms |Supported|Future| |---|---| -|Windows 7+|Android (community contribution, in progress)| -|Linux (various)|iOS| +|Windows 7+|iOS| +|Linux (various)| | |Mac OS X| | +|Android (community contribution, Preview)| | #### Languages |Supported|Future| |---|---| -|Python (3.5, 3.6, 3.7)|Java (community contribution, in progress)| +|Python (3.5, 3.6, 3.7)| Javascript | |C++| | |C#| | |C| | |Ruby (community project)| | +|Java | | ### Accelerators and Execution Providers @@ -77,14 +79,16 @@ To achieve the best performance on a growing set of compute targets across cloud |Supported|Future| |---|---| -|MLAS (Microsoft Linear Algebra Subprograms)|Android NN API (in progress)| -|Intel DNNL / MKL-ML|ARM Compute Library (community contribution by NXP, in progress)| +|MLAS (Microsoft Linear Algebra Subprograms)|AMD GPU| +|Intel DNNL / MKL-ML|Xilinx FPGA| |Intel nGraph| | |NVIDIA CUDA| | |NVIDIA TensorRT| | |Intel OpenVINO| | |Nuphar Model Compiler| | |Microsoft Direct ML| | +|Android NN API (Preview)| | +|ARM Compute Library (Preview)| | #### CUDA operator coverage To maximize performance potential, we will be continually adding additional CUDA implementations for supported operators. @@ -115,7 +119,6 @@ As more operators are added to the ONNX spec, ONNX Runtime will provide implemen A few specific items include: * Sparse Tensor support * Generic function logic without separate kernels -* Data processing featurizers for traditional ML #### Investments in popular converters We work with the OSS and ONNX community to ensure popular frameworks can export or be converted to ONNX format. @@ -139,9 +142,10 @@ these. If you've identified any integration ideas or opportunities and have ques Some of these products include: * [AzureML](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-onnx): simplify the process to train, convert, and deploy ONNX models to Azure - * [Model Interpretability](https://docs.microsoft.com/en-us/azure/machine-learning/service/machine-learning-interpretability-explainability): explainability for ONNX models +* [Model Interpretability](https://docs.microsoft.com/en-us/azure/machine-learning/service/machine-learning-interpretability-explainability): explainability for ONNX models * [ML.NET](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-onnx): inference ONNX models in .NET * [PyTorch](https://pytorch.org/docs/stable/onnx.html): improve coverage for exporting trained models to ONNX * [Windows](https://docs.microsoft.com/en-us/windows/ai/windows-ml/index): run ONNX models on Windows devices using the built-in Windows ML APIs. Windows ML APIs will be included in the ONNX Runtime builds and binaries to enable Windows developers to get OS-independent updates +* [SQL Database Edge](https://docs.microsoft.com/en-us/azure/sql-database-edge/deploy-onnx): predict with ONNX models in SQL Database Edge, an optimized relational database engine geared for IoT and IoT Edge deployments Have an idea or feature request? [Contribute](https://github.com/microsoft/onnxruntime/blob/master/CONTRIBUTING.md) or [let us know](https://github.com/microsoft/onnxruntime/blob/master/.github/ISSUE_TEMPLATE/feature_request.md)!