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Updates to roadmap (#3155)

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* Updates to roadmap

* remove redundant directML

* Add JS to future investments
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Faith Xu 2020-03-16 18:19:07 -07:00 committed by GitHub
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18
docs/Roadmap.md
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@ -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)!