mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-06-29 03:30:52 +00:00
Readme updates (#570)
* Update readme * Update table * Formatting updates * Formatting * Formatting * Update README.md * Minor updates * Update README.md * Update README.md
This commit is contained in:
Faith Xu
Pranav Sharma
parent
a79c09388f
commit
e272feb60d
1 changed files with 15 additions and 13 deletions
28
README.md
28
README.md
|
|
@ -23,7 +23,7 @@ ONNX Runtime continuously strives to provide top performance for a broad and gro
|
|||
### Alignment with ONNX Releases
|
||||
ONNX Runtime provides comprehensive support of the ONNX spec and can be used to run all models based on ONNX v1.2.1 and higher. See ONNX version release details [here](https://github.com/onnx/onnx/releases).
|
||||
|
||||
As of January 2019, ONNX Runtime supports ONNX 1.3. We will soon add support for the recently released ONNX 1.4.
|
||||
As of March 2019, ONNX Runtime supports ONNX 1.4.
|
||||
|
||||
### Traditional ML support
|
||||
ONNX Runtime fully supports the ONNX-ML profile of the ONNX spec for traditional ML scenarios.
|
||||
|
|
@ -31,11 +31,11 @@ ONNX Runtime fully supports the ONNX-ML profile of the ONNX spec for traditional
|
|||
## High Performance
|
||||
You can use ONNX Runtime with both CPU and GPU hardware. You can also plug in additional execution providers to ONNX Runtime. With many graph optimizations and various accelerators, ONNX Runtime can often provide lower latency and higher efficiency compared to other runtimes. This provides smoother end-to-end customer experiences and lower costs from improved machine utilization.
|
||||
|
||||
Currently ONNX Runtime supports CUDA and MKL-DNN (with option to build with MKL) for computation acceleration. To add an execution provider, please refer to [this page](docs/AddingExecutionProvider.md).
|
||||
Currently ONNX Runtime supports CUDA, MLAS (Microsoft Linear Algebra Subprograms), MKL-DNN, and MKL-ML for computation acceleration. See more details on available build options [here](https://github.com/Microsoft/onnxruntime/blob/master/BUILD.md) or refer to [this page](docs/AddingExecutionProvider.md) to add a new execution provider.
|
||||
|
||||
We are continuously working to integrate new execution providers to provide improvements in latency and efficiency. We have ongoing collaborations to integrate the following with ONNX Runtime:
|
||||
* Intel MKL-DNN and nGraph
|
||||
* NVIDIA TensorRT
|
||||
* Intel MKL-DNN and nGraph
|
||||
* NVIDIA TensorRT
|
||||
|
||||
## Cross Platform
|
||||
ONNX Runtime offers:
|
||||
|
|
@ -44,25 +44,27 @@ ONNX Runtime offers:
|
|||
|
||||
See API documentation and package installation instructions [below](#Installation).
|
||||
|
||||
Looking ahead: To broaden the reach of the runtime, we will continue investments to make ONNX Runtime available and compatible with more platforms. These include but are not limited to:
|
||||
* C# for Mac
|
||||
* [ARM](BUILD.md##arm-builds)
|
||||
Looking ahead: To broaden the reach of the runtime, we will continue investments to make ONNX Runtime available and compatible with more platforms. If you have specific scenarios that are not currently supported, please share your suggestions via [Github Issues](https://github.com/microsoft/onnxruntime/issues).
|
||||
|
||||
# Getting Started
|
||||
If you need a model:
|
||||
* Check out the [ONNX Model Zoo](https://github.com/onnx/models) for ready-to-use pre-trained models.
|
||||
* To get an ONNX model by exporting from various frameworks, see [ONNX Tutorials](https://github.com/onnx/tutorials).
|
||||
|
||||
If you already have an ONNX model, just [install the runtime](#Installation) for your machine to try it out. One easy way to deploy the model on the cloud is by using [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service). See detailed instructions [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx).
|
||||
If you already have an ONNX model, just [install the runtime](#Installation) for your machine to try it out. One easy way to deploy the model on the cloud is by using [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service). See [detailed instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) and [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment/onnx).
|
||||
|
||||
|
||||
# Installation
|
||||
## APIs and Official Builds
|
||||
| API Documentation | CPU package | GPU package |
|
||||
| API Documentation | CPU package | GPU package* |
|
||||
|-----|-------------|-------------|
|
||||
| [Python](https://aka.ms/onnxruntime-python) | [Windows/Linux/Mac](https://pypi.org/project/onnxruntime/)| [Windows/Linux](https://pypi.org/project/onnxruntime-gpu/)<br>(Compatible with CUDA9.1 and cuDNN 7.3) |
|
||||
| [C#](docs/CSharp_API.md) | [Windows/Linux](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime/)<br>Mac - Coming Soon| [Windows/Linux](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.Gpu/)<br>Mac - Coming Soon|
|
||||
| [C](docs/C_API.md) | [Windows/Linux](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime/)<br>Mac - Coming Soon | [Windows/Linux](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.Gpu/)<br>Mac - Coming Soon |
|
||||
| [C++](onnxruntime/core/session/inference_session.h) | TBD | TBD |
|
||||
| [Python](https://aka.ms/onnxruntime-python)** | [Available on Pypi](https://pypi.org/project/onnxruntime)<br/><ul><li> Windows: x64</li><li>Linux: x64</li><li>Mac OS X: x64</li></ul><br/> | [Available on Pypi](https://pypi.org/project/onnxruntime-gpu) <br/><ul><li> Windows: x64</li><li>Linux: x64</li></ul><br/><br/> |
|
||||
| [C#](docs/CSharp_API.md) | [Available on Nuget](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime/)<br/><ul><li>Windows: x64</li><li>Linux: x64</li><li>Mac OS X: x64</li></ul>| [Available on Nuget](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.Gpu/)<br/><ul><li> Windows: x64</li><li>Linux: x64</li></ul><br/>|
|
||||
| [C](docs/C_API.md) | [Available on Nuget](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime/)<br/><ul><li>Windows: x64</li><li>Linux: x64</li><li>Mac OS X: x64</li></ul><br/>[Files (.zip, .tgz)](https://aka.ms/onnxruntime-release)<br/><ul><li>Windows: x64, x86</li><li>Linux: x64, x86</li><li>Mac OS X: x64</li></ul> | [Available on Nuget](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.Gpu/)<br/><ul><li>Windows: x64</li><li>Linux: x64</li></ul><br/><br/>[Files (.zip, .tgz)](https://aka.ms/onnxruntime-release)<br/><ul><li>Windows: x64</li><li>Linux: x64</li></ul><br/> |
|
||||
| [C++](onnxruntime/core/session/inference_session.h) | [Build from source](https://github.com/Microsoft/onnxruntime/blob/master/BUILD.md) | [Build from source](https://github.com/Microsoft/onnxruntime/blob/master/BUILD.md) |
|
||||
|
||||
*Requires CUDA 9.1 and cuDNN 7.3<br/>
|
||||
**Compatible with Python 3.5-3.7
|
||||
|
||||
## System Requirements
|
||||
* ONNX Runtime binaries in CPU packages use OpenMP and depends on the library being available at runtime in the
|
||||
|
|
|
|||
Loading…
Reference in a new issue