[DOCS] Add Node.js API to docs for 24.0 (#23083)
Porting: https://github.com/openvinotoolkit/openvino/pull/23055 Adding article about Node.js API in the OpenVINO toolkit.
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Sebastian Golebiewski
GitHub
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@ -10,9 +10,10 @@ API Reference
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:maxdepth: 2
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:hidden:
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ie_python_api/api
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c_cpp_api/group__ov__cpp__api
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c_cpp_api/group__ov__c__api
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ie_python_api/api
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nodejs_api/nodejs_api.rst
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@ -0,0 +1,223 @@
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OpenVINO Node.js API
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=====================
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.. meta::
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:description: Explore Node.js API and implementation of its features in Intel®
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Distribution of OpenVINO™ Toolkit.
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OpenVINO Node.js API is distributed as an *openvino-node* npm package that contains JavaScript
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wrappers with TypeScript types descriptions and a script that downloads the OpenVINO Node.js
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bindings for current OS.⠀
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System requirements
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###################
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.. list-table::
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:header-rows: 1
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* - Operating System
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- Architecture
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- Software
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* - Windows, Linux, macOS
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- x86, ARM (Windows ARM not supported)
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- `Node.js version 20.5.1 and higher <https://nodejs.org/en/download/>`__
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Install openvino-node package
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#############################
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To install the package, use the following command:
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.. code-block:: sh
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npm install openvino-node
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.. note::
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The *openvino-node* npm package runs in Node.js environment only and provides
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a subset of :doc:`OpenVINO Runtime C++ API <../c_cpp_api/group__ov__cpp__api>`.
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Use openvino-node package
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#########################
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1. Import openvino-node package. Use the ``addon`` property to reach general exposed entities:
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.. code-block:: js
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const { addon: ov } = require('openvino-node');
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2. Load and compile a model, then prepare a tensor with input data. Finally, run inference
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on the model with it to get the model output tensor:
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.. code-block:: js
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const { addon: ov } = require('openvino-node');
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// Load model
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const core = new ov.Core();
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const model = await ov.readModel('path/to/model', 'path/to/model/weights');
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// Compile model
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const compiledModel = await ov.compileModel(model, 'CPU');
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// Prepare tensor with input data
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const tensorData = new Float32Array(image.data);
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const shape = [1, image.rows, image.cols, 3];
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const inputTensor = new ov.Tensor(ov.element.f32, shape, tensorData);
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const inferRequest = compiledModel.createInferRequest();
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const modelOutput = inferRequest.infer([inputTensor]);
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For more extensive examples of use, refer to the following scripts:
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- `Hello Classification Sample <https://github.com/openvinotoolkit/openvino/blob/releases/2024/0/samples/js/node/hello_classification/hello_classification.js>`__
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- `Hello Reshape SSD Sample <https://github.com/openvinotoolkit/openvino/blob/releases/2024/0/samples/js/node/hello_reshape_ssd/hello_reshape_ssd.js>`__
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- `Image Classification Async Sample <https://github.com/openvinotoolkit/openvino/blob/releases/2024/0/samples/js/node/classification_sample_async/classification_sample_async.js>`__
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OpenVINO API features
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#####################
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.. list-table::
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:widths: 15 85
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:class: nodejs-features
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* - ``addon``
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-
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.. code-block:: ts
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Core()
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Tensor()
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PartialShape()
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element
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preprocess:
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resizeAlgorithms
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PrePostProcessor()
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* - ``CompiledModel``
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-
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.. code-block:: ts
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outputs: Output[]
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inputs: Output[]
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constructor()
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output(nameOrId?: string | number): Output
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input(nameOrId?: string | number): Output
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createInferRequest(): InferRequest
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* - ``Core``
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-
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.. code-block:: ts
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constructor()
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compileModel(model: Model, device: string, config?: { [option: string]: string }): Promise<CompiledModel>
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compileModelSync(model: Model, device: string, config?: { [option: string]: string }): CompiledModel
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readModel(modelPath: string, binPath?: string): Promise<Model>
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readModel(modelBuffer: Uint8Array, weightsBuffer?: Uint8Array): Promise<Model>;
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readModelSync(modelPath: string, binPath?: string): Model
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readModelSync(modelBuffer: Uint8Array, weightsBuffer?: Uint8Array): Model;
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* - ``InferRequest``
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-
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.. code-block:: ts
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constructor()
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setTensor(name: string, tensor: Tensor): void
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setInputTensor(idxOrTensor: number | Tensor, tensor?: Tensor): void
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setOutputTensor(idxOrTensor: number | Tensor, tensor?: Tensor): void
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getTensor(nameOrOutput: string | Output): Tensor
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getInputTensor(idx?: number): Tensor
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getOutputTensor(idx?: number): Tensor
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getCompiledModel(): CompiledModel
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inferAsync(inputData?: { [inputName: string]: Tensor |SupportedTypedArray} | Tensor[] | SupportedTypedArray[]): Promise<{ [outputName: string] : Tensor}>;
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infer(inputData?: { [inputName: string]: Tensor |SupportedTypedArray} | Tensor[] | SupportedTypedArray[]): { [outputName: string] : Tensor};
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* - ``InputInfo``
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-
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.. code-block:: ts
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tensor(): InputTensorInfo;
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preprocess(): PreProcessSteps;
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model(): InputModelInfo;
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* - ``InputModelInfo``
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-
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.. code-block:: ts
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setLayout(layout: string): InputModelInfo;
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* - ``InputTensorInfo``
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-
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.. code-block:: ts
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setElementType(elementType: element | elementTypeString ): InputTensorInfo;
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setLayout(layout: string): InputTensorInfo;
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setShape(shape: number[]): InputTensorInfo;
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* - ``Model``
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-
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.. code-block:: ts
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outputs: Output[]
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inputs: Output[]
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output(nameOrId?: string | number): Output
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input(nameOrId?: string | number): Output
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getName(): string
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* - ``Output``
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-
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.. code-block:: ts
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anyName: string;
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shape: number[];
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constructor()
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toString(): string
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getAnyName(): string
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getShape(): number[]
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getPartialShape(): number[]
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* - ``OutputInfo``
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-
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.. code-block:: ts
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tensor(): OutputTensorInfo;
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* - ``OutputTensorInfo``
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-
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.. code-block:: ts
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setElementType(elementType: element | elementTypeString ): InputTensorInfo;
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setLayout(layout: string): InputTensorInfo;
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* - ``PrePostProcessor``
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-
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.. code-block:: ts
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constructor(model: Model)
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build(): PrePostProcessor
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input(): InputInfo
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output(): OutputInfo
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* - ``preprocess.element``
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- u8, u16, u32, i8, i16, i32, i64, f32, f64
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* - ``preprocess.resizeAlgorithm``
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- RESIZE_CUBIC, RESIZE_LINEAR
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* - ``PreProcessSteps``
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-
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.. code-block:: ts
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resize(algorithm: resizeAlgorithm | string): PreProcessSteps;
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* - ``Tensor``
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-
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.. code-block:: ts
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data: number[]
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constructor(type: element, shape: number[], tensorData?: number[] | SupportedTypedArray): Tensor
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getElementType(): element
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getShape(): number[]
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getData(): number[]
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