# ONNX Runtime Pipelines[[optimum.onnxruntime.pipeline]]

#### optimum.onnxruntime.pipeline[[optimum.onnxruntime.pipeline]]

[Source](https://github.com/huggingface/optimum-onnx/blob/vr_102/optimum/onnxruntime/pipelines.py#L150)

Utility factory method to build a `Pipeline` with an ONNX Runtime model, similar to `transformers.pipeline`.

A pipeline consists of:

- One or more components for pre-processing model inputs, such as a [tokenizer](tokenizer),
[image_processor](image_processor), [feature_extractor](feature_extractor), or [processor](processors).
- A [model](model) that generates predictions from the inputs.
- Optional post-processing steps to refine the model's output, which can also be handled by processors.

While there are such optional arguments as `tokenizer`, `feature_extractor`, `image_processor`, and `processor`,
they shouldn't be specified all at once. If these components are not provided, `pipeline` will try to load
required ones automatically. In case you want to provide these components explicitly, please refer to a
specific pipeline in order to get more details regarding what components are required.

Examples:
```python
>>> from optimum.onnxruntime import pipeline

>>> # Sentiment analysis pipeline
>>> analyzer = pipeline("sentiment-analysis")

>>> # Question answering pipeline, specifying the checkpoint identifier
>>> oracle = pipeline(
...     "question-answering", model="distilbert/distilbert-base-cased-distilled-squad", tokenizer="google-bert/bert-base-cased"
... )

>>> # Named entity recognition pipeline, passing in a specific model and tokenizer
>>> model = ORTModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
>>> recognizer = pipeline("ner", model=model, tokenizer=tokenizer)
```

**Parameters:**

task (`str`) : The task defining which pipeline will be returned. Currently accepted tasks are:  - `"audio-classification"`: will return a `AudioClassificationPipeline`. - `"automatic-speech-recognition"`: will return a `AutomaticSpeechRecognitionPipeline`. - `"depth-estimation"`: will return a `DepthEstimationPipeline`. - `"document-question-answering"`: will return a `DocumentQuestionAnsweringPipeline`. - `"feature-extraction"`: will return a `FeatureExtractionPipeline`. - `"fill-mask"`: will return a `FillMaskPipeline`:. - `"image-classification"`: will return a `ImageClassificationPipeline`. - `"image-feature-extraction"`: will return an `ImageFeatureExtractionPipeline`. - `"image-segmentation"`: will return a `ImageSegmentationPipeline`. - `"image-text-to-text"`: will return a `ImageTextToTextPipeline`. - `"image-to-image"`: will return a `ImageToImagePipeline`. - `"image-to-text"`: will return a `ImageToTextPipeline`. - `"mask-generation"`: will return a `MaskGenerationPipeline`. - `"object-detection"`: will return a `ObjectDetectionPipeline`. - `"question-answering"`: will return a `QuestionAnsweringPipeline`. - `"summarization"`: will return a `SummarizationPipeline`. - `"table-question-answering"`: will return a `TableQuestionAnsweringPipeline`. - `"text2text-generation"`: will return a `Text2TextGenerationPipeline`. - `"text-classification"` (alias `"sentiment-analysis"` available): will return a `TextClassificationPipeline`. - `"text-generation"`: will return a `TextGenerationPipeline`:. - `"text-to-audio"` (alias `"text-to-speech"` available): will return a `TextToAudioPipeline`:. - `"token-classification"` (alias `"ner"` available): will return a `TokenClassificationPipeline`. - `"translation"`: will return a `TranslationPipeline`. - `"translation_xx_to_yy"`: will return a `TranslationPipeline`. - `"video-classification"`: will return a `VideoClassificationPipeline`. - `"visual-question-answering"`: will return a `VisualQuestionAnsweringPipeline`. - `"zero-shot-classification"`: will return a `ZeroShotClassificationPipeline`. - `"zero-shot-image-classification"`: will return a `ZeroShotImageClassificationPipeline`. - `"zero-shot-audio-classification"`: will return a `ZeroShotAudioClassificationPipeline`. - `"zero-shot-object-detection"`: will return a `ZeroShotObjectDetectionPipeline`. 

model (`str` or `ORTModel`, *optional*) : The model that will be used by the pipeline to make predictions. This can be a model identifier or an actual instance of a ONNX Runtime model inheriting from `ORTModel`.  If not provided, the default for the `task` will be loaded.

config (`str` or `PretrainedConfig`, *optional*) : The configuration that will be used by the pipeline to instantiate the model. This can be a model identifier or an actual pretrained model configuration inheriting from `PretrainedConfig`.  If not provided, the default configuration file for the requested model will be used. That means that if `model` is given, its default configuration will be used. However, if `model` is not supplied, this `task`'s default model's config is used instead.

tokenizer (`str` or `PreTrainedTokenizer`, *optional*) : The tokenizer that will be used by the pipeline to encode data for the model. This can be a model identifier or an actual pretrained tokenizer inheriting from `PreTrainedTokenizer`.  If not provided, the default tokenizer for the given `model` will be loaded (if it is a string). If `model` is not specified or not a string, then the default tokenizer for `config` is loaded (if it is a string). However, if `config` is also not given or not a string, then the default tokenizer for the given `task` will be loaded.

feature_extractor (`str` or `PreTrainedFeatureExtractor`, *optional*) : The feature extractor that will be used by the pipeline to encode data for the model. This can be a model identifier or an actual pretrained feature extractor inheriting from `PreTrainedFeatureExtractor`.  Feature extractors are used for non-NLP models, such as Speech or Vision models as well as multi-modal models. Multi-modal models will also require a tokenizer to be passed.  If not provided, the default feature extractor for the given `model` will be loaded (if it is a string). If `model` is not specified or not a string, then the default feature extractor for `config` is loaded (if it is a string). However, if `config` is also not given or not a string, then the default feature extractor for the given `task` will be loaded.

image_processor (`str` or `BaseImageProcessor`, *optional*) : The image processor that will be used by the pipeline to preprocess images for the model. This can be a model identifier or an actual image processor inheriting from `BaseImageProcessor`.  Image processors are used for Vision models and multi-modal models that require image inputs. Multi-modal models will also require a tokenizer to be passed.  If not provided, the default image processor for the given `model` will be loaded (if it is a string). If `model` is not specified or not a string, then the default image processor for `config` is loaded (if it is a string).

processor (`str` or `ProcessorMixin`, *optional*) : The processor that will be used by the pipeline to preprocess data for the model. This can be a model identifier or an actual processor inheriting from `ProcessorMixin`.  Processors are used for multi-modal models that require multi-modal inputs, for example, a model that requires both text and image inputs.  If not provided, the default processor for the given `model` will be loaded (if it is a string). If `model` is not specified or not a string, then the default processor for `config` is loaded (if it is a string).

revision (`str`, *optional*, defaults to `"main"`) : When passing a task name or a string model identifier: The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.

use_fast (`bool`, *optional*, defaults to `True`) : Whether or not to use a Fast tokenizer if possible (a `PreTrainedTokenizerFast`).

use_auth_token (`str` or *bool*, *optional*) : The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `hf auth login` (stored in `~/.huggingface`).

device (`int` or `str` or `torch.device`) : Defines the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank like `1`) on which this pipeline will be allocated.

device_map (`str` or `dict[str, Union[int, str, torch.device]`, *optional*) : Sent directly as `model_kwargs` (just a simpler shortcut). When `accelerate` library is present, set `device_map="auto"` to compute the most optimized `device_map` automatically (see [here](https://huggingface.co/docs/accelerate/main/en/package_reference/big_modeling#accelerate.cpu_offload) for more information).    Do not use `device_map` AND `device` at the same time as they will conflict   

torch_dtype (`str` or `torch.dtype`, *optional*) : Sent directly as `model_kwargs` (just a simpler shortcut) to use the available precision for this model (`torch.float16`, `torch.bfloat16`, ... or `"auto"`).

trust_remote_code (`bool`, *optional*, defaults to `False`) : Whether or not to allow for custom code defined on the Hub in their own modeling, configuration, tokenization or even pipeline files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.

model_kwargs (`dict[str, Any]`, *optional*) : Additional dictionary of keyword arguments passed along to the model's `from_pretrained(..., **model_kwargs)` function.

kwargs (`dict[str, Any]`, *optional*) : Additional keyword arguments passed along to the specific pipeline init (see the documentation for the corresponding pipeline class for possible values).

**Returns:**

``Pipeline``

A suitable pipeline for the task.