openvino/docs/OV_Runtime_UG/Python_API_exclusives.md

OpenVINO™ Python API Exclusives

OpenVINO™ Runtime Python API offers additional features and helpers to enhance user experience. The main goal of Python API is to provide user-friendly and simple yet powerful tool for Python users.

Easier Model Compilation

CompiledModel can be easily created with the helper method. It hides the creation of Core and applies AUTO inference mode by default.

@snippet docs/snippets/ov_python_exclusives.py auto_compilation

Model/CompiledModel Inputs and Outputs

Besides functions aligned to C++ API, some of them have their Python counterparts or extensions. For example, Model and CompiledModel inputs/outputs can be accessed via properties.

@snippet docs/snippets/ov_python_exclusives.py properties_example

Refer to Python API documentation on which helper functions or properties are available for different classes.

Working with Tensor

Python API allows passing data as tensors. The Tensor object holds a copy of the data from the given array. The dtype of numpy arrays is converted to OpenVINO™ types automatically.

@snippet docs/snippets/ov_python_exclusives.py tensor_basics

Shared Memory Mode

Tensor objects can share the memory with numpy arrays. By specifying the shared_memory argument, the Tensor object does not copy data. Instead, it has access to the memory of the numpy array.

@snippet docs/snippets/ov_python_exclusives.py tensor_shared_mode

Running Inference

Python API supports extra calling methods to synchronous and asynchronous modes for inference.

All infer methods allow users to pass data as popular numpy arrays, gathered in either Python dicts or lists.

@snippet docs/snippets/ov_python_exclusives.py passing_numpy_array

Results from inference can be obtained in various ways:

@snippet docs/snippets/ov_python_exclusives.py getting_results

Synchronous Mode - Extended

Python API provides different synchronous calls to infer model, which block the application execution. Additionally, these calls return results of inference:

@snippet docs/snippets/ov_python_exclusives.py sync_infer

AsyncInferQueue

Asynchronous mode pipelines can be supported with a wrapper class called AsyncInferQueue. This class automatically spawns the pool of InferRequest objects (also called "jobs") and provides synchronization mechanisms to control the flow of the pipeline.

Each job is distinguishable by a unique id, which is in the range from 0 up to the number of jobs specified in the AsyncInferQueue constructor.

The start_async function call is not required to be synchronized - it waits for any available job if the queue is busy/overloaded. Every AsyncInferQueue code block should end with the wait_all function which provides the "global" synchronization of all jobs in the pool and ensure that access to them is safe.

@snippet docs/snippets/ov_python_exclusives.py asyncinferqueue

Acquiring Results from Requests

After the call to wait_all, jobs and their data can be safely accessed. Acquiring a specific job with [id] will return the InferRequest object, which will result in seamless retrieval of the output data.

@snippet docs/snippets/ov_python_exclusives.py asyncinferqueue_access

Setting Callbacks

Another feature of AsyncInferQueue is the ability to set callbacks. When callback is set, any job that ends inference calls upon the Python function. The callback function must have two arguments: one is the request that calls the callback, which provides the InferRequest API; the other is called "userdata", which provides the possibility of passing runtime values. Those values can be of any Python type and later used within the callback function.

The callback of AsyncInferQueue is uniform for every job. When executed, GIL is acquired to ensure safety of data manipulation inside the function.

@snippet docs/snippets/ov_python_exclusives.py asyncinferqueue_set_callback

Working with u1, u4 and i4 Element Types

Since OpenVINO™ supports low precision element types, there are a few ways to handle them in Python. To create an input tensor with such element types, you may need to pack your data in the new numpy array, with which the byte size matches the original input size: @snippet docs/snippets/ov_python_exclusives.py packing_data

To extract low precision values from a tensor into the numpy array, you can use the following helper: @snippet docs/snippets/ov_python_exclusives.py unpacking

Release of GIL

Some functions in Python API release the Global Lock Interpreter (GIL) while running work-intensive code. This can help you achieve more parallelism in your application, using Python threads. For more information about GIL, refer to the Python documentation.

@snippet docs/snippets/ov_python_exclusives.py releasing_gil

Note

: While GIL is released, functions can still modify and/or operate on Python objects in C++. Hence, there is no reference counting. You should pay attention to thread safety in case sharing of these objects with another thread occurs. It might affect code only if multiple threads are spawned in Python.

List of Functions that Release the GIL

• openvino.runtime.AsyncInferQueue.start_async
• openvino.runtime.AsyncInferQueue.is_ready
• openvino.runtime.AsyncInferQueue.wait_all
• openvino.runtime.AsyncInferQueue.get_idle_request_id
• openvino.runtime.CompiledModel.create_infer_request
• openvino.runtime.CompiledModel.infer_new_request
• openvino.runtime.CompiledModel.call
• openvino.runtime.CompiledModel.export
• openvino.runtime.CompiledModel.get_runtime_model
• openvino.runtime.Core.compile_model
• openvino.runtime.Core.read_model
• openvino.runtime.Core.import_model
• openvino.runtime.Core.query_model
• openvino.runtime.Core.get_available_devices
• openvino.runtime.InferRequest.infer
• openvino.runtime.InferRequest.start_async
• openvino.runtime.InferRequest.wait
• openvino.runtime.InferRequest.wait_for
• openvino.runtime.InferRequest.get_profiling_info
• openvino.runtime.InferRequest.query_state
• openvino.runtime.Model.reshape
• openvino.preprocess.PrePostProcessor.build