Python API
##########
This document lists the application programming interface (API) that the
generated to Python. With these APIs, applications can:

 - upload a model to the ONNC bench SaaS,
 - compile the neural network, and
 - download the compiled model from ONNC bench SaaS

----

Workspace
=========
**Workspace** class represents a workspace in the remote service.

.. code-block:: python

  from onnc.bench import Workspace;

Upload A Model
--------------
**upload_model** creates a remote workspace in ONNC bench SaaS and upload the model into the workspace.

.. code-block:: python

  onnc.bench.Workspace.upload_model(model,
                                    samples,
                                    input_name = None,
                                    output_name = None,
                                    input_as_nchw: str = "auto")


model: Union[str, object]
  A file or a directory path to a serialized model file, or a neurl network model object.

samples: Union[str, object]
  A file or a directory path to a serialized *Numpy* dataset, or a Numpy dataset object.

input_name: str
  The input tensor name of the given model.

output_name: str
  The output tensor name of the given model.

input_as_nchw: str
  This parameter is used to define the input format. It should be one of the *noset*, *auto*, or *as_input_name*.

return
  A python dict object contains `success`, `model_id`, and `sample_id`.

  example
    .. code-block:: python

      {
        'success': True,
        'model_id': '',
        'sample_id': ''
      }

Compile The Neural Network
--------------------------
**compile** asks ONNC bench SaaS to transform the given model into C function calls.

.. code-block:: python

  onnc.bench.Workspace.compile(board, ram_size=0)

board: str
  The name of the supported SoC board, for example, ``NUMAKER_IOT_M487``.

ram_size: int
  Limit of free RAM size in the board. The value depends on the environmental conditions, such as OS, application.

return
  A python dict object contains  `success`, `loadable_id`, `board`, `ram_size`, and `report`.

  example
    .. code-block:: python

      {
        'success': success,
        'loadable_id': loadable_id,
        'board': board,
        'ram_size': ram_size,
        'report': {'ram': ram, 'rom': rom}
      }

Download The Compiled Model
---------------------------
**download** downloads the produced C function calls from ONNC bench SaaS.

.. code-block:: python

  onnc.bench.Workspace.download(download_path, unzip=True)

download_path: str
  The path to save the file.

unzip: bool
  Unzip the results when true.