Correct various grammar and style issues in the ML device library documentation:
- fix subject-verb agreement for "API which supports" - use compound word "Workflow" instead of "Work flow" - fix parallel construction for model load and start - use plural "feature sets" - rewrite grammatically broken sentence about rte_ml_dev_info_get - add missing article before "number of queue pairs" - use consistent terminology "operations" not "packets" - fix malformed sentence about dequeue API format - add missing word "with" in quantize section Signed-off-by: Stephen Hemminger <[email protected]> --- doc/guides/prog_guide/mldev.rst | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/doc/guides/prog_guide/mldev.rst b/doc/guides/prog_guide/mldev.rst index 61661b998b..094a67cbdb 100644 --- a/doc/guides/prog_guide/mldev.rst +++ b/doc/guides/prog_guide/mldev.rst @@ -6,7 +6,7 @@ Machine Learning (ML) Device Library The Machine Learning (ML) Device library provides a Machine Learning device framework for the management and provisioning of hardware and software ML poll mode drivers, -defining an API which support a number of ML operations +defining an API which supports a number of ML operations including device handling and inference processing. The ML model creation and training is outside of the scope of this library. @@ -16,7 +16,7 @@ The ML framework is built on the following model: .. figure:: img/mldev_flow.* - Work flow of inference on MLDEV + Workflow of inference on MLDEV ML Device A hardware or software-based implementation of ML device API @@ -28,7 +28,7 @@ ML Model required to make predictions on live data. Once the model is created and trained outside of the DPDK scope, the model can be loaded via ``rte_ml_model_load()`` - and then start it using ``rte_ml_model_start()`` API function. + and then started using ``rte_ml_model_start()`` API function. The ``rte_ml_model_params_update()`` can be used to update the model parameters such as weights and bias without unloading the model using ``rte_ml_model_unload()``. @@ -79,9 +79,9 @@ Each device, whether virtual or physical is uniquely designated by two identifie Device Features and Capabilities ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -ML devices may support different feature set. -In order to get the supported PMD feature ``rte_ml_dev_info_get()`` API -which return the info of the device and its supported features. +ML devices may support different feature sets. +To get the supported PMD features, use the ``rte_ml_dev_info_get()`` API, +which returns the info of the device and its supported features. Device Configuration @@ -106,7 +106,7 @@ maximum size of model and so on. Configuration of Queue Pairs ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Each ML device can be configured with number of queue pairs. +Each ML device can be configured with a number of queue pairs. Each queue pair is configured using ``rte_ml_dev_queue_pair_setup()`` @@ -162,9 +162,9 @@ to specify the device queue pair to schedule the processing on. The ``nb_ops`` parameter is the number of operations to process which are supplied in the ``ops`` array of ``rte_ml_op`` structures. The enqueue function returns the number of operations it enqueued for processing, -a return value equal to ``nb_ops`` means that all packets have been enqueued. +a return value equal to ``nb_ops`` means that all operations have been enqueued. -The dequeue API uses the same format as the enqueue API of processed +The dequeue API uses the same format as the enqueue API, but the ``nb_ops`` and ``ops`` parameters are now used to specify the max processed operations the user wishes to retrieve and the location in which to store them. @@ -193,7 +193,7 @@ from a higher precision type to a lower precision type and vice-versa. ML library provides the functions ``rte_ml_io_quantize()`` and ``rte_ml_io_dequantize()`` to enable data type conversions. User needs to provide the address of the quantized and dequantized data buffers -to the functions, along the number of the batches in the buffers. +to the functions, along with the number of batches in the buffers. For quantization, the dequantized data is assumed to be of the type ``dtype`` provided by the ``rte_ml_model_info::input`` -- 2.51.0
