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Deprecate OpenVINO tutorial; CL no longer supports computer-vision-openvino (#1177)

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* Deprecate OpenVINO tutorial; CL no longer supports computer-vision-openvino.

- Closes #1166
- openvino was also removed from containers-basic bundle
- See also: https://community.clearlinux.org/t/openvino-in-clear-linux-os-moving-to-docker/4566

Signed-off-by: Michael Vincerra <michael.vincerra@intel.com>

* Remove ref to computer-vision-openvino in developer-workstation.

- Remove ref to openVINO in tutorials index.

Signed-off-by: Michael Vincerra <michael.vincerra@intel.com>
This commit is contained in:
michael vincerra
2020-05-19 11:54:20 -07:00
committed by GitHub
parent 659052ef96
commit 3a84441854
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source/guides/maintenance/developer-workstation.rst
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@@ -78,9 +78,6 @@ tools you need to start. Consider these profiles as a starting point.
* - Work with deep learning and edge-optimized models.
- `computer-vision-models <https://clearlinux.org/software/bundle/computer-vision-models/>`_
* - Basic OpenVINO™ toolkit.
- `computer-vision-openvino <https://clearlinux.org/software/bundle/computer-vision-openvino/>`_
* - API helper for cloud access.
- `cloud-api <https://clearlinux.org/software/bundle/cloud-api/>`_
source/tutorials/index.rst
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@@ -37,7 +37,6 @@ sorted by difficulty level! Learn about :ref:`how we evaluate tutorials <tutoria
- :ref:`hpc`
- :ref:`kubernetes-bp`
- :ref:`nvidia-cuda`
- :ref:`openvino`
- :ref:`php`
- :ref:`vmware-workstation`
- :ref:`wp-install`
source/tutorials/openvino.rst
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@@ -1,290 +0,0 @@
.. _openvino:
OpenVINO™ for Deep Learning
###########################
This tutorial shows how to install OpenVINO™ on |CL-ATTR|, run an
OpenVINO sample application for image classification, and run a benchmark_app
for estimating inference performance---using Squeezenet 1.1.
.. contents::
:local:
:depth: 1
Prerequisites
*************
* |CL| installed on the host OS
Install OpenVINO
****************
OpenVINO in |CL| offers pre-built OpenVINO sample applications with which
developers can try inferencing immediately.
#. In |CL| OpenVINO is included in the :command:`computer-vision-basic`
bundle. To install OpenVINO, enter:
.. code-block:: bash
sudo swupd bundle-add computer-vision-basic
#. OpenVINO Inference Engine libraries are located in :file:`/usr/lib64/`
To view one added package, enter:
.. code-block:: bash
ls /usr/lib64/libinference_engine.so
If bundle installation is successful, the output shows:
.. code-block:: console
/usr/lib64/libinference_engine.so
#. To view the OpenVINO Model Optimizer, enter:
.. code-block:: console
ls /usr/share/openvino/model-optimizer
#. To view the OpenVINO sample application Executables, enter:
.. code-block:: bash
ls /usr/bin/benchmark_app \
/usr/bin/classification_sample_async \
/usr/bin/hello_classification \
/usr/bin/hello_nv12_input_classification \
/usr/bin/hello_query_device \
/usr/bin/hello_reshape_ssd \
/usr/bin/object_detection_sample_ssd \
/usr/bin/speech_sample \
/usr/bin/style_transfer_sample \
.. note::
If bundle installation is successful, the above files should appear.
#. To view the pre-built OpenVINO sample application source code, enter:
.. code-block:: bash
ls /usr/share/doc/inference_engine/samples
In the next section, you learn how to use an OpenVINO sample application.
Run OpenVINO sample application
*******************************
After installing OpenVINO on |CL|, you need a model against which to test.
In this example, we use the public squeezenet 1.1 model for image
classification. Test results vary based on the system used.
Use model to test
=================
#. If you don’t have any model, you can download an
**intel_model** or a public model using OpenVINO Model Downloader.
- Check the list of public models you can download from
:file:`/usr/share/open_model_zoo/models/public`
- Check the list of Intel® models you can download from
:file:`/usr/share/open_model_zoo/intel_models`
#. View the location of OpenVINO Model Downloader:
.. code-block:: console
cd /usr/share/open_model_zoo/tools/downloader
#. In general, download models with the following command:
.. code-block:: bash
python3 downloader.py --name <model_name> -o <downloading_path>
.. note::
* Where :file:`<model_name>` is the one you chose from previous step
* Where :file:`<downloading_path>` is your project directory
#. For this example, enter:
.. code-block:: bash
python3 downloader.py --name squeezenet1.1 -o $HOME/.
#. After running this command, the model appears as downloading at your
:file:`$HOME/classification/squeezenet/1.1/caffe` as follows:
.. code-block:: console
###############|| Downloading topologies ||###############
========= Downloading /$HOME/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel
... 100%, 4834 KB, 2839 KB/s, 1 seconds passed
...
Convert model to IR format
==========================
#. As necessary, follow the instruction on :ref:`convert-dl-models`
to convert deep learning models.
#. Navigate to the model:
.. code-block:: bash
cd $HOME/classification/squeezenet/1.1/caffe
#. Enter the command:
.. code-block:: bash
python3 /usr/share/openvino/model-optimizer/mo.py --input_model squeezenet1.1.caffemodel
The output will show these files being generated:
.. code-block:: console
squeezenet1.1.xml
squeezenet1.1.bin
#. Finally, enter :command:`ls` to view the newly added model and files.
Run image classification
========================
This sample application demonstrates how to run the Image Classification in asynchronous mode on supported devices. In this example, we use the image of a specific type of automobile to test the inference engine. Squeezenet 1.1 is designed to perform image classification and has been trained on the `ImageNet`_ database.
#. We provide an image of an automobile, shown in Figure 1. For ease of use,
save this image into the :file:`classification` model directory.
.. figure:: ../_figures/openvino/automobile.png
:height: 375 px
:width: 500 px
:scale: 100 %
:alt: Photo by Goh Rhy Yan on Unsplash
Figure 1: Photo by Goh Rhy Yan on Unsplash
#. To execute the sample application enter the command:
.. code-block:: bash
classification_sample_async -i <path_to_image> -m <path_to_model_ir> -d <device>
.. note::
* Where :file:`<path_to_image>` is the image that you selected
* Where :file:`<path_to_model_ir>` is the path to the IR model file
* Where :file:`<device>` is your choice of CPU, GPU, etc.
#. In this case, we replace the :file:`<path_to_image>` with the previously
saved image for CPU inferencing.
.. code-block:: bash
classification_sample_async -i ./automobile.png -m squeezenet1.1.xml
.. note::
If you do not specify the :file:`device`, the CPU is used by default.
#. The results show the highest probability is 67% for a sports car.
.. code-block:: bash
classid probability
------- -----------
817 0.6717085
511 0.1611409
+-----------------------+-----------------------------------+
|:command:`classid` 817 | :command:`sports car, sport car` |
+-----------------------+-----------------------------------+
|:command:`classid` 511 |:command:`convertible` |
+-----------------------+-----------------------------------+
.. note:
Label definitions are provided by `ImageNet`_.
#. Next, add :command:`-d GPU` to the end of the above command for GPU
inferencing.
.. code-block:: bash
classification_sample_async -i ./automobile.png -m squeezenet1.1.xml -d GPU
Run benchmark_app
*****************
This sample application demonstrates how to use benchmark application to
estimate deep learning inference **performance** on supported devices.
We use the same image of an automobile, Figure 1, from the previous section.
#. To execute this sample application, enter:
.. code-block:: bash
benchmark_app -i <path_to_image> -m <path_to_model> -d <device>
.. note::
* Where :file:`<path_to_image>` is the image that you selected
* Where :file:`<path_to_model_ir>` is the path to the IR model file
* Where :file:`<device>` is local your choice of CPU, GPU, etc.
#. Change directory:
.. code-block:: bash
cd $HOME/classification/squeezenet/1.1/caffe
#. Enter the following command for CPU inferencing.
.. code-block:: bash
benchmark_app -i ./automobile.png -m squeezenet1.1.xml
#. For the CPU, the results show a :guilabel:`Throughput` of 243.202 FPS.
.. code-block:: console
:linenos:
:emphasize-lines: 4
Count: 1464 iterations
Duration: 60196.8 ms
Latency: 164.104 ms
Throughput: 243.202 FPS
#. Next, add :command:`-d GPU` to the end of the same command for GPU
inferencing.
.. code-block:: bash
benchmark_app -i ./automobile.png -m squeezenet1.1.xml -d GPU
#. For the GPU, the results show a :guilabel:`Throughput` of 372.677 FPS.
.. code-block:: console
:linenos:
:emphasize-lines: 4
Count: 2240 iterations
Duration: 60105.7 ms
Latency: 107.554 ms
Throughput: 372.677 FPS
.. _ImageNet: http://image-net.org/