Commit e2322b66 authored by Leona C's avatar Leona C

Previously-tested MXNet detail may not be current

parent 177372c3
......@@ -21,9 +21,9 @@ from a framework on a CPU, GPU, or ASIC; it can also be used with an
*Interpreter* mode, which is primarily intended for testing, to analyze a
program, or to help a framework developer customize targeted solutions.
.. nGraph also provides a way to use the advanced tensor compiler PlaidML
.. as a backend; you can learn more about this backend and how to build it
.. from source in our documentation: :ref:`ngraph_plaidml_backend`.
nGraph also provides a way to use the advanced tensor compiler PlaidML
as a backend; you can learn more about this backend and how to build it
from source in our documentation: :ref:`ngraph_plaidml_backend`.
.. csv-table::
:header: "Backend", "Current nGraph support", "Future nGraph support"
......@@ -31,7 +31,6 @@ program, or to help a framework developer customize targeted solutions.
Intel® Architecture Processors (CPUs), Yes, Yes
Intel® Nervana™ Neural Network Processor™ (NNPs), Yes, Yes
NVIDIA\* CUDA (GPUs), Yes, Some
AMD\* GPUs, Yes, Some
......
......@@ -10,7 +10,7 @@ workloads:
* :ref:`tensorflow_valid`
* :ref:`mxnet_valid`
* :ref:`onnx_valid`
* :doc:`../../project/extras/testing_latency.rst`
* :ref:`testing_latency`
.. _tensorflow_valid:
......
.. contribution-guide:
.. project/contribution-guide.rst:
.._contribution_guide:
##################
Contribution guide
......@@ -261,5 +264,8 @@ it is automatically enforced and reduces merge conflicts.
To contribute documentation for your code, please see the :doc:`doc-contributor-README`.
.. include:: doc-contributor-README.rst
.. _Apache 2: https://www.apache.org/licenses/LICENSE-2.0
.. _repo wiki: https://github.com/NervanaSystems/ngraph/wiki
\ No newline at end of file
.. project/extras/testing_latency.rst:
.. _testing_latency:
Testing latency
===============
.. important:: This tutorial was tested using previous versions. While it is
not currently or officially supported in the latest nGraph Compiler
stack |version|, some configuration options may still work.
Many open-source DL frameworks provide a layer where experts in data science
can make use of optimizations contributed by machine learning engineers. Having
a common API benefits both: it simplifies deployment and makes it easier for ML
......
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