[GitHub] [incubator-mxnet] apeforest commented on a change in pull request #16654: Multithreaded Inference Support

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apeforest commented on a change in pull request #16654: Multithreaded Inference Support
URL: https://github.com/apache/incubator-mxnet/pull/16654#discussion_r365505834
 
 

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 File path: docs/static_site/src/pages/api/cpp/docs/tutorials/multi_threaded_inference.md
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+--
+layout: page_api
+title: Multi Threaded Inference
+action: Get Started
+action_url: /get_started
+permalink: /api/cpp/docs/tutorials/multi_threaded_inference
+is_tutorial: true
+tag: cpp
+--
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+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
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+<!--- under the License. -->
+
+## Multi Threaded Inference API
+
+A long standing request from MXNet users has been to invoke parallel inference on a model from multiple threads while sharing the parameters.
+With this use case in mind, the threadsafe version of CachedOp was added to provide a way for customers to do multi-threaded inference for MXNet users.
+This doc attempts to do the following:
+1. Discuss the current state of thread safety in MXNet
+2. Explain how one can use C API and thread safe version of cached op, along with CPP package to achieve iultithreaded inference. This will be useful for end users as well as frontend developers of different language bindings
+3. Discuss the limitations of the above approach
+4. Future Work
+
+## Current state of Thread Safety in MXNet
+
+Examining the current state of thread safety in MXNet we can arrive to the following conclusion:
+
+1. MXNet Dependency Engine is thread safe (except for WaitToRead invoked inside a spawned thread. Please see Limitations section)
+2. Graph Executor which is Module/Symbolic/C Predict API backend is not thread safe
+3. Cached Op (Gluon Backend) is not thread safe
+
+The CachedOpThreadSafe and corresponding C APIs were added to address point 3 above and provide a way
+for MXNet users to do multi-threaded inference.
+
+```
+/*!
+ * \brief create cached operator, allows to choose thread_safe version
+ * of cachedop
+ */
+MXNET_DLL int MXCreateCachedOpEX(SymbolHandle handle,
+                                 int num_flags,
+                                 const char** keys,
+                                 const char** vals,
+                                 CachedOpHandle *out,
+                                 bool thread_safe DEFAULT(false));
+```
+
+## Multithreaded inference in MXNet with C API and CPP Package
+
+### Prerequisites
+To complete this tutorial you need to:
+- Learn the basics about [MXNet C++ API](/api/cpp)
+- Build MXNet from source with make/cmake
+- Build the multi-threaded inference example
+
+### Setup the MXNet C++ API
+To use the C++ API in MXNet, you need to build MXNet from source with C++ package. Please follow the [built from source guide](/get_started/ubuntu_setup.html), and [C++ Package documentation](/api/cpp)
+The summary of those two documents is that you need to build MXNet from source with `USE_CPP_PACKAGE` flag set to 1. For example: `make -j USE_CPP_PACKAGE=1 USE_CUDA=1 USE_CUDNN=1`.
+This example requires a build with CUDA and CUDNN.
+
+### Build the example
+If you have built mxnet from source with make, then do the following:
+
+```bash
+$ cd example/multi_threaded_inference
+$ make
+```
+
+If you have built mxnet from source with cmake, please uncomment the specific lines for cmake build or set the following environment variables: `MKLDNN_BUILD_DIR (default is $(MXNET_ROOT)/3rdparty/mkldnn/build)`, `MKLDNN_INCLUDE_DIR (default is $(MXNET_ROOT)/3rdparty/mkldnn/include)`, `MXNET_LIB_DIR (default is $(MXNET_ROOT)/lib)`.
+
+### Download the model and run multi threaded inference example
+To download a model use the `get_model.py` script. This downloads a model to run inference.
+
+```python
+python3 get_model.py --model <model_name>
+```
+e.g.
+```python
+python3 get_model.py --model imagenet1k-inception-bn
+```
+Only the supported models with `get_model.py` work with multi threaded inference.
 
 Review comment:
   Where can the user find the supported model names? Can you provide a link here?

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