anirudh2290 commented on a change in pull request #16654: Multithreaded Inference Support URL: https://github.com/apache/incubator-mxnet/pull/16654#discussion_r366079769
########## File path: docs/static_site/src/pages/api/cpp/docs/tutorials/multi_threaded_inference.md ########## @@ -0,0 +1,199 @@ +-- +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 +-- +<!--- Licensed to the Apache Software Foundation (ASF) under one --> +<!--- or more contributor license agreements. See the NOTICE file --> +<!--- distributed with this work for additional information --> +<!--- regarding copyright ownership. The ASF licenses this file --> +<!--- to you under the Apache License, Version 2.0 (the --> +<!--- "License"); you may not use this file except in compliance --> +<!--- with the License. You may obtain a copy of the License at --> + +<!--- http://www.apache.org/licenses/LICENSE-2.0 --> + +<!--- Unless required by applicable law or agreed to in writing, --> +<!--- software distributed under the License is distributed on an --> +<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --> +<!--- KIND, either express or implied. See the License for the --> +<!--- specific language governing permissions and limitations --> +<!--- 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 $(MXNET_ROOT)/lib)`. + +### Download the model and run multi threaded inference example +To download a model use the `get_model.py` script. This downloads a model to run inference. + +```python +python3 get_model.py --model <model_name> +``` +e.g. +```python +python3 get_model.py --model imagenet1k-inception-bn +``` +Only the supported models with `get_model.py` work with multi threaded inference. + +To run the multi threaded inference example: + +First export `LD_LIBRARY_PATH`: + +```bash +$ export LD_LIBRARY_PATH=<MXNET_LIB_DIR>:$LD_LIBRARY_PATH +``` + +```bash +$ ./multi_threaded_inference [model_name] [num_threads] [is_gpu] [file_names] +``` +e.g. + +```bash +./multi_threaded_inference imagenet1k-inception-bn 2 1 grace_hopper.jpg dog.jpg +``` + +The above script spawns 2 threads, shares the same cachedop and params among two threads, and runs inference on GPU. It returns the inference results in the order in which files are provided. + +NOTE: This example is to demonstrate the multi-threaded-inference with cached op. The inference results work well only with specific models (e.g. imagenet1k-inception-bn). The results may not necessarily be very accurate because of different preprocessing step required etc. + +### Code walkthrough multi-threaded inference with CachedOp + +The multi threaded inference example (`multi_threaded_inference.cc`) involves the following steps: + +1. Parse arguments and load input image into ndarray +2. Prepare input data and load parameters, copying data to a specific context +3. Preparing arguments to pass to the CachedOp and calling C API to **create cached op** +4. Prepare lambda function which will run in spawned threads. Call C API to **invoke cached op** within the lambda function. +5. Spawn multiple threads and wait for all threads to complete. +6. Post process data to obtain inference results and cleanup. + +### Step 1: Parse arguments and load input image into ndarray + +[https://github.com/apache/incubator-mxnet/example/multi_threaded_inference/multi_threaded_inference.cc#L299-L341](multi_threaded_inference.cc#L299-L341) + +The above code parses arguments, loads the image file into a ndarray with a specific shape. There are a few things that are set by default and not configurable. For example, `static_alloc` and `static_shape` are by default set to true. + + +### Step 2: Prepare input data and load parameters, copying data to a specific context + +[https://github.com/apache/incubator-mxnet/example/multi_threaded_inference/multi_threaded_inference.cc#L147-L205](multi_threaded_inference.cc#L147-L205) + +The above code loads params and copies input data and params to specific context. + +### Step 3: Preparing arguments to pass to the CachedOp and calling C API to create cached op + +[https://github.com/apache/incubator-mxnet/example/multi_threaded_inference/multi_threaded_inference.cc#L207-L233](multi_threaded_inference.cc#L207-233) + +The above code prepares `flag_key_cstrs` and `flag_val_cstrs` to be passed the Cached op. +The C API call is made with `MXCreateCachedOpEX`. This will lead to creation of thread safe cached +op since the `thread_safe` (which is the last parameter to `MXCreateCachedOpEX`) is set to +true. When this is set to false, it will invoke CachedOp instead of CachedOpThreadSafe. + + +### Step 4: Prepare lambda function which will run in spawned threads + +[https://github.com/apache/incubator-mxnet/example/multi_threaded_inference/multi_threaded_inference.cc#L248-L262](multi_threaded_inference.cc#L248-262) + +The above creates the lambda function taking the thread number as the argument. +If `random_sleep` is set it will sleep for a random number (secs) generated between 0 to 5 seconds. Review comment: it can be 0-10 also, i just wanted to simulate a multi-threaded scenario where different threads call forward at different times. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
