AI-pipeline
Remote Tensor API Sample
This AI pipeline implements zero-copy between SYCL and OpenVINO through the Remote Tensor API of the GPU Plugin.
- Introduction
The development of SYCL simplifies the use of OpenCL, which can fully exploit the computing power of GPU in the pipeline. Meanwhile, SYCL has more flexibility to do customized pre- and post-processing of OpenVINO. To further optimize the pipeline, developers can use GPU Plugin to avoid the memory copy overhead between SYCL and OpenVINO. The GPU plugin provides the ov::RemoteContext and ov::RemoteTensor interfaces for video memory sharing and interoperability with existing native APIs, such as OpenCL, Microsoft DirectX, or VAAPI. For details, please refer to the online documentation of OpenVINO.
Based on the pseudocode of the online documentation, here we provide a simple pipeline sample with Remote Tensor API. Because in the rapid iteration of oneAPI, sometimes customers need quick verification so that this sample can be used for testing. OneAPI also provides a real-world, end-to-end example, which optimizes PointPillars for lidar object detection.
- Components
SYCL preprocessing is based on the Sepia Filter sample, which demonstrates how to convert a color image to a Sepia tone image, a monochromatic image with a distinctive Brown Gray color. The sample program works by offloading the compute-intensive conversion of each pixel to Sepia tone using SYCL*-compliant code for CPU and GPU.
OpenVINO inferencing is based on the OpenVINO classification sample, the input from SYCL filtered image in the device will be sent into OpenVINO as a remote tensor without a memory copy.
Remote Tensor API: Create RemoteContext from SYCL pre-processing’s native handle. After model compiling, do memory sharing between the application and GPU plugin with from cl::Buffer to remote tensor.
- Build Sample on Linux
Download the source code from sandbox. Prepare the model and images.
To run the sample, you need to specify a model and image:
Use pre-trained models from the Open Model Zoo. The models can be downloaded using the Model Downloader. Use images from the media files collection.
Run on Intel NUC Core 11 iGPU with OpenVINO 2022.2 and oneAPI 2022.3.
./intel64/hello_nv12_input_classification_oneAPI../model/FP32/alexnet.xml ../image/dog512.bmp GPU 2
Sample Output:
Warning: With the updating of OpenVINO and oneAPI, different versions may cause problems with the tools in the common directory or the new SYCL header name. Please use the same version or debug following the corresponding release instructions.
Leverage the power of Model Caching in your AI Applications
Authors: Devang Aggarwal, Eddy Kim, Preetha Veeramalai
Choosing the right type of hardware for deep learning tasks is a critical step in the AI development workflow. Here at Intel, we provide developers, like yourself, with a variety of hardware options to meet your compute requirements. From Intel® CPUs to Intel® GPUs, there are a wide array of hardware platforms available to meet your needs. When it comes to inferencing on different hardware, the little things matter. For example, the loading of deep learning models, which can be a lengthy process and can lead to a difficult user experience on application startup.
Are there ways to achieve faster model loading time on such devices?
Short answer is, yes, there are ways; one way is to handle the model loading time. Model loading performs several time-consuming device-specific optimizations and network compilations, which can also result in developers seeing a relatively higher first inference latency. These delays can lead to a difficult user experience during application startup. This problem can be solved through a mechanism called Model Caching. Model Caching solves the issue of model loading time by caching the final optimized model directly into a file. Reusing cached networks can significantly reduce the model loading time.
Model Caching
With OpenVINO 2022.3, model caching is currently implemented as a preview feature. To accelerate first inference latency on Intel® GPU, not only should the kernel source code be compiled in a form that can be executed on the GPU, but also various optimization passes must be performed. Kernel caching reuses only the kernels, but model caching reuses even the output of the optimization passes, so the model loading time can be further reduced. Before model caching, kernel caching was used in the same manner: by setting the CACHE_DIR configuration key to a folder where the cache should be stored. Now, to use the preview feature of model caching, set the OV_GPU_CACHE_MODEL environment variable to 1. Since the extension of the cache file created by kernel caching is “cl_cache” and the extension of the cache file created by model caching is “blob”, it is possible to check whether model caching is activated through this.
Note: Currently this is a preview feature with OpenVINO 2022.3. This feature will be fully available in OpenVINO 2023.0.
Developers can now also leverage this preview feature from OpenVINO™ Toolkit in OpenVINO™ Execution Provider for ONNX Runtime, a product that accelerates inferencing of ONNX models using ONNX Runtime API’s while using the OpenVINO™ toolkit as a backend. With the OpenVINO™ Execution Provider, ONNX Runtime delivers better inferencing performance on the same hardware compared to generic acceleration on Intel® CPU, GPU, and VPU. Additionally, by using model caching, OpenVINO™ Execution Provider can speed up the first inference latency of deep learning models on Intel® GPU.
In OpenVINO™ Execution Provider for ONNX Runtime, the model caching feature can been abled by setting the ONNX Runtime config option ‘use_compiled_network’ to True while using the C++/Python API’s. This config option acts like a switch to enable and disable the model caching feature that saves the final optimized model into a .blob file during the very first inference of the model on Intel® hardware.
The blobs are loaded from a directory named ‘ov_compiled_blobs’ relative to the executable path by default. This path however can be overridden using the ONNX Runtime config option ‘blob_dump_path’ which is used to explicitly specify the path where you would like to dump and load the blobs files from when already using theuse_compiled_network (model caching) setting.
Refer to Configuration Options for more information about using these options.
Conclusion
With the Model Caching feature, the deep learning model loading time should significantly decrease. You can now utilize this feature in both the Intel® Distribution of OpenVINO™ Toolkit and OpenVINO™ Execution Provider for ONNX Runtime and experience better first inference latency for your AI models.
Notices & Disclaimers
Intel technologies may require enabled hardware, software or service activation.
No product or component can be absolutely secure.
Your costs and results may vary.
©Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or itssubsidiaries. Other names and brands maybe claimed as the property of others.
Intel® DL Streamer Optimize Media-AI pipeline on Intel® Data Center Flex dGPU by Docker
Introduction
In this blog is about How to use DL-streamer to build a complete Media-AI pipeline (Including: Video Access, Media Decode, AI Inference, Media Encode and Result Export). And the pipeline will be accelerated by OpenVINO™ and optimize to run on Flex dGPU(Intel® Data Center Flex dGPU)
Requirement
- DL-streamer
Intel® Deep Learning Streamer (Intel® DL Streamer)Pipeline Framework is an easy way to construct media analytics pipelines using Intel® Distribution of OpenVINO™ Toolkit. It leverages the open source media framework GStreamer to provide optimized media operations and Deep Learning Inference Engine from OpenVINO™ Toolkit to provide optimized inference.
- OpenVINO
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference which can boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common task.
- Docker (Optional)
Docker is an open-source platform that enables developers to build, deploy, run, update, and manage containers—standardized, executable components that combine application source code with the operating system (OS) libraries and dependencies required to run that code in any environment.
Install DL-Streamer and OpenVINO™ via Docker
Images for Intel® Data Center GPU Flex Series
Images 2022.2.0-ubuntu20-gpu419.40* are intended for Intel® Data Center GPU Flex Series and include
1. Intel®DL Streamer 2022.2-release
3. Drivers for Intel® Data Center GPU Flex Series, drivers version 419.40
Four images are listed below, images -devel additionally contain samples and development files, images with -dpcpp additionally contain Intel® oneAPI DPC++/C++ Compiler
Runtime image that includes GStreamer* Pipeline Framework elements
Developer image that builds on runtime image containing samples,development files and a model downloader.
Runtime image including elements built with Intel® oneAPI DPC++/C++ Compiler
Developer image for elements built with Intel® oneAPI DPC++/C++ Compiler
Taking “2022.2.0-ubuntu20-gpu419.40” docker images as a sample to show how to pull the docker image from docker hub.
DL-Streamer Media-AI pipeline quick start example
Make sure the pre-requirement had already installed, there is a very basic introduction to using object detection models(yolov4) to build a DL-streamer pipeline.
Step 1.Download video and yolov4-tf model file
Download video, Enter the following link into your browser to download the related files
https://www.pexels.com/photo/5325136/download
Download yolov4-tf model
Step 2.Enter Docker and copy the files into docker container
Create andenter the docker container
Open another terminal for file copy into container ,copy video and model into docker container
Step 3. Run an objectdetection Media-AI pipeline
By the following script, we can run pipeline the Media-AI objection detection on the Flex dGPU in the docker container.
If want to encode the detection result and save as video file, can use the follow script
The encoded video file will save in the container and can be copied out in new terminal.
PS. Instruction aboutDL-streamer CLI parameter
decodebin: Auto-magically constructs a decoding pipeline using available decoders and demuxers via auto-plugging.
vaapipostproc: Consists in various post processing algorithms to be applied to VA surfaces. For e.g. scaling, deinterlacing (bob, motion-adaptive, motion-compensated), noise reduction or sharpening.
gvadetect: Performs object detection on a full-frame or region of interest (ROI) using object detection models such as YOLO v3-v5, MobileNet-SSD, Faster-RCNN etc. Outputs the ROI for detected objects.
gvatrack: Performs object tracking using zero-term, zero-term-imageless, or short-term-imageless tracking algorithms. Zero-term tracking assigns unique object IDs and requires object detection to run on every frame. Short-term tracking allows to track objects between frames, there by reducing the need to run object detection on each frame. Imageless tracking forms object associations based on the movement and shape of objects, and it does not use image data.
gvafpscounter: Measures frames per second across multiple streams in a single process.
Tuning Tips
Users can refer the different platform using case which were supported by OpenVINO™ and the device profiling API to realize performance tuning of your inference program between CPU, iGPU, dGPU. It will also be helpful to developer finding out the place where has the potential space of performance improvement.
An end-to-end workflow with training on Habana® Gaudi® Processor and post-training quantization and Inference using OpenVINO™ toolkit
Authors: Sachin Rastogi, Maajid N Khan, Akhila Vidiyala
Background:
Brain tumors are abnormal growths of braincells and can be benign (non-cancerous) or malignant (cancerous). Accurate diagnosis and treatment of brain tumors are critical for the patient's prognosis, and one important step in this process is the segmentation of the tumor in medical images. This involves identifying the boundaries of the tumor and separating it from the surrounding healthy brain tissue.
MRI is a non-invasive imaging technique that uses a strong magnetic field and radio waves to produce detailed images of the brain. MRI scans can provide high-resolution images of the brain, including the location and size of tumors. Traditionally, trained professionals, such as radiologists or medical image analysts, perform manual segmentation of brain tumors. However, this process is time-consuming and subject to human error, leading to the development of automated methods using machine learning.
Introduction:
As demand for deep learning applications grows for medical imaging, so does the need for cost-effective cloud solutions that can efficiently train Deep Learning models. With the Amazon EC2 DL1 instances powered by Gaudi® accelerators from Habana® Labs (An Intel® company), you can train deep learning models for medical image segmentation at a reduced cost of up to 40% than the current generation GPU-based EC2 instances.
Medical Imaging AI solutions often need to be deployed on various hardware platforms, including both new and older systems. The usage of Intel® Distribution of OpenVINO™ toolkit makes it easier to deploy these solutions on systems with Intel® Architecture.
This reference implementation demonstrates how this toolkit can be used to detect and distinguish between healthy and cancerous tissue in MRI scans. It can be used on a range of Intel® Architecture platforms, including CPUs, integrated GPUs, and VPUs, with no need to modify the code when switching between platforms. This allows developers to choose the hardware that meets their needs in terms of performance, cost, and power consumption.
The Challenge:
Identify and separate cancerous tumors from healthy tissue in an MRI scan of the brain with the best price performance.
The Solution:
One approach to brain tumor segmentation using machine learning is to use supervised learning, where the algorithm is trained on a dataset of labelled brain images, with the tumor regions already identified by experts. The algorithm can then learn to identify these tumor regions in new images.
Convolutional neural networks (CNNs) are a type of machine learning model that has been successful in image classification and segmentation tasks and are often used for brain tumor segmentation. In a CNN, the input image is passed through multiple layers of filters that learn to recognize specific features in the image. The output of the CNN is a segmented image, with each pixel classified as either part of the tumor or healthy tissue.
Another approach to brain tumor segmentation is to use unsupervised learning, where the algorithm is not given any labelled examples and must learn to identify patterns in the data on its own. One unsupervised method for brain tumor segmentation is to use clustering algorithms, which can group similar pixels together and identify the tumor region as a separate cluster. However, unsupervised learning is not commonly used for brain tumor segmentation due to the complexity and variability of the data.
Regardless of the approach used, the performance of brain tumor segmentation algorithms can be evaluated using metrics such as dice coefficient, Jaccard index, and sensitivity.
Our medical imaging AI solution is designed to be used widely and in a cost-effective manner. Our approach ensures that the accuracy of the model is not compromised while still being affordable. We have used a U-Net 2D model that can be trained using the Habana® Gaudi® platform and the Medical Decathlon dataset (BraTS 2017 Brain Tumor Dataset) to achieve the best possible accuracy for image segmentation. The model can then be used for inferencing with the OpenVINO™ on Intel® Architecture.
This reference implementation provides an AWS*cloud-based generic AI workflow, which showcases U-Net-2D model-based image segmentation with the medical decathlon dataset. The reference implementation is available for use by Docker containers and Helm chart.
Training:
Primarily, we are leveraging AWS* EC2 DL1workflows to train U-Net 2D models for the end-to-end pipeline. We are consistently seeing cost savings compared to existing GPU-based instances across model types, enabling us to achieve much better Time-to-Market for existing models or training much larger and more complex models.
AWS*DL1 instances with Gaudi® accelerators offer the best price-performance savings compared to other GPU offerings in the market. The models were trained using the Pytorch framework.
The reference training code with detailed instructions is available here.
Inference and Optimization:
Intel® OpenVINO™ is an inference solution that optimizes and accelerates the computation of AI workloads on Intel® hardware. The trained Pytorch models were converted to ONNX (Open Neural Network Exchange) model representation format and then further optimized to the OpenVINO™ format or Intermediate representation (IR) of OpenVINO™ using the Model Optimizer tool from OpenVINO™.
TheFP32-optimized IR models outperformed using OpenVINO™ runtime in terms of throughput compared to other Deep Learning framework runtimes on the same Intel® hardware.
Asa next step, the FP32 IR model was further optimized and converted to lower8-bit precision with post-training quantization using the default quantization algorithm from the Post Training Optimization Tool (POT) from the OpenVINO™ toolkit. This inherently leads to a jump in the model’s performance, in terms of its processing speed and throughput, for you get a higher FPS when dealing with video streams with very negligible loss in accuracy.
TheINT8 IR models performed extremely well for inference on Intel® CPU(Central Processing Unit) 3rd Generation Intel® Xeon.
The reference inference code with detailed instructions is available here.
GitHub: https://github.com/intel/cv-training-and-inference-openvino/tree/main/gaudi-segmentation-unet-ptq
Developer Catalog: https://www.intel.com/content/www/us/en/developer/articles/reference-implementation/gaudi-processor-training-and-inference-openvino.html
Inference Result:
We are using OpenVINO™ Model Optimizer(MO) to convert the trained ONNX FP32 model to FP32 OpenVINO™ or Intermediate Representation(IR) format. The FP32prediction shown here is from a test image from the training dataset which was never used for training. The prediction is from a trained model which was trained for 8 epochs with 8 HPU multi-card training on an AWS* EC2 DL1 Instance with 400/484 images from the training folder.
Quantization (Recommended to use if you need the better performance of the model)
Quantization is the process of converting a deep learning model’s weight to a lower precision requiring less computation. This inherently leads to an increase in model performance, in terms of its processing speed and throughput, you will see a higher throughput(FPS) when dealing with video streams. We are using OpenVINO™ POT for the Default Quantization Algorithm to quantize the FP32 OpenVINO™ format model into the INT8 OpenVINO™ format model.
The INT8 prediction shown here is from a testimage from a training dataset that was never used for training. The predictionis from a quantized model which we quantized using POT with a calibrationdataset of 300 samples.
This application is available on the Intel® Developer Catalog for the developers to use as it is or use as a base code to bootstrap their customized solution. Intel® Developer Catalog offers reference implementations and software packages to build modular applications using containerized building blocks. Using the containerized building blocks the developers can rapidly develop deployable solutions.
Conclusion:
In conclusion, brain tumor segmentation using machine learning can help improve the accuracy and efficiency of the diagnosis and treatment of brain tumors.
There are several challenges and limitations to using machine learning for brain tumor segmentation. One of the main challenges is the limited availability of annotated data, as it is time consuming and expensive to annotate large datasets of medical images. In addition, there is a high degree of variability and complexity in the data, as brain tumors can have different shapes, sizes, and intensity patterns on MRI scans. This can make it difficult for the machine learning algorithm to generalize and accurately classify tumors in new data.
Another challenge is the potential for bias in the training data, as the dataset may not be representative of the entire population. This can lead to inaccurate or biased results if the algorithm is not properly trained or validated.
While there are still challenges to be overcome, the use of machine learning in medical image analysis shows great promise for improving patient care.
Notices & Disclaimers:
Intel technologies may require enabled hardware, software or service activation.
No product or component can be absolutely secure.
Your costs and results may vary.
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.