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deep learning NVIDIA PowerEdge GPU

Deep Learning Performance with MLPerf Inference v0.7 Benchmark

Rakshith Vasudev Frank Han Dharmesh Patel

Wed, 21 Oct 2020 17:28:57 -0000


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MLPerf is a benchmarking suite that measures the performance of Machine Learning (ML) workloads. It focuses on the most important aspects of the ML life cycle:

  • Training—The MLPerf training benchmark suite measures how fast a system can train ML models. 
  • Inference—The MLPerf inference benchmark measures how fast a system can perform ML inference by using a trained model in various deployment scenarios.

This blog outlines the MLPerf inference v0.7 data center closed results on Dell EMC PowerEdge R7525 and DSS8440 servers with NVIDIA GPUs running the MLPerf inference benchmarks. Our results show optimal inference performance for the systems and configurations on which we chose to run inference benchmarks.  

In the MLPerf inference evaluation framework, the LoadGen load generator sends inference queries to the system under test (SUT). In our case, the SUTs are carefully chosen PowerEdge R7525 and DSS8440 servers with various GPU configurations. The SUT uses a backend (for example, TensorRT, TensorFlow, or PyTorch) to perform inferencing and sends the results back to LoadGen.

MLPerf has identified four different scenarios that enable representative testing of a wide variety of inference platforms and use cases. The main differences between these scenarios are based on how the queries are sent and received:

  • Offline—One query with all samples is sent to the SUT. The SUT can send the results back once or multiple times in any order. The performance metric is samples per second. 
  • Server—The queries are sent to the SUT following a Poisson distribution (to model real-world random events). One query has one sample. The performance metric is queries per second (QPS) within latency bound.
  • Single-stream—One sample per query is sent to the SUT. The next query is not sent until the previous response is received. The performance metric is 90th percentile latency.
  • Multi-stream—A query with N samples is sent with a fixed interval. The performance metric is max N when the latency of all queries is within a latency bound.

MLPerf Inference  Rules describes detailed inference rules and latency constraints. This blog only focuses on Offline and Server scenarios, which are designed for data center environments. Single-stream and Multi-stream scenarios are designed for nondatacenter (edge and IoT) settings.

MLPerf Inference results can be submitted under either of the following divisions:

  • Closed division—The Closed division is intended to provide an “apples to apples” comparison of hardware platforms or software frameworks. It requires using the same model and optimizer as the reference implementation.

    The Closed division requires using preprocessing, postprocessing, and model that is equivalent to the reference or alternative implementation. It allows calibration for quantization and does not allow any retraining. MLPerf provides a reference implementation of each benchmark. The benchmark implementation must use a model that is equivalent, as defined in MLPerf Inference  Rules, to the model used in the reference implementation.

  • Open division—The Open division is intended to foster faster models and optimizers and allows any ML approach that can reach the target quality. It allows using arbitrary preprocessing or postprocessing and model, including retraining. The benchmark implementation may use a different model to perform the same task.

To allow the apples-to-apples comparison of Dell EMC results and enable our customers and partners to repeat our results, we chose to conduct testing under the Closed division, as shown in the results in this blog.

Criteria for MLPerf Inference v0.7 benchmark result submission  

The following table describes the MLPerf benchmark expectations:

Table 1: Available benchmarks in the Closed division for MLPerf inference v0.7 with their expectations.





QSL size

Required quality

Required server latency constraint


Image classification


ImageNet (224x224)


99% of FP32 (76.46%)

15 ms


Object detection (large)


COCO (1200x1200)


99% of FP32 (0.20 mAP)

100 ms


Medical image segmentation


BraTS 2019 (224x224x160)


99% of FP32 and 99.9% of FP32 (0.85300 mean DICE score)





Librispeech dev-clean (samples < 15 seconds)


99% of FP32 (1 - WER, where WER=7.452253714852645%)

1000 ms


Language processing


SQuAD v1.1 (max_seq_len=384)


99% of FP32 and 99.9% of FP32 (f1_score=90.874%)

130 ms




1 TB Click Logs


99% of FP32 and 99.9% of FP32 (AUC=80.25%)

30 ms

For any benchmark, it is essential for the result submission to meet all the specifications in this table. For example, if we choose the Resnet50 model, then the submission must meet the 76.46 percent target accuracy and the latency must be within 15 ms for the ImageNet dataset.

Each data center benchmark requires the scenarios in the following table:

Table 2: Tasks and corresponding required scenarios for data center benchmark suite in MLPerf inference v0.7.



Required scenarios


Image classification

Server, Offline


Object detection (large)

Server, Offline


Medical image segmentation




Server, Offline


Language processing

Server, Offline



Server, Offline

SUT configurations

We selected the following servers with different types of NVIDIA GPUs as our SUT to conduct data center inference benchmarks:


The following provides the results of the MLPerf Inference v0.7 benchmark.  

For the Offline scenario, the performance metric is Offline samples per second. For the Server scenario, the performance metric is queries per second (QPS). In general, the metrics represent throughput.

The following graphs include performance metrics for the Offline and Server scenarios. A higher throughput is a better result.



Figure 1: Resnet50 v1.5 Offline and Server scenario with 99 percent accuracy target


Figure 2: SSD w/ Resnet34 Offline and Server scenario with 99 percent accuracy target

Figure 3: DLRM Offline and Server scenario with 99 percent accuracy target



Figure 4: DLRM Offline and Server scenario with 99.9 percent accuracy target

Figure 5: 3D-Unet using the 99 and 99.9 percent accuracy targets.

Note: The 99 and 99.9 percent accuracy targets with DLRM and 3D-Unet show the same performance because the accuracy targets were met early.



Figure 6: BERT Offline and Server scenario with 99 percent accuracy target


Figure 7: BERT Offline and Server scenario with 99.9 percent accuracy target.


Figure 8: RNN-T Offline and Server scenario with 99 percent accuracy target

Performance per GPU

For an estimate of per GPU performance, we divided the results in the previous section by the number of GPUs on the system. We observed that the performance scales linearly as we increase the number of GPUs. That is, as we add more cards, the performance of the system is multiplied by the number of cards times the performance per card. We will provide this information in a subsequent blog post. 

The following figure shows the approximate per GPU performance:


Figure 9: Approximate per card performance for the Resnet50 Offline scenario

The R7525_QuadroRTX8000x3 and DSS8440_QuadroRTX8000x10 systems both use the RTX8000 card. Therefore, performance per card for these two systems is about the same. The A100 cards yield the highest performance; the T4 cards yield the lowest performance. 


In this blog, we quantified the MLPerf inference v0.7 performance on Dell EMC DSS8440 and PowerEdge R7525 severs with NVIDIA A100, RTX8000, and T4 GPUs with Resnet50, SSD w/ Resnet34, DLRM, BERT, RNN-T, and 3D-Unet benchmarks. These benchmarks span tasks from vision to recommendation. Dell EMC servers delivered top inference performance normalized to processor count among commercially available results. We found that the A100 GPU delivered the best overall performance and best performance-per-watt while the RTX GPUs delivered the best performance-per-dollar. If constrained in a limited power environment, T4 GPUs deliver the best performance-per-watt.

Next steps

In future blogs, we plan to describe how to:

  • Run and performance tune MLPerf inference v0.7
  • Size the system (server and GPU configurations) correctly based on the type of workload (area and task)
  • Understand per-card, per-watt, and per-dollar metrics to determine your infrastructure needs 
  • Understand MLPerf training results on recently released R7525 PowerEdge servers with NVIDIA A100 GPUs
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AI Kubernetes

Bare Metal Compared with Kubernetes

Sam Lucido

Thu, 04 Jun 2020 16:19:26 -0000


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It has been fascinating to watch the tide of application containerization build from stateless cloud native web applications to every type of data-centric workload. These workloads include high performance computing (HPC), machine learning and deep learning (ML/DL), and now most major SQL and NoSQL databases. As an example, I recently read the following Dell Technologies knowledge base article: Bare Metal vs Kubernetes: Distributed Training with TensorFlow.

Bare metal and bare metal server refer to implementations of applications that are directly on the physical hardware without virtualization, containerization, and cloud hosting. Many times, bare metal is compared to virtualization and containerization is used to contrast performance and manageability features. For example, contrasting an application on bare metal to an application running in a container can provide insights into the potential performance differences between the two implementations.

Figure 1: Comparison of bare metal to containers implementations

A screenshot of a cell phone

Description automatically generated

Containers encapsulate an application with supporting binaries and libraries to run on one shared operating system. The container’s runtime engine or management applications, such as Kubernetes, manage the container. Because of the shared operating system, a container’s infrastructure is lightweight, providing more reason to understand the differences in terms of performance.

In the case of comparing bare metal with Kubernetes, distributed training with TensorFlow performance was measured in terms of throughput. That is, we measured the number of images per second when training CheXNet. Five tests were run in which each test consecutively added more GPUs across the bare metal and Kubernetes systems. The solid data points in Figure 2 show that the tests were run using 1, 2, 3, 4, and 8 GPUs.

Figure 2: Running CheXNet training on Kubernetes compared to bare metal

A close up of a map

Description automatically generated

Figure 2 shows that the Kubernetes container configuration was similar in terms of performance to the bare metal configuration through 4 GPUs. The test through 8 GPUs shows an eight percent increase for bare metal compared with Kubernetes. However, the article that I referenced offers factors that might contribute to the delta:

  • The bare metal system takes advantage of the full bandwidth and latency of raw InfiniBand while the Kubernetes configuration uses software defined networking using flannel.
  • The Kubernetes configuration uses IP over InfiniBand, which can reduce available bandwidth.

Studies like this are useful because they provide performance insight that customers can use. I hope we see more studies that encompass other workloads. For example, a study about Oracle and SQL Server databases in containers compared with running on bare metal would be interesting. The goal would be to understand how a Kubernetes ecosystem can support a broad ecosystem of different workloads.

Hope you like the blog!




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deep learning AI Spark

Deep Learning on Spark is Getting Interesting

Phil Hummel

Tue, 02 Jun 2020 18:57:09 -0000


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The year 2012 will be remembered in history as a break out year for data analytics. Deep learnings meteoric rise to prominence can largely be attributed to the 2012 introduction of convolution neural networks (CNN)for image classification using the ImageNet dataset during the Large-Scale Visual Recognition Challenge (LSVRC) [1].  It was a historic event after a very, very long incubation period for deep learning that started with mathematical theory work in the 1940s, 50s, and 60s.  The prior history of neural networks and deep learning development is a fascination and should not be forgotten, but it is not an overstatement to say that 2012 was the breakout year for deep learning.

Coincidentally, 2012 was also a breakout year for in-memory distributed computing.  A group of researchers from the University of AMPlab published a paper with an unusual title that changed the world of data analytics. “Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing”. [2] This paper describes how the initial creators developed an efficient, general-purpose and fault-tolerant in-memory data abstraction for sharing data in cluster applications.  The effort was motivated by the short-comings of both MapReduce and other distributed-memory programming models for processing iterative algorithms and interactive data mining jobs.

The ongoing development of so many application libraries that all leverage Spark’s RDD abstraction including GraphX for creating graphs and graph-parallel computation, Spark Streaming for scalable fault-tolerant streaming applications and MLlib for scalable machine learning is proof that Spark achieved the original goal of being a general-purpose programming environment.  The rest of this article will describe the development and integration of deep learning libraries – a now extremely useful class of iterative algorithms that Spark was designed to address.  The importance of the role that deep learning was going to have on data analytics and artificial intelligence was just starting to emerge at the same time Spark was created so the combination of the two developments has been interesting to watch.

MLlib – The original machine learning library for Spark

MLlib development started not long after the AMPlab code was transferred to the Apache Software Foundation in 2013.  It is not really a deep learning library however there is an option for developing Multilayer perceptron classifiers [3] based on the feedforward artificial neural network with backpropagation implemented for learning the model.  Fully connected neural networks were quickly abandoned after the development of more sophisticated models constructed using convolutional, recursive, and recurrent networks. 

Fully connected shallow and deep networks are making a comeback as alternatives to tree-based models for both regression and classification.  There is also a lot of current interest in various forms of autoencoders used to learn latent (hidden) compressed representations of data dimension reduction and self-supervised classification.  MLlib, therefore, can be best characterized as a machine learning library with some limited neural network capability.

BigDL – Intel open sources a full-featured deep learning library for Spark

BigDL is a distributed deep learning library for Apache Spark.  BigDL implements distributed, data-parallel training directly on top of the functional compute model using the core Spark features of copy-on-write and coarse-grained operations.  The framework has been referenced in applications as diverse as transfer learning-based image classification, object detection and feature extraction, sequence-to-sequence prediction for precipitation nowcasting, neural collaborative filtering for recommendations, and more.  Contributors and users include a wide range of industries including Mastercard, World Bank, Cray, Talroo, University of California San Francisco (UCSF), JD, UnionPay, Telefonica, GigaSpaces. [4]

Engineers with Dell EMC and Intel recently completed a white paper demonstrating the use of deep learning development tools from the Intel Analytics Zoo [5] to build an integrated pipeline on Apache Spark ending with a deep neural network model to predict diseases from chest X-rays. [6]   Tools and examples in the Analytics Zoo give data scientists the ability to train and deploy BigDL, TensorFlow, and Keras models on Apache Spark clusters. Application developers can also use the resources from the Analytics Zoo to deploy production class intelligent applications through model extractions capable of being served in any Java, Scala, or other Java virtual machine (JVM) language. 

The researchers conclude that modern deep learning applications can be developed and deployed at scale on an existing Hadoop and Spark cluster. This approach avoids the need to move data to a different deep learning cluster and eliminates the operational complexities of provisioning and maintaining yet another distributed computing environment.  The open-source software that is described in the white paper is available from Github. [7] – Sparkling Water for Spark

H2O is fast, scalable, open-source machine learning, and deep learning for smarter applications. Much like MLlib, the H20 algorithms cover a wide range of useful machine learning techniques but only fully connected MLPs for deep learning.  With H2O, enterprises like PayPal, Nielsen Catalina, Cisco, and others can use all their data without sampling to get accurate predictions faster. [8]  Dell EMC, Intel, and recently developed a joint reference architecture that outlines both technical considerations and sizing guidance for an on-premises enterprise AI platform. [9]

The engineers show how running software on optimized Dell EMC infrastructure with the latest Intel® Xeon® Scalable processors and NVMe storage, enables organizations to use AI to improve customer experiences, streamline business processes, and decrease waste and fraud. Validated software included the H2O Driverless AI enterprise platform and the H2O and H2O Sparkling Water open-source software platforms. Sparkling Water is designed to be executed as a regular Spark application. It provides a way to initialize H2O services on Spark and access data stored in both Spark and H2O data structures. H20 Sparkling Water algorithms are designed to take advantage of the distributed in-memory computing of existing Spark clusters.  Results from H2O can easily be deployed using H2O low-latency pipelines or within Spark for scoring.

H2O Sparkling Water cluster performance was evaluated on three- and five-node clusters. In this mode, H2O launches through Spark workers, and Spark manages the job scheduling and communications between the nodes. Three and five Dell EMC PowerEdge R740xd Servers with Intel Xeon Gold 6248 processors were used to train XGBoost and GBM models using the mortgage data set derived from the Fannie Mae Single-Family Loan Performance data set.

Spark and GPUs

Many data scientists familiar with Spark for machine learning have been waiting for official support for GPUs.  The advantages realized from modern neural network models like the CNN entry in the 2012 LSVRC would not have been fully realized without the work of NVIDIA and others on new acceleration hardware.  NVIDIA’s GPU technology like the Volta V100 has morphed into a class of advanced, enterprise-class ML/DL accelerators that reduce training time for all types of neural network configurations including CCN, RNN (recurrent neural networks) and GAN (generative adversarial networks) to mention just a few of the most popular forms.  Deep learning researchers see many advantages to building end-to-data model training “pipelines” that take advantage of the generalized distributed computing capability of Spark for everything from data cleaning and shaping through to scale-out training using integration with GPUs.

NVIDIA recently announced that it has been working with Apache Spark’s open source community to bring native GPU acceleration to the next version of the big data processing framework, Spark 3.0 [10]  The Apache Spark community is distributing a preview release of Spark 3.0 to encourage wide-scale community testing of the upcoming release.  The preview is not a stable release of the expected API specification or functionality.  No firm date for the general availability of Spark 3.0 has been released but organizations exploring options for distributed deep learning with GPUs should start evaluating the proposed features and advantages of Spark 3.0.

Cloudera is also giving developers and data science an opportunity to do testing and evaluation with the preview release of Spark 3.0.  The current GA version of the Cloudera Runtime includes the Apache Spark 3.0 preview 2 as part of their CDS 3 (Experimental) Powered by Apache Spark release. [11] Full Spark 3.0 preview 2 documentation including many code samples is available from the Apache Spark website [12] 

What’s next

It’s been 8 years since the breakout events for deep learning and distributed computing with Spark were announced.  We have seen tremendous adoption of both deep learning and Spark for all types of analytics use cases from medical imaging to language processing to manufacturing control and beyond.  We are just now poised to see new breakthroughs in the merging of Spark and deep learning, especially with the addition of support for hardware accelerators.  IT professionals and data scientists are still too heavily burdened with the hidden technical debt overhead for managing machine learning systems. [13]  The integration of accelerated deep learning with the power of the Spark generalized distributed computing platform will give both the IT and data science communities a capable and manageable environment to develop and host end-to-end data analysis pipelines in a common framework.  


[1] Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164.

[2] Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauly, M., ... & Stoica, I. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In Presented as part of the 9th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 12) (pp. 15-28).

[3] Apache Spark (June 2020) Multilayer perceptron classifier

[4] Dai, J. J., Wang, Y., Qiu, X., Ding, D., Zhang, Y., Wang, Y., ... & Wang, J. (2019, November). Bigdl: A distributed deep learning framework for big data. In Proceedings of the ACM Symposium on Cloud Computing (pp. 50-60).

[5] Intel Analytics Zoo (June 2020)

[6] Chandrasekaran, Bala (Dell EMC) Yang, Yuhao (Intel) Govindan, Sajan (Intel) Abd, Mehmood (Dell EMC) A. A. R. U. D. (2019).  Deep Learning on Apache Spark and Analytics Zoo.

[7] Dell AI Engineering (June 2020)  BigDL Image Processing Examples

[8] Candel, A., Parmar, V., LeDell, E., and Arora, A. (Apr 2020). Deep Learning with H2O

[9] Reference Architectures for (February 2020) Dell Technologies

[10] Woodie, Alex (May 2020) Spark 3.0 to Get Native GPU Acceleration datanami

[11] CDS 3 (Experimental) Powered by Apache Spark Overview (June 2020)

[12] Spark Overview (June 2020)

[13] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. In Advances in neural information processing systems (pp. 2503-2511).

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AI deep learning HPC

Accelerating Insights with Distributed Deep Learning

Luke Wilson Ph.D. Michael Bennett

Wed, 15 Apr 2020 19:18:38 -0000


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Originally published on Aug 6, 2018 1:17:46 PM 

Artificial intelligence (AI) is transforming the way businesses compete in today’s marketplace. Whether it’s improving business intelligence, streamlining supply chain or operational efficiencies, or creating new products, services, or capabilities for customers, AI should be a strategic component of any company’s digital transformation.

Deep neural networks have demonstrated astonishing abilities to identify objects, detect fraudulent behaviors, predict trends, recommend products, enable enhanced customer support through chatbots, convert voice to text and translate one language to another, and produce a whole host of other benefits for companies and researchers. They can categorize and summarize images, text, and audio recordings with human-level capability, but to do so they first need to be trained.

Deep learning, the process of training a neural network, can sometimes take days, weeks, or months, and effort and expertise is required to produce a neural network of sufficient quality to trust your business or research decisions on its recommendations. Most successful production systems go through many iterations of training, tuning and testing during development. Distributed deep learning can speed up this process, reducing the total time to tune and test so that your data science team can develop the right model faster, but requires a method to allow aggregation of knowledge between systems.

There are several evolving methods for efficiently implementing distributed deep learning, and the way in which you distribute the training of neural networks depends on your technology environment. Whether your compute environment is container native, high performance computing (HPC), or Hadoop/Spark clusters for Big Data analytics, your time to insight can be accelerated by using distributed deep learning. In this article we are going to explain and compare systems that use a centralized or replicated parameter server approach, a peer-to-peer approach, and finally a hybrid of these two developed specifically for Hadoop distributed big data environments.

Distributed Deep Learning in Container Native Environments

Container native (e.g., Kubernetes, Docker Swarm, OpenShift, etc.) have become the standard for many DevOps environments, where rapid, in-production software updates are the norm and bursts of computation may be shifted to public clouds. Most deep learning frameworks support distributed deep learning for these types of environments using a parameter server-based model that allows multiple processes to look at training data simultaneously, while aggregating knowledge into a single, central model.

The process of performing parameter server-based training starts with specifying the number of workers (processes that will look at training data) and parameter servers (processes that will handle the aggregation of error reduction information, backpropagate those adjustments, and update the workers). Additional parameters servers can act as replicas for improved load balancing.

Parameter server model for distributed deep learning

Worker processes are given a mini-batch of training data to test and evaluate, and upon completion of that mini-batch, report the differences (gradients) between produced and expected output back to the parameter server(s). The parameter server(s) will then handle the training of the network and transmitting copies of the updated model back to the workers to use in the next round.

This model is ideal for container native environments, where parameter server processes and worker processes can be naturally separated. Orchestration systems, such as Kubernetes, allow neural network models to be trained in container native environments using multiple hardware resources to improve training time. Additionally, many deep learning frameworks support parameter server-based distributed training, such as TensorFlow, PyTorch, Caffe2, and Cognitive Toolkit.

Distributed Deep Learning in HPC Environments

High performance computing (HPC) environments are generally built to support the execution of multi-node applications that are developed and executed using the single process, multiple data (SPMD) methodology, where data exchange is performed over high-bandwidth, low-latency networks, such as Mellanox InfiniBand and Intel OPA. These multi-node codes take advantage of these networks through the Message Passing Interface (MPI), which abstracts communications into send/receive and collective constructs.

Deep learning can be distributed with MPI using a communication pattern called Ring-AllReduce. In Ring-AllReduce each process is identical, unlike in the parameter-server model where processes are either workers or servers. The Horovod package by Uber (available for TensorFlow, Keras, and PyTorch) and the mpi_collectives contributions from Baidu (available in TensorFlow) use MPI Ring-AllReduce to exchange loss and gradient information between replicas of the neural network being trained. This peer-based approach means that all nodes in the solution are working to train the network, rather than some nodes acting solely as aggregators/distributors (as in the parameter server model). This can potentially lead to faster model convergence.

Ring-AllReduce model for distributed deep learning

The Dell EMC Ready Solutions for AI, Deep Learning with NVIDIA allows users to take advantage of high-bandwidth Mellanox InfiniBand EDR networking, fast Dell EMC Isilon storage, accelerated compute with NVIDIA V100 GPUs, and optimized TensorFlow, Keras, or Pytorch with Horovod frameworks to help produce insights faster. 

Distributed Deep Learning in Hadoop/Spark Environments

Hadoop and other Big Data platforms achieve extremely high performance for distributed processing but are not designed to support long running, stateful applications. Several approaches exist for executing distributed training under Apache Spark. Yahoo developed TensorFlowOnSpark, accomplishing the goal with an architecture that leveraged Spark for scheduling Tensorflow operations and RDMA for direct tensor communication between servers.

BigDL is a distributed deep learning library for Apache Spark. Unlike Yahoo’s TensorflowOnSpark, BigDL not only enables distributed training - it is designed from the ground up to work on Big Data systems. To enable efficient distributed training BigDL takes a data-parallel approach to training with synchronous mini-batch SGD (Stochastic Gradient Descent). Training data is partitioned into RDD samples and distributed to each worker. Model training is done in an iterative process that first computes gradients locally on each worker by taking advantage of locally stored partitions of the training data and model to perform in memory transformations. Then an AllReduce function schedules workers with tasks to calculate and update weights. Finally, a broadcast syncs the distributed copies of model with updated weights.

BigDL implementation of AllReduce functionality

The Dell EMC Ready Solutions for AI, Machine Learning with Hadoop is configured to allow users to take advantage of the power of distributed deep learning with Intel BigDL and Apache Spark. It supports loading models and weights from other frameworks such as Tensorflow, Caffe and Torch to then be leveraged for training or inferencing. BigDL is a great way for users to quickly begin training neural networks using Apache Spark, widely recognized for how simple it makes data processing.

One more note on Hadoop and Spark environments: The Intel team working on BigDL has built and compiled high-level pipeline APIs, built-in deep learning models, and reference use cases into the Intel Analytics Zoo library. Analytics Zoo is based on BigDL but helps make it even easier to use through these high-level pipeline APIs designed to work with Spark Dataframes and built in models for things like object detection and image classification.


Regardless of whether you preferred server infrastructure is container native, HPC clusters, or Hadoop/Spark-enabled data lakes, distributed deep learning can help your data science team develop neural network models faster. Our Dell EMC Ready Solutions for Artificial Intelligence can work in any of these environments to help jumpstart your business’s AI journey. For more information on the Dell EMC Ready Solutions for Artificial Intelligence, go to

Lucas A. Wilson, Ph.D. is the Chief Data Scientist in Dell EMC's HPC & AI Innovation Lab. (Twitter: @lucasawilson)

Michael Bennett is a Senior Principal Engineer at Dell EMC working on Ready Solutions.

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deep learning AI HPC

Training an AI Radiologist with Distributed Deep Learning

Luke Wilson Ph.D.

Wed, 15 Apr 2020 19:18:38 -0000


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Originally published on Aug 16, 2018 11:14:00 AM

The potential of neural networks to transform healthcare is evident. From image classification to dictation and translation, neural networks are achieving or exceeding human capabilities. And they are only getting better at these tasks as the quantity of data increases.

But there’s another way in which neural networks can potentially transform the healthcare industry: Knowledge can be replicated at virtually no cost. Take radiology as an example: To train 100 radiologists, you must teach each individual person the skills necessary to identify diseases in x-ray images of patients’ bodies. To make 100 AI-enabled radiologist assistants, you take the neural network model you trained to read x-ray images and load it into 100 different devices.

The hurdle is training the model. It takes a large amount of cleaned, curated, labeled data to train an image classification model. Once you’ve prepared the training data, it can take days, weeks, or even months to train a neural network. Even once you’ve trained a neural network model, it might not be smart enough to perform the desired task. So, you try again. And again. Eventually, you will train a model that passes the test and can be used out in the world.

Workflow for developing neural network modelsIn this post, I’m going to talk about how to reduce the time spent in the Train/Test/Tune cycle by speeding up the training portion with distributed deep learning, using a test case we developed in Dell EMC’s HPC & AI Innovation Lab to classify pathologies in chest x-ray images. Through a combination of distributed deep learning, optimizer selection, and neural network topology selection, we were able to not only speed the process of training models from days to minutes, we were also able to improve the classification accuracy significantly. 

Starting Point: Stanford University’s CheXNet

We began by surveying the landscape of AI projects in healthcare, and Andrew Ng’s group at Stanford University provided our starting point. CheXNet was a project to demonstrate a neural network’s ability to accurately classify cases of pneumonia in chest x-ray images.

The dataset that Stanford used was ChestXray14, which was developed and made available by the United States’ National Institutes of Health (NIH). The dataset contains over 120,000 images of frontal chest x-rays, each potentially labeled with one or more of fourteen different thoracic pathologies. The data set is very unbalanced, with more than half of the data set images having no listed pathologies.

Stanford decided to use DenseNet, a neural network topology which had just been announced as the Best Paper at the 2017 Conference on Computer Vision and Pattern Recognition (CVPR), to solve the problem. The DenseNet topology is a deep network of repeating blocks over convolutions linked with residual connections. Blocks end with a batch normalization, followed by some additional convolution and pooling to link the blocks. At the end of the network, a fully connected layer is used to perform the classification.

An illustration of the DenseNet topology (source: Kaggle)

Stanford’s team used a DenseNet topology with the layer weights pretrained on ImageNet and replaced the original ImageNet classification layer with a new fully connected layer of 14 neurons, one for each pathology in the ChestXray14 dataset. 

Building CheXNet in Keras

It’s sounds like it would be difficult to setup. Thankfully, Keras (provided with TensorFlow) provides a simple, straightforward way of taking standard neural network topologies and bolting-on new classification layers.

from tensorflow import keras
from keras.applications import DenseNet121

orig_net = DenseNet121(include_top=False, weights='imagenet', input_shape=(256,256,3)) 

In this code snippet, we are importing the original DenseNet neural network (DenseNet121) and removing the classification layer with the include_top=False argument. We also automatically import the pretrained ImageNet weights and set the image size to 256x256, with 3 channels (red, green, blue).

With the original network imported, we can begin to construct the classification layer. If you look at the illustration of DenseNet above, you will notice that the classification layer is preceded by a pooling layer. We can add this pooling layer back to the new network with a single Keras function call, and we can call the resulting topology the neural network's filters, or the part of the neural network which extracts all the key features used for classification. 

from keras.layers import GlobalAveragePooling2D

filters = GlobalAveragePooling2D()(orig_net.output) 

The next task is to define the classification layer. The ChestXray14 dataset has 14 labeled pathologies, so we have one neuron for each label. We also activate each neuron with the sigmoid activation function, and use the output of the feature filter portion of our network as the input to the classifiers. 

from keras.layers import Dense

classifiers = Dense(14, activation='sigmoid', bias_initializer='ones')(filters)  

The choice of sigmoid as an activation function is due to the multi-label nature of the data set. For problems where only one label ever applies to a given image (e.g., dog, cat, sandwich), a softmax activation would be preferable. In the case of ChestXray14, images can show signs of multiple pathologies, and the model should rightfully identify high probabilities for multiple classifications when appropriate.

Finally, we can put the feature filters and the classifiers together to create a single, trainable model.

from keras.models import Model  
chexnet = Model(inputs=orig_net.inputs, outputs=classifiers)  

With the final model configuration in place, the model can then be compiled and trained.

Accelerating the Train/Test/Tune Cycle with Distributed Deep Learning

To produce better models sooner, we need to accelerate the Train/Test/Tune cycle. Because testing and tuning are mostly sequential, training is the best place to look for potential optimization.

How exactly do we speed up the training process? In Accelerating Insights with Distributed Deep Learning, Michael Bennett and I discuss the three ways in which deep learning can be accelerated by distributing work and parallelizing the process:

  • Parameter server models such as in Caffe or distributed TensorFlow,
  • Ring-AllReduce approaches such as Uber’s Horovod, and
  • Hybrid approaches for Hadoop/Spark environments such as Intel BigDL.

Which approach you pick depends on your deep learning framework of choice and the compute environment that you will be using. For the tests described here we performed the training in house on the Zenith supercomputer in the Dell EMC HPC & AI Innovation Lab. The ring-allreduce approach enabled by Uber’s Horovod framework made the most sense for taking advantage of a system tuned for HPC workloads, and which takes advantage of Intel Omni-Path (OPA) networking for fast inter-node communication. The ring-allreduce approach would also be appropriate for solutions such as the Dell EMC Ready Solutions for AI, Deep Learning with NVIDIA.

The MPI-RingAllreduce approach to distributed deep learning

Horovod is an MPI-based framework for performing reduction operations between identical copies of the otherwise sequential training script. Because it is MPI-based, you will need to be sure that an MPI compiler (mpicc) is available in the working environment before installing horovod.

Adding Horovod to a Keras-defined Model

Adding Horovod to any Keras-defined neural network model only requires a few code modifications:

  1. Initializing the MPI environment,
  2. Broadcasting initial random weights or checkpoint weights to all workers,
  3. Wrapping the optimizer function to enable multi-node gradient summation,
  4. Average metrics among workers, and
  5. Limiting checkpoint writing to a single worker.

Horovod also provides helper functions and callbacks for optional capabilities that are useful when performing distributed deep learning, such as learning-rate warmup/decay and metric averaging.

Initializing the MPI Environment

Initializing the MPI environment in Horovod only requires calling the init method:

import horovod.keras as hvd  

This will ensure that the MPI_Init function is called, setting up the communications structure and assigning ranks to all workers.

Broadcasting Weights

Broadcasting the neuron weights is done using a callback to the Keras method. In fact, many of Horovod’s features are implemented as callbacks to, so it’s worthwhile to define a callback list object for holding all the callbacks.

callbacks = [ hvd.callbacks.BroadcastGlobalVariablesCallback(0) ] 

You’ll notice that the BroadcastGlobalVariablesCallback takes a single argument that’s been set to 0. This is the root worker, which will be responsible for reading checkpoint files or generating new initial weights, broadcasting weights at the beginning of the training run, and writing checkpoint files periodically so that work is not lost if a training job fails or terminates.

Wrapping the Optimizer Function

The optimizer function must be wrapped so that it can aggregate error information from all workers before executing. Horovod’s DistributedOptimizer function can wrap any optimizer which inherits Keras’ base Optimizer class, including SGD, Adam, Adadelta, Adagrad, and others.

import keras.optimizers  
opt = hvd.DistributedOptimizer(keras.optimizers.Adadelta(lr=1.0)) 

The distributed optimizer will now use the MPI_Allgather collective to aggregate error information from training batches onto all workers, rather than collecting them only to the root worker. This allows the workers to independently update their models rather than waiting for the root to re-broadcast updated weights before beginning the next training batch.

Averaging Metrics

Between steps error metrics need to be averaged to calculate global loss. Horovod provides another callback function to do this called MetricAverageCallback.

callbacks = [ hvd.callbacks.BroadcastGlobalVariablesCallback(0),  

This will ensure that optimizations are performed on the global metrics, not the metrics local to each worker.

Writing Checkpoints from a Single Worker

When using distributed deep learning, it’s important that only one worker write checkpoint files to ensure that multiple workers writing to the same file does not produce a race condition, which could lead to checkpoint corruption.

Checkpoint writing in Keras is enabled by another callback to However, we only want to call this callback from one worker instead of all workers. By convention, we use worker 0 for this task, but technically we could use any worker for this task. The one good thing about worker 0 is that even if you decide to run your distributed deep learning job with only 1 worker, that worker will be worker 0.

callbacks = [ ... ]  
if hvd.rank() == 0:  

Result: A Smarter Model, Faster!

Once a neural network can be trained in a distributed fashion across multiple workers, the Train/Test/Tune cycle can be sped up dramatically.

The figure below shows exactly how dramatically. The three tests shown are the training speed of the Keras DenseNet model on a single Zenith node without distributed deep learning (far left), the Keras DenseNet model with distributed deep learning on 32 Zenith nodes (64 MPI processes, 2 MPI processes per node, center), and a Keras VGG16 version using distributed deep learning on 64 Zenith nodes (128 MPI processes, 2 MPI processes per node, far right). By using 32 nodes instead of a single node, distributed deep learning was able to provide a 47x improvement in training speed, taking the training time for 10 epochs on the ChestXray14 data set from 2 days (50 hours) to less than 2 hours!

Performance comparisons of Keras models with distributed deep learning using Horovod

The VGG variant, trained on 128 Zenith nodes, was able to complete the same number of epochs as was required for the single-node DenseNet version to train in less than an hour, although it required more epochs to train. It also, however, was able to converge to a higher-quality solution. This VGG-based model outperformed the baseline, single-node model in 4 of 14 conditions, and was able to achieve nearly 90% accuracy in classifying emphysema.

Accuracy comparison of baseline single-node DenseNet model vs VGG variant with Horovod


In this post we’ve shown you how to accelerate the Train/Test/Tune cycle when developing neural network-based models by speeding up the training phase with distributed deep learning. We walked through the process of transforming a Keras-based model to take advantage of multiple nodes using the Horovod framework, and how these few simple code changes, coupled with some additional compute infrastructure, can reduce the time needed to train a model from days to minutes, allowing more time for the testing and tuning pieces of the cycle. More time for tuning means higher-quality models, which means better outcomes for patients, customers, or whomever will benefit from the deployment of your model.

Lucas A. Wilson, Ph.D. is the Chief Data Scientist in Dell EMC's HPC & AI Innovation Lab. (Twitter: @lucasawilson)

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Challenges of Large-batch Training of Deep Learning Models

Vineet Gundecha

Wed, 15 Apr 2020 21:22:49 -0000


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Originally published on Aug 27, 2018 1:29:28 PM

The process of training a deep neural network is akin to finding the minimum of a function in a very high-dimensional space. Deep neural networks are usually trained using stochastic gradient descent (or one of its variants). A small batch (usually 16-512), randomly sampled from the training set, is used to approximate the gradients of the loss function (the optimization objective) with respect to the weights. The computed gradient is essentially an average of the gradients for each data-point in the batch. The natural way to parallelize the training across multiple nodes/workers is to increase the batch size and have each node compute the gradients on a different chunk of the batch. Distributed deep learning differs from traditional HPC workloads where scaling out only affects how the computation is distributed but not the outcome.

Challenges of large-batch training

It has been consistently observed that the use of large batches leads to poor generalization performance, meaning that models trained with large batches perform poorly on test data. One of the primary reason for this is that large batches tend to converge to sharp minima of the training function, which tend to generalize less well. Small batches tend to favor flat minima that result in better generalization. The stochasticity afforded by small batches encourages the weights to escape the basins of attraction of sharp minima. Also, models trained with small batches are shown to converge farther away from the starting point. Large batches tend to be attracted to the minimum closest to the starting point and lack the exploratory properties of small batches.

The number of gradient updates per pass of the data is reduced when using large batches. This is sometimes compensated by scaling the learning rate with the batch size. But simply using a higher learning rate can destabilize the training. Another approach is to just train the model longer, but this can lead to overfitting. Thus, there’s much more to distributed training than just scaling out to multiple nodes.

An illustration showing how sharp minima lead to poor generalization. The sharp minimum of the training function corresponds to a maximum of the testing function which hurts the model's performance on test data 

How can we make large batches work?

There has been a lot of interesting research recently in making large-batch training more feasible. The training time for ImageNet has now been reduced from weeks to minutes by using batches as large as 32K without sacrificing accuracy. The following methods are known to alleviate some of the problems described above:

  1. Scaling the learning rate
    The learning rate is multiplied by k, when the batch size is multiplied by k. However, this rule does not hold in the first few epochs of the training since the weights are changing rapidly. This can be alleviated by using a warm-up phase. The idea is to start with a small value of the learning rate and gradually ramp up to the linearly scaled value.

  2. Layer-wise adaptive rate scaling
    A different learning rate is used for each layer. A global learning rate is chosen and it is scaled for each layer by the ratio of the Euclidean norm of the weights to Euclidean norm of the gradients for that layer.

  3. Using regular SGD with momentum rather than Adam
    Adam is known to make convergence faster and more stable. It is usually the default optimizer choice when training deep models. However, Adam seems to settle to less optimal minima, especially when using large batches. Using regular SGD with momentum, although more noisy than Adam, has shown improved generalization.

  4. Topologies also make a difference
    In a previous blog post, my colleague Luke showed how using VGG16 instead of DenseNet121 considerably sped up the training for a model that identified thoracic pathologies from chest x-rays while improving area under ROC in multiple categories. Shallow models are usually easier to train, especially when using large batches.


Large-batch distributed training can significantly reduce training time but it comes with its own challenges. Improving generalization when using large batches is an active area of research, and as new methods are developed, the time to train a model will keep going down.

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Training Neural Network Models for Financial Services with Intel® Xeon Processors

Pei Yang Ph.D.

Thu, 16 Apr 2020 19:53:13 -0000


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Originally published on Nov 5, 2018 9:10:17 AM 

Time series is a very important type of data in the financial services industry. Interest rates, stock prices, exchange rates, and option prices are good examples for this type of data. Time series forecasting plays a critical role when financial institutions design investment strategies and make decisions. Traditionally, statistical models such as SMA (simple moving average), SES (simple exponential smoothing), and ARIMA (autoregressive integrated moving average) are widely used to perform time series forecasting tasks.

Neural networks are promising alternatives, as they are more robust for such regression problems due to flexibility in model architectures (e.g., there are many hyperparameters that we can tune, such as number of layers, number of neurons, learning rate, etc.). Recently applications of neural network models in the time series forecasting area have been gaining more and more attention from statistical and data science communities.

In this blog, we will firstly discuss about some basic properties that a machine learning model must have to perform financial service tasks. Then we will design our model based on these requirements and show how to train the model in parallel on HPC cluster with Intel® Xeon processors.

Requirements from Financial Institutions

High-accuracy and low-latency are two import properties that financial service institutions expect from a quality time series forecasting model.

High Accuracy  A high level of accuracy in the forecasting model helps companies lower the risk of losing money in investments. Neural networks are believed to be good at capturing the dynamics in time series and hence yield more accurate predictions. There are many hyperparameters in the model so that data scientists and quantitative researchers can tune them to obtain the optimal model. Moreover, data science community believes that ensemble learning tends to improve prediction accuracy significantly. The flexibility of model architecture provides us a good variety of model members for ensemble learning.

Low Latency  Operations in financial services are time-sensitive.  For example, high frequency trading usually requires models to finish training and prediction within very short time periods. For deep neural network models, low latency can be guaranteed by distributed training with Horovod or distributed TensorFlow. Intel® Xeon multi-core processors, coupled with Intel’s MKL optimized TensorFlow, prove to be a good infrastructure option for such distributed training.

With these requirements in mind, we propose an ensemble learning model as in Figure 1, which is a combination of MLP (Multi-Layer Perceptron), CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) models. Because architecture topologies for MLP, CNN and LSTM are quite different, the ensemble model has a good variety in members, which helps reduce risk of overfitting and produces more reliable predictions. The member models are trained at the same time over multiple nodes with Intel® Xeon processors. If more models need to be integrated, we just add more nodes into the system so that the overall training time stays short. With neural network models and HPC power of the Intel® Xeon processors, this system meets the requirements from financial service institutions.

Training high accuracy ensemble model on HPC cluster with Intel® Xeon processors

Fast Training with Intel® Xeon Scalable Processors

Our tests used Dell EMC’s Zenith supercomputer which consists of 422 Dell EMC PowerEdge C6420 nodes, each with 2 Intel® Xeon Scalable Gold 6148 processors. Figure 2 shows an example of time-to-train for training MLP, CNN and LSTM models with different numbers of processes. The data set used is the 10-Year Treasury Inflation-Indexed Security data. For this example, running distributed training with 40 processes is the most efficient, primarily due to the data size in this time series is small and the neural network models we used did not have many layers. With this setting, model training can finish within 10 seconds, much faster than training the models with one processor that has only a few cores, which typically takes more than one minute. Regarding accuracy, the ensemble model can predict this interest rate with MAE (mean absolute error) less than 0.0005. Typical values for this interest rate is around 0.01, so the relative error is less than 5%.

Training time comparison: Each of the models is trained on a single Dell EMC PowerEdge C6420 with 2x Intel Xeon® Scalable 6148 processors


With both high-accuracy and low-latency being very critical for time series forecasting in financial services, neural network models trained in parallel using Intel® Xeon Scalable processors stand out as very promising options for financial institutions. And as financial institutions need to train more complicated models to forecast many time series with high accuracy at the same time, the need for parallel processing will only grow.

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Neural Network Inference Using Intel® OpenVINO™

Vineet Gundecha

Thu, 16 Apr 2020 20:37:58 -0000


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Originally published on Nov 9, 2018 2:12:18 PM 

Deploying trained neural network models for inference on different platforms is a challenging task. The inference environment is usually different than the training environment which is typically a data center or a server farm. The inference platform may be power constrained and limited from a software perspective. The model might be trained using one of the many available deep learning frameworks such as Tensorflow, PyTorch, Keras, Caffe, MXNet, etc. Intel® OpenVINO™ provides tools to convert trained models into a framework agnostic representation, including tools to reduce the memory footprint of the model using quantization and graph optimization. It also provides dedicated inference APIs that are optimized for specific hardware platforms, such as Intel® Programmable Acceleration Cards, and Intel® Movidius™ Vision Processing Units. 

The Intel® OpenVINO™ toolkit


  1. The Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices. It is a Python script which takes as input a trained Tensorflow/Caffe model and produces an Intermediate Representation (IR) which consists of a .xml file containing the model definition and a .bin file containing the model weights.
  2. The Inference Engine is a C++ library with a set of C++ classes to infer input data (images) and get a result. The C++ library provides an API to read the Intermediate Representation, set the input and output formats, and execute the model on devices. Each supported target device has a plugin which is a DLL/shared library. It also has support for heterogenous execution to distribute workload across devices. It supports implementing custom layers on a CPU while executing the rest of the model on a accelerator device.


  1. Using the Model Optimizer, convert a trained model to produce an optimized Intermediate Representation (IR) of the model based on the trained network topology, weights, and bias values.
  2. Test the model in the Intermediate Representation format using the Inference Engine in the target environment with the validation application or the sample applications.
  3. Integrate the Inference Engine into your application to deploy the model in the target environment.

Using the Model Optimizer to convert a Keras model to IR

The model optimizer doesn’t natively support Keras model files. However, because Keras uses Tensorflow as its backend, a Keras model can be saved as a Tensorflow checkpoint which can be loaded into the model optimizer. A Keras model can be converted to an IR using the following steps

  1. Save the Keras model as a Tensorflow checkpoint. Make sure the learning phase is set to 0. Get the name of the output node.
import tensorflow as tf 
from keras.applications import Resnet50 
from keras import backend as K 
from keras.models import Sequential, Model

K.set_learning_phase(0)   # Set the learning phase to 0
model = ResNet50(weights='imagenet', input_shape=(256, 256, 3))  
config = model.get_config()
weights = model.get_weights()
model = Sequential.from_config(config)
output_node =':')[0]  # We need this in the next step
graph_file = "resnet50_graph.pb" 
ckpt_file = "resnet50.ckpt"
saver = tf.train.Saver(sharded=True)
tf.train.write_graph(sess.graph_def, '', graph_file), ckpt_file)                                                    

2. Run the Tensorflow freeze_graph program to generate a frozen graph from the saved checkpoint.

tensorflow/bazel-bin/tensorflow/python/tools/freeze_graph --input_graph=./resnet50_graph.pb --input_checkpoint=./resnet50.ckpt --output_node_names=Softmax --output_graph=resnet_frozen.pb

3. Use the script and the frozen graph to generate the IR. The model weights can be quantized to FP16.

 python --input_model=resnet50_frozen.pb --output_dir=./ --input_shape=[1,224,224,3] --           data_type=FP16          


 The C++ library provides utilities to read an IR, select a plugin depending on the target device, and run the model.

  1. Read the Intermediate Representation - Using the InferenceEngine::CNNNetReader class, read an Intermediate Representation file into a CNNNetwork class. This class represents the network in host memory.
  2. Prepare inputs and outputs format - After loading the network, specify input and output precision, and the layout on the network. For these specification, use the CNNNetwork::getInputInfo() and CNNNetwork::getOutputInfo()
  3. Select Plugin - Select the plugin on which to load your network. Create the plugin with the InferenceEngine::PluginDispatcher load helper class. Pass per device loading configurations specific to this device and register extensions to this device.
  4. Compile and Load - Use the plugin interface wrapper class InferenceEngine::InferencePlugin to call the LoadNetwork() API to compile and load the network on the device. Pass in the per-target load configuration for this compilation and load operation.
  5. Set input data - With the network loaded, you have an ExecutableNetwork object. Use this object to create an InferRequest in which you signal the input buffers to use for input and output. Specify a device-allocated memory and copy it into the device memory directly, or tell the device to use your application memory to save a copy.
  6. Execute - With the input and output memory now defined, choose your execution mode:
    • Synchronously - Infer() method. Blocks until inference finishes.
    • Asynchronously - StartAsync() method. Check status with the wait() method (0 timeout), wait, or specify a completion callback.
  7. Get the output - After inference is completed, get the output memory or read the memory you provided earlier. Do this with the InferRequest GetBlob API.

The classification_sample and classification_sample_async programs perform inference using the steps mentioned above. We use these samples in the next section to perform inference on an Intel® FPGA.

Using the Intel® Programmable Acceleration Card with Intel® Arria® 10GX FPGA for inference

The OpenVINO toolkit supports using the PAC as a target device for running low power inference. The steps for setting up the card are detailed here. The pre-processing and post-processing is performed on the host while the execution of the model is performed on the card. The toolkit contains bitstreams for different topologies.

Programming the bitstream

aocl program <device_id> <open_vino_install_directory>/a10_dcp_bitstreams/2-0-1_RC_FP16_ResNet50-101.aocx

The Hetero plugin can be used with CPU as the fallback device for layers that are not supported by the FPGA. The -pc flag prints performance details for each layer

./classification_sample_async -d HETERO:FPGA,CPU -i <path/to/input/image.png> -m <path/to/ir>/resnet50_frozen.xml            


 Intel® OpenVINO™ toolkit is a great way to quickly integrate trained models into applications and deploy them in different production environments. The complete documentation for the toolkit can be found at

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Deep Neural Network Inference Performance on Intel FPGAs using Intel OpenVINO

Vineet Gundecha

Thu, 16 Apr 2020 21:09:49 -0000


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Originally published on Nov 16, 2018 9:22:39 AM 

Inference is the process of running a trained neural network to process new inputs and make predictions. Training is usually performed offline in a data center or a server farm. Inference can be performed in a variety of environments depending on the use case. Intel® FPGAs provide a low power, high throughput solution for running inference. In this blog, we look at using the Intel® Programmable Acceleration Card (PAC) with Intel® Arria® 10GX FPGA for running inference on a Convolutional Neural Network (CNN) model trained for identifying thoracic pathologies.

Advantages of using Intel® FPGAs

System Acceleration: Intel® FPGAs accelerate and aid the compute and connectivity required to collect and process the massive quantities of information around us by controlling the data path. In addition to FPGAs being used as compute offload, they can also directly receive data and process it inline without going through the host system. This frees the processor to manage other system events and enables higher real time system performance.

Power Efficiency: Intel® FPGAs have over 8 TB/s of on-die memory bandwidth. Therefore, solutions tend to keep the data on the device tightly coupled with the next computation. This minimizes the need to access external memory and results in a more efficient circuit implementation in the FPGA where data can be paralleled, pipelined, and processed on every clock cycle. These circuits can be run at significantly lower clock frequencies than traditional general-purpose processors and results in very powerful and efficient solutions.

Future Proofing: In addition to system acceleration and power efficiency, Intel® FPGAs help future proof systems. With such a dynamic technology as machine learning, which is evolving and changing constantly, Intel® FPGAs provide flexibility unavailable in fixed devices. As precisions drop from 32-bit to 8-bit and even binary/ternary networks, an FPGA has the flexibility to support those changes instantly. As next generation architectures and methodologies are developed, FPGAs will be there to implement them.

Model and software

The model is a Resnet-50 CNN trained on the NIH chest x-ray dataset. The dataset contains over 100,000 chest x-rays, each labelled with one or more pathologies. The model was trained on 512 Intel® Xeon® Scalable Gold 6148 processors in 11.25 minutes on the Zenith cluster at DellEMC.

The model is trained using Tensorflow 1.6. We use the Intel® OpenVINO™ R3 toolkit to deploy the model on the FPGA. The Intel® OpenVINO™ toolkit is a collection of software tools to facilitate the deployment of deep learning models. This OpenVINO blog post details the procedure to convert a Tensorflow model to a format that can be run on the FPGA.


In this section, we look at the power consumption and throughput numbers on the Dell EMC PowerEdge R740 and R640 servers.

Using the Dell EMC PowerEdge R740 with 2x Intel® Xeon® Scalable Gold 6136 (300W) and 4x Intel® PACs

The figures below show the power consumption and throughput numbers for running the model on Intel® PACs, and in combination with Intel® Xeon® Scalable Gold 6136. We observe that the addition of a single Intel® PAC adds only 43W to the system power while providing the ability to inference over 100 chest X-rays per second. The additional power and inference performance scales linearly with the addition of more Intel® PACs. At a system level, wee see a 2.3x improvement in throughput and 116% improvement in efficiency (images per sec per Watt) when using 4x Intel® PACs with 2x Intel® Xeon® Scalable Gold 6136.

Inference performance tests using ResNet-50 topology. FP11 precision. Image size is 224x224x3. Power measured via racadm

Performance per watt tests using ResNet-50 topology. FP11 precision. Image size is 224x224x3. Power measured via racadm

Using the Dell EMC PowerEdge R640 with 2x Intel® Xeon® Scalable Gold 5118 (210W) and 2x Intel® PACs

We also used a server with lower idle power. We see a 2.6x improvement in system performance in this case. As before, each Intel® PAC linearly adds performance to the system, adding more than 100 inferences per second for 43W (2.44 images/sec/W).

Inference performance tests using ResNet-50 topology. FP11 precision. Image size is 224x224x3. Power measured via racadm

Performance per watt tests using ResNet-50 topology. FP11 precision. Image size is 224x224x3. Power measured via racadm 


Intel® FPGAs coupled with Intel® OpenVINO™ provide a complete solution for deploying deep learning models in production. FPGAs offer low power and flexibility that make them very suitable as an accelerator device for deep learning workloads.

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