TensorFlow is the most popular machine learning framework nowadays. TensorFlow Lite (TFLite), open sourced in late 2017, is TensorFlow’s runtime designed for mobile devices, esp. Android cell phones. TFLite is getting more and more mature. One the most interesting new components introduced recently are its GPU delegate and new NNAPI delegate. The GPU delegate uses Open GL ES compute shader on Android platforms and Metal shade on iOS devices. The original NNAPI delegate is an all-or-nothing design (if one of the ops in the compute graph is not supported by NNAPI, the whole graph is not delegated). The new one is a per-op design. When an op in a graph is not supported by NNAPI, the op is automatically fell back to the CPU runtime. I’ll have a quick review TFLite and its interpreter, then walk the audience through example usage of the two delegates and important source code of them.
The session will present HPC challenges in fuelling machine learning and deep learning into the simulations. Besides, we will present a user-centric view of IBM Watson ML Community Edition and the newly IBM inference system IC922 adoption into AIops of large HPC clusters (from deployment to inference).
This presentation describes the components of GPU ecosystem for compute, provides overview of existing ecosystems, and contains a case study on NVIDIA Nsight
This is a presentation I gave on last GPGPU workshop we did on April 2013.
The usage of GPGPU is expanding, and creates a continuum from Mobile to HPC. At the same time, question is whether the GPGPU languages are the right ones (well, no) and aren't we wasting resources on re-developing the same SW stack instead of converging.
TensorFlow is the most popular machine learning framework nowadays. TensorFlow Lite (TFLite), open sourced in late 2017, is TensorFlow’s runtime designed for mobile devices, esp. Android cell phones. TFLite is getting more and more mature. One the most interesting new components introduced recently are its GPU delegate and new NNAPI delegate. The GPU delegate uses Open GL ES compute shader on Android platforms and Metal shade on iOS devices. The original NNAPI delegate is an all-or-nothing design (if one of the ops in the compute graph is not supported by NNAPI, the whole graph is not delegated). The new one is a per-op design. When an op in a graph is not supported by NNAPI, the op is automatically fell back to the CPU runtime. I’ll have a quick review TFLite and its interpreter, then walk the audience through example usage of the two delegates and important source code of them.
The session will present HPC challenges in fuelling machine learning and deep learning into the simulations. Besides, we will present a user-centric view of IBM Watson ML Community Edition and the newly IBM inference system IC922 adoption into AIops of large HPC clusters (from deployment to inference).
This presentation describes the components of GPU ecosystem for compute, provides overview of existing ecosystems, and contains a case study on NVIDIA Nsight
This is a presentation I gave on last GPGPU workshop we did on April 2013.
The usage of GPGPU is expanding, and creates a continuum from Mobile to HPC. At the same time, question is whether the GPGPU languages are the right ones (well, no) and aren't we wasting resources on re-developing the same SW stack instead of converging.
Fast Insights to Optimized Vectorization and Memory Using Cache-aware Rooflin...Intel® Software
Integrated into Intel® Advisor, Cache-aware Roofline Modeling (CARM) provides insight into how an application behaves by helping to determine a) how optimally it works on a given hardware, b) the main factors that limit performance, c) if the workload is memory or compute-bound, and d) the right strategy to improve application performance.
Software AI Accelerators: The Next Frontier | Software for AI Optimization Su...Intel® Software
Software AI Accelerators deliver orders of magnitude performance gain for AI across deep learning, classical machine learning, and graph analytics and are key to enabling AI Everywhere. Get started on your AI Developer Journey @ software.intel.com/ai.
Scalability for All: Unreal Engine* 4 with Intel Intel® Software
Unreal Engine* 4 is a high-performance game engine for game developers. Learn how Intel and Epic Games* worked together to improve engine performance both for CPUs and GPUs and how developers can take advantage of it.
PT-4057, Automated CUDA-to-OpenCL™ Translation with CU2CL: What's Next?, by W...AMD Developer Central
Presentation PT-4057, Automated CUDA-to-OpenCL™ Translation with CU2CL: What's Next?, by Wu Feng and Mark Gardner at the AMD Developer Summit (APU13) November 11-13, 2013.
Fast Scalable Easy Machine Learning with OpenPOWER, GPUs and DockerIndrajit Poddar
Transparently accelerated Deep Learning workloads on OpenPOWER systems and GPUs using easy to use open source frameworks such as Caffe, Torch, Tensorflow, Theano.
Keynote (Nandini Ramani) - The Role of Java in Heterogeneous Computing & How ...AMD Developer Central
Keynote presentation, The Role of Java in Heterogeneous Computing, and How You Can Help, by Nandini Ramani, VP, Java Platform, Oracle Corporation, at the AMD Developer Summit (APU13), Nov. 11-13, 2013.
Presentation from the 16th Open Source Hardware Users' Group Meeting held at the C4CC in London on the 23 February 2012. Event details at: http://oshug.org/event/16
Early Benchmarking Results for Neuromorphic ComputingDESMOND YUEN
An update on the Intel Neuromorphic Research Community’s growth and benchmark results, including the addition of new corporate members and numerous new benchmarking updates computed on Intel’s neuromorphic test chip, Loihi.
End-to-End Deep Learning with Horovod on Apache SparkDatabricks
Data processing and deep learning are often split into two pipelines, one for ETL processing, the second for model training. Enabling deep learning frameworks to integrate seamlessly with ETL jobs allows for more streamlined production jobs, with faster iteration between feature engineering and model training.
ROCm and Distributed Deep Learning on Spark and TensorFlowDatabricks
ROCm, the Radeon Open Ecosystem, is an open-source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. In this talk, we describe how Apache Spark is a key enabling platform for distributed deep learning on ROCm, as it enables different deep learning frameworks to be embedded in Spark workflows in a secure end-to-end machine learning pipeline. We will analyse the different frameworks for integrating Spark with Tensorflow on ROCm, from Horovod to HopsML to Databrick's Project Hydrogen. We will also examine the surprising places where bottlenecks can surface when training models (everything from object stores to the Data Scientists themselves), and we will investigate ways to get around these bottlenecks. The talk will include a live demonstration of training and inference for a Tensorflow application embedded in a Spark pipeline written in a Jupyter notebook on Hopsworks with ROCm.
Fast Insights to Optimized Vectorization and Memory Using Cache-aware Rooflin...Intel® Software
Integrated into Intel® Advisor, Cache-aware Roofline Modeling (CARM) provides insight into how an application behaves by helping to determine a) how optimally it works on a given hardware, b) the main factors that limit performance, c) if the workload is memory or compute-bound, and d) the right strategy to improve application performance.
Software AI Accelerators: The Next Frontier | Software for AI Optimization Su...Intel® Software
Software AI Accelerators deliver orders of magnitude performance gain for AI across deep learning, classical machine learning, and graph analytics and are key to enabling AI Everywhere. Get started on your AI Developer Journey @ software.intel.com/ai.
Scalability for All: Unreal Engine* 4 with Intel Intel® Software
Unreal Engine* 4 is a high-performance game engine for game developers. Learn how Intel and Epic Games* worked together to improve engine performance both for CPUs and GPUs and how developers can take advantage of it.
PT-4057, Automated CUDA-to-OpenCL™ Translation with CU2CL: What's Next?, by W...AMD Developer Central
Presentation PT-4057, Automated CUDA-to-OpenCL™ Translation with CU2CL: What's Next?, by Wu Feng and Mark Gardner at the AMD Developer Summit (APU13) November 11-13, 2013.
Fast Scalable Easy Machine Learning with OpenPOWER, GPUs and DockerIndrajit Poddar
Transparently accelerated Deep Learning workloads on OpenPOWER systems and GPUs using easy to use open source frameworks such as Caffe, Torch, Tensorflow, Theano.
Keynote (Nandini Ramani) - The Role of Java in Heterogeneous Computing & How ...AMD Developer Central
Keynote presentation, The Role of Java in Heterogeneous Computing, and How You Can Help, by Nandini Ramani, VP, Java Platform, Oracle Corporation, at the AMD Developer Summit (APU13), Nov. 11-13, 2013.
Presentation from the 16th Open Source Hardware Users' Group Meeting held at the C4CC in London on the 23 February 2012. Event details at: http://oshug.org/event/16
Early Benchmarking Results for Neuromorphic ComputingDESMOND YUEN
An update on the Intel Neuromorphic Research Community’s growth and benchmark results, including the addition of new corporate members and numerous new benchmarking updates computed on Intel’s neuromorphic test chip, Loihi.
End-to-End Deep Learning with Horovod on Apache SparkDatabricks
Data processing and deep learning are often split into two pipelines, one for ETL processing, the second for model training. Enabling deep learning frameworks to integrate seamlessly with ETL jobs allows for more streamlined production jobs, with faster iteration between feature engineering and model training.
ROCm and Distributed Deep Learning on Spark and TensorFlowDatabricks
ROCm, the Radeon Open Ecosystem, is an open-source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. In this talk, we describe how Apache Spark is a key enabling platform for distributed deep learning on ROCm, as it enables different deep learning frameworks to be embedded in Spark workflows in a secure end-to-end machine learning pipeline. We will analyse the different frameworks for integrating Spark with Tensorflow on ROCm, from Horovod to HopsML to Databrick's Project Hydrogen. We will also examine the surprising places where bottlenecks can surface when training models (everything from object stores to the Data Scientists themselves), and we will investigate ways to get around these bottlenecks. The talk will include a live demonstration of training and inference for a Tensorflow application embedded in a Spark pipeline written in a Jupyter notebook on Hopsworks with ROCm.
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
Running Emerging AI Applications on Big Data Platforms with Ray On Apache SparkDatabricks
With the rapid evolution of AI in recent years, we need to embrace advanced and emerging AI technologies to gain insights and make decisions based on massive amounts of data. Ray (https://github.com/ray-project/ray) is a fast and simple framework open-sourced by UC Berkeley RISELab particularly designed for easily building advanced AI applications in a distributed fashion.
An Introduction to Google Colab as a development Platform using Python as a programming language. Mentioning Tips that are beneficial to make the development experience much easier.
PyTorch vs TensorFlow: The Force Is Strong With Which One? | Which One You Sh...Edureka!
( ** Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka comparison PPT of "PyTorch vs TensorFlow" provides you with a detailed comparison between the top 2 Python Deep Learning Frameworks.
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Deep Learning Neural Network Acceleration at the Edge - Andrea GalloLinaro
Short
The growing amount of data captured by sensors and the real time constraints imply that not only big data analytics but also Machine Learning (ML) inference shall be executed at the edge. The multiple options for neural network acceleration in Arm-based platforms provide an unprecedented opportunity for new intelligent devices. It also raises the risk of fragmentation and duplication of efforts when multiple frameworks shall support multiple accelerators.
Andrea Gallo, Linaro VP of Segment Groups, will summarise the existing NN frameworks, accelerator solutions, and will describe the efforts underway in the Arm ecosystem.
Abstract
The dramatically growing amount of data captured by sensors and the ever more stringent requirements for latency and real time constraints are paving the way for edge computing, and this implies that not only big data analytics but also Machine Learning (ML) inference shall be executed at the edge. The multiple options for neural network acceleration in recent Arm-based platforms provides an unprecedented opportunity for new intelligent devices with ML inference. It also raises the risk of fragmentation and duplication of efforts when multiple frameworks shall support multiple accelerators.
Andrea Gallo, Linaro VP of Segment Groups, will summarise the existing NN frameworks, model description formats, accelerator solutions, low cost development boards and will describe the efforts underway to identify the best technologies to improve the consolidation and enable the competitive innovative advantage from all vendors.
Audience
The session will be useful for executives to engineers. Executives will gain a deeper understanding of the issues and opportunities. Engineers at NN acceleration IP design houses will take away ideas for how to collaborate in the open source community on their area of expertise, how to evaluate the performance and accelerate multiple NN frameworks without modifying them for each new IP, whether it be targeting edge computing gateways, smart devices or simple microcontrollers.
Benefits to the Ecosystem
The AI deep learning neural network ecosystem is starting just now and it has similar implications with open source as GPU and video accelerators had in the early days with user space drivers, binary blobs, proprietary APIs and all possible ways to protect their IPs. The session will outline a proposal for a collaborative ecosystem effort to create a common framework to manage multiple NN accelerators while at the same time avoiding to modify deep learning frameworks with multiple forks.
Intro - End to end ML with Kubeflow @ SignalConf 2018Holden Karau
There are many great tools for training machine learning tools, ranging from sci-kit to Apache Spark, and tensorflow. However many of these systems largely leave open the question how to use our models outside of the batch world (like in a reactive application). Different options exist for persisting the results and using them for live training, and we will explore the trade-offs of the different formats and their corresponding serving/prediction layers.
The Implementing AI: High Performance Architectures webinar, hosted by KTN and eFutures, was the fourth event in the Implementing AI summer webinar series.
Every business is increasing the use of artificial intelligence to gain efficiency and to make better decisions. These new demands for data processing are not well delivered by traditional computer architectures. Enterprises, developers, data scientists, and researchers need new platforms that unify all AI workloads, simplifying infrastructure and accelerating ROI. This has led to the development of high performance and specialised hardware devices to meet these new demands.
The focus of this webinar was the impact of processing AI data on data centres - particularly from the technology perspective. The webinar had four presentations from experts, covering the opportunities, implementation techniques and Case Studies, followed by a panel Q&A session.
TensorFlow XLAの中では、
XLA Client を Pythonで利用できるようになっています。
また、2018年2月に開催されたSysMLの論文(JAX@Google)についても追記しました。
In TensorFlow XLA,
XLA Client is now available in Python.
Also added about SysML's paper (JAX @ Google) held in February 2018.
Tiramisu : A Code Optimization Framework for High Performance Systems
https://www.csail.mit.edu/research/tiramisu-framework-code-optimization-and-code-generation
の概要です。
ドキュメントがほとんどないので、ソースコード解析をやって、サンプルプログラムの内容について、調べてみました。
1. Cloud Deep Learning Chips
Training & Inference
Created date:2019.12.07
Updated date : 2019.12.15/17/25
@Vengineer
2. This is a summary of learning and inference
chips for deep learning in the cloud.
Each company's chips, product photos and
images are borrowed from the URL on the same
page.
3. Habana Labs:Goya (DRAM)
Intel Nervana:NNP-I (DRAM)
Google:TPU v1 (SRAM)
Groq (SRAM)
Alibaba:Hanguang(含光) 800 (SRAM)
The inference chip does not have an
interconnect itself.
InferenceTraining
Google:TPU v2/v3 (HBM2)
Intel Nervana:NNP-T (HBM2)
Habana Labs:Gaudi (HBM2)
Alphaics:RAP (HBM2 option)
Huawei:Ascend 910 (HBM2)
Graphcore:GC2 (SRAM)
Cerebras:CS-1 (SRAM)
The training chip has its own
interconnect.
https://vengineer.hatenablog.com/entry/2019/11/05/060000
https://github.com/basicmi/AI-Chip
https://twitter.com/jwangARK/status/1189560904872058880
21. Glow: A community-driven approach to AI
infrastructure specification
https://engineering.fb.com/ml-applications/glow-a-community-driven-approach-to-ai-infrastructure/
https://github.com/pytorch/glow
22. Intel and Baidu Continue Collaboration
across AI, AD and 5G
https://newsroom.intel.com/articles/intel-baidu-continue-collaboration-across-ai-ad-5g/
● BaiduBrain* (Baidu’s AI platform),
● PaddlePaddle* (Baidu’s deep learning platform)
● DuerOS* (Baidu’s AI-powered voice assistant platform)
● Apollo* (Baidu’s autonomous driving platform)
● Intel® Xeon® Scalable platform
● Intel® Optane™ DC Persistent Memory
● Intel® Optane™ DC SSD
● silicon photonics
● Ethernet
● Intel AI accelerators and Intel software stack
23. Training PyTorch models on Cloud TPU
Pods
https://cloud.google.com/tpu/docs/tutorials/pytorch-pod
github : https://github.com/pytorch/xla
24. MICROSOFT AND GRAPHCORE
COLLABORATE TO ACCELERATE
ARTIFICIAL INTELLIGENCE
https://www.graphcore.ai/posts/microsoft-and-graphcore-collaborate-to-accelerate-artificial-intellig
ence
Today we are very excited to share details of our collaboration with Microsoft,
announcing preview of Graphcore® Intelligence Processing Units (IPUs) on
Microsoft Azure.
● Graphcore IPUs with Dell EMC DSS 8440 Server
● Graphcore also delivers a full training runtime for ONNX and is working closely with the ONNX
organisation to include this in the ONNX standard environment. Initial PyTorch support is
available in Q4 2019 with full advanced feature support becoming available in early 2020.
25. Baidu, Facebook and Microsoft work together to
define the OCP Accelerator Module
specification
https://www.opencompute.org/blog/baidu-facebook-and-microsoft-work-together-to-define-the-oc
p-accelerator-module-specification
https://146a55aca6f00848c565-a7635525d40ac1c70300198708936b4e.ssl.cf1.rackcdn.com/images/22fa829b159a4c
ea7b33aa12bc2c61909e52d077.pdf
Other than Apple and Amazon =>
26. I am a computer engineer,
not a deep learning craftsman
ありがとうございました。
Thanks
@Vengineer
ソースコード解析職人
Source code analysis craftsman