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© 2021, Amazon Web Services, Inc. or its Affiliates.
Heidi Pan, Director of Data Analytics Software, Intel
Jing Xu, AI Technical Consulting Engineer, Intel
AWS & Intel Research Webinar Series
Scale your research workloads faster with Intel
© 2021, Amazon Web Services, Inc. or its Affiliates.
Amazon Confidential | © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved.
What does Intel do with Amazon?
Amazon
EC2
VMware
Cloud on
AWS
AWS IoT
Core
AWS
Greengrass
Amazon
SageMaker
AWS Deep
Learning
AMIs
AWS
DeepLens
Amazon S3
AWS
Deepracer
Amazon
Echo Look
Amazon
Echo Show
Cloud &
data center
Things &
devices
Alexa Voice
Services
SAP on
AWS
AWS
Outposts
Intel is a very deep partner
of AWS and will be for a long
time.That’s not changing.
Andy Jassy,
CEO, AWS
Joint priorities:
Strategic migrations
AI/ML
HPC
SAP
Hybrid
Edge
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
3
Bridging the Gap from Ideation to Results
Ideas Results
Intel SW+HW
PRODUCTIVITY & PERFORMANCE
 More experimentation
 Reproducible results
 AI/analytics at scale
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
4
Example: Project DisCo
UC Davis Complexity Sciences Center
Problem: How to predict
extreme weather patterns?
massive data, no ground truth
Results:
Effectively segmented
complex high-dim data
Winner of 2019 HPC
Innovation Excellence Award
Intel Extension for Scikit-learn (fka daal4py)
on 1024 Xeon nodes
computational limitations
process 89.5 TB in 6.6 minutes
code lags theoretical development
easily iterate w/ high-level Python APIs
Idea: local causal state
unsupervised physics-based
https://www.nextplatform.com/2020/04/15/python-delivers-big-on-complex-unlabeled-data/
Visit www.Intel.com/PerformanceIndex for workloads and
configurations. Results may vary​.
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
5
Intel’s Optimized Data Science Software
O P T I MI Z E D
A I / A N A L Y T I C S
P A C K A G E S
Intel Extension
for Scikit-learn
• • •
Familiar, easy-to-use APIs, up to 10-100X faster
optimized to fully utilize modern parallel hardware
Visit www.Intel.com/PerformanceIndex for workloads and
configurations. Results may vary​.
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
6
Example: Identifying Melanoma in Skin Lesion Images
Society of Imaging Informatics in Medicine Kaggle competition
ADD TWO LINES
https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a
Unoptimized
scikit-learn baseline w/ Intel Extension for sklearn
Performance
(Cascade
Lake
Gold
2sx16c)
48x faster
1x
Optimized
Same numeric behavior
as defined by scikit-learn consortium
Repeatable run-to-run
Visit www.Intel.com/PerformanceIndex for workloads and
configurations. Results may vary​.
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
7
Example: AWS AutoGluon Acceleration of KNN
One of Most
Expensive Algos
(and timed out
with large data)
25x faster
via Intel Ext for Sklearn
10x faster
via AWS opts
One of Least
Expensive Algos
(and handles
large data)
https://github.com/awslabs/autogluon/pull/1049
Visit www.Intel.com/PerformanceIndex for workloads and
configurations. Results may vary​.
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
8
Do More Experimentation
before
exp
alternative
de
with Intel
acceleration
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
9
Or Save Cost
p3dn.24xlarge
RAPIDS cuML
8 NVIDIA V100 GPUs
Intel Ext for sklearn
8 Intel CPU nodes
2.64x cheaper
(and 2.76x faster)
1x
c5.24xlarge
Cost
As measured by K-means on the largest dataset that fits into GPU nodes (out-of-memory error on GPU otherwise)
https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1
Visit www.Intel.com/PerformanceIndex for workloads and
configurations. Results may vary​.
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
10
Scale Up and Down Instances with Same Software
Visit www.Intel.com/PerformanceIndex for workloads and
configurations. Results may vary​.
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
11
Do Even More Experimentation
A Day in the Life of a Data Scientist
Everything Else Experimentation
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
12
Modin
Explore data at scale
TB-scale data analytics with the ease of pandas APIs
Unoptimized
pandas baseline w/ Modin
Time
on
1
CPU
node
11 minutes
2 hours
Optimized
Data Ingest of 1 Billion Rows of “NYC Taxi” data
(1.6TB in-mem on single node)
Visit www.Intel.com/PerformanceIndex for workloads and
configurations. Results may vary​.
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
13
Modin
Explore data at scale
TB-scale data analytics with the ease of pandas APIs
20M Rows
pandas baseline w/ pandas
Time
on
1
CPU
node
74X slower
1B Rows (Unoptimized)
1x
50x larger data
w/ Modin
1B Rows (Optimized)
Time Required to Analyze TB vs GB scale data
(“Query #2” on NYC Taxi data: 1B rows vs 20M rows)
2X
df.groupby("passenger_count").agg({"total_amount": "mean"})
Visit www.Intel.com/PerformanceIndex for workloads and
configurations. Results may vary​.
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
14
Modin
…and scale infinitely to the Cloud
1-line to run on cloud
as infinite extension of
local compute
Automatically spawned cluster
with mirrored Python environment
https://medium.com/intel-analytics-software/data-science-at-scale-with-modin-5319175e6b9a
110101010011010011010101001110011101100111010100100101110101010010
010100010101010001010100101010101010101011010101011010110100001011
101001111101010101011101010101101001000100101010110101011110101000
110101010011010011010101001110011101100111010100101010111010100101
010100010101010001010100101010101010101011010101010101010101010011
101001111101010101011101010101101001000100101010110001010101110100
110101010011010011010101001110011101100111010100100101011010010010
010100010101010001010100101010101010101011010101011010101010101010
101001111101010101011101010101101001000100101010110001111010111010
010100010101010001010100101010101010101010101010101010101111101010
101001111101010101011101010101101001000101001100101111010101011101
110101010001101010101001110101010101010111110101000101011101010111
001010101101010101001101010010010010010111100110110101010101111010
110101011010110101110111010001001110001000101010100101110101011101
110101010011010011010101001110011101100111010100101010101110101010
010100010101010001010100101010101010101011010101010101110101110101
101001111101010101011101010101101001000100101010111010111010100000
110101010001101010101001110101010101010101010101011010100010100111
001010101101010101001101010010010010010110101010100001010101101010
110101011010110101110111010001001110001000101010001010101010101011
110101010011010011010101001110011101100110101111000101010101110100
110101010011010011010101001110011101100111010100000101010101010101
0101000101010100010101
1010011111010101010111
0010101011010101010011
0101001001001001011100
0101101011010111011101
0001001110001011010101
0110100110101010011100
1110110010101000101010
Visit www.Intel.com/PerformanceIndex for workloads and
configurations. Results may vary​.
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
15
SigOpt
Experiment, optimize, and analyze models at scale
Accelerate and amplify the impact of modelers everywhere
https://aws.amazon.com/machine-learning/partner-solutions/hyperparameter-optimization-sigopt/
Visit https://sigopt.com/resources/ and www.Intel.com/PerformanceIndex for
workloads and configurations. Results may vary​.
el Corporation. SigOpt and the SigOpt logo are trademarks of Intel Corporation or its subsidiaries
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
16
SigOpt
Experiment, optimize, and analyze models at scale
Accelerate and amplify the impact of modelers everywhere
© Intel Corporation. SigOpt and the SigOpt logo are trademarks of Intel Corporation or its subsidiaries
Visit https://sigopt.com/resources/ and www.Intel.com/PerformanceIndex for
workloads and configurations. Results may vary​.
Copyright © 2021, Intel Corporation. All rights reserved.
*Other names and brands may be claimed as the property of others.
Optimization Notice
17
Bridging the Gap from Ideation to Results
Ideas Results
Intel SW+HW
PRODUCTIVITY & PERFORMANCE
 More experimentation
 Reproducibleresults
 AI/analytics at scale
https://software.intel.com/ai
Visit www.Intel.com/PerformanceIndex for workloads and
configurations. Results may vary​.
Notices & Disclaimers
Performance varies by use, configuration and other factors. Learn more at
www.Intel.com/PerformanceIndex​​.
Performance results are based on testing as of dates shown in configurations and may not reflect all
publicly available ​updates. See backup for configuration details. No product or component can be
absolutely secure.
Intel does not control or audit third-party data. You should consult other sources to evaluate accuracy.
Your costs and results may vary.
Intel and SigOpt technologies may require enabled hardware, software or service activation.
© Intel Corporation. Intel, SigOpt, the Intel logo, the SigOpt 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.
AWS & Intel Webinar Series - Accelerating AI Research

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AWS & Intel Webinar Series - Accelerating AI Research

  • 1. © 2021, Amazon Web Services, Inc. or its Affiliates. Heidi Pan, Director of Data Analytics Software, Intel Jing Xu, AI Technical Consulting Engineer, Intel AWS & Intel Research Webinar Series Scale your research workloads faster with Intel
  • 2. © 2021, Amazon Web Services, Inc. or its Affiliates. Amazon Confidential | © 2021, Amazon Web Services, Inc. or its affiliates. All rights reserved. What does Intel do with Amazon? Amazon EC2 VMware Cloud on AWS AWS IoT Core AWS Greengrass Amazon SageMaker AWS Deep Learning AMIs AWS DeepLens Amazon S3 AWS Deepracer Amazon Echo Look Amazon Echo Show Cloud & data center Things & devices Alexa Voice Services SAP on AWS AWS Outposts Intel is a very deep partner of AWS and will be for a long time.That’s not changing. Andy Jassy, CEO, AWS Joint priorities: Strategic migrations AI/ML HPC SAP Hybrid Edge
  • 3. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 3 Bridging the Gap from Ideation to Results Ideas Results Intel SW+HW PRODUCTIVITY & PERFORMANCE  More experimentation  Reproducible results  AI/analytics at scale
  • 4. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 4 Example: Project DisCo UC Davis Complexity Sciences Center Problem: How to predict extreme weather patterns? massive data, no ground truth Results: Effectively segmented complex high-dim data Winner of 2019 HPC Innovation Excellence Award Intel Extension for Scikit-learn (fka daal4py) on 1024 Xeon nodes computational limitations process 89.5 TB in 6.6 minutes code lags theoretical development easily iterate w/ high-level Python APIs Idea: local causal state unsupervised physics-based https://www.nextplatform.com/2020/04/15/python-delivers-big-on-complex-unlabeled-data/ Visit www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 5. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 5 Intel’s Optimized Data Science Software O P T I MI Z E D A I / A N A L Y T I C S P A C K A G E S Intel Extension for Scikit-learn • • • Familiar, easy-to-use APIs, up to 10-100X faster optimized to fully utilize modern parallel hardware Visit www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 6. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 6 Example: Identifying Melanoma in Skin Lesion Images Society of Imaging Informatics in Medicine Kaggle competition ADD TWO LINES https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a Unoptimized scikit-learn baseline w/ Intel Extension for sklearn Performance (Cascade Lake Gold 2sx16c) 48x faster 1x Optimized Same numeric behavior as defined by scikit-learn consortium Repeatable run-to-run Visit www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 7. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 7 Example: AWS AutoGluon Acceleration of KNN One of Most Expensive Algos (and timed out with large data) 25x faster via Intel Ext for Sklearn 10x faster via AWS opts One of Least Expensive Algos (and handles large data) https://github.com/awslabs/autogluon/pull/1049 Visit www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 8. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 8 Do More Experimentation before exp alternative de with Intel acceleration
  • 9. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 9 Or Save Cost p3dn.24xlarge RAPIDS cuML 8 NVIDIA V100 GPUs Intel Ext for sklearn 8 Intel CPU nodes 2.64x cheaper (and 2.76x faster) 1x c5.24xlarge Cost As measured by K-means on the largest dataset that fits into GPU nodes (out-of-memory error on GPU otherwise) https://medium.com/intel-analytics-software/accelerate-k-means-clustering-6385088788a1 Visit www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 10. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 10 Scale Up and Down Instances with Same Software Visit www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 11. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 11 Do Even More Experimentation A Day in the Life of a Data Scientist Everything Else Experimentation
  • 12. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 12 Modin Explore data at scale TB-scale data analytics with the ease of pandas APIs Unoptimized pandas baseline w/ Modin Time on 1 CPU node 11 minutes 2 hours Optimized Data Ingest of 1 Billion Rows of “NYC Taxi” data (1.6TB in-mem on single node) Visit www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 13. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 13 Modin Explore data at scale TB-scale data analytics with the ease of pandas APIs 20M Rows pandas baseline w/ pandas Time on 1 CPU node 74X slower 1B Rows (Unoptimized) 1x 50x larger data w/ Modin 1B Rows (Optimized) Time Required to Analyze TB vs GB scale data (“Query #2” on NYC Taxi data: 1B rows vs 20M rows) 2X df.groupby("passenger_count").agg({"total_amount": "mean"}) Visit www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 14. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 14 Modin …and scale infinitely to the Cloud 1-line to run on cloud as infinite extension of local compute Automatically spawned cluster with mirrored Python environment https://medium.com/intel-analytics-software/data-science-at-scale-with-modin-5319175e6b9a 110101010011010011010101001110011101100111010100100101110101010010 010100010101010001010100101010101010101011010101011010110100001011 101001111101010101011101010101101001000100101010110101011110101000 110101010011010011010101001110011101100111010100101010111010100101 010100010101010001010100101010101010101011010101010101010101010011 101001111101010101011101010101101001000100101010110001010101110100 110101010011010011010101001110011101100111010100100101011010010010 010100010101010001010100101010101010101011010101011010101010101010 101001111101010101011101010101101001000100101010110001111010111010 010100010101010001010100101010101010101010101010101010101111101010 101001111101010101011101010101101001000101001100101111010101011101 110101010001101010101001110101010101010111110101000101011101010111 001010101101010101001101010010010010010111100110110101010101111010 110101011010110101110111010001001110001000101010100101110101011101 110101010011010011010101001110011101100111010100101010101110101010 010100010101010001010100101010101010101011010101010101110101110101 101001111101010101011101010101101001000100101010111010111010100000 110101010001101010101001110101010101010101010101011010100010100111 001010101101010101001101010010010010010110101010100001010101101010 110101011010110101110111010001001110001000101010001010101010101011 110101010011010011010101001110011101100110101111000101010101110100 110101010011010011010101001110011101100111010100000101010101010101 0101000101010100010101 1010011111010101010111 0010101011010101010011 0101001001001001011100 0101101011010111011101 0001001110001011010101 0110100110101010011100 1110110010101000101010 Visit www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 15. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 15 SigOpt Experiment, optimize, and analyze models at scale Accelerate and amplify the impact of modelers everywhere https://aws.amazon.com/machine-learning/partner-solutions/hyperparameter-optimization-sigopt/ Visit https://sigopt.com/resources/ and www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​. el Corporation. SigOpt and the SigOpt logo are trademarks of Intel Corporation or its subsidiaries
  • 16. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 16 SigOpt Experiment, optimize, and analyze models at scale Accelerate and amplify the impact of modelers everywhere © Intel Corporation. SigOpt and the SigOpt logo are trademarks of Intel Corporation or its subsidiaries Visit https://sigopt.com/resources/ and www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 17. Copyright © 2021, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization Notice 17 Bridging the Gap from Ideation to Results Ideas Results Intel SW+HW PRODUCTIVITY & PERFORMANCE  More experimentation  Reproducibleresults  AI/analytics at scale https://software.intel.com/ai Visit www.Intel.com/PerformanceIndex for workloads and configurations. Results may vary​.
  • 18. Notices & Disclaimers Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex​​. Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available ​updates. See backup for configuration details. No product or component can be absolutely secure. Intel does not control or audit third-party data. You should consult other sources to evaluate accuracy. Your costs and results may vary. Intel and SigOpt technologies may require enabled hardware, software or service activation. © Intel Corporation. Intel, SigOpt, the Intel logo, the SigOpt 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.

Editor's Notes

  1. More than 14 years of engineer to engineer relationships reflect our shared customer obsession, constantly iterating and listening to what Amazon, their customers and partners want and delivering that in optimized solutions built upon custom silicon SKUs. Amazon Web Services (AWS) and Intel have developed a variety of specialized instances for high performance computing, big data, artificial intelligence and the Internet of Things to meet the needs of organizations today and into the future. Intel® Platinum Xeon processor families are the foundation of the cloud computing services deployed by AWS. Amazon EC2 instances powered by Intel® processors have the largest breadth, global reach and availability of compute instances across AWS geographies (24 regions, 76 availability regions). Whether deploying hybrid cloud with AWS Outposts, SAP HANA with High Memory instances or High Performance Computing with c5n or z1d instances, Intel architecture is at the heart of Amazon EC2 (AWS) instances. Intel processors feature: Our focus has always been on delivering cutting edge technology PLUS value – i.e., delivering the highest performance at the lowest costs. We also have a world class supply chain that enables AWS to scale appropriately with the right products based on customer demand. If you look at the range of AWS instances powered by Intel technology, we’ve worked together to design instances – be they compute- or memory- or storage-optimized ones – to ensure demanding enterprise applications can run in an optimized and scalable manner in the cloud. Goal is one architecture from edge to cloud so that any optimization we make in the cloud can also benefit the edge. Some examples of projects we’ve worked on with Amazon during the last 14 years: Sagemaker, AMI – our engineers worked with AWS to optimize ML frameworks, like MXNet and Tensorflow with Amazon so when you use Amazon MLservices, you know you’re getting the maximum performance possible. This is an iterative process – we have to validate performance with every new software release to comprehend new features and make continual improvements. Deeplens – in the area of computer vision, Intel and AWS collaborated on a developer kit for building ML models in the cloud and then deploying them at the edge. Some amazing examples of how customers are using this include healthcare patient tracking, supply chain management for manufacturing and product improvements. AWS Deepracer -- In the area of AI, Intel and Amazon collaborated to make reinforcement learning accessible to broader developer audiences through the cool AWS DeepRacer* program - a fun and accessible reinforcement learning platform used to make self-driving cars a reality – all with optimized Intel technology inside. On the Alexa side, we worked with Lab126 on devices such as the Echo Show and Echo Look. On the infrastructure side, Intel works with Amazon to tailor instances for a wide range of customer needs. A few examples of specialized instances include: Amazon EC2 High Memory instances purpose built to run large in-memory databases, including production deployments of SAP HANA, in the cloud. (SKL and CLX) Amazon EC2 z1d instances offer both high compute capacity and a high memory footprint. z1d instances deliver a sustained all core frequency of up to 4.0 GHz, the fastest of any cloud instance. (SKX) C5n instances are ideal for HPC workloads, data lakes, and network appliances such as firewalls and routers that can take advantage of improved network throughput (up to 100 Gbps) and packet rate performance. VMware Cloud on AWS runs on the storage optimized i3en (CLX) instance offers up to 60 TB of NVMe SSD instance storage, the lowest price per GB offered by Amazon EC2.