The Case for Data Centric Hyperconvergence
•Download as PPTX, PDF•
0 likes•62 views
End-to-end deep learning on unstructured data showcasing data centric hypeconvergence.
Recommended
More Related Content
Similar to The Case for Data Centric Hyperconvergence
Similar to The Case for Data Centric Hyperconvergence (13)
Recently uploaded
Recently uploaded (20)
The Case for Data Centric Hyperconvergence
- 1. Data-Centric Hyperconvergence with OpenStack Swift & Storlets Mail: eran@itsonlyme.name IRC: eranrom Twitter: @EranRom
- 8. Takeaways 1. Storlets can be used to do REAL stuff (and quite easily) 2. Storlets enable cost efficient data centric services
- 9. End-to-End Deep Learning on Unstructured Data
- 10. End-to-End Deep Learning on Unstructured Data Training Set ~100 tagged pictures of Trump, Obama, Merkel and Bibi obama merkel
- 11. End-to-End Deep Learning on Unstructured Data Training Set obama merkel Test Set Videos of Trump, Obama, Merkel and Bibi
- 12. Step 1: Data Preparation Training Set Extracted Training Set 1. Identify face location 2. Crop 3. Resize Extract face storlet merkel merkel
- 13. Step 2: Supervised Learning Extracted Training Set model Train model storlet trump obama merkel bibi
- 14. model Step 3: Model Testing Video Recognize face storlet Test set bibi
- 15. End-to-End Deep Learning on Unstructured Data Training Set Extracted Training Set model Video Extract face storlet Train model storlet X10 0 Test set Recognize face storlet
- 16. Demo Setup: S2AIO with Jupyter Notebook Swift and Storlets all in one
- 17. Local Scripts & S3 Vs. S2AIO Swift and Storlets all in oneS3 S3 Client With OpenCV and SKLearn
- 18. Local Scripts & S3 Vs. S2AIO Swift and Storlets all in oneS3 S3 Client With OpenCV and SKLearn Dedicated M4.2XLarge (8 CPUs 32GB RAM)
- 19. S2aio on EC2 Vs. EC2/S3 Dedicated M4X2Large (8 VCPUs, 32GB Ram, High Network Performance) 0 10 20 30 40 50 60 70 Extract Train Recognize Seconds EC2 Swift & Storlets EC2 & S3
- 20. But the point is…
- 21. Sources: Ethernet: http://www.ethernetalliance.org/roadmap/ Infiniband: http://www.infinibandta.org/content/pages.php?pg=technology_overview 1 1.5 2 2.5 3 4 7.5 30 50 1 1.5 5 6 8 1 10 10 20 1.00 1.79 3.57 3.57 0 10 20 30 40 50 60 2010 2011 2012 2013 2014 2015 2016 2017 2018-2020 GrowthFactor Storage Vs. Networking Growth SSD HDD Ethernet Infiniband
- 22. 16 Disks and 4 Network Ports Servers 800.00 128.00 80.00 14.290.00 100.00 200.00 300.00 400.00 500.00 600.00 700.00 800.00 900.00 SSD HDD Ethernet Infiniband Storage Vs. Networking Growth
- 23. Thank You! All Demo Code: https://github.com/eranr/e2emlstorlets My Blog: http://itsonlyme.name/blog
Editor's Notes
- Storlets are about co-locating storage and compute. That is, instead of bringing the data to the compute, bring the compute, which is much smaller, to the data. The Stork is the Storlets project mascot
- More specifically, storlets allow to co-locate Dockerized computations inside Openstack Swift in a serverless fashion
- Swift is a massively scalable storage system that has a simple API to store and retrieve data blobs taking care of data redundancy via e.g. replication across failure domains.
- We use Docker to run the compute near the data in a secured and isolated manner.
- By serverless we mean that an end user can upload to Swift the program to run as done for any other data blob, and we will take care of the rest.
- This is what I refer to as a data centric hyper convergence. Like traditional hyperconvergence the idea is to have a storage compute and networking solution that can horizontally scale. Traditional hyperconvergence though is focused on general purpose virtual environments and many times go hand in hand with high end flash arrays. This is being marketed as A solution for big data analytics over semi-structure data. Here we are focusing on unstructured data, which is the majority of the data. Hyperconvergence and data centric hyper convergence are complimentary technologies where one can think of the data centric part as ‘transforming’ the unstructured data to semi-structured data that can be consumed with traditional big data machinery. As such I think that data centric hyperconvergence should also have a data management component in the mix, e.g. metadata search.
- The graph shows the growth factor of a single SSD/HDD and single networking ports. In Ethernet we see growth from 10Gb in 2010 to a 100 in 2014. Today we start seeing 200Gb Infiniband started at 56Gb in 2011 and like ethernet were in 100 in 2014 and are now at 200 HDD were not growing as fast, with the X8 factor due to Helium filled HDDs In SSDs, however, we see a really big growth, with Seagate announcing 60TB drive last year, and Toshiba 100TB drive to come out this year. Now, consider that in a typical storage server there are much more disks then network ports…
- Considering a 16 disks server with 4 network ports we see a much bigger difference in the growth factor.