IBM Storage Reference Architecture for AI applied to Autonomous Driving (AD)

Transcription

1 IBM Reference Architecture for AI applied to Autonomous Driving (AD) Frank Kraemer IBM Systems Architect Nvidia GTC /2018

2 Autonomous Driving = See + Think + Act The Automotive Industry has to solve this highly complex problem

3 Automotive Sensor Setup for AD Each data source: Sensors sets: Data collection volume: ~ 2 Gbit/s ~ 30 Gbit/s ~ TB/h 3

4 Automotive Industry generates large amounts of data of data (sensor / video) is very costly. Handling of these data is difficult i.e. due to high required bandwidth. For testing purposes sensor / video data are much more complex in comparison to discrete bus signals, electronic values, etc. Sources: Images from Sensor / video data must be synchronously captured, stored, modified and executed with other testing data such as CAN, FlexRay, Radar, LiDAR, HiSonic, etc. most common formats are: ADTF v2/3 (digitalwerk) RTMaps (Intempora) MDF4 and ROS/rosbag.

5 Data Management for ADAS/AD development and test is challenging Test Drives R&D Labs: tagging o Europe o USA o China o Japan o Asia o Africa TB / day / car > 200h / 1h driving R&D Labs: developing & testing & (re-)simulation & AI training >5 PB of data for each car project PB data in total Training Data as a Service (TDaaS)

6 The IBM AD Solution Approach 1. How to implement & operate an efficient storage, workflow and management system? The Data Foundation 2. How to distribute data globally within an enterprise and partners? 3. How to preserve digital data for decades with optimized costs? 4. How to analyze sensor and video data with fast analytics and modern BigData tools? 5. How to run Machine Learning (ML) and AI training with Nvidia GPU technology at scale? 6. How to do efficient IT workload and resource scheduling? IBM Object (COS) IBM High-Speed WAN File Transfer IBM Aspera / Mass Data Migration / Cloud IBM Cold Archiving IBM Spectrum Protect / Cold / Low Cost / Tape IBM Analytics HDFS Hortonworks HDP, DSX, Spark, IBM Enterprise-Class AI Power9 AC922, PowerAI, AI Vision IBM Spectrum Computing IBM Spectrum Discover (MetaOcean) IBM AREMA

7 The IBM storage architecture based on Spectrum Scale, COS and Tape IBM Spectrum Scale (HOT) File based storage with Object & HDFS support High End I/O performance Information Lifecycle Management (ILM) Sub Micro-seconds access time IBM Cloud Object (S3) (WARM) Site Fault Tolerant Geo Dispersed and WW scale Easy to Deploy Milli-seconds access time Backup DR Tiering Archive Data sharing Compressed Encrypted Integrity Validated Replicated Transparent Cloud Tiering IBM Cloud Private Cloud Azure AWS S3 ICP IBM Spectrum Archive & Tape (COLD) Lowest TCO Tape ILM target especially frozen archive Long term retention and Minutes access time Access as files via LTFS Reduced floor space requirements and energy consumption Up to 260PB native capacity in a single Tape Library Tiering from flash, to disk, to tape, to cloud. Cloud appears as external storage pool. Auto Tiering & migration. High performance Read/Write operations. Public cloud-ready. Support of multi cloud environments.

8 Building-block HOT High Performance I/O File Client workstations Users, Containers and applications New Gen applications Traditional applications HPC & HTC Compute farm DGX / AC922 Management API Advanced GUI RESTful API Compression NFS File POSIX SMB Analytics Transparent HDFS Block OpenStack Cinder Manila iscsi Glance Swift GLOBAL Namespace Object Transparent Cloud S3 Encryption File Audit Logging Immutability AFM-DR DR Site Site B Site A Powered by IBM Spectrum Scale Automated data placement and data migration S3 Data Cloud Site C Spectrum Scale RAID Transparent Cloud Tier (TCT) Cloud Data Sharing Worldwide File Data Distribution (AFM) Flash NVMe Disk Tape Shared Nothing Cluster (FPO) JBOD/JBOF ESS

9 IBM Analytics & Hortonworks (HDP) / Hadoop Automotive Customer Use Case: Major automotive OEM was experiencing significant difficulties and costs associated with storing and processing huge volumes of Video, Radar and Lidar files within legacy Network Attached (NAS) system. Data necessary for development of Autonomous Vehicle machine learning algorithms. Today, storing multiple Petabytes of video and binary data with HDP Data Lake, aiming to grow to the tens of Petabytes. Dramatically reduced data management costs and user productivity. Provided foundation for Autonomous Driving research. IBM Reference customer for Spectrum Scale and HDP.

10 System x3650 M4 System x3650 M4 System x3650 M4 System x3650 M4 System x3650 M4 System x3650 M4 EXP3524 EXP3524 EXP3524 EXP3524 EXP3524 EXP3524 EXP nd Generation IBM Elastic Server (ESS) Family Model GS1S 24 SSD Speed Model GL1S: 1 Enclosures, 9U 82 NL-SAS, 2 SSD Capacity Model GL6S: 6 Enclosures, 28U 502 NL-SAS, 2 SSD 14 GB/s Model GS2S 48 SSD Model GH14S: 1 2U24 Enclosure SSD 4 5U84 Enclosure HDD 334 NL-SAS, 24 SSD Model GH24S: 2 2U24 Enclosure SSD 4 5U84 Enclosure HDD 334 NL-SAS, 48 SSD 6 GB/s Model GL4S: 4 Enclosures, 20U 334 NL-SAS, 2 SSD 26 GB/s Model GL2S: 2 Enclosures, 12U 166 NL-SAS, 2 SSD Model GS4S 96 SSD 40 GB/s 38 GB/s 40 GB/s 12 GB/s 24 GB/s 36 GB/s 10

11 Presentation at ATZ Live 04/2018 in Wiesbaden, Germany Dr. Michael Hafner, Head of Automated Driving and Active Safety at Mercedes-Benz, talks about sensors, safety, and the road map that developers are following. Artifical Intelligence is key to understand Sensor Data Relevant data is needed to finalize the Software Development.

12 Workload and data flow for AI flow is complex IBM Reference Architecture for AI Infrastructure New Data Data Source Data Preparation Model Training Inference Traditional Business Data Parallel Hyper-Parameter Search & Optimization Sensor Data Data from collaboration partners Data from mobile app and social media Heavy IO Pre-Processing Training Dataset Network Models Hyper- Parameters Instrumentation AI Deep Learning Frameworks (Tensorflow, Caffe, ) Monitor & Advise Iterate Deploy in Production using Trained Model Trained Model Legacy Data Testing Dataset Distributed & Elastic Deep Learning (Fabric) Years of Data Hours and weeks of preparation Weeks and months of training Sub Seconds to results

13 Reference IBM Spectrum Scale ESS CORAL 2.5 TB/sec single stream IOR as requested from ORNL 1 TB/sec 1MB sequential read/write as stated in CORAL RFP Single Node 16 GB/sec sequential read/write as requested from ORNL 50K creates/sec per shared directory as stated in CORAL RFP 2.6 Million 32K file creates/sec as requested from ORNL Summit s 250-petabyte storage system is delivered by a cluster of 77x IBM ESS Systems that will deliver 2.5 TBs of data. Summit will have the capacity of 30B files and 30B directories and will be able create files at a rate of over 2.6 million I/O file operations per second.

14 Global Data Distribution via IBM Aspera Automotive company synchronizes petabytes of vehicle field test data & video from on-site locations to worldwide R&D teams at high-speed with IBM Aspera FASP. IBM Aspera for Global Data Distribution

15 IBM can help 1. Significantly increased development efficiency by reducing manual efforts for video tagging, eliminated wasted time for data search and manual data copy/move processes and by automating workflows. 3. Increase the entire flexibility of your organization through the ability to move work-load from one place to another. 2. Significantly increased test through-put, means allowing you to run more test cases in less time, therefore increasing time-tomarket as well as the quality of your camera and ADAS products. 4. to reduce IT costs for local storage hardware by globally centralizing data in a private cloud and object store, from which project- and demand specific video data are downloaded to local test labs. 5. to guarantee long-year data verifiability and recoverability of test data with a comparable cheap tape storage solution for potential warranty cases.

16 Question to win a prize How much data does a single test/dev car generate in an 8 hour shift per day? a) 1-5 TB per day b) TB per day c) 1-5 PB per day 16