Never Underestimate the Power of a Single Node - Karthik Ramasamy, Uber

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Recent developments in GPU hardware and storage technology have changed how we do data analysis and machine learning. These technologies on a single node have grown many folds in the last five years while the growth in network speed has lagged behind. I will talk about the overall ML lifecycle and challenges we face in doing ML at scale, from protecting your Uber accounts to making self driving cars a reality. Then I want to focus on an important part of ML lifecycle which is data/ML exploration and experimentation. In large companies like Uber, data scientists are inclined to use shared Hadoop infra for all their needs. For data exploration, this is inefficient for the user and also makes the cluster run slow. I will talk about our new solution to tackle this problem by using a high powered node that lets us to work with 100s of GB to few TBs of data interactively without paying the overhead of a distributed system. I will also talk about some of the interesting machine learning and infrastructure problems that I face in my new role in Uber’s self driving team.

Data & Analytics

Never Underestimate the
Power of a Single Node
Karthik Ramasamy
Uber
@_cartick

About Me
LinkedIn - Hadoop & Security
LogBase - Hadoop, In-Memory DBs, & Machine Learning
Uber Risk - Machine Learning, Deep Learning, & Security
Uber ATG - Self Driving & Deep Learning

Distributed ≠ Fast

Data Science Workflow

Finding Relevant Data
Building a Working Set
Exploratory Data Analysis
Feature Engineering
ML Exploration
ML Production

Storage

Seq. Read 200MB/s 500MB/s 2-3GB/s 2GB/s
Seq. Write 200MB/s 500MB/s 1-2GB/s 1.5GB/s
Rnd. Read 0.3MB/s 400MB/s 700MB/s 1.8GB/s
Price/TB $43 $350 $600 $1200
Approximate numbers

CPU
Chipset
Memory
x16 PCIe
SATA Drivers
x16 PCIe
x8 PCIe
x8 PCIe
x4 PCIe - NVMe #1
x4 PCIe - NVMe #2
x4 PCIe - NVMe #3
x4 PCIe

Compute

Nobody predicted the rise of GPUs, not even
NVIDIA!

Compute over time graph
Intel Xeon 8100 series 1.5 TFlops DP
NVIDIA V100 GPU 7.8 TFlops DP, 15.7 TFlops SP, 125 TFlops MP

GPU trends

8xV100 GPU Server

AMD Radeon SSG

Our Implementation

Threadripper 16
Core OC@4GHz
Chipset
128GB DDR4 OC
Memory
NVIDIA 1080 Ti
x16 PCIe
2TB Backup SATA
Driver
NVIDIA 1080 Ti
x16 PCIe
10GBE NIC
x4 PCIe
NVIDIA 1050 Ti
x8 PCIe
Samsung 2TB
x4 PCIe - NVMe #1
Samsung 2TB
x4 PCIe - NVMe #1
Samsung 512GB
x4 PCIe - NVMe #1
x4 PCIe

EXT4 FS
Docker (NVIDIA runtime)
PySpark XGBoost TensorFlow
Pandas/
PyData
Jupyter Notebooks

Local PySpark
● Easiest to install - pip install pyspark
● Configured in local mode - master=local[*] (local[16])
● Large driver memory (>90GB)
● R/W local files directly (No HDFS)

Upto:
100MB
Workstation
100ms
1GB 1s
10GB 10s
100GB 2mins
1TB 20mins
10TB
Hadoop/Cluster
3hr
100TB 12hr
1PB 24hr+

What works?
● DS workload on couple of TBs of data
● Doesn’t need to update the data often
● ML Focused jobs

What doesn’t work?
● ETL workload
● Scheduled jobs
● DS/DA workload on large data set (>2TB)

Upto:
100MB
Workstation
200ms
1GB 2s
10GB 20s
100GB 1min
1TB 5min
10TB 10min
100TB
Hadoop/Cluster
12hr
1PB 24hr+

Thanks!

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