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1 .Data Lake to AI on GPUs
2 . CPUs can no longer handle the growing data demands
of data science workloads
Slow Process Suboptimal Infrastructure
Preparing data and training models Hundreds to tens of thousands of CPU
can take days or even weeks. servers are needed in data centers.
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3 . GPUs are well known for accelerating the training of
machine learning and deep learning models.
Performance
Machine
improvements
Learning
increase at scale.
Deep Learning 40x Improvement
over CPU.
(Neural Networks)
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4 . But data preparation still happens on CPUs, and can’t
keep up with GPU accelerated machine learning.
• Apache Spark
Query ETL ML Train
• Apache Spark + GPU ML
ML
Query ETL Train
Enterprise GPU users find it challenging to “Feed the Beast”.
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5 . An end-to-end analytics solution on GPUs is the only
way to maximize GPU power.
RAPIDS (All GPU)
ML
Query ETL
Train
Expertise: Expertise: Expertise:
· GPU DBMS · CUDA · Python
· GPU Columnar Analytics · Machine Learning · Data Science
· Data Lakes · Deep Learning · Machine Learning
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6 . RAPIDS, the end-to-end GPU analytics ecosystem
import cudf
A set of open source libraries for GPU from cuml import KNN
accelerating data preparation and
import numpy as np
machine learning. np_float = np.array([
[1,2,3], #Point 1
[1,2,3], #Point 2
[1,2,3], #Point 3
]).astype('float32')
Data Preparation Model Training Visualization gdf_float = cudf.DataFrame()
gdf_float['dim_0'] = np.ascontiguousarray(np_float[:,0])
gdf_float['dim_1'] = np.ascontiguousarray(np_float[:,1])
gdf_float['dim_2'] = np.ascontiguousarray(np_float[:,2])
cuDF cuML cuGRAPH
print('n_samples = 3, n_dims = 3')
print(gdf_float)
Data Preparation Machine Learning Graph Analytics
knn_float = KNN(n_gpus=1)
knn_float.fit(gdf_float)
Distance,Index = knn_float.query(gdf_float,k=3)
# Get 3 nearest neighbors
In GPU Memory print(Index)
print(Distance)
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7 . BlazingSQL: The GPU SQL Engine on RAPIDS
A SQL engine built on RAPIDS.
Query enterprise data lakes lightning fast with full interoperability with the RAPIDS stack.
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8 . BlazingSQL, The GPU SQL Engine for RAPIDS
from blazingsql import BlazingContext
A SQL engine built on RAPIDS. Query
enterprise data lakes lightning fast with bc = BlazingContext()
full interoperability with RAPIDS stack.
#Register Filesystem
bc.hdfs('data', host='129.13.0.12',
port=54310)
cuDF
Data Preparation # Create Table
bc.create_table('performance',
file_type='parquet',
path='hdfs://data/performance/')
cuML
Machine Learning In GPU Memory #Execute Query
result_gdf = bc.run_query('SELECT * FROM
performance WHERE
YEAR(maturity_date)>2005')
cuGRAPH
Graph Analytics
print(result_gdf)
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9 . Getting Started Demo
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10 . BlazingSQL + XGBoost Loan Risk Demo
Train a model to assess risk of new mortgage loans based
on Fannie Mae loan performance data
Mortgage Data
4.22M Loans ETL/
148M Perf. Records Feature Engineering XGBoost Training
CSV Files on HDFS
+ +
4 Nodes 1 Nodes
+ +
8 vCPUs per node 16 vCPUs per node
CLUSTER CLUSTER
30GB RAM 1 Tesla T4 GPU
2560 16GB
CUDA Cores VRAM
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11 . RAPIDS + BlazingSQL outperforms traditional
CPU pipelines
Demo Timings (ETL Phase)
3.8GB
(1 x T4)
3.8GB
(4 Nodes)
15.6GB
(1 x T4)
15.6GB
(4 Nodes)
TIME IN SECONDS
0’’ 1000’’ 2000’’ 3000’’
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12 . Scale up the data on a DGX
4 x V100 GPUs
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13 . BlazingSQL + Graphistry Netflow Analysis
Visually analyze the VAST netflow data set inside Graphistry in order
to quickly detect anomalous events.
Netflow Data ETL Visualization
65M Events
1,440 Devices
2 Weeks
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14 . Benchmarks
Netflow Demo Timings (ETL Only)
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15 . Benefits of BlazingSQL
Data Lake to RAPIDS Blazing Fast.
Query data from Data Lakes Massive time savings with our
directly with SQL in to GPU GPU accelerated ETL pipeline.
memory, let RAPIDS do the rest.
Minimal Code Changes Required. Stateless and Simple.
RAPIDS with BlazingSQL mirrors Underlying services being
Pandas and SQL interfaces for stateless reduces complexity
seamless onboarding. and increase extensibility.
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16 . Upcoming BlazingSQL Releases
VO.1 VO.2 VO.3 VO.4 VO.5
Query Direct Query String Distributed Physical Plan
GDFs Flat Files Support Scheduler Optimizer
Use the PyBlazing Integrate FileSystem API, String support and string SQL queries are fanned Partition culling for where
connection to execute SQL adding the ability to operation support. out across multiple GPUs clauses and joins.
queries on GDFs that are directly query flat files and servers.
loaded by the cuDF API (Apache Parquet & CSV)
inside distributed file
systems.
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17 . Get Started
BlazingSQL is quick to get up and running using either
DockerHub or Conda Install:
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