![@blazingdb@blazingdb
RAPIDS, the end-to-end GPU analytics ecosystem
cuDF
Data Preparation
cuML
Machine Learning
cuGRAPH
Graph Analytics
Model TrainingData Preparation Visualization
A set of open source libraries for GPU
accelerating data preparation and
machine learning.
In GPU Memory
import cudf
from cuml import KNN
import numpy as np
np_float = np.array([
[1,2,3], #Point 1
[1,2,3], #Point 2
[1,2,3], #Point 3
]).astype('float32')
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])
print('n_samples = 3, n_dims = 3')
print(gdf_float)
knn_float = KNN(n_gpus=1)
knn_float.fit(gdf_float)
Distance,Index = knn_float.query(gdf_float,k=3)
# Get 3 nearest neighbors
print(Index)
print(Distance)](https://image.slidesharecdn.com/gtcsj2019-blazingsqlrapidssummary-190325202336/85/BlazingSQL-RAPIDS-AI-at-GTC-San-Jose-2019-6-320.jpg)
BlazingSQL + RAPIDS AI at GTC San Jose 2019
- 1. Data Lake to AI on GPUs
- 2. @blazingdb@blazingdb CPUs can no longer handle the growing data demands of data science workloads Slow Process Suboptimal Infrastructure Hundreds to tens of thousands of CPU servers are needed in data centers. Preparing data and training models can take days or even weeks.
- 3. @blazingdb@blazingdb GPUs are well known for accelerating the training of machine learning and deep learning models. Deep Learning (Neural Networks) Machine Learning Performance improvements increase at scale. 40x Improvement over CPU.
- 4. @blazingdb@blazingdb But data preparation still happens on CPUs, and can’t keep up with GPU accelerated machine learning. • Apache Spark Query ETL ML Train Enterprise GPU users find it challenging to “Feed the Beast”. • Apache Spark + GPU ML Query ETL ML Train
- 5. @blazingdb@blazingdb An end-to-end analytics solution on GPUs is the only way to maximize GPU power. Expertise: · GPU DBMS · GPU Columnar Analytics · Data Lakes Expertise: · CUDA · Machine Learning · Deep Learning Expertise: · Python · Data Science · Machine Learning Query ETL ML Train RAPIDS (All GPU)
- 6. @blazingdb@blazingdb RAPIDS, the end-to-end GPU analytics ecosystem cuDF Data Preparation cuML Machine Learning cuGRAPH Graph Analytics Model TrainingData Preparation Visualization A set of open source libraries for GPU accelerating data preparation and machine learning. In GPU Memory import cudf from cuml import KNN import numpy as np np_float = np.array([ [1,2,3], #Point 1 [1,2,3], #Point 2 [1,2,3], #Point 3 ]).astype('float32') 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]) print('n_samples = 3, n_dims = 3') print(gdf_float) knn_float = KNN(n_gpus=1) knn_float.fit(gdf_float) Distance,Index = knn_float.query(gdf_float,k=3) # Get 3 nearest neighbors print(Index) print(Distance)
- 7. @blazingdb@blazingdb 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.
- 8. @blazingdb@blazingdb cuDF Data Preparation BlazingSQL, The GPU SQL Engine for RAPIDS cuML Machine Learning cuGRAPH Graph Analytics A SQL engine built on RAPIDS. Query enterprise data lakes lightning fast with full interoperability with RAPIDS stack. In GPU Memory from blazingsql import BlazingContext bc = BlazingContext() #Register Filesystem bc.hdfs('data', host='129.13.0.12', port=54310) # Create Table bc.create_table('performance', file_type='parquet', path='hdfs://data/performance/') #Execute Query result_gdf = bc.run_query('SELECT * FROM performance WHERE YEAR(maturity_date)>2005') print(result_gdf)
- 10. @blazingdb@blazingdb BlazingSQL + XGBoost Loan Risk Demo Train a model to assess risk of new mortgage loans based on Fannie Mae loan performance data ETL/ Feature Engineering XGBoost Training Mortgage Data 4.22M Loans 148M Perf. Records CSV Files on HDFS CLUSTER + CLUSTER 1 Nodes 16 vCPUs per node 1 Tesla T4 GPU 2560 CUDA Cores 16GB VRAM + + 4 Nodes 8 vCPUs per node +30GB RAM
- 11. @blazingdb@blazingdb RAPIDS + BlazingSQL outperforms traditional CPU pipelines Demo Timings (ETL Phase) 3.8GB 0’’ 1000’’ 2000’’ 3000’’ (1 x T4) 3.8GB (4 Nodes) 15.6GB (1 x T4) 15.6GB (4 Nodes) TIME IN SECONDS
- 12. @blazingdb@blazingdb Scale up the data on a DGX 4 x V100 GPUs
- 13. @blazingdb@blazingdb BlazingSQL + Graphistry Netflow Analysis Visually analyze the VAST netflow data set inside Graphistry in order to quickly detect anomalous events. ETL VisualizationNetflow Data 65M Events 2 Weeks 1,440 Devices
- 14. @blazingdb@blazingdb Benchmarks Netflow Demo Timings (ETL Only)
- 15. @blazingdb@blazingdb Stateless and Simple. Underlying services being stateless reduces complexity and increase extensibility. Benefits of BlazingSQL Blazing Fast. Massive time savings with our GPU accelerated ETL pipeline. Data Lake to RAPIDS Query data from Data Lakes directly with SQL in to GPU memory, let RAPIDS do the rest. Minimal Code Changes Required. RAPIDS with BlazingSQL mirrors Pandas and SQL interfaces for seamless onboarding.
- 16. @blazingdb@blazingdb Upcoming BlazingSQL Releases Use the PyBlazing connection to execute SQL queries on GDFs that are loaded by the cuDF API Integrate FileSystem API, adding the ability to directly query flat files (Apache Parquet & CSV) inside distributed file systems. SQL queries are fanned out across multiple GPUs and servers. String support and string operation support. Query GDFs Direct Query Flat Files Distributed Scheduler String Support Physical Plan Optimizer Partition culling for where clauses and joins. VO.1 VO.2 VO.3 VO.4 VO.5
- 17. @blazingdb@blazingdb Get Started BlazingSQL is quick to get up and running using either DockerHub or Conda Install: