SNEAK PEEK

Learn Python GPU coding and data science from top project creators and contributors!

 

GET GOING WITH PYTHON
GPU DATA COMPUTING & DATA SCIENCE

Pick and choose from learning paths carefully planned by RAPIDS GPU project creators and experts

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ANALYSTS

Friendly introductions to what you need to know

Get going with Jupyter notebooks, loading large files, filtering, statistics, visualization, and machine learnings... all with automatic GPU acceleration!

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PYDATA PRO'S

Ramp up and scale quickly with GPU replacements

Setup, learn the packages, go from CPU to single-GPU to multi-GPU, best practices, reference workflows, and interact with the community leaders

 

LEARNING PATHS OPTIMIZED
FOR YOUR INDUSTRY'S USE CASES

Built from years of experience working with top tech companies, enterprises, and federal agencies

 

WE TAILOR TO YOUR ANALYTICS GOALS

  • Upcoming: Security & fraud investigations and analytics

  • Next: Supply chain optimization, sales & marketing analytics, and genomics

  • Contact for future & custom!

RAMP UP, GO LIVE, AND INCREASE IMPACT

  • Specialized tracks and materials

  • Industry-specific datasets

  • Ready-to-go solution skeletons

  • Instructors who delivered similar projects

  • Peers tackling related problems

PICK YOUR PATH

Cover the fundamentals in the best way for you

Webinars
DIY tutorials
Expert-led labs
Community chat
Private trainings

 

BY THE GPU COMMUNITY,
FOR THE GPU COMMUNITY

You may have seen our instructors speak at your favorite events, including:
Amazon Re:Invent, GDC, Nvidia GTC, O'Reilly Strata, Strange Loop, JupyterCon, PyCon, BlackHat, BlueHat, DefCon, BSides, ISSA, GraphConnect, Oakland, WWW, ForwardJS, SplunkConf, and more. We helped start the GPU dataframe computing movement and are core members of the GPU Open Analytics Initiative and RAPIDS.ai. We look forward to meeting you!

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Open-Source SQL
on RAPIDS

Our solutions staff is experienced in delivering in helping enterprise data scientists and data architects scale solutions all the way from sales & marketing to supplychain optimization

Graphistry logo

100X Graph Investigation Visualization & Automation

Graphistry is the first RAPIDS-native visual analytics platform. We work with data teams to power and deploy data-intensive graph projects. Heavy experience with security & fraud analytics, and growing in  fintech,  genomics, and sales & marketing.

Coiled

Scaling Python Simply

Founded by creators of Dask, Coiled helps you run at maximum speed and minimum cost.

... MORE TO BE ANNOUNCED!

(Your Name Here!)

We are bringing together project creators, full-time users, and expert communicators to help spread the knowledge. Newest member announces coming soon!

 

FREE PUBLIC TRAINING

Upcoming community sessions

 
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[PAST] GPU SECURITY ANALYTICS 1:
THE TOUR

July 16th @ 11a PT / 2p ET

GUEST INSTRUCTOR:

  • Leo Meyerovich (CEO @ Graphistry, Inc.) 

LIVE STREAM (40min):

  • Incident Response - Network log mapping: Netflow (ssh, dns, http, ..) to exfil analysis & dynamic visual network map

  • Threat Research - Scam  domains analysis: Using AT&T AlienVault OTX DNS data and graph analytics

  • Hunting - Killchain mapping & clustering: Over ELK winlogs (Project Mordor APT data)

  • Covered GPU tech: Python Jupyter notebooks, BlazingSQL, cuDF (dataframes & regex), cuML/UMAP, cuGraph, Apache Arrow, Graphistry

OPTIONAL LIVE LAB (1hr):

  • Load and explore your first large file  using GPUs: Netflow & Zeek/CoreLight

  • Extra emphasis on log analysis, viz, & graph

  • Tech: Jupyter, cuDF, Graphistry, ...

[PAST] RAPIDS DATA SCIENCE 1:
THE TOUR

July 28th @ 11a PT / 2p ET
(Session links below)

GUEST INSTRUCTOR: 

  • Rodrigo Aramburu (CEO @ BlazingSQL)​

LIVE STREAM (40min):

  • RAPIDS stack: GPU components and fundamentals

  • Data manipulation: Use GPU dataframes and SQL to inspect and transform data

  • Data visualization: Render datasets in different charts both on and off the GPU

  • Machine learning: Analyze dataframes with GPU ML libraries

  • Covered GPU tech: Python Jupyter Notebooks, BlazingSQL, cuDF (DataFrames), cuML, Apache Arrow, Dask, cuXFilter, Datashader, Matplotlib...

OPTIONAL LIVE LAB (1hr):

  • Load and explore large file using GPUs

  • Emphasis on cuDF and SQL

  • Tech: Jupyter, cuDF, BlazingSQL, Dask, cuML, Datashader

[NEXT] SCALING TO WAY MORE DATA THAN 1 GPU CAN HANDLE

August 11th @ 11a PT / 2p ET
(Taking place of deferred Dask session, which is now August 18th)

(Special session as part of Dask postponement)

GUEST INSTRUCTOR: 

  • Felipe Aramburu (CTO @ BlazingSQL)​

LIVE STREAM + HANDS-ON (1hr):

  • RAPIDS stack: Python GPU components

  • BlazingSQL: Load & analyze more data than 1 GPU can handle with automatic out-of-core memory handling

  • Dask-cuDF: Run on multiple GPUs

  • Mixed format: Split between overview, hands-on, and discussion

[NEXT] EASY PYTHON MULTI-GPU PROGRAMMING WITH DASK-CUDF

August 18th @ 11a PT / 2p ET
(Previously was August 11th)

GUEST INSTRUCTOR:

Matthew Rocklin (Dask creator, Coiled founder, ex-Nvidia)

LIVE STREAM (40min):

  • dask-cudf: A Python multi-GPU library for running RAPIDS GPU code over multiple dask workers

  • Dask: Python multiprocessing 

  • RAPIDS: Python GPU ecosystem, 

  • cuDF: Python GPU dataframes in RAPIDS

OPTIONAL LIVE LAB (1hr):
Hands-on to load in a large dataset and easily compute over it using dask-cudf across multiple GPU nodes. Experience for yourself how the RAPIDS ecosystem recently won the TPCx-BB big data benchmark!

SCHEDULE PRIVATE TRAINING

 

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