Jacob Tomlinson - What is my GPU doing? | JupyterCon 2020
Brief Summary
With the growing number of GPU accelerated Python packages it is more important than ever to understand what your GPU devices are doing. You need to know how they are performing and how well they are being utilized. This talk will demonstrate a couple of open-source Python packages to help you access utilization information programmatically and view the data in realtime dashboards.
Outline
This talk will walk you through collecting and viewing various utilization information from NVIDIA GPU devices with PyNVML and NVDashboard. Understanding what your GPUs are doing and how well they are being utilized can be tricky. Until now this information is generally accessed via the command line but with these libraries you can access this information in your browser, or in your code.
PyNVML is an open-source Python wrapper for the NVIDIA Management Library (NVML), which is a C-based API for monitoring and managing various states of NVIDIA GPU devices. NVML is directly used by the better-known NVIDIA System Management Interface (nvidia-smi).
NVDashboard is an open-source browser-based dashboard written in Python using Bokeh. It displays data collected with PyNVML in various time series and utilization plots. There is also a Jupyter Lab extension which enables you to view these dashboards in your Jupyter session right next to your notebooks. We will run through various examples of using these libraries, from installing NVDashboard and getting it running in your Jupyter session to building your own simple diagnostics tools with PyNVML. It doesn’t matter whether you are new to GPU development in Python or an experienced user, by the end of this session you will leave with a good understanding of getting detailed information about what your hardware is up to.
----
JupyterCon brings together data scientists, business analysts, researchers, educators, developers, core Project contributors, and tool creators for in-depth training, insightful keynotes, networking, and
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Playlist
Uploads from JupyterCon · JupyterCon · 39 of 60
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
▶
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Interview Joshua Patterson NVIDIA
JupyterCon
Dave Stuart - Jupyter as an Enterprise “Do It Yourself” (DIY) Analytic Platform | JupyterCon 2020
JupyterCon
Jeffrey Mew - Supercharge your Data Science workflow | JupyterCon 2020
JupyterCon
Michelle Ufford- Supercharging SQL Users with Jupyter Notebooks | JupyterCon 2020
JupyterCon
Alan Yu - What we learned from introducing Jupyter Notebooks to the SQL community | JupyterCon 2020
JupyterCon
Chris Holdgraf- 2i2c: sustaining open source through hosted Jupyter infrastructure | JupyterCon 2020
JupyterCon
Yiwen Li - Intro to Elyra - an AI centric extension for JupyterLab | JupyterCon 2020
JupyterCon
Luciano Resende - What's new on Elyra - A set of AI centric JupyterLab extensions | JupyterCon 2020
JupyterCon
Alan Chin - Explore and Extend AI Pipeline Runtimes with Elyra and JupyterLab | JupyterCon 2020
JupyterCon
Eduardo Blancas- Streamline your Data Science projects with Ploomber | JupyterCon 2020
JupyterCon
Thorin Tabor - Democratizing the accessibility of computational workflows | JupyterCon 2020
JupyterCon
Simon Willison- Using Datasette with Jupyter to publish your data | JupyterCon 2020
JupyterCon
Brendan O'Brien - Using Qri (“query”) to fetch, query, combine and publish datasets.|JupyterCon 2020
JupyterCon
Georgiana Dolocan - Putting the JupyterHub puzzle pieces together | JupyterCon 2020
JupyterCon
Yuvi Panda- Running nonjupyter applications on JupyterHub with jupyter-server-proxy| JupyterCon 2020
JupyterCon
Richard Wagner- The Streetwise Guide to JupyterHub Security | JupyterCon 2020
JupyterCon
TamNguyen- Handling Custom Jupyter Data Sources | JupyterCon 2020
JupyterCon
Immanuel Bayer- ipyannotator - the infinitely hackable annotation framework | JupyterCon 2020
JupyterCon
Rebecca Kelly- A shared Python, R and Q Jupyter Notebook - A Quant Sandbox Dream |JupyterCon 2020
JupyterCon
Itay Dafna - Leap of faith: Transitioning from Excel to Jupyter-based applications | JupyterCon 2020
JupyterCon
Damián Avila - Using the Jupyterverse to power MADS | JupyterCon 2020
JupyterCon
Chiin Rui Tan- From Zero to Hero | JupyterCon 2020
JupyterCon
Firas Moosvi- Teaching an Active Learning class with Jupyter Book| JupyterCon 2020
JupyterCon
Daniel Mietchen- Jupyter in the Wikimedia ecosystem | JupyterCon 2020
JupyterCon
Qiusheng Wu- How Jupyter and geemap enable interactive mapping and analysis | JupyterCon 2020
JupyterCon
Stephanie Juneau- Jupyterenabled astrophysical analysis for researchers and students|JupyterCon 2020
JupyterCon
Denton Gentry- The Care and Feeding of JupyterHub for Climate Solution Models| JupyterCon 2020
JupyterCon
Tingkai Liu- FlyBrainLab: Interactive Computing in the Connectomic/Synaptomic Era | JupyterCon 2020
JupyterCon
Kunal Bhalla- A Notebook Style Guide| JupyterCon 2020
JupyterCon
Julia Wagemann - How to avoid 'Death by Jupyter Notebooks' | JupyterCon 2020
JupyterCon
David Pugh - Best practices for managing Jupyter-based data science | JupyterCon 2020
JupyterCon
Karla Spuldaro - Debugging notebooks and python scripts in JupyterLab | JupyterCon 2020
JupyterCon
Shreyas Dalia - assert browserTest == True # Frontend Testing JupyterLab | JupyterCon 2020
JupyterCon
Chris Holdgraf - The new Jupyter Book stack | JupyterCon 2020
JupyterCon
Hamel Husain - Fastpages - A new, open source Jupyter notebook blogging system | JupyterCon 2020
JupyterCon
Marc Wouts - Jupytext: Jupyter Notebooks as Markdown Documents | JupyterCon 2020
JupyterCon
Sheeba Samuel- ProvBook |JupyterCon 2020
JupyterCon
Philipp Rudiger - To Jupyter and back again | JupyterCon 2020
JupyterCon
Jacob Tomlinson - What is my GPU doing? | JupyterCon 2020
JupyterCon
Afshin Darian - A visual debugger in Jupyter | JupyterCon 2020
JupyterCon
Eric Charles - Jupyter Real Time Collaboration| JupyterCon 2020
JupyterCon
Devin Robison - Optimizing model performance | JupyterCon 2020
JupyterCon
Junhua zhao - PayPal Notebooks: ML & Data Science experience | JupyterCon 2020
JupyterCon
April Wang - Redesigning Notebooks for Better Collaboration | JupyterCon 2020
JupyterCon
Bryan Weber - Distributing and Collecting Jupyter Notebooks for Manual Grading| JupyterCon 2020
JupyterCon
Georgiana Dolocan - The Littlest JupyterHub distribution | JupyterCon 2020
JupyterCon
Tim Metzler - Electronic Examination using Jupyter Notebook | JupyterCon 2020
JupyterCon
Blaine Mooers - Why develop a snippet library for Jupyter in your subject domain? | JupyterCon 2020
JupyterCon
Ryan Abernathey - Cloud Native Repositories for Big Scientific Data | JupyterCon 2020
JupyterCon
Tanya Rai - Introducing Bento: Jupyter Notebooks @ Facebook | JupyterCon 2020
JupyterCon
Kenton McHenry - From Papers to Notebooks | JupyterCon 2020
JupyterCon
Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
JupyterCon
Ana Ruvalcaba - Community building is a sustainability strategy | JupyterCon 2020
JupyterCon
Martin Renou - Xeus: an ecosystem of Jupyter kernels | JupyterCon 2020
JupyterCon
Michael Wilson - Teaching teenagers to understand Dark Energy | JupyterCon 2020
JupyterCon
Davide De Marchi - Voilà dashboards for policy support | JupyterCon 2020
JupyterCon
Marcos Lopez Caniego - ESASky's JupyterLab widget| JupyterCon 2020
JupyterCon
Praveen Kanamarlapud - Kernel Life Cycle Management | JupyterCon 2020
JupyterCon
Aaron Bray - Pulse Physiology Engine | JupyterCon 2020
JupyterCon
Aaron Watters - Using WebGL2 transform/feedback in Jupyter widgets | JupyterCon 2020
JupyterCon
More on: Tool Use & Function Calling
View skill →
🎓
Tutor Explanation
DeepCamp AI