The document discusses the integration of Globus services with JupyterHub to enhance collaborative data science, enabling authentication, data access, movement, sharing, and search within Jupyter notebooks. It emphasizes the need for scalable, reproducible environments for data analysis through containers and incorporating parallel computing using the Parsl library. Additionally, it highlights features like remote data access, secure token management, and APIs for seamless interactions with distributed storage systems.

1. Ian Foster
foster@uchicago.edu
JupyterCon, New York, August 23, 2018
Scaling collaborative
data science
with Globus and Jupyter

2. Andre Schleife
UIUC
Modeling stopping power
with time-dependent
density functional theory
Hydrogen in Gold, v=2.0
16,000 CPU-hours per simulation
SampleExperimental
sca ering
Material
composi on
Simulated
structure
Simulated
sca ering
La 60%
Sr 40%
Evolu onary op miza on
786,432 CPUs, 10 PFLOPS (1016 flops) supercomputer
Argonne Leadership Computing Facility

3. @python_app
Logan Ward

4. But data are big and distributed, and
our science is collaborative
(1) Query
(2) Transfer
(3) Learn
materialsdatafacility.org
petrel.alcf.anl.gov
Cooley: 290 TeraFLOPS
(4) Share
2 PB, 80 Gbps Globus-enabled store
3.2M materials data
We need multi-credential,
multi-service authentication
and big data management

5. operated by UChicago for researchers worldwide
Auto-
mate
globus.org
Globus services

6. • Multi-user Hub
• Configurable HTTP proxy
• Multiple single-user Jupyter
notebook servers
Recall: JupyterHub components
Hub
Configurable HTTP proxy
Authenticator
User database
Spawner
Notebook
/api/auth
Browser
/hub/
/user/[name]/

7. • Multi-user Hub
• Configurable HTTP proxy
• Multiple single-user Jupyter
notebook servers
Recall: JupyterHub components
We want to grant notebooks
access to the world
• Tokens for remote services
• APIs for remote actions: e.g.,
Globus data management Hub
Configurable HTTP proxy
Authenticator
User database
Spawner
Notebook
/api/auth
Browser
/hub/
/user/[name]/
Cooley Globus
Petrel

8. Securing JupyterHub with Globus Auth
We provide a simple
Globus OAuth plugin
• 100s of identity providers
(can restrict which ones)
• 1000 registered clients, apps
• Custom scopes
• Tokens passed into notebook
environment
JupyterHub OAuthenticator

9. Use within JupyterHub is easy
https://github.com/jupyterhub/oauthenticator#globus-setup

10. Tokens are easily used within notebooks
Login
REST APIs
{“tokens”:…
{“tokens”:…
REST APIs
REST APIs
Bearer a45cd…
Globus Transfer
Globus Search
Globus Publish
Your App
Another App
Hub
Configurable HTTP proxy
Authenticator
User database Notebook
/hub/
/user/[name]/
Spawner
/api/auth
Browser

11. In particular, you can access Globus services
Globus Transfer
• Uniform access to
distributed storage (Posix,
S3, Ceph, HPSS, Google
Drive, Hadoop, Lustre,, …)
• HTTPS; GridFTP for high-
speed, reliable, third-party
transfers
• Shared endpoints: User-
managed access control
• Web, REST, CLI access
• HIPAA compliant 12,000 active Globus Connect endpoints
(including most universities and labs)

12. In particular, you can access Globus services
Globus Search
• Cloud-hosted, schema agnostic
• Scale to billions of objects
Globus Identifiers
• Digital object ids for your data
• DataCite or other metadata
Globus Publication platform
• Customized publication pipelines Canadian Federated Research
Data Repository: https://frdr.ca/

13. Demonstration
What we’re going to do:
• Login into our JupyterCon JupyterHub*
• Launch (spawn) a Notebook Server
• Get tokens
• Access some Globus APIs
• Download some data
• Plot it
• PUT the result on an HTTPS endpoint
*Zero to JupyterHub: Fast JupyterHub on Kubernetes
https://zero-to-jupyterhub.readthedocs.io

14. https://jupyter.demo.globus.org/
Login to Start Tutorial

15. The story so far …
• Globus APIs enable authentication, data access, data
movement, data sharing, data search
• Can be used in notebooks and in JupyterHub/Lab to
access any data anywhere and to secure any resource

16. But wait, there’s more!
• Globus APIs enable authentication, data access, data
movement, data sharing, data search
• Can be used in notebooks and in JupyterHub/Lab to
access any data anywhere and to secure any resource
• Create a containerized data science ecosystem that
encompasses laptops, servers, clouds, HPC

17. Container
Registry
I AM
cont ai ner met adat a
cont ai ner r eci pes
ALCF Petrel
cont ai ner s
Supercomputer
compute
compute
compute
compute
JupyterHub
Notebook Server
Containers are staged
to local file systems
Users select the container
to execute their custom
Jupyter environment
The same containers can be used for
both Jupyter notebook server and
compute nodes, for consistency
Unified IAM platform
scalable for
distributed projects
Container definitions
are tracked in version
control systems
A registry for container
discovery and referencing
Containers can be used for
other tasks: analysis; ML; etc.
Containers
everywhere

18. And more …
• Globus APIs enable authentication, data access, data
movement, data sharing, data search
• Can be used in notebooks and in JupyterHub/Lab to
access any data anywhere and to secure any resource
• Create a containerized data science ecosystem that
encompasses laptops, servers, clouds, HPC
• Incorporate seamless parallel computing via Parsl

19. Python parallel library
• Tasks exposed as
functions (Python or bash)
• Python code to glue functions together
• Globus for auth and data movement
(Data) science applications require:
• Interactivity
• Scalability
- Need more than a desktop
• Reproducibility
- Publish code and documentation
Our solution: JupyterHub + Parsl
 Interactive computing environment
 Notebooks for publication
 Can run on dedicated hardware
parsl-project.org
Interactive, scalable, reproducible data analysis
@python_app
def compute_features(chunk):
for f in featurizers:
chunk = f.featurize_dataframe(chunk, 'atoms')
return chunk
chunks = [compute_features(chunk)
for chunk in np.array_split(data, chunks)]

20. @python_app

21. And more …
• Globus APIs enable authentication, data access, data
movement, data sharing, data search
• Can be used in notebooks and in JupyterHub/Lab to
access any data anywhere and to secure any resource
• Create a containerized data science ecosystem that
encompasses laptops, servers, clouds, HPC
• Incorporate seamless parallel computing via Parsl
• Jupyter notebooks for rules-based automation
– Notebooks are triggered by events (e.g., new data available)
– Notebooks trigger events (e.g., computation completed)

22. globus.org

23. And more …
• Globus APIs enable authentication, data access, data
movement, data sharing, data search
• Can be used in notebooks and in JupyterHub/Lab to
access any data anywhere and to secure any resource
• Create a containerized data science ecosystem that
encompasses laptops, servers, clouds, HPC
• Incorporate seamless parallel computing via Parsl
• Jupyter notebooks for rules-based automation
• Integration with JupyterLab (student summer project)

24. Juan David Garrido

25. Browse data on
local storage

26. Search remote
storage systems

27. Select files on
remote storage

28. Transfer data to
local storage

29. There it is!

31. Search remote
databases

32. Select materials
data

33. Inspect
materials data

34. Ben Blaiszik Steve TueckeKyle Chard Jim Pruyne Logan WardRachana
Ananthakrishnan
Ryan Chard Mike Papka Rick Wagner
I reported on the work of many talented people
And others from the Globus team, the University of Chicago, and Argonne Nat Lab
We are grateful to our sponsors
DLHub Globus
IMaD
Petrel
Argonne Leadership
Computing Facility
At JupyterCon 2018

35. For more information
“Globus APIs enable authentication, data access, data
movement, data sharing, data search”
 See https://docs.globus.org
“Can be used in notebooks and in JupyterHub/Lab to
access any data anywhere and to secure any resource”
 Tutorial: https://jupyter.demo.globus.org
Blog: https://www.globus.org/blog/using-globus-jupyter-notebooks
“But wait, there’s more!”
 Talk to me, or check back in a few months
foster@uchicago.edu