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TRANSFORM 2020 has ended
Welcome to the first global, virtual TRANSFORM! The Software Underground is excited to bring you lots of interesting and useful sessions on the digital subsurface. The schedule is still taking shape, but this event is a bit different from most conferences: the sessions are all fully participatory and interactive. Get ready for a conversation!

If you are viewing the schedule on the web, you can select the timezone you wish to see in the menu on the right. In the mobile app, times are shown in UTC.

Sign up or log in to bookmark your favorites and sync them to your phone or calendar.

Saturday, June 6
 

07:00 UTC

Hackathon Live: Welcome!
Instructors

Saturday June 6, 2020 07:00 - 07:30 UTC
Zoom

07:00 UTC

Hackathon Day 1
TIMES ARE UTC



Instructors

Saturday June 6, 2020 07:00 - Sunday June 7, 2020 00:00 UTC
Zoom

12:00 UTC

On-Demand Seismic Processing with AWS
On-Demand Seismic Processing with AWS

The focus is on using AWS’ serverless tools to perform on-demand seismic processing, keeping cost optimization in mind. In the notebook, I walk through streaming seismic SEGY data from S3 storage, reading the headers with custom Python code, and then provisioning Lambda functions to process the data in parallel. I do a simple “mean trace amplitude” calculation, but there is no reason the code cannot point to a more complex Lambda algorithm or an AWS machine learning service.

Here is the GitHub repository with all the required files, minus SEGY files: https://github.com/vavourak/serverless_seismic_processing

Instructors
avatar for Constantine Vavourakis

Constantine Vavourakis

Solutions Architect, AWS

Co-hosts

Saturday June 6, 2020 12:00 - 13:00 UTC
Zoom

15:00 UTC

Hackathon Live: catch up!
Instructors
Co-hosts

Saturday June 6, 2020 15:00 - 15:30 UTC
Zoom

23:00 UTC

Hackathon Live: catch up!
Instructors
Co-hosts
CS

Chance Sanger

Engineer and Software Developer
avatar for Will Sanger

Will Sanger

Geophysicist, Schlumberger
I love learning about viz, ML... and the amazing things people are doing with open code in the geosciences. Day job is data processing geophysicist.


Saturday June 6, 2020 23:00 - 23:30 UTC
Zoom
 
Sunday, June 7
 

07:00 UTC

Hackathon Live: catch up!
Instructors
Co-hosts

Sunday June 7, 2020 07:00 - 07:30 UTC
Zoom

07:00 UTC

Hackathon Day 2
TIMES ARE UTC

Instructors

Sunday June 7, 2020 07:00 - Monday June 8, 2020 00:00 UTC
Zoom

15:00 UTC

Hackathon Live: catch up!
Instructors
Co-hosts

Sunday June 7, 2020 15:00 - 15:30 UTC
Zoom

23:00 UTC

Hackathon Live: catch up!
Instructors
Co-hosts
CS

Chance Sanger

Engineer and Software Developer
avatar for Will Sanger

Will Sanger

Geophysicist, Schlumberger
I love learning about viz, ML... and the amazing things people are doing with open code in the geosciences. Day job is data processing geophysicist.


Sunday June 7, 2020 23:00 - 23:30 UTC
Zoom
 
Monday, June 8
 

08:00 UTC

Tutorial: Getting started with Python
This session is open to all but is aimed at people who have never coded at all or who don't know Python. Ideally you should have watched the setup videos for Windows or Linux and/or followed the instructions in the Installation guide for the tutorials that contained the information in those videos and more. This session is meant as a demo but if you followed those installation steps, you should be able to follow along.


Content
  • Simple math, logic and flow control
  • Modelling Darcy's law with a function
  • Computing fold wavelengths with the Ramberg-Biot equation
  • Geophysical wavelets with bruges
  • Mapping DEM's with matplotlib
  • Data wrangling and QC well data with welly
  • Scanning a seismic cube with segyio

Instructors
avatar for Robert Leckenby

Robert Leckenby

Digital Geoscientist, Agile


Monday June 8, 2020 08:00 - 11:00 UTC
YouTube Live

13:00 UTC

Unsession: What is good career advice in 2020?
An Unsession is a new way to collaborate scientifically at conferences (e.g. see this report from AAPG ACE 2018). Help us figure out how to make it work remotely!

We will facilitate an informal, inclusive — and fun! — parallel discussion on the topic in question. Please come ready to share your opinions and experiences with a handful of fellow subsurface scientists and engineers.

Join the session >> https://swu.ng/t20-mon-unsession

Please note the participation will be limited to 200 people.

Monday June 8, 2020 13:00 - 16:00 UTC
Zoom

18:00 UTC

Tutorial: Open Source Spatial Data Analytics in Python with GeostatsPy
Tutorial: Open Source Spatial Data Analytics in Python with GeostatsPy
 
'Build from zero, learn some spatial data analytics / geostatistics tools in Python.  All welcome!'

Unsure if You Want to Attend?

I model uncertainty as part of my job, but I actually don't like uncertainty in my life. Let me remove all uncertainty from this choice!

I have posted all of the Tutorial on GitHub @ https://github.com/GeostatsGuy/GeostatsPy_Intro_Course:

1. Lectures as PDFs with a fast treatment of fundamental spatial data analytics concepts
2. Interactive workflows in Python Jupyter Notebooks with GeostatPy, ipywidget and matplotlib packages
3. Well-documented workflows in Python Jupyter with GeostatsPy, demonstrated complete workflows to help get you started
4. Realistic, synthetic subsurface/spatial datasets for the demonstrations

Check it out, try it out, then decide if this is a good investment of your valuable time. Hope to see you there!

About the Lecturer

  Howdy Folks, I'm Michael Pyrcz, an associate professor at The University of Texas at Austin, appointed in the Cockrell School of Engineering and the Jackson School of Geosciences. I teach and conduct research on spatial data analytics, geostatistics and machine learning and I record all my lectures on my YouTube channel at https://www.youtube.com/GeostatsGuyLectures and post my well-documented demonstrations/exercises at: https://github.com/GeostatsGuy. I work in C++, Python, Fortran, R etc.

GeostatsPy Python Package Background

 Geostatistics and spatial data analytics are essential tools for data-drive spatiotemporal modeling workflows that are commonly applied to support decision making for subsurface resource development (ground water, minerals and hydrocarbons).  As an applied branch of statistics they add:
  1. spatiotmeporal context,
  2. geoscience information integration, 
  3. accounting for spatial data and model scales
  4. uncertainty models
When I started teaching my graduate level 'Stochastic Subsurface Modeling' course, I failed to find the Python solution to support the students' experiential learning and completion of the term-long subsurface modeling project. So I spent the weekends writing the GeostatsPy package (just days ahead of the students).  I'm excited to see wide use and contributions to open source package.

Tutorial Description

Goals
  1. Introduce the Package: Expand familarity with the functionality of the GeostatsPy package
  2. Add it to Your Toolbox: Provide experience with a variety of spatial data analytics workflows
  3. You're Welcome: Invite community contributions to the GeostatsPy open source project
How Will We Accomplish All of That?
  1. lectures: a limited amount time for overview lectures to provide basic theory
  2. demonstrations: walk throughs of well-documented workflows
  3. exercises: hands-on challenges for experiential learning
Topics Covered

  We will cover common topics/workflows for spatial data analytics, including:
  1. Variogram Calculation
  2. Variogram Modeling
  3. Spatial Estimation
 Prerequisites

Please have the following installed locally:
  1. Anaconda / Jupyter Notebooks
  2. GeostatsPy (available at PyPi, use 'pip install GeostatsPy' to install)
More Information
  • Times are in UTC and the location is virtual.
  • You can reach me at mpyrcz@austin.utexas.edu.
I'm happy to discuss,

Michael

MICHAEL J. PYRCZ, Ph.D., P.Eng.,  Associate Professor
H.B. Harkings, Jr. Professor of Petroleum Engineering
Hildebrand Department of Petroleum and Geosystems Engineering
Jackson School of Geosciences and Bureau of Economic Geology
The University of Texas at Austin  |  512-471-3252  |  www.pge.utexas.edu
Twitter (@GeostatsGuy) | YouTube | LinkedIn | Webpage | Book | GoogleScholar




Instructors
avatar for Michael (GeostatsGuy) Pyrcz

Michael (GeostatsGuy) Pyrcz

Associate Professor, The University of Texas at Austin
I moved to academia after 13 years modelling the subsurface in industry. Now I teach and conduct research on spatial data analytics, geostatistics and machine learning. All my lectures are on YouTube @ https://www.youtube.com/GeostatsGuyLectures and all my supporting well-documented... Read More →


Monday June 8, 2020 18:00 - 21:00 UTC
YouTube Live

18:00 UTC

Tutorial: Using Python Subsurface Tools (No Install Required)
Hello!

This will be an introductory session on some subsurface tools that can be used in Google Colab (https://colab.research.google.com/notebooks/basic_features_overview.ipynb).

If you want to follow along, here is the github!
-> https://github.com/ThomasMGeo/Transform2020 <-

You will be able to run everything in your own python install as well if you prefer it that way. Even if you are a hardcore python programmer, might be a good chance to check out how Colab operates. It's the next phase of sharing notebooks with colleagues!

Tools include:

- lasio
- striplog
- pandas
- matplotlib

Good chance of some bonus content as well! Will probably change 10 minutes before the presentation, and probably during. If you are a subsurface python pro, consider dropping in and helping out! This is for everyone, minimal to zero python experience required.

Instructors
avatar for Thomas Martin

Thomas Martin

Graduate Student, Colorado School of Mines
Python, deepwater stratigraphy, ML, using Google Colab as a teaching tool, beer, etc!


Monday June 8, 2020 18:00 - 21:00 UTC
YouTube Live
 
Tuesday, June 9
 

08:00 UTC

Tutorial: Geological modeling with GemPy
The objective of this course is to teach geoscientists, engineers and researchers all the basics about 3D structural geological modeling using our open-source Python library GemPy.

They will learn about the most important theoretical principles behind its implicit modeling engine, its advantages, and how to use GemPy most efficiently.

We will start by building a relatively simple geological model from scratch and introduce functionalities as we add features step by step. Subsequently, participants will learn more about adequate data management and how to import data and models into GemPy, including from other modeling software. Towards the end, we will give an outlook into advanced approaches such as the quantification of geological uncertainty.

Instructors
avatar for Miguel de la Varga

Miguel de la Varga

CEO/ Researcher, Terranigma Solutions/RWTH Aachen


Tuesday June 9, 2020 08:00 - 11:00 UTC
YouTube Live

13:00 UTC

Unsession: What open tools are needed now?
TIMES ARE UTC

An Unsession is a new way to collaborate scientifically at conferences (e.g. see this report from AAPG ACE 2018). Help us figure out how to make it work remotely!

We will facilitate an informal, inclusive — and fun! — parallel discussion on the topic in question. Please come ready to share your opinions and experiences with a handful of fellow subsurface scientists and engineers.

Please note the participation will be limited to 200 people.

Tuesday June 9, 2020 13:00 - 16:00 UTC
Zoom

18:00 UTC

Tutorial: Digital subsurface data loaders
We will look at segyio (for SEG-Y data) and DLISIO (for DLIS log data).

Instructors

Tuesday June 9, 2020 18:00 - 21:00 UTC
YouTube Live

18:00 UTC

Tutorial: Geophysical inversion in SimPEG
 In this tutorial, we will provide a hand-on overview of using SimPEG to simulate and invert geophysical data. The examples we plan to work through use Direct Current (DC) Resistivity and Induced Polarization (IP) data from the Century Zinc Deposit in Australia.

Starting from field data in a text file we will learn how to
  • load those data into SimPEG 
  • construct a survey object that contains the geometry of the sources and receivers
  • set up and run a forward simulation 
  • define the inverse problem consisting of a data misfit and regularization
  • run an inversion and discuss inversion strategies


Here are the links to the materials for this session



And some additional resources for getting involved in the SimPEG community

Instructors
avatar for Lindsey Heagy

Lindsey Heagy

UC Berkeley
Postdoc in the UC Berkeley Department of Statistics and soon to be Assistant Prof in the Dept. of Earth, Ocean and Atmospheric Sciences at UBC. Interested in geophysical simulations, inversions and data science for characterizing the subsurface. Contributor to open-source software... Read More →


Tuesday June 9, 2020 18:00 - 21:00 UTC
YouTube Live
 
Thursday, June 11
 

08:00 UTC

Tutorial: From scattered data to gridded products using Verde
This tutorial will be a hands-on tour of Verde, a Python package for processing and gridding geophysical/geospatial data with a twist of machine learning.
BEFORE THE TUTORIAL: Please go to github.com/fatiando/transform2020 and follow the setup instructions. There is also information on where to ask questions and what is going to happen in the tutorial.

We'll start with a real dataset and work our way towards producing one or more gridded products. The way there will take us through:

  • Loading some data
  • Generating and handling coordinates and projections (using pyproj)
  • Splitting training and testing data for validation
  • Data decimation with blocked means/medians to avoid aliasing
  • 2D trend estimation
  • Gridding with bi-harmonic splines
  • Combining everything into a data processing pipeline
  • Cross-validation of data distrubuted spatially on the Earth (including parallel execution)

UPDATE: Given the size of the tutorial, this will mainly be some live coding to showcase what Verde can do with participants following along. There will be some short exercises that can be done individually after the tutorial is finished. We will finish a bit earlier than the scheduled time to leave room for more questions/chat at the end.  

Instructors
avatar for Leonardo Uieda

Leonardo Uieda

Lecturer, University of Liverpool
Geophysicist specializing in the development of methods for determining the inner structure of the Earth from geophysical observations, mainly disturbances in the Earth's gravity and magnetic fields. Developer of open-source software for processing, modeling, and visualizing geophysical... Read More →


Thursday June 11, 2020 08:00 - 11:00 UTC
YouTube Live

08:00 UTC

Tutorial: Geophysical modeling with Devito
In support of #ShutDownSTEM we are rescheduling all TRANSFORM events on Wednesday 10 June. https://softwareunderground.org/blog/2020/6/7/shutdownstem-at-transform

This event will be moved to another day, probably this week. Please watch this space.


----

Devito is a domain specific language (DSL) and compiler for finite difference schemes. This workshop will provide attendees with an introduction to the core elements of the Devito DSL. A breakdown of the agenda is as follows:
  • Introduction to the DSL - Expressing PDEs in Devito
  • Introduction to Devito operators
  • Building wave-propagators
  • Expressing boundary conditions


Github workshop page: https://github.com/devitocodes/transform2020 

Instructors
avatar for Rhodri Nelson

Rhodri Nelson

Imperial College London


Thursday June 11, 2020 08:00 - 11:00 UTC
YouTube Live

13:00 UTC

Lightning talks ⚡
TIMES ARE UTC — Note this is a 2-hour session.

Lightning talks are 5 minutes long and start every 10 minutes.

Slots start at 1300 UTC and go every 10 minutes. There are 12 slots per day. You can claim a slot by following this link. First come, first served.

Claim a slot now.

Co-hosts

Thursday June 11, 2020 13:00 - 15:00 UTC
YouTube Live

18:00 UTC

Tutorial: Geologic image processing with Python
We'll cover the image processing operations you need to know to solve common geologic and geophysical problems across scales using Python. Images are more than just photographs, and we'll focus on methods that can be applied to any regularly sampled dataset, be it a thin section, an elevation map, a seismic volume, or a satellite image.  If you've ever wondered how to chain Python libraries together to accomplish tasks you do in ImageJ, ArcGIS Spatial Analyist, or Photoshop, this tutorial is right for you.

We'll focus primarly on using basic numpy operations, scipy.ndimage, and scikit-image, but we'll do some work with more specalized libraries as well.

Materials are in the git repository here: https://github.com/joferkington/geo_image_processing_tutorial   The material is still under active development at the moment, but feel free to browse around.  If you're not familiar with how to work with this repository, you can run the tutorial in binder.

While the details may change, here's the current outline:
  • Overview / Introduction
  • Seamount Detection Example
    • Thresholding
    • Filtering
    • Segmentation
    • Simplification
  • Slope and Hillshade of Topographic Data
    • Gradients
  • Grain Detection in Thin Sections
    • Using gradients for more than slope
  • Lineament Analysis from Aerial Photography
    • Edge detection
    • Hough Transform
    • Structure Tensor
  • What is color?
    • Satellite data examples

Instructors
avatar for Joe Kington

Joe Kington

I\\'m a structural geologist by training currently working as a software developer. I\\'m certainly not an expert in any domain, but a common theme across my career has been applying image processing techniques to scientific datasets. I\\'ve been working with scientific python for... Read More →


Thursday June 11, 2020 18:00 - 21:00 UTC
YouTube Live

18:00 UTC

Tutorial: Intro to interactive visualization in Jupyter for well data
In support of #ShutDownSTEM we are rescheduling all TRANSFORM events on Wednesday 10 June. https://softwareunderground.org/blog/2020/6/7/shutdownstem-at-transform

This event will be moved to another day, probably this week. Please watch this space.


----

This tutorial will take you through some of the available python packages that make it easy to create interactive plots and dashboards for wellbore data. We will also discuss some of the "soft stuff" on making effective visuals that resonate with geoscience colleages who code and don't code (and maybe management too!)

Audience: This tutorial is great for beginners who already have python up and working and for those who are more experienced with python who might want to explore questions of trust and communication when it comes to showing your code-based work. We will work with relatively simple python code to provide the building blocks to advance upon.

Dataset will be open source well based data - but we will look at both numerical and string components. We will use the following data from the Australia offshore well database for two wells (Pharos-1 and Poseidon North-1) both their mud logs and their cuttings descriptions.

Data files are available here:

Pharos-1
  1. Las mud log: https://drive.google.com/file/d/1Lws7PztcgZlKT4TQNfmojDesKSOIZ56X/view?usp=sharing
  2. Lithology report: https://drive.google.com/file/d/1splcE2E_SXjsRq4olHmlqFPHwDNtSLZO/view?usp=sharing

Poseidon North:
  1. Las Mud Log: https://drive.google.com/file/d/1j1sLFl8Z31qQTfrGoDU6gHsX0yx1ShKA/view?usp=sharing
  2. Lithology report: https://drive.google.com/file/d/1w4OJln6mzw-hDCe3YTRVVAsTLI5RDIcv/view?usp=sharing

Prerequisites:

REQUIRED:
You will need Google Drive and ensure the files above are available on your root level of your drive (/content/drive/My Drive/).

OPTIONAL:
pip install the following packages: panel, hvplot, holoviews, plotly_express if you prefer to download the notebook and work locally. We will use Google collab so there is no need to install anything unless you want to give it a shot on your local computer.

Please note:
We will run this tutorial using Google Colab for the first several sections. Due to issues with the implementation of Holoviews on Google's Jupyter environment, I will switch over to a demo in regular local Jupyter notebook for the last section.

Notebooks are available here:
1. Google Colab (run in playground mode)
2. Jupyter Demo with Widgets and Simple Dashboarding

Agenda:
We will cover the following:
  • Communicating your code with interactive visuals  - what visualizations do you pick and why?  How can you establish trust with non-coding colleages by using good visuals?  We will discuss points on familiarity, colors, storytelling, and decisions on interactivity for you to understand which visuals will help you deliver your message best.
  • Data formatting for ease in visualization based on the points above.
  • Simple wrapper python packages - working off a provided dataset loaded into pandas, we will take a look at some one liners of code that allow you to visualize your data in some pretty cool ways with minimum effort.  Great for those off the cuff requests to show something with short notice or to look cool in hackathons in little time.  We will go through the functionalities in both plotly_express and hvplot and some of the key attributes that we can change to make the plots more effective.
  • Dashboarding in Jupyter - with all these different visuals you can make, how can you combine them to be interactive together?  We will go through making a simple dashboard using Holoviews in Jupyter and discuss/experiment with how to best format the interactivity to deliver a message or give a live demo, bringing it back to how you establish trust in your code with your visualizations.

Instructors
avatar for Ashley Russell

Ashley Russell

Equinor
Geologist working within data science team in Equinor. Goals are to change how geoscientists view their data and how to work with data in new ways though code.


Thursday June 11, 2020 18:00 - 21:00 UTC
YouTube Live

18:00 UTC

Tutorial: Scalable and resilient systems architecture
Welcome to your new assignment!

You are a Cloud Consultant who is beginning an assignment at Unicorn Reservoir Charachterization (URC) * a new tool for geological analysis that "will change how much your rocks rock!"

In this session, you will meet with the URC team, and design a scalable architecture that meets their requirements. What are their requirements? What will this system do? How does the data go from a rock to a data science notebook?

Well, to answer those questions, you simply have to show up and ask them.

In this session, you will be an Architect responsible to translating business requirements to technical requirements, white boarding out an architectural diagram, and then figuring out how to take your great ideas and translate them into actionable next steps.

In this session, you'll meet some of the key stakeholders at URC, learn about their goals, and design a system to meet their vision of "Being the Rock Stars of the Reservoir Characterization World"

  • Note that Unicorn Reservoir Characterization (URC) is a fictitious company and any overlap with real companies and products is purely coincidental
Learning Objectives
  • Understand the basics of translating business requirements to technical requirements
  • Knowledge about key AWS Services and where to learn more
  • Techniques for whiteboarding successfully
  • What the AWS Well-Architected Framework is and how to use it
  • How to take those great ideas and architecture and do something with them
Background

Data engineering and systems architecture is a necessary precursor to delivering data-driven subsurface insights. As a geologist or geophysicist, systems design typically isn’t something you learn in school, yet it can be a necessary skill set to progress your career and drive meaningful change.

Session Overview

This session will present an overview of the key concepts and design principles for building a scalable and cloud-optimized infrastructure in line with the AWS Well-Architected Framework, with a specific focus on Energy-Industry use cases and accompanying challenges for subsurface professionals. Attendees will leave this session with techniques for outlining technical requirements, knowledge of key AWS services and solutions, an overview of the AWS Well-Architected Framework, and an understanding of how to design for scalable and resilient solutions that can be implemented to improve subsurface workflows.

Agenda
  • Working backwards: User stories to drive technical requirements
  • Services, solutions, and scalability
  • Whiteboarding as a superpower
  • The AWS Well-Architected Framework
  • Now what? How to communicate and drive architectural improvements


Instructors
avatar for Liz Dennett

Liz Dennett

Sr. Partner Solutions Architect, AWS
Liz Percak Dennett is a passionate technologist 10+ years of experience and deep domain expertise in oil and gas geology. Through this interdisciplinary skill set, she has demonstrated success translating academic advances to scalable industry solutions in a wide range of use cases... Read More →


Thursday June 11, 2020 18:00 - 21:00 UTC
YouTube Live
 
Friday, June 12
 

08:00 UTC

Tutorial: Geospatial analysis in Python
Important note: This talk is being rescheduled as a result of the #ShutDownSTEM initiative. Further details are available here: https://softwareunderground.org/blog/2020/6/7/shutdownstem-at-transform

I will update this once it has been rescheduled, but for now, please consider taking the time that would have been spent on this tutorial to read the links in the blogpost and consider what else can be done to aid and support marginalised voices in STEM in general and the geosciences in particular.

---

This tutorial is intended to give an overview of some vector geospatial capabilities within Pythonpredominantly using geopandas. This should be accessible to most people who have some familiarity with PythonSome basic knowledge of pandas would help, but is not required. Matplotlib will be used for some of the plotting; knowing how to make a simple plot is going to make parts of this tutorial simpler.
Topics to be covered:
  • File I/O and GeoDataFrames
  • Plotting Data
  • Spatial Selections and subsetting
  • Neighbours and distances
  • Basemaps (Demo, more than a tutorial)
  • Spatial Joins (time permitting)
Please make sure that your environment has geopandas, mapclassify, rasterio, contextily, matplotlib, numpy, and jupyter, along with their dependencies installed.
The majority of this tutorial will be done in jupyter notebooks, as a mix of live coding and hands-on exercises for participants, if they wish to engage.
The course materials, an environment file for installing everything on your own PC are available in this GitHub repo.There is also an option to use a Binder to run it remotely, with no install, by clicking this finely crafted link.

Instructors
MB

Martin Bentley

digital geoscientist, Agile Scientific


Friday June 12, 2020 08:00 - 11:00 UTC
YouTube Live

08:00 UTC

Tutorial: Scalable model training with Ray Tune
As a starting point I assume you are familiar with basic sklearn model pipelines and the “shape” of NN training code. We’ll be using pytorch for that bit.

UPDATED

Then the tutorial will focus on Ray and its libraries. Exploring some of the training code patterns used, and the Ray/Raytune API. Once your code can be run by Ray then it’s easy to use ray features and train models on a multi core machine though to a multinode gpu cluster without much change to that core code. Ray does the heavy lifting.

So we’ll step through various ray features taking a more verbose and relaxed path then some of the ray docs, and (hopefully) with some examples relevant to the subsurface domain.

We’ll spend most of our time looking at SOTA Hyperparameter optimisation with Raytune, Ray itself and [hopefully] plpaying with a Ray Cluster.

But we’ll also at least pay homage to RayRLib (Reinforcement Learning) RaySGD (Distributed Training) and look at the very recent addition Ray Serve.

Some parts of the tutorial you will be able to run on a local machine. More if you have a GPU available. Some parts may involve launching AWS clusters and that part will probably be “follow along” only. 


Instructors

Friday June 12, 2020 08:00 - 11:00 UTC
YouTube Live

13:00 UTC

Lightning talks ⚡⚡
TIMES ARE UTC — Note this is a 2-hour session.

Lightning talks are 5 minutes long and start every 10 minutes.

Slots start at 1300 UTC and go every 10 minutes. There are 12 slots per day. You can claim a slot by following this link. First come, first served.

Claim a slot now.

Co-hosts

Friday June 12, 2020 13:00 - 15:00 UTC
YouTube Live

18:00 UTC

Tutorial: Idea to MVP
We'll show you how to get from an idea in your head to a Minimum Viable Product on the web. This session will help you transform from mild-mannered subsurface professional to digital solution superhero!

The specific use case we are looking at is putting a machine learning model on the web. We'll primarily be using scikit-learn for our (very simple) machine learning model, and flask for the web server. We will mention some other use cases in passing: dashboards, calculators, database queries, etc.

We will also spend a little time asking you to think about some of your own ideas, and helping you map those from your head into a solid plan for a minimum viable product of your own.

Instructors
avatar for Brendon Hall

Brendon Hall

Enthought
avatar for Matt Hall

Matt Hall

Agile
I\\'m a geoscientist in Canada. I have a PhD in sedimentology from the University of Manchester and worked at Statoil, Landmark, and ConocoPhillips. I started Agile in 2010.


Friday June 12, 2020 18:00 - 21:00 UTC
YouTube Live
 
Saturday, June 13
 

07:00 UTC

Hackathon Live: catch up!
Instructors
Co-hosts

Saturday June 13, 2020 07:00 - 07:30 UTC
Zoom

07:00 UTC

Hackathon Day 3
TIMES ARE UTC

Moderators
Saturday June 13, 2020 07:00 - Sunday June 14, 2020 00:00 UTC
Zoom

15:00 UTC

Hackathon Live: catch up!
Instructors
Co-hosts
avatar for Elisa Heim

Elisa Heim

PhD student, RWTH Aachen


Saturday June 13, 2020 15:00 - 15:30 UTC
Zoom

23:00 UTC

Hackathon Live: catch up!
Instructors
Co-hosts
CS

Chance Sanger

Engineer and Software Developer
avatar for Will Sanger

Will Sanger

Geophysicist, Schlumberger
I love learning about viz, ML... and the amazing things people are doing with open code in the geosciences. Day job is data processing geophysicist.


Saturday June 13, 2020 23:00 - 23:30 UTC
Zoom
 
Sunday, June 14
 

07:00 UTC

Hackathon Live: catch up!
Instructors
Co-hosts

Sunday June 14, 2020 07:00 - 07:30 UTC
Zoom

07:00 UTC

Hackathon Day 4
TIMES ARE UTC

Instructors

Sunday June 14, 2020 07:00 - 22:30 UTC
Zoom

14:00 UTC

 
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