Massive Computational Experiments, Painlessly

Watch Ali Zaidi as he described distributed tools on Azure for data scientists.

https://www.youtube.com/watch?v=TeJUQA0gw4s

Watch Riccardo Murri from the university of Zurich address challenges for taking scientific computing to the cloud:

Lecture 08 video:

Watch Greg Kurtzer (CEO of Sylabs.io) give an in-depth explanation of container technologies, in particular Singularity.

Lecture 6: “Some reflections about data science“ by John Chambers

Full lecture video:

Part 1) XYZ Studies, A paradigm for research in data science:

Part 2) Science in the cloud:

Watch lecture 3 as Hatef Monajemi discusses automation in data science and the necessity of Experiment Management Systems (EMS) in data science.

https://www.youtube.com/watch?v=ock3-evBS2I&feature=youtu.be

Watch Mark Piercy from Stanford Research Computing Center (SRCC) talk about cluster computing basics and the features of Stanford medium-risk research cluster Sherlock.

Full Video of Lecture 01:

See Lecture Slides from SDSS2018

See Lecture Slides from SDSS2018

Link to the video of this lecture: https://www.youtube.com/watch?v=gAIXyT71Ja8&feature=youtu.be

Abstract:

With increasing computational demands due to ambitious data science studies and the scarcity of computational resources (e.g., GPUs) on university campuses, researchers are forced inescapably to adopt cloud-based solutions for their computing needs.

In this lecture, we lay out the foundation of a new computing model in which researchers build their own ephemeral personal clusters on the cloud, conduct their experiments, and destroy their clusters when they are no longer needed. This is a departure from traditional research computing model where many researchers share a in-house HPC cluster (e.g., Sherlock) with a set of determined policies. The new model will integrate building personal clusters seamlessly with experiment design, job management, data harvesting and data analysis.

Deep learning (DL) research is a prime example that requires massive computational resources, and in particular access to many GPUs. In the lecture, we will review deep learning and explain the computations that are involved in a DL experiment. We will then teach how to do these experiments at scale on Google Cloud push-button: With one push of a button, the researcher builds her own personal cluster and with another push she will fire up 1000's of jobs to her cluster on the cloud.

Venue: Thornt110

Time : 3:00 PM on Monday Nov 27

Title : Push-button Deep Learning on the Cloud

Abstract:

With increasing computational demands due to ambitious data science studies and the scarcity of computational resources (e.g., GPUs) on university campuses, researchers are forced inescapably to adopt cloud-based solutions for their computing needs.

In this lecture, we lay out the foundation of a new computing model in which researchers build their own ephemeral personal clusters on the cloud, conduct their experiments, and destroy their clusters when they are no longer needed. This is a departure from traditional research computing model where many researchers share a in-house HPC cluster (e.g., Sherlock) with a set of determined policies. The new model will integrate building personal clusters seamlessly with experiment design, job management, data harvesting and data analysis.

Deep learning (DL) research is a prime example that requires massive computational resources, and in particular access to many GPUs. In the lecture, we will review deep learning and explain the computations that are involved in a DL experiment. We will then teach how to do these experiments at scale on Google Cloud push-button: With one push of a button, the researcher builds her own personal cluster and with another push she will fire up 1000's of jobs to her cluster on the cloud.

Riccardo Murri gave an exceptional lecture in Stats285. Check out the video here:

Lecture03: Occupy The Cloud (Eric Jonas):

Lecture04: Reproducibility in Computational Science (Victoria Stodden):

You may now find slides for lecture 02 at

Stay tuned for the video!

The slides of the first lecture now are now available on Stats285 website:

Follow course updates on https://twitter.com/stats285 starting next week.

Great guest speakers this quarter for Stats285. It is going to be a fun quarter at Stanford: https://stats285.github.io