Add JupyterCon 2020

jupytercon-2020/category.json

@@ -0,0 +1,3 @@
+{
+  "title": "JupyterCon 2020"
+}

jupytercon-2020/videos/aaron-bray-pulse-physiology-engine-jupytercon-2020.json

@@ -0,0 +1,36 @@
+{
+  "description": "Brief Summary\nThe Pulse Physiology Engine is a C++ based, open source, dynamic, faster than real time human physiology simulator that drives medical education, research, and training technologies. The engine enables accurate and consistent physiology simulation across the medical community. The engine can be used as a standalone application or integrated with simulators, sensors, and models of all fidelity.\n\nOutline\nIn this talk, we will present our Pulse Physiology Engine along with how we have integrated it with Jupyter Notebooks.\n\nThe Pulse Physiology engine\u2019s role in scientific research is to simulate a patient\u2019s physiological condition during disease, trauma, and treatment. We have paired our engine with multiple simulation modalities for training medical professionals and caregivers in appropriate trauma treatment. Combining our engine with Jupyter Notebooks will allow more users to easily interact with the models to explore the changes in physiology throughout disease and injury and how treatment and timing affect the patient\u2019s recovery.\n\nOur notebooks are targeted to developers, researchers, and students to explore the many aspects of human physiology available in Pulse. Pulse provides a Python API for creating customized patients and exploring how various insults, injuries and underlying patient conditions change the underlying physiology and abilities of a patient.\n\nInteractivity is extremely important to using Pulse and we will discuss various user interface options we have explored to provide end users an intuitive and interactive experience in our Notebooks. Specifically, viewer widgets used to plot real-time data from Pulse in 2D graph views.",
+  "duration": 739,
+  "language": "eng",
+  "recorded": "2020-10-05",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://web.archive.org/web/20201030085456/https://jupytercon.github.io/jupytercon2020-website/"
+    },
+    {
+      "label": "https://jupytercon.com/about/#Organizing%20Committee\u00a0",
+      "url": "https://jupytercon.com/about/#Organizing%20Committee\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/sponsors/\u00a0",
+      "url": "https://jupytercon.com/sponsors/\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/\u00a0",
+      "url": "https://jupytercon.com/\u00a0"
+    }
+  ],
+  "speakers": [
+    "Aaron Bray"
+  ],
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/jPh2NL_tZgc/sddefault.jpg",
+  "title": "Pulse Physiology Engine",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=jPh2NL_tZgc"
+    }
+  ]
+}

jupytercon-2020/videos/aaron-richter-high-performance-jupyter-faster-workloads-with-dask-and-rapids-jupytercon-202.json

@@ -0,0 +1,36 @@
+{
+  "description": "Brief Summary\nWhat happens when you run out of processing power on your laptop? You could scale up - get more efficient hardware, or scale out - add more machines. Whichever you choose, there are great tools for accomplishing scale within the Jupyter ecosystem. This talk presents Dask and RAPIDS for parallel and GPU computing, and how to launch and manage clusters all within JupyterLab.\n\nOutline\nWhat happens when you run out of processing power on your laptop? You could scale up - get more efficient hardware, or scale out - add more machines. Whichever you choose, there are great tools for accomplishing scale with the Python and Jupyter ecosystem. Dask is a parallel computing framework that scales from your laptop to a cluster of thousands of machines. RAPIDS is a GPU-computing framework that pushes traditional CPU workloads to the GPU. Dask and RAPIDS together allow you to scale both up and out! This talk will help you navigate this exciting new world, and show how easy it is to get your workloads running faster in Jupyter.\n\nOutline\n\nThe state of single-node CPU workloads: why do we need clusters and GPU computing?\n\nIntro to Dask (Python-native cluster computing)\n\nIntro to RAPIDS (Python-native GPU computing)\n\nCode examples:\n\nLaunch Dask cluster and monitor in JupyterLab with dask-labextension\n\nLarge-scale data processing across the cluster\n\nFast ML model training with RAPIDS\n\nPrerequisites: a working knowledge of data science with Python (pandas, numpy, scikit-learn, etc.). No cluster computing experience necessary - this is what you will learn from the talk!",
+  "duration": 1670,
+  "language": "eng",
+  "recorded": "2020-10-05",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://web.archive.org/web/20201030085456/https://jupytercon.github.io/jupytercon2020-website/"
+    },
+    {
+      "label": "https://jupytercon.com/about/#Organizing%20Committee\u00a0",
+      "url": "https://jupytercon.com/about/#Organizing%20Committee\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/sponsors/\u00a0",
+      "url": "https://jupytercon.com/sponsors/\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/\u00a0",
+      "url": "https://jupytercon.com/\u00a0"
+    }
+  ],
+  "speakers": [
+    "Aaron Richter"
+  ],
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/dCk-uQIhmAQ/sddefault.jpg",
+  "title": "High performance Jupyter: faster workloads with Dask and RAPIDS",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=dCk-uQIhmAQ"
+    }
+  ]
+}

jupytercon-2020/videos/aaron-watters-using-webgl2-transform-feedback-in-jupyter-widgets-jupytercon-2020.json

@@ -0,0 +1,36 @@
+{
+  "description": "Brief Summary\nThe transform/feedback mechanism of WebGL2 allows Javascript programs running in a browser to use the GPU to implement novel graphics processing stages and other computations. This talk outlines the transform/feedback processing model and describes how it can be embedded in a Jupyter context to generate iso-surfaces, clustering, graph layouts, and other highly parallel computations.\n\nOutline\nThe transform/feedback mechanism of WebGL2 allows Javascript programs running in a browser to use the graphics processor (GPU) to implement novel graphics processing stages and other computations. This talk outlines the transform/feedback processing model and describes a three dimensional isosurface generator implemented using transform/feedback as well as WebGL instanced rendering. The isosurface generator is integrated with the three.js graphics modelling library to create visualizations of scientific data for use in web pages or Jupyter notebooks. The talk also demonstrates some uses of isosurfaces for visualizing and exploring spatial genomics, quantum physics, astrophysical phenomena, and other mathematical structures. The talk will also explore other possible applications of transform/feedback to implement particle system simulations, matrix operations, classification algorithms, statistical clustering or other large scale highly parallel computations. This presentation discusses components of the feedWebGL2 github library which provides several abstraction layers built over the basic WebGL2 Javascript API and also exposes the transform feedback functionality to Jupyter notebooks using a widget interface.",
+  "duration": 1546,
+  "language": "eng",
+  "recorded": "2020-10-05",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://web.archive.org/web/20201030085456/https://jupytercon.github.io/jupytercon2020-website/"
+    },
+    {
+      "label": "https://jupytercon.com/about/#Organizing%20Committee\u00a0",
+      "url": "https://jupytercon.com/about/#Organizing%20Committee\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/sponsors/\u00a0",
+      "url": "https://jupytercon.com/sponsors/\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/\u00a0",
+      "url": "https://jupytercon.com/\u00a0"
+    }
+  ],
+  "speakers": [
+    "Aaron Watters"
+  ],
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/zJmZ2v_lIeg/sddefault.jpg",
+  "title": "Using WebGL2 transform/feedback in Jupyter widgets",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=zJmZ2v_lIeg"
+    }
+  ]
+}

jupytercon-2020/videos/alex-remedios-introducing-a-simple-notebook-continuous-integration-workflow-jupytercon-2020.json

@@ -0,0 +1,36 @@
+{
+  "description": "Brief Summary\nNotebooks have been an unexpected innovation in how software is written. They have lowered the bar for writing scripts, unlocking huge amounts of scientific productivity.\n\nBut are they going to make it into 'production software'?\n\nI'm going to introduce Treebeard's notebook CI framework and how we built it with Papermill, Repo2Docker, and GitHub Actions.\n\nOutline\nIn this talk I will describe the evolution of notebook use-cases and the great potential of investing in adjacent tooling.\n\nGiven my perspective from working in devops on large websites, I'd like to impress that the Jupyter project has discovered not just a great product for exploratory data science, but for software engineering more generally.\n\nFinally I'd like to introduce the problem area we are working on -- continuous integration, in order to accelerate what we see as a beneficial progression in software.\n\nBackground required:\n\nIntermediate-level programming\nBasic infrastructure knowledge (awareness of Docker, CI)",
+  "duration": 700,
+  "language": "eng",
+  "recorded": "2020-10-05",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://web.archive.org/web/20201030085456/https://jupytercon.github.io/jupytercon2020-website/"
+    },
+    {
+      "label": "https://jupytercon.com/about/#Organizing%20Committee\u00a0",
+      "url": "https://jupytercon.com/about/#Organizing%20Committee\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/sponsors/\u00a0",
+      "url": "https://jupytercon.com/sponsors/\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/\u00a0",
+      "url": "https://jupytercon.com/\u00a0"
+    }
+  ],
+  "speakers": [
+    "Alex Remedios"
+  ],
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/_zy7iLZ7-ts/sddefault.jpg",
+  "title": "Introducing a simple notebook continuous integration workflow",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=_zy7iLZ7-ts"
+    }
+  ]
+}

jupytercon-2020/videos/andrew-duberstein-pydeck-high-scale-geospatial-visualization-for-python-jupytercon-2020.json

@@ -0,0 +1,36 @@
+{
+  "description": "Brief Summary\nWith social graphs, genomics, and sensor data visualizations, data scientists often need to render massive spatial data sets. In 2018, Uber released pydeck, an open source Python library for rendering beautiful high scale data visualizations, built on top of Uber's deck.gl library. We'll go over how pydeck was written and how to use pydeck to visualize large-scale data sets. See more at pydeck.gl.\n\nOutline\nAfter this talk, you'll know what your options are for mapping geospatial data, what pydeck is, and how its Jupyter integration works. Attendees are expected to know Python scripting, Pandas, and Jupyter. Ideal attendees have experience in data science, data analytics, or machine learning engineering, and have a passion for geospatial data.",
+  "duration": 1330,
+  "language": "eng",
+  "recorded": "2020-10-05",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://web.archive.org/web/20201030085456/https://jupytercon.github.io/jupytercon2020-website/"
+    },
+    {
+      "label": "https://jupytercon.com/about/#Organizing%20Committee\u00a0",
+      "url": "https://jupytercon.com/about/#Organizing%20Committee\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/sponsors/\u00a0",
+      "url": "https://jupytercon.com/sponsors/\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/\u00a0",
+      "url": "https://jupytercon.com/\u00a0"
+    }
+  ],
+  "speakers": [
+    "Andrew Duberstein"
+  ],
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/i-dGU80hNOw/sddefault.jpg",
+  "title": "Pydeck: High-scale geospatial visualization for Python",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=i-dGU80hNOw"
+    }
+  ]
+}

jupytercon-2020/videos/animashree-anandkumar-next-generation-frameworks-for-large-scale-ai-juyptercon-2020.json

@@ -0,0 +1,36 @@
+{
+  "description": "Brief Summary\nI will introduce latest frameworks from NVIDIA that enables large scale AI and research that exploits them.\n\nOutline\nAbstract: The deep-learning revolution has achieved impressive progress through the convergence of data, algorithms, and computing infrastructure. The availability of web-scale labeled data and parallelism of GPUs enabled us to harness the power of neural networks. However, for further progress, we cannot solely rely on bigger models. We need to reduce our dependence on labeled data, and design algorithms that can incorporate more structure and domain knowledge. Examples include tensors, graphs, physical laws, and simulations. I will describe efficient frameworks that enable developers to easily prototype such models, e.g. Tensorly to incorporate tensorized architectures, NVIDIA Isaac to incorporate physically valid simulations and NVIDIA RAPIDS for end-to-end data analytics. I will then lay out some outstanding problems in this area.\n\n_____\n\nJupyterCon brings together data scientists, business analysts, researchers, educators, developers, core Project\u00a0contributors, and tool creators for in-depth training, insightful keynotes, networking, and practical talks exploring\u00a0the Project Jupyter ecosystem.\n\nhttps://jupytercon.com/\u00a0\n\nJupyterCon is possible thanks to the generous support of our sponsors, and the labor of many volunteer organizers.\u00a0\n\nhttps://jupytercon.com/sponsors/\u00a0\nhttps://jupytercon.com/about/#Organizing%20Committee",
+  "duration": 3625,
+  "language": "eng",
+  "recorded": "2020-10-05",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://web.archive.org/web/20201030085456/https://jupytercon.github.io/jupytercon2020-website/"
+    },
+    {
+      "label": "https://jupytercon.com/about/#Organizing%20Committee",
+      "url": "https://jupytercon.com/about/#Organizing%20Committee"
+    },
+    {
+      "label": "https://jupytercon.com/sponsors/\u00a0",
+      "url": "https://jupytercon.com/sponsors/\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/\u00a0",
+      "url": "https://jupytercon.com/\u00a0"
+    }
+  ],
+  "speakers": [
+    "Animashree Anandkumar"
+  ],
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/U2aqdYrJh-I/maxresdefault.jpg",
+  "title": "Next-generation frameworks for Large-scale AI",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=U2aqdYrJh-I"
+    }
+  ]
+}

jupytercon-2020/videos/anjali-mathur-role-of-jupyter-notebook-in-performing-data-science-activities-jupytercon-2020.json

@@ -0,0 +1,36 @@
+{
+  "description": "Brief Summary\nThe proposal is about data science in educational institutes, . An educational institute, always deals with huge amount of students data. The data input starts from admission process and get enhanced year by year on the basis of courses, exams and events opted by a student. Here Data Science plays a bigger role. Jupyter Notebook is extremely helpful to perform data science activities properly.\n\nOutline\nThe Proposal is about an applications of Data Science (a case study), i.e. how data science plays a bigger role in educational institutes. The target audience is intermediate level that may have basic skills of data science and python language.\n\nThe proposal is briefly focuses on Student Registration Process and designing and framing course time table. 1. Student Registration Process:- In this section, our focus would be on following points, a. Total number of Registrations. b. Actual number of registrations. c. Course wise registrations. d. How many courses are there with zero registrations, minimum and maximum registrations. 2. Designing and Framing Course Time Table:- In this section, our focus would be on following points, a. Generation of course time table on the basis of faculty details. b. Finding details of an individual faculty and individual course on the basis of time table details.",
+  "duration": 727,
+  "language": "eng",
+  "recorded": "2020-10-05",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://web.archive.org/web/20201030085456/https://jupytercon.github.io/jupytercon2020-website/"
+    },
+    {
+      "label": "https://jupytercon.com/about/#Organizing%20Committee\u00a0",
+      "url": "https://jupytercon.com/about/#Organizing%20Committee\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/sponsors/\u00a0",
+      "url": "https://jupytercon.com/sponsors/\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/\u00a0",
+      "url": "https://jupytercon.com/\u00a0"
+    }
+  ],
+  "speakers": [
+    "Anjali Mathur"
+  ],
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/qxy34nqTL3w/sddefault.jpg",
+  "title": "Role of Jupyter Notebook in performing Data Science activities.",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=qxy34nqTL3w"
+    }
+  ]
+}

jupytercon-2020/videos/arnim-bleier-a-persistent-binderhub-jupytercon-2020.json

@@ -0,0 +1,36 @@
+{
+  "description": "Brief Summary\nIn this talk, we introduce GESIS Notebooks, a public JupyterHub deployment that enables users to persistently import projects using the mechanisms of MyBinder. The talk is relevant for researchers and lecturers that want to work persistently with one of the many existing binder-ready projects as well as for system administrators.\n\nOutline\nKenan Erdogan and Arnim Bleier offer an overview of GESIS Notebooks (notebooks.gesis.org), a JupyterHub deployment that enables users to import and update projects using the mechanisms of MyBinder. The platform drastically lowers the bar for researchers, lecturers, and students to start their own persistent cloud-based notebook server with an environment that is tailored to their needs or to build on one of the many existing binder-ready analysis pipelines. The cornerstone of our infrastructure, the Persistent BinderHub, enables us to support the heterogeneous computational needs of our community while reducing the costs of maintaining this infrastructure as compared to other alternatives.\n\nThe 30-minute talk begins with an overview of the computational needs in the Computational Social Science community. We then showcase the use and utility of the platform from a user perspective. Finally, we demonstrate how the open-source platform can be deployed from an administrator\u2019s point of view.",
+  "duration": 1549,
+  "language": "eng",
+  "recorded": "2020-10-05",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://web.archive.org/web/20201030085456/https://jupytercon.github.io/jupytercon2020-website/"
+    },
+    {
+      "label": "https://jupytercon.com/about/#Organizing%20Committee\u00a0",
+      "url": "https://jupytercon.com/about/#Organizing%20Committee\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/sponsors/\u00a0",
+      "url": "https://jupytercon.com/sponsors/\u00a0"
+    },
+    {
+      "label": "https://jupytercon.com/\u00a0",
+      "url": "https://jupytercon.com/\u00a0"
+    }
+  ],
+  "speakers": [
+    "Arnim Bleier"
+  ],
+  "tags": [],
+  "thumbnail_url": "https://i.ytimg.com/vi/dwidVhPtQT4/sddefault.jpg",
+  "title": "A Persistent BinderHub",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=dwidVhPtQT4"
+    }
+  ]
+}