From jeffrey.fischer at gmail.com Fri Feb 18 16:31:10 2022 From: jeffrey.fischer at gmail.com (Jeff Fischer) Date: Fri, 18 Feb 2022 13:31:10 -0800 Subject: [Baypiggies] Next week's talks: Scaling AI Workloads with the Ray Ecosystem and a lightning talk on Docker and Kubernetes Message-ID: *Thursday February 24th 7:00 pm - 8:30 pm* This month, we'll have a talk from Jules Damji about Ray, the open source platform for distributed programming. We will also have a lightning talk from Tyler Suard about Docker and Kubernetes. Come and join us! Lightning talk: Docker and Kubernetes: Getting Doom to Run on Your Friends Computer in 1993 *Speaker:* Tyler Suard *Abstract:* The absolute simplest approach to Docker and Kubernetes ever, with an example from the flyest time in history: the 90?s! *Speaker bio:* Tyler Suard is a software engineer at Facebook. He likes cats, his girlfriend, and inventing things. Main Talk: Scaling AI Workloads with the Ray Ecosystem *Speaker: *Jules S. Damji *Abstract:* Today, AI applications are becoming pervasive across all sectors of our industry. Driven by a few fundamental trends, there is no indication of slowing down. In fact, the trend continues rapidly, making distributed computing at scale a norm and necessity. But distributed computing is not easy. It has its challenges. Building distributed applications today requires tons of expertise. For many developers, it is out of reach. Current solutions to these challenges have their shortcomings and tradeoff. Ray aims to address these shortcomings. As a general-purpose distributed computing framework, it makes programming a cluster of machines as easy as programming a laptop, thereby enabling many more developers and practitioners to take advantage of the advances in cloud computing and scale their machine learning workloads to solve harder problems, without needing to be experts in distributed systems. Besides a core general-purpose distributed-compute system, Ray encompasses a collection of state-of-the-art native libraries targeting scalable machine learning. These include libraries for hyperparameter tuning, distributed training, reinforcement learning, model serving, and last-mile ML data pre-processing and ingestion for model training. This talk will introduce Ray?s overview; survey its ecosystem of both native and integrated ML libraries; and discuss key applications and developments in the Ray ecosystem, drawing upon lessons from discussions with practitioners over the years of developing Ray with the community?and at Anyscale. In particular, we will demonstrate how you can easily scale three common ML workloads, from your laptop to the cluster, with Ray?s native libraries: training, hyperparameter tuning and optimization (HPO), and large-scale batch inference. Using the popular XGBoost for classification, we will show how you can scale model training, hyperparameter tuning, and inference?from a laptop or single node to a Ray cluster, with tangible performance difference when using Ray. The takeaways from this talk are : Why distributed computing has become the norm and necessity, not an exception Learn Ray?s architecture, core concepts, and programming primitives Understand Ray?s ecosystem of scalable ML libraries Easily extend or transition your laptop to a Ray cluster Scale three ML workloads using Ray?s native libraries: Training on a single node vs. Ray cluster, using XGBoost with/without Ray Tuning HPO using XGBoost with Ray and Ray Tune Inferencing at scale, using XGBoost with/without Ray *Speaker bio:* Jules S. Damji is a lead developer advocate at Anyscale Inc, an MLflow contributor, and co-author of Learning Spark, 2nd Edition. He is a hands-on developer with over 25 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/LoudCloud, VeriSign, ProQuest, Hortonworks, and Databricks, building large-scale distributed systems. He holds a B.Sc and M.Sc in computer science (from Oregon State University and Cal State, Chico respectively), and an MA in political advocacy and communication (from Johns Hopkins University). Code of Conduct https://baypiggies.net/pages/code_of_conduct.html Interactions online have less nuance than in-person interactions. Please be Open, Considerate and Respectful. Also, please refrain from discussing topics unrelated to the Python community or the technical content of the meeting. RSVP We will conduct the meeting via Zoom meeting. Please RSVP on MeetUp at https://www.meetup.com/BAyPIGgies/events/283390500/. When you RSVP "Yes" to this event, the link to the Zoom meeting will become visible in MeetUp. If you do not have a MeetUp account and would like to attend the meeting, just contact the organizers. 2022 Call for Talks We are looking for speakers for 2022. We are looking for technical talks of interest to Python developers, either about the language and core libraries itself, popular libraries/platforms using Python (for example, Pandas andTensorFlow in Data Science, Flask and Django in web applications, Ansible in DevOPs), or other experiences using Python. You can apply for an online talk here: https://forms.gle/PqxrExC2t858xtfMA -------------- next part -------------- An HTML attachment was scrubbed... URL: From jiffyclub at gmail.com Sat Feb 5 14:50:34 2022 From: jiffyclub at gmail.com (Matt Davis) Date: Sat, 05 Feb 2022 19:50:34 -0000 Subject: [Baypiggies] Call for Proposals for SciPy 2022! Message-ID: Hi Bay Piggies, We are reaching out to you about the 21st annual Scientific Computing with Python Conference ? also known as SciPy 2022 ? that will be held July 11-17, 2022 in Austin, Texas. The call for submissions is now open, and we would love to receive proposals from folks in your organization. We seek submissions for talks and posters by February 11. This year, in addition to the general conference track and domain mini-symposia, we are accepting submissions for two specialized tracks: (1) Machine Learning and Data Science, and (2) Data Life Cycle. We are also seeking tutorial submissions by February 15. This year's conference will be held in-person in Austin, Texas. Registration opens February 3 and early bird rates end May 1. Scholarships are available this year ? if you?d like to apply for a scholarship to attend, please fill out this form . The deadline for scholarships is March 13. The conference will conclude with two days of Sprints focused on developing and maintaining open source Python scientific software. In addition we are proud to offer again our Mentorship Program and networking events with focused topics like ?Careers in Data Science.? You can find out more about our programming at https://www.scipy2022.scipy.org/about. The full schedule will be published later in May. In the meantime, please check out last year?s schedule and videos of last year?s talks and events to get a better idea of what SciPy conferences are like. SciPy 2022 is shaping up to be a fantastic conference. We hope you'll join us in July! SciPy Organizers -------------- next part -------------- An HTML attachment was scrubbed... URL: From chityala at gmail.com Mon Feb 28 11:46:11 2022 From: chityala at gmail.com (Ravi Chityala) Date: Mon, 28 Feb 2022 08:46:11 -0800 Subject: [Baypiggies] UC Berkeley Extension courses Spring 2022 Message-ID: Hello All, Dr. Sridevi Pudipeddi, a Lecturer in the Master's in Data Science Program at UC Berkeley main campus will be teaching two 10-week long courses at the UC Berkeley extension. Due to the 'shelter at home' policy, the class will be held "live online" via Zoom. So anyone from anywhere can join this class. Her LinkedIn profile can be found at https://www.linkedin.com/in/sridevi-pudipeddi/ The details of the two live online courses are as follows: Python for Data Analysis and Scientific Computing - 10 weeks meets on Tuesdays from 6-9 pm starts March 1st and ends May 3rd https://extension.berkeley.edu/search/publicCourseSearchDetails.do?method=load&courseId=17545604 Introduction to Machine Learning using Python - 10 weeks meets on Thursdays from 6-9 pm starts March 3rd and ends May 5th https://extension.berkeley.edu/search/publicCourseSearchDetails.do?method=load&courseId=30544037 UC Berkeley Extension is accredited by various bodies through the UC Berkeley. Hence, many employers will reimburse the cost of the course. If you have any questions, reach out to Sri at sridevip at berkeley.edu Thanks, Ravi From jeffrey.fischer at gmail.com Mon Feb 28 12:59:30 2022 From: jeffrey.fischer at gmail.com (Jeff Fischer) Date: Mon, 28 Feb 2022 09:59:30 -0800 Subject: [Baypiggies] ML talk this Thursday Message-ID: Hi everyone, I work at C3.AI , a company in Redwood City that builds machine learning applications for industrial and finance users. One of our data scientists, Mehdi Maasoumy, is giving a talk at our office this Thursday evening. The talk is titled "Digital Transformation: From Asset Reliability to Stochastic Optimization of Supply Chains". I think it is going to be mostly focused on algorithms and how we apply AI to business problems. If you are interested, the link is here: https://www.meetup.com/artificial-intelligence-and-machine-learning-with-c3-ai/events/283894462/ We're also hiring Python programmers in three kinds of roles: data scientists, developers on our platform infrastructure, and JupyterLab integration developers. If you might be interested, feel free to reach out to me, or come to the event. Thanks! Regards, Jeff -------------- next part -------------- An HTML attachment was scrubbed... URL: