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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20220201T090000
DTEND;TZID=Europe/Stockholm:20220203T120000
DTSTAMP:20260421T225802
CREATED:20211122T115138Z
LAST-MODIFIED:20220114T083155Z
UID:11670-1643706000-1643889600@enccs.se
SUMMARY:Upscaling A.I. with Containers
DESCRIPTION:Overview\nActive practitioners in deep learning primarily develop and train their networks on local computing devices. Nonetheless\, the accuracy of such networks is dependent on their depth and the amount of training data. Moreover\, since local computing devices such as laptops or clusters provide limited computational capacity\, training a deep neural network (NN) requires access to large clusters\, i.e.\, supercomputers. \nIn this workshop\, we will learn the basics of containers\, how to work with Docker and Singularity\, and how to run distributed training using the Horovod framework on a supercomputer. \nThe workshop will be entirely online using zoom. \nOutcomes\n\nYou will be able to create\, deploy\, and update containers locally or on a cluster.\nYou will train\, and upscale traditional Convolutional NN (CNN) in TensorFlow.\nYou will learn to upscale CNN using Horovod.\n\nPrerequisites\nBasic knowledge of UNIX OS and familiarity with NNs are required. \nPreliminary Agenda\nDay 1 – Tuesday 1 February 2022\n[ninja_tables id=”11680″] \nDay 2 – Wednesday 2 February 2022\n[ninja_tables id=”11681″] \nDay 3 – Thursday 3 February 2022\n[ninja_tables id=”11682″] \nRegistration\nTo register for this event\, please follow this link https://events.prace-ri.eu/event/1292/registrations/952/. \nFor questions regarding this event please contact us at training@enccs.se. \n————\nThis training is intended for users established in the European Union or a country associated to Horizon 2020.
URL:https://enccs.se/events/2022-02-upscaling-ai-with-containers/
LOCATION:Online
CATEGORIES:ENCCS Event
ATTACH;FMTTYPE=image/jpeg:https://media.enccs.se/2021/11/AI-with-containers.jpg
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20220207T090000
DTEND;TZID=Europe/Stockholm:20220209T123000
DTSTAMP:20260421T225802
CREATED:20211220T075014Z
LAST-MODIFIED:20220114T083136Z
UID:11912-1644224400-1644409800@enccs.se
SUMMARY:GROMACS Workshop - ENCCS/CSC/BioExcel
DESCRIPTION:Overview\nThis course gives advanced practical tips on how to run GROMACS MD simulations efficiently on modern hardware including both CPUs and GPUs. In addition to speeding up MD simulations\, also workflow automation\, advanced sampling techniques\, and future developments are discussed. The course consists of lectures and hands-on exercises. GROMACS will be used in the exercise sessions. \nThe event is organized in collaboration with BioExcel\, CSC\, ENCCS and supported by EuroCC. \nLearning Outcome\nAfter the course the participants should have the skills and knowledge needed to 1) efficiently use CPU and GPU resources in GROMACS simulations 2) apply workflows and python API for GROMAC simulations and 3) run QM/MM simulations. \nPrerequisites\nThe participants are required to be familiar with MD\, have working knowledge of GROMACS\, and basics of BioBB\, also basic Python and Linux/Unix skills are required. The fundamentals of MD or basic usage of GROMACS are not covered on this course. Please consult the following materials\, if you’re uncertain of your Python\, Linux or GROMACS or BioBB skills: \n\nLectures on the basis of molecular dynamics simulation (videolinks: part I\, part II)\nMD with GROMACS tutorials (link)\nA basic Python introduction (link\, available also via CSC Notebooks)\, in addition to the very basics\, knowing functions\, decorators and ‘with’ context manager will be useful.\nA short guide to Jupyter Notebook (pdf)\nLinux commands\, bash shell\, a quiz and a link to intro course (link)\nBioBB introductory videos (link)\, installation tutorials (link) and demonstration workflows (link)\n\nGPU Compute Resources\nCSC’s Puhti supercomputer will be used for the hands-on exercises. \nLecturers\n\nDr. Alessandra Villa\, PDC Center for High Performance Computing\, KTH Royal Institute of Technology\nDr. Berk Hess\, Theoretical and Computational Biophysics\, KTH Royal Institute of Technology\nDr. Adam Hospital\, Molecular Modelling and Bioinformatics\, Institute of Research in Biomedicine\, Barcelona\nDr. Eric Irrgang\, Department of Molecular Physiology and Biological Physics\, University of Virginia\, Charlottesville\nDr. Dmitry Morozov\, Department of Chemistry\, University of Jyväskylä\nDr. Artem Zhmurov\, The EuroCC National Competence Center Sweden (ENCCS)\, Stockholm\n\nAgenda & Registration\nFor updated agenda and registration please follow this link https://ssl.eventilla.com/attend/mWeRZ \n————\nThis training is intended for users established in the European Union or a country associated to Horizon 2020.
URL:https://enccs.se/events/gromacs-2022-02/
LOCATION:Online
CATEGORIES:Collaboration Event,ENCCS Event
ATTACH;FMTTYPE=image/jpeg:https://media.enccs.se/2021/05/Gromacs-logo.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20220215T090000
DTEND;TZID=Europe/Stockholm:20220216T120000
DTSTAMP:20260421T225802
CREATED:20211126T091447Z
LAST-MODIFIED:20220204T170718Z
UID:11704-1644915600-1645012800@enccs.se
SUMMARY:Julia for High-Performance Scientific Computing
DESCRIPTION:Overview\nJulia is a modern high-level programming language which is both fast (on par with traditional HPC languages like Fortran and C) and relatively easy to write like Python or Matlab. It thus solves the “two-language problem”\, i.e. when prototype code in a high-level language needs to be combined with or rewritten in a lower-level language to improve performance. Although Julia is a general-purpose language\, many of its features are particularly useful for numerical scientific computation\, and a wide range of both domain-specific and general libraries are available for statistics\, machine learning and numerical modeling. The language supports parallelization for both shared-memory and distributed HPC architectures\, and native Julia libraries are available for running on GPUs from different vendors. \nThis online workshop will start by briefly covering the basics of Julia’s syntax and features\, and then introduce methods and libraries which are useful for writing high-performance code for modern HPC systems. After attending the workshop you will: \n\nBe comfortable with Julia’s syntax\, in-built package manager\, and development tools.\nUnderstand core language features like its type system\, multiple dispatch\, and composability.\nBe able to write your own Julia packages from scratch.\nHave an overview of Julia’s parallelization and GPU-porting strategies and know-how to get started using them.\nBe familiar with key Julia libraries for scientific modeling\, visualization\, and machine learning.\n\nPrerequisites\nThe workshop is intended for researchers who are familiar with one or more other languages like Python\, R\, Matlab\, C/C++ or Fortran but would like to learn an exciting modern high-performance language.\nBasic experience with working in a terminal is also beneficial. Participants are expected to install Julia\, Jupyter and Zoom before the workshop starts. \nPreliminary Agenda\nTuesday 15 February 2022 \n[ninja_tables id=”12602″] \nWednesday 16 February 2022 \n[ninja_tables id=”12603″] \nRegistration\nPlease register by following this link https://events.prace-ri.eu/event/1294/registrations/954/ \nFor questions regarding this event please contact us at training@enccs.se. \n————\nThis training is intended for users established in the European Union or a country associated to Horizon 2020.
URL:https://enccs.se/events/2022-02-julia-for-high-performance-scientific-computing/
LOCATION:Online
CATEGORIES:ENCCS Event
ATTACH;FMTTYPE=image/jpeg:https://media.enccs.se/2021/11/julia-enccs-2-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20220222T090000
DTEND;TZID=Europe/Stockholm:20220223T120000
DTSTAMP:20260421T225802
CREATED:20211126T093907Z
LAST-MODIFIED:20220218T150334Z
UID:11711-1645520400-1645617600@enccs.se
SUMMARY:Introduction to Deep-Learning
DESCRIPTION:Overview\nThe use of Deep Learning has seen a sharp increase of popularity and applicability over the last decade. While Deep Learning can be a useful tool for researchers from a wide range of domains\, taking the first steps in the world of Deep Learning can be somewhat intimidating. This introduction aims to cover the basics of Deep Learning in a practical and hands-on manner\, so that upon completion\, you will be able to train your first neural network and understand what next steps to take to improve the model. \nWe start with explaining the basic concepts of neural networks\, and then go through the different steps of a Deep Learning workflow. Learners will learn how to prepare data for deep learning\, how to implement a basic Deep Learning model in Python with Keras\, how to monitor and troubleshoot the training process and how to implement different layer types such as convolutional layers. \nAfter attending the workshop\, you should be able to: \n\nPrepare input data for use for deep learning\nDesign and train a Deep Neural Network\nTroubleshoot the learning process\nMeasure the performance of the network\nVisualise data and results\nRe-use existing network architectures with and without pre-trained weights\n\nPrerequisites\nParticipants are expected to have basic Python programming skills and familiarity with the Pandas package. Basic knowledge of statistics is also beneficial. \nPreliminary Agenda\nTuesday 22 February 2022 \n[ninja_tables id=”13063″] \nWednesday 23 February 2022 \n[ninja_tables id=”13064″] \nRegistration\nPlease register by following this link https://events.prace-ri.eu/event/1295/registrations/955/ \nFor questions regarding this event please contact us at training@enccs.se. \n————\nThis training is intended for users established in the European Union or a country associated to Horizon 2020.
URL:https://enccs.se/events/2022-02-introduction-to-deep-learning/
LOCATION:Online
CATEGORIES:ENCCS Event
ATTACH;FMTTYPE=image/jpeg:https://media.enccs.se/2021/11/Deep-learning-2-1.jpg
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