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Improve your
Julia Collaboration

Looking for better Julia Collaboration? Learn new ways to sync your projects, manage access and permissions, and use version control.  

Register for Webinar Now

Julia collaboration has taken a revolutionary leap forward with JuliaHub Projects.

 
WEBINAR
Friday, March 10th | 1:30 PM EST

Improving Julia Collaboration with JuliaHub Projects

Julia collaboration has taken a leap forward with JuliaHub Projects. JuliaHub now gives you new ways to encapsulate your team's files, folders, datasets, code, and sharing permission through the lens of a project.

This webinar showcases how your team can seamlessly collaborate and ensure that projects are constantly synced, as well as how git-based version control and unprecedented traceability features, unique to JuliaHub Projects, will keep your team aligned through:

  • File Sharing and File Browsing

  • Group Access Controls and Permissions

  • Traceability

Join Deep Datta to see for yourself how JuliaHub Projects can revolutionize your Julia collaboration. 

Register now to reserve your spot!

REGISTER NOW

 

Meet Your Speaker

Deep Datta-1

Deep Datta

Product Manager

Deep Datta recently joined Julia Computing as Product Director. Previously, he was senior product manager at JFrog where he led their partnership team through building DevOps-focused integrations. He also helped the Conan open-source community develop a central repository for C / C++ packages called ConanCenter. With over a decade of startup experience, Deep has worked in product R&D, engineering, and open-source program management.

 

JuliaHub Projects allows you to

  • Determine Access

    Choose access and levels of access for team members.

  • Folders + Files

    Select how folders and files persist across products and applications.

  • Local Branch

    Allow individual team members to create their own local branch of the project's content, work independently on code, and merge those changes back to the master project.

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More Info About JuliaHub

Search Everything in One Place

  • Public & private code all together

  • Semantic search understands Julia syntax

  • Regex search for really tricky cases

Deploy Julia Apps

  • Build interactive apps & deploy easily

  • Bare Kubernetes app deploys also supported - to the cloud, to existing or air-gapped on-prem k8s clusters

  • SSO-integrated user auth

Scale with Ease

  • Grab a 1000-core cluster for Monte Carlo simulations

  • Use a beefy GPU machine to train ML models

  • Built-in support for DataSets.jl

Model Discovery

Combine models with tools like DiffEqFlux and NeuralPDE to discover missing physics and generate digital twins.

Combine with Pre-Built Models and Digital Twins

Grab complete models from the JuliaSim Model Store and compose the pieces to accelerate the design process.

Specialized Numerical Environments

Use the latest numerical tools, like discontinuity-aware differential equation solvers, high-performance steady state solvers, and domain-specific environments.

Blending classical physical modeling with modern Scientific Machine Learning techniques.

Accelerate

JuliaSim is a next generation cloud-based simulation platform, combining the latest techniques in Scientific Machine Learning with equation-based digital twin modeling and simulation. Our modern ML-based techniques accelerate simulation by up to 500x, changing the paradigm of what is possible with computational design. The premise of the software is to facilitate the design and accelerate challenging real-life models of considerable complexity.

Integrate

JuliaSim allows the user to import models directly from the Model Store (more information below) into the Julia environment, making it easy to build large complex simulations. The user-friendly GUI facilitates the process and makes simulation more accessible to a wider audience.

Specialize

JuliaSim produces surrogates of blackbox (and regular) dynamical systems using Continuous Time Echo State Networks (CTESNs). This novel technique allows, amongst other features, for implicit training in parameter space to stabilize the ill-conditioning present in stiff systems.

Learn more about the JuliaSim Ecosystem

Julia Computing delivers JuliaSim as an answer to accelerating simulations through digital-twin (or surrogate) modeling. By blending classical, physical modeling with advanced scientific machine learning (SciML) techniques, JuliaSim provides a next-generation platform for building, accelerating, and analyzing models.

Register Now