Blog

Portable & Reproducible, Across Virtual & Bare Metal

Posted by Dav Clark on May 14, 2020 7:13:44 AM

Working exclusively in a single cloud isn't possible for most people, and that is not just because it is expensive. Real work requires significantly flexibility around deployment.

For example, sensitive data typically can't go in the cloud. Or maybe each of your three clients uses a different cloud, or maybe you spend significant time on a laptop. 

It would be nice if things would "just work" wherever you want them to, but the barriers are many and large. Git & Docker skills are table stakes. Typos & hard coded variables rule the day. No matter how careful you are, stuff goes wrong. Maybe your collaborators don't have the same level of care and technical skill you do.

Who knows? The possibilities are endless.

Well, it used to be hard. There is a new container native system that moves reproducible work between machines (virtual or bare metal) with a few clicks.

No need to know Docker or Git. No need to be obsessive about best practices. No need to worry who is on what machine. 

We will demo it here using Dask and DigitalOcean for context. In the demo we:

  1. Create a 32-core Droplet (i.e. instance) on Digital Ocean
  2. Install the open source Gigantum Client on the Droplet
  3. Import a Dask Project from Gigantum Hub and run it
  4. Sync your work to Gigantum Hub to save it for later.
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Topics: Reproducibility, Data Science, Open Science

Rebooting reproducibility: From re-execution to replication

Posted by Tyler Whitehouse, Dav Clark and Emmy Tsang on Jul 12, 2019 12:26:00 PM

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Computational reproducibility should be trivial but it is not. Though code and data are increasingly shared, the community has realised that many other factors affect reproducibility, a typical example of which is the difficulty in reconstructing a work’s original library dependencies and software versions. The required level of detail documenting such aspects scales with the complexity of the problem, making the creation of user-friendly solutions very challenging.

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Topics: Reproducibility, Data Science

Making Reproducibility Reproducible

gigantum blog post 12

Reproducibility doesn’t have to be magic, anymore. This image is provided by Abstruse Goose under the Creative Commons License

TL;DR - We believe the following

  • Approaches to the transmission of scientific knowledge are currently broken, mainly due to the criticality of software in modern research.
  • Calling re-execution of static results “reproducibility” isn’t enough. Reproducibility should be functionally equivalent to collaboration.
  • Academic emphasis on best practices is ineffective and should switch to a product based approach that minimizes effort rather than maximizes it.
  • By focusing on the needs of the end user, people can actually improve how scientific knowledge is communicated and shared.
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Topics: Science, Reproducibility, Data Science, Containers, Jupyter

Gigantum – a simple way to create and share reproducible data science and research

Today, we present Gigantum, an open source platform for creating and collaborating on computational and analytic work, complete with:

  • Automated, high-resolution versioning of code, data and environment for reproducibility and rollback
  • Work and version history illustrated in a browsable activity feed
  • Streamlined environment management with customization via Docker snippets
  • One-click transfer between laptop and cloud for easy sharing
  • Seamless integration with development environments such as JupyterLab
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Topics: Data Science, Software

Extending Git Commit Metadata In Gigantum

Posted by Dean Kleissas - Co-founder and CTO at Gigantum on Jul 20, 2018 12:27:00 PM

At Gigantum, we are building an open-source tool for developing, executing, and sharing data science projects that automates the creation of versioned and containerized code. This way your work is always accessible, reproducible, and transparent. Our ultimate goal is to make science and data science more efficient and reproducible, and we want people to directly access and build on each other’s work without all of the technical hassles. You can learn more about Gigantum, try the Client in the cloud, or download and install it locally at our website: https://gigantum.com

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Topics: Open Science, Git, Software