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Author: Jason

Tensorwerk Announcement

A Letter From Luca Antiga, Co-Founder and CEO of Tensorwerk

Since its inception, Tensorwerk has been about people. First and foremost, people like Rick Izzo and Sherin Thomas, who were there with me materializing our vision of accessible, democratic, data-driven software.

It has also been about the many people with whom we’ve collaborated along the way, exploring synergies (as corporate jargon would put it), or excitedly sharing each other’s ideas (as a regular human would). This group includes the team at Grid. When we met Will and Luis, it became immediately clear that we shared a common vision for the future of machine learning and software development at large, one that was simultaneously more ambitious than anything attempted previously and also firmly grounded in a desire to make technology more accessible, easy to use, and intuitive than it had ever been. Out of our mutual desire to solve big challenges in machine learning like abstraction and composition grew a determination to empower developers with the next generation of deep learning products.

Finally, and perhaps most importantly, it has been about the expansive and constantly growing network of people who use, think about, innovate, develop, and scale up machine learning technology on a daily basis. By working with Grid and the PyTorch Lightning community, our goal is to serve that community more effectively and more profoundly than ever before. We hope, for example, to apply the expertise we’ve developed to make enterprise-level model serving accessible not only to large enterprises, but to the broader machine learning community as well. As deep learning becomes an increasingly integral aspect of software development, and as that development moves towards a combination of prescriptive and data-driven software, we have sought to build out a paradigm that enables the people who will be developing within it to do amazing things.

When I think about how Tensorwerk fits into Grid, I think about the people at the heart of these overlapping, ambitious, and exciting endeavors. Not just the inspiring people with whom I work every single day, but also the developers from across the world who use our tooling to do an array of outstanding things, and the people we have yet to bring into the fold.

We’ve got plenty of challenges ahead of us, and I’m excited to approach them together.

Luca Antiga
Co-Founder, CEO

⚡️ Read the press release here

Luca Antiga, Tensorwerk

How Computer Vision Researchers Use Grid For Their Infrastructure Requirements

The Project:

Clutterbot is a New Zealand-based startup that builds autonomous cleaning robots. Syed Riaz, the Senior Machine Learning Engineer for Clutterbot, focuses on computer vision use cases to solve real world challenges. His expertise in object detection, tracking and multitask learning is helping the company develop a working prototype.

Regular cleaning is an essential task for improving health and reducing stress, but takes time away from our already busy lives. Clutterbot’s cleaning robot gives that time back so that users can focus on family or other personal activities. It also encourages a sustainable lifestyle by reducing the environmental impact of cleaning and organizing a space.

Clutterbot uses Grid to build a cleaning robot without worrying about MLOps infrastructure
The company uses state-of-the-art computer vision and artificial intelligence to navigate home environments, find toys/objects on the floor and organize them by category into containers. A mobile app houses and manages this entire process.

The Challenge:

The biggest challenge for Syed and his team was that they had to spend a considerable amount of time building an ML (Machine Learning) infrastructure, which was both costly and resource-intensive. After the team had completed their significant research and development phase, the need to build and manage this infrastructure prevented them from working on the project itself. Building this infrastructure on their own caused delays in prototyping new models, and implementing those technologies from scratch also grew into a costly and significant investment.

Not wanting to waste time and resources building out their own ML infrastructure led Clutterbot to look for a solution that would manage this facet of their pipeline.

The Solution:

A Clutterbot team member first learned about Grid after using PyTorch Lightning and was initially drawn to its convenient and easily-implemented features such as Sessions for quick prototyping and model training.

Grid Sessions are an interactive environment in which you can develop, analyze and prototype models or ideas on a live machine. Grid allocates the hardware you need on demand, so you only pay for what you need when you need it.

The Sessions feature, as well as its pre-installed tools, allowed the Clutterbot team to mount numerous GPUs automatically, enabling them to train their model faster. They were also able to pause their session on demand without losing any progress. Pausing a session, for example overnight or over the weekend, typically saves a considerable amount in training costs when nobody is available to monitor the session. Whenever you’re ready to resume, a simple click continues the session.

Because of Grid’s easily-implemented features, Clutterbot no longer had to worry about developing their own ML infrastructure from the ground up. This auto-restart feature allowed them to train their models on demand, whenever they needed to. That enabled them to avoid paying for training costs they didn’t need, as they would have with AWS. Instead, they were able to focus on solving the research, development, and business problems that mattered to them.

The team benefited from:

  • Not having to write a whole lot of code from scratch!
  • Flexible sessions which allowed them to spin up multiple GPUs and easily shut down when completed.
  • Not having to worry about the boilerplate, how to deal with CUDA and normal installation of a variety of drivers – everything is tied in with sessions.
  • Customizable settings that suit you and your team’s workflow.

Getting Started with Grid:

Interested in learning more about how Grid can help you manage deep learning model development for your next project? Get started with Grid’s free community tier account (and get $25 in free credits!) by clicking here. Also, explore our documentation and join the Slack community to learn more about what the Grid platform can do for you.