Insights from the deep learning industry.
One of the core design paradigms of Valohai is technology agnosticism. Building on top of the file system and in our case Docker means that we support running very different kinds of applications, scripts, languages and frameworks on top of Valohai. This means most systems are Valohai-ready because of these common abstractions. The same is true for TensorBoard as well.
Last week we had the pleasure of joining our partner SwiftStack at our joint booth at the NVIDIA GTC 2019 conference in San Jose. GTC touts itself as the premier AI conference and it sure was.
In this blog post we’ll look at which parts a machine learning platform consists of and compare building your own infrastructure from scratch to buying a ready-made service that does everything for you.
Running a local notebook is great for early data exploration and model tinkering, there’s no doubt about it. But eventually you’ll outgrow it and want to scale up and train the model in the cloud with easy parallel executions, full version control and robust deployment. (Letting you reproduce your experiments and share them with team members at any time.)
SwiftStack and Valohai, in joint partnership, announce the world’s first peta-scale ML solution that covers everything from computation to data management in a multi-cloud environment. The solution provides a global namespace removing silos and enabling universal access to all your data in all your machine learning use-cases. It has built-in support for Azure, Google Cloud, AWS and SwiftStack.
We may live in the era of “Big Data,” and yet the access to it is somewhat restricted; especially, when we talk about high-quality data. This blogpost will address the question of acquiring data for your Machine Learning projects from the perspective of EU and US copyright laws.
In part 1 we introduced Q-learning as a concept with a pen and paper example. In part 2 we implemented the example in code and demonstrated how to execute it in the cloud. In this third part, we will move our Q-learning approach from a Q-table to a deep neural net.
When we founded Valohai two years ago, we were lucky to make friends with team leads for Uber’s Michelangelo machine learning platform. Michelangelo has been an inspiration in building Valohai for the other 99.999...% of companies that aren’t Uber but still need to speed up their machine learning through automation.
In part 1, we looked at the theory behind Q-learning using a very simple dungeon game with two strategies: the accountant and the gambler. This second part takes these examples, turns them into Python code and trains them in the cloud, using the Valohai deep learning management platform.