Valohai blog

Insights from the deep learning industry.

TensorBoard + Valohai Tutorial

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.

Machine Learning at NVIDIA GTC 2019

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.

Build vs. Buy – A Scalable Machine Learning Infrastructure

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.

Automatic Version Control Meets Jupyter Notebooks

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.)

Multi-Cloud Data & Infrastructure Solution for Machine Learning

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.

EU/US Copyright Law and Implications on ML Training Data

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.

Reinforcement Learning Tutorial Part 3: Basic Deep Q-Learning

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.

Michelangelo – Machine Learning Infrastructure at Uber

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.

Reinforcement Learning Tutorial Part 2: Cloud Q-learning

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.

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