Microsoft's Cognitive Toolkit or CNTK is an open source framework for building Deep Learning models. This relatively new framework has been gaining traction so we decided to make sure Valohai supports it well. One of the benefits over competing frameworks has been CNTK’s ground up support for multi-node, multi-GPU training, something that for instance TensorFlow has been struggling to tackle well. If you are doing work on really large datasets, you should maybe give it a try.
Synthetic data is artificially created information rather than recorded from real-world events. A simple example would be generating a user profile for John Doe rather than using an actual user profile. This way you can theoretically generate vast amounts of training data for deep learning models and with infinite possibilities.
You might have heard that every individual subject to automated decision making by machine learning models has a right to an explanation of the result. I bet you feel drops of sweat forming on your forehead when you receive an inquiry from a manager saying that he needs details about how a certain decision was made. If thinking about this scenario gives you chills, you are in the right place. Read further and learn how to tackle the transparency issue.
When meeting with teams that are working with machine learning today, there is one point above everything else that I try to teach. It is the importance of storing and versioning of machine learning experiments and especially how many things there actually are that need to be stored.
You know what really grinds my gears? When I have a deep learning model that I want to train and I have to SSH into my AWS instance, install all the drivers and libraries, run my code and then forget to shut down my machine! Once, I ended up forgetting one up over the weekend that cost my employer over $10 000!!!
Recreating experiments inside Valohai could be a whole lot easier and we’ve heard your cries!
With the latest release, live today, whenever you copy an old experiment and want to re-run it Valohai now copies the tags and title over from the previous experiment. Tags are also now automatically propagated down to individual executions when you create a task with several ones e.g. during a hyperparameter sweep.
We also improved the creation of new experiments. When creating a new execution you now have a dropdown for selecting your Docker image. We pre-fill the filterable box with our list of recommended Docker images but you can naturally point to any custom made Docker image as well. And naturally, if you have defined a default one in your valohai.yaml file that will be the default one selected from the get-go.
We have also fixed several bugs and made a handful of smaller fixes in the UI and the API that you can read more about in the patch notes.
All of us have seen those fear mongering headlines about how artificial intelligence is going to steal our jobs and how we should be very careful with biased AI algorithms. Bias means that the algorithm favors certain groups of people or otherwise guides decisions towards an unfair outcome. Bias can mean giving a raise only to white male employees, increasing criminal risk factors of certain ethnic groups and filling your news feed only with topics and point of views that you are currently consuming – instead of giving a broad, balanced view of the world and educating you.
Valohai and Microsoft cross lightsabers in the battle for artificial intelligence, through Microsoft’s global ScaleUp Program.
Just lately we’ve been playing around with IBM PowerAI in order to ensure our customers can leverage it in large-scale on-premise training. PowerAI in itself is IBM’s solution for deep learning consisting of software and hardware to help you quickly train deep learning models. Today we’re happy to announce that Valohai fully supports PowerAI and our customers can start using it!