We just published one of our newest and biggest features: Model optimizations. This allows you to automatically find hyperparameters based on the KPI of any python model, no matter which framework you use. Basic idea:
learning_rate from 0.005 to 0.5)
In our user interface AETROS Trainer you can start as many optimizations as you want, watch its progress, adjust hyperparameter spaces, compare or export the results.
See our main documentation: Automatic hyperparameter optimization.
You can now define hyperparameters in a even more detailed way. You can choose between seven types: String, Number, Boolean, Group (dict), Choice: String (selectbox), Choice: Number(selectbox), Choice: Group (select group). And of course, you can overwrite those hyperparameters per job.Read more ...
With our improved jobs browser you can now create own job categories, export jobs as CSV and see continuous integration builds. If you hover with your mouse over a particular job you see now additionally all used hyperparameters and custom information. Through the implementation of a pagination you can now browse hundreds or thousands of jobs without performance issues.Read more ...
You can now connect external server with AETROS using
aetros-cli so you can start and monitor training jobs across multiple external servers with just one command.
This makes it super easy to distribute your training jobs across multiple servers without using ssh (and start each job manually).
More documentation about that feature can be seen at External server infrastructure.
We are excited to announce that we won the NVIDIA Inception "cool demo" contest! The price is a brand new NVIDIA Pascal Titan X, that we will definitely use to train a lot of new hot deep learning models. Thank you very much, NVIDIA!Read more ...