Automatic hyperparameter optimization: Easier than ever

24. February 2017, Feature

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:

  1. You define hyperparameters (for example learning_rate)
  2. Its spaces (for example learning_rate from 0.005 to 0.5)
  3. We start the training script of your model several times with hyperparameters within the given space
  4. In your model you send us your KPI (accuracy for example)
  5. We can determine which hyperparameter performed well and which didn't
  6. Calculate further hyperparameter and start automatically as many training runs as you want (at wish parallel distributed across multiple servers)

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.


  • Automatic search of better hyperparameters
  • Three different optimization algorithms (Random, TPE, Annealing)
  • Automatic training job distribution across multiple servers
  • Very detailed and convenient hyperparameter space definition through interface
  • Runtime constrains like max epochs and max time
  • Watch the process, results and metrics in real-time in AETROS Trainer
  • Completely based on Git
  • All results can be exported as CSV

Full documentation

See our main documentation: Automatic hyperparameter optimization.

Improved hyperparameters

23. February 2017, Feature

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.

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Better jobs browser

20. February 2017, Feature

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.

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New feature: External servers / job scheduler

25. January 2017, Feature

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.

New Website, new version and won NVIDIA contest

25. January 2017, Company

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!

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