Enterprise training suite for
modern deep learning

Explore all features
Neural network
Monitor and compare experiments

Major features at a glance

More Monitor experiments

Supervise the progress and all information of your experiment in real-time.

More Compare experiments

Compare all aspects of multiple experiments side-by-side in real-time.

More History tracking

All experiments with its metrics, hyperparameters, informations, files and more are stored automatically.

More Server management

Manage multiple training servers through one interface. Start and stop jobs with ease.

More Framework independent

Supports any model written in Python, no matter which framework. Ready to use Callback hook for Keras.

Git integration

We store git information and fetch your sourcecode automatically.

More Notifications

Get an email or slack notification once your job is done or crashed.

More Hyperparameter optimization

Fully automated and scaleable hyperparameter optimization.


Share models and experiments with your colleagues and watch together in real-time.

Monitor your

Supervise key performance indicators
and custom metrics in real time of your model
created in the model designer or your
custom python model (framework independent).

Also see all information you need to replicate
the experiment: Used hyperparameters, environment,
own additional information, files, etc.

Manage all experiments

See all current running training jobs aka experiment or history to see which
model and hyper-parameters performed best. With custom channels you can add as many metrics as you need.

Own server

With our aetros-cli you can easily connect your server to AETROS Trainer allowing you to start jobs without fighting with dozens of open ssh connections, while keeping all your data in your own server infrastructure.
AETROS has absolutely no access to your training data (images, csv files, etc). More information in our docs External server.

aetros server --secure-key=my-secure-key
Connected to aetros.com as marcj/alpha

Custom metrics

Send logs and metrics generated by your model
to AETROS Trainer using our Python SDK to
see in real-time your performance indicators.
More information in our docs Python SDK Channels.

job = aetros.backend.start_job('model/name')
accuracy_channel = job.create_channel('accuracy')
for i in range(0, 100):
accuracy_channel.send(x=i, y=...)
job.progress(i, 100)

Compare experiments

Compare every aspect of multiple experiments in very detail.
You can attach custom additional information at each job (e.g. which datasource and split ratio you used)
and compare side by side. Metrics as graphs, hyperparameters and uploaded files can be compared as well.

Automatic hyperparameter optimization

With our fully automated and scaleable hyperparameter optimization based on Hyperop you find easier and faster better hyperparameters for your model. More information in our documentation.

Deep neural network

Model designer

You can quickly create a deep neural network model using our model designer. For example a regression or image classification, without deep understanding of neural networks.

You can train the model completely on your own server infrastructure.
Python code based on the Keras framework is automatically generated and versioned.


Datasets in AETROS Trainer allows you to quickly create small datasets and link your created model with your own data.

You data stays in your data center and never reaches AETROS.
Use built-in feature image augmentation to prevent overfitting of your model.


With activated insights you get automatically a snapshot of all layers of your model per epoch and other insights like a confusion matrix.

Simple deployment

You can access your trained model via our API. The only thing you need to do is to train your model and tag your job with a version number.

You can also download the state of your trained model and run it in your own infrastructure.

curl http://predictor1.aetros.com/AETROS/digit-convolution@1.0.0 -F "input=@/path_to/local/image.jpg"
{ "prediction": [ { "class": 8, "prediction": 0.13521507382393 }, { "class": 9, "prediction": 0.1149984151125 }, { "class": 4, "prediction": 0.10820455849171 }, ], "times": { "prediction": 0.1549129486084, "prepare_fetch_input": 0.1016149520874 } }

Video demo

Simple Keras integration

Are you ready?

Improve your current machine learning workflow
or start digging into machine learning using our neural network designer.

Register now