With Bender, you create Algos within Experiments, and ask Bender some Suggestions of Hyperparameters to try for these Algos, benchmark your algo with these hyperparameters, and then submit the results as a Trial.

You can then compare the Trials to get the best set of Hyper-Parameters for a given Algo or compare your Algos and get the best results possible for your Experiment!


An Experiment is closely tied to a problem.

You want for example to recognize automatically handwritten digits? That is a problem: you can create an Experiment for example named digits_recogition.

You want to find the shortest path to go somewhere? That’s another problem: therefore you create another Experiment.

Every experiment has an unique id in the form of: 0597ca48-66f7-42be-9021-12ec57d4251e

Every bender client allows you to create, load or delete an Experiment, just check out the specific documentation to learn how.


An Algo is simply a way to respond to an experiment: a way to answer the given problem.

Getting back to our example of digit recognition problem, there is plenty different ways to solve the problem: one would be a certain kind of neural network, another would be a random forest algorithm, etc.

Some are better than others but Bender is here to maximise the performances of each Algo and allows you to also compare them to find the best way to answer your Experiment.

For each Algo you want to specify a set of Hyperparameters that Bender will optimize. These parameters can be the learning rate of your neural network, the number of trees in your random forest, etc.


A Trial is Bender’s food. To train Bender on an Algo you created, he needs data to improve himself.

Each time you try a set of Hyperparameters, you want to make Bender know about it, also giving him a performance indicator associated with this Hyperparameters set (a loss, an accuracy, etc.).

In short: a Trial is a Hyperparameters set associated with a performance metric.


Of course it’s not up to you to decide which Hyperparameters to use or not. Just ask Bender to give you some new ones: that is a Suggestion.

Therefore you can automate the whole process of optimizing your hyperparameters efficiently and quicky.