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Important Parameters

As you can see from the results above, the default behavior is to execute a short test benchmark. However, we can specify a different benchmark, provide different constraints, and even run the experiment in a container or on AWS. There are many parameters for the script, but the most important ones are:

Framework (required)

  • The AutoML framework or baseline to evaluate and is not case-sensitive. See integrated frameworks for a list of supported frameworks. In the above example, this benchmarked framework randomforest.

Benchmark (optional, default='test')

  • The benchmark suite is the dataset or set of datasets to evaluate the framework on. These can be defined as on OpenML as a study or task (formatted as openml/s/X or openml/t/Y respectively) or in a local file. The default is a short evaluation on two folds of iris, kc2, and cholesterol.

Constraints (optional, default='test')

  • The constraints applied to the benchmark as defined by default in constraints.yaml. These include time constraints, memory constrains, the number of available cpu cores, and more. Default constraint is test (2 folds for 10 min each).

    Constraints are not enforced!

    These constraints are forwarded to the AutoML framework if possible but, except for runtime constraints, are generally not enforced. It is advised when benchmarking to use an environment that mimics the given constraints.

    Constraints can be overriden by benchmark

    A benchmark definition can override constraints on a task level. This is useful if you want to define a benchmark which has different constraints for different tasks. The default "test" benchmark does this to limit runtime to 60 seconds instead of 600 seconds, which is useful to get quick results for its small datasets. For more information, see defining a benchmark.

Mode (optional, default='local')

  • The benchmark can be run in four modes:

    • local: install a local virtual environment and run the benchmark on your machine.
    • docker: create a docker image with the virtual environment and run the benchmark in a container on your machine. If a local or remote image already exists, that will be used instead. Requires Docker.
    • singularity: create a singularity image with the virtual environment and run the benchmark in a container on your machine. Requires Singularity.
    • aws: run the benchmark on AWS EC2 instances. It is possible to run directly on the instance or have the EC2 instance run in docker mode. Requires valid AWS credentials to be configured, for more information see Running on AWS.

For a full list of parameters available, run:

python --help