Eisen offers a Command Line Interface (CLI) which allows model training, validation, testing and serving. The CLI leverages configuration files in JSON format to build complex workflows to accomplish these tasks. A JSON file can be built using Eisen’s Domain Specific Language (DSL), or be obtained through Eisen Builder utility that can be reached at http://builder.eisen.ai
Due to the fact that Eisen’s DSL is not excessively complicated, and due to the fact that Eisen Builder can produce configuration files for arbitrary complex workflows, the DSL documentation is still pending. This will be addressed in a future documentation update.
Eisen-CLI is included in the distribution of eisen and can therefore be obtained by executing
$ pip install eisen
Otherwise, it is possible to obtain Eisen-CLI by installing only the eisen-cli package
$ pip install eisen-deploy
Using Eisen-Deploy can be achieved by importing the necessary modules directly in your code.
Command line utilities for training, validation, testing and deployment are included in Eisen CLI. The interface and purpose of each implementation is detailed here:
The CLI exposes functionality to the user by implementing argument parsing and and interface. Workflows are then executed by specific functions that parse configuration files and instantiate objects according to needs.
These functions are detailed here:
eisen_training(configuration, epochs, data_dir, artifacts_dir, resume)¶
This function parses a configuration file and creates all the necessary objects and worflow components to execute training. Everything will be built according to the configuration file.
configuration (Path) – path of configuration file for the job
epochs (int) – number of epochs requested for training
data_dir (Path) – base path for the data of this job
artifacts_dir (Path) – base path for the artifacts of this job
resume (bool) – use pre-existing artifacts to resume training