Bring your parameters to Valohai (without valohai-utils)

• Defining parameters allows you to easily rerun, sort, and keep track of executions based on the parameter values used to run them.
• You can easily create (or copy) an execution and change the parameters in the UI, without changing your code.
• Defining parameters allows you to start creating Tasks where you run multiple parallel executions with different parameter combinations.
• Each step has its own parameter configuration in valohai.yaml.
• A parameter can be a type of a string, integer, float, and flag (=boolean).
• Parameters are passed as command-line arguments to your code.
  • Edit how parameters are passed to your code using pass-as.
  • You can also parse parameter values from YAML and JSON files inside your execution. See file-based configuration.

Define parameters in Python:

import argparse

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--iterations', type=int, default=10)
    return parser.parse_args()

args = parse_args()

print(args.iterations)

Create a valohai.yaml configuration files and define your step in it:

- step:
    name: Train model
    image: tensorflow/tensorflow:1.13.1
    command: python myfile.py {parameters}
    parameters:
      - name: iterations
        type: integer
        default: 100