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Parameter Placeholders

After you define parameters in your valohai.yaml, you can add various placeholders to your commands which get replaced with the actual values.

It is common to use these in the following manner:

- step:
    name: train-model
    image: tensorflow/tensorflow:0.12.1-devel-gpu
    command: python train.py {parameters}
    parameters:
      - name: max_steps
        pass-as: --max_steps={v}
        type: integer
        default: 300

This makes it easy to track used parameters for automated hyperparameter optimization and following how different parameters affect your model accuracy.

{parameters} placeholder

{parameters} injects all parameters to its position in the commands.

For example:

- step:
    name: train-model
    image: python:3.6
    command:
      - python train.py {parameters}
    parameters:
      - name: max-steps
        type: integer
        description: Number of steps to run the trainer
        default: 300
      - name: learning-rate
        type: float
        pass-as: --lr={v}
        description: Initial learning rate
        default: 0.001
      - name: architecture
        type: string
        pass-as: arc {v}
        default: 10xRELU-SoftMax
        optional: true

The above would generate the following command by default:

python train.py --max-steps=300 --lr=0.001 arc 10xRELU-SoftMax

When a value is undefined, the parameter will appear with its default value, except for the type flag. Flags will only ever appear, if they are defined with value set to true.

{parameter:} placeholder

You can also use singular parameters using the {parameter:<NAME>} syntax.

For example:

- step:
    name: preprocess-and-train
    image: python:3.6
    command:
      - python preprocess.py {parameter:train-split}
      - python train.py {parameter:learning-rate}
    parameters:
      - name: train-split
        type: integer
        default: 80
      - name: learning-rate
        pass-as: --lr={v}
        type: float
        default: 0.001

The above would generate the following commands by default:

python preprocess.py --train-split=80
python train.py --lr=0.001

{parameter-value:} placeholder

If you wish to ignore pass-as definition, you can use {parameter-value:<NAME>} to pass only the parameter value. This is essentially the same as defining pass-as: "{v}".

For example:

- step:
    name: preprocess
    image: python:3.6
    command:
      - python preprocess.py {parameter-value:train-split} {parameter-value:style}
    parameters:
      - name: train-split
        type: integer
        default: 80
      - name: style
        pass-as: -s={v}
        type: string
        default: nested

The above would generate the following command by default:

python preprocess.py 80 nested

There are no limits how many {parameters}, {parameter:<NAME>}, and {parameter-value:<NAME>} placeholders you can have in a set of commands so use them to your heart’s content!

Placeholders in inputs

Parameter placeholders can also be used in inputs, for example:

- step:
    name: train-model
    image: tensorflow/tensorflow:0.12.1-devel-gpu
    command: python train.py
    parameters:
      - name: device_id
        type: integer
        default: 455
      - name: family
        type: string
        default: "katti"
    inputs:
      - name: images
        default: s3://factories/{parameter:device_id}/images/*
      - name: prod-model
        default: datum://prod-model-{parameter:family}
      - name: sensor-data
        default: dataset://sensor-data-{parameter:family}/{parameter:device_id}

Multiple value placeholders

Parameters can have multiple values, and they can be used as command placeholders. In this case, the default should be a list.

Multiple inputs

Multiple value parameters are not supported as input placeholders.

- step:
    name: train-model
    image: tensorflow/tensorflow:0.12.1-devel-gpu
    command: python train.py {parameters}
    parameters:
      - name: seed_values
        type: integer
        multiple: separate
        default: [455, 922, 1344]

When used as a command placeholder, multiple value representation can be customized.

When using multiple: separate, items are joined using a comma into a single value, and passed as a single command argument. The joining can be customized into another character with the multiple-separator property. The default values [455, 922, 1344] would result in the following command:

python train.py --seed_values=455,922,1344

When using multiple: repeat instead, each item is passed as a repeating command argument. The same defaults result in the following command:

python train.py --seed_values=455 --seed_values=922 --seed_values=1344

You can also read parameters from configuration files as JSON or YAML, which can be easier with complicated parameters that are difficult to parse. Multiple value parameters are represented as a list in file configurations, no parsing and joining required. 

import json

# FYI: alternate YAML format can be found in /valohai/config/parameters.yaml
with open("/valohai/config/parameters.json") as f: 
    params = json.load(f)

print(params["seed_values"])  # => [455, 922, 1344]

valohai-utils can also read parameters from file configuration, but note that command line parameters are parsed first. If you wish to read parameters from the file using valohai-utils, do not also use {parameters} placeholders.

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