Use parameters – Valohai Help

This section will show you how to use parameters in Valohai. Defining parameters will allow you to easily rerun your executions and experiment with a different set of values.

In this section you will learn:

How to define Valohai parameters
How to change parameter values between executions both in the CLI and in the UI
Defining Valohai parameters will allow you to easily rerun your executions and experiment with a different set of values.

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 it’s own parameter configuration in valohai.yaml.
A parameter can be type of a string, integer, float, and flag.
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.

Let’s start by defining parameters for our train-model step.

Update the valohai.yaml file with two new parameter definitions:

- step:
    name: train-model
    command:
      - pip install -r requirements.txt
      - python train.py {parameters}
    image: tensorflow/tensorflow:2.6.0
    parameters:
      - name: epoch
        type: integer
        default: 5
      - name: learning_rate
        type: float
        default: 0.001

Update train.py to use the parameter epoch in model.fit and use the learning_rate parameter value in the optimizer.

Read the parameter value during an execution with valohai.parameters('myparam').value

import numpy as np
import tensorflow as tf
import valohai

input_path = 'mnist.npz'
with np.load(input_path, allow_pickle=True) as f:
    x_train, y_train = f['x_train'], f['y_train']
    x_test, y_test = f['x_test'], f['y_test']

x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10)
])

optimizer = tf.keras.optimizers.Adam(learning_rate=valohai.parameters('learning_rate').value)

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

model.compile(optimizer=optimizer,
            loss=loss_fn,
            metrics=['accuracy'])

model.fit(x_train, y_train, epochs=valohai.parameters('epoch').value)

model.evaluate(x_test,  y_test, verbose=2)

output_path = valohai.outputs().path('model.h5')
model.save(output_path)

Run in Valohai

Finally run a new Valohai execution.

vh exec run train-model --adhoc

Rerun an execution with different parameter values

Open your project on app.valohai.com

Open the latest execution
Click Copy
Scroll down to the Parameters section
Change the value of epochs
Click Create execution

You can also run a new execution with different parameter values from the command line:

vh exec run train-model --adhoc --epoch=10

Core concept: Parameters
Core conept: Hyperparameter search
Tutorial: Running a grid search

Next: Download data to your job ⇒

Run in Valohai

Rerun an execution with different parameter values

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