Run Basic Execution

Executions are Valohai's core compute unit, think of them as containerized ML jobs that run your code with specific parameters and inputs.

This guide shows you how to create and run executions using both the web interface and command line.

What You Need

Before running an execution:

• A project with a valohai.yaml file containing at least one step

• Your code committed to the linked repository

• Sufficient compute quota in your organization

Method 1: Web Interface

Navigate to your project and click Create Execution.

Configure your execution:

1. Select the step you want to run

2. Set parameters (or use defaults)

3. Add input files if your step requires them

4. Choose your compute environment

5. Click Create Execution

The execution will queue automatically and start when resources are available.

Method 2: Command Line

Basic Execution

Run a step with default settings:

vh exec run step-name

With Parameters

Override the default parameter values:

vh exec run step-name --epochs 100 --learning-rate 0.001

With Inputs

Override the default input files or datasets:

vh exec run step-name --dataset=https://example.com/data.zip

Combined Example

vh exec run train-model \
  --dataset=s3://my-bucket/training-data.zip \
  --epochs 50 \
  --batch-size 32

💡 Use vh exec run --help to see all available options.

Running local code with --adhoc:

During development, you often want to test changes without committing to Git. Use the --adhoc flag to run your local code directly:

vh execution run --adhoc

This packages your local changes, uploads them to your data store, and downloads them on the worker for the execution. Everything stays fully reproducible, Valohai tracks the exact code snapshot used.

Execution Lifecycle

Your execution moves through these states:

State	Description
created	Waiting for quota or queuing
queued	Waiting for available compute resources
started	Currently running your code
stopping	Graceful shutdown in progress
complete	Finished successfully
error	Failed with an error
stopped	Manually cancelled

Only the started state runs your actual code.

Monitor Your Execution

Real-time Logs

Web UI: Click on any running execution to view live logs.

CLI: Stream logs to your terminal:

vh exec logs <execution-id> --stream

Get execution status:

vh exec list --count 5

Execution Environment

Each execution runs in an isolated container with:

• Your code at /valohai/repository/ (working directory)

• Input files at /valohai/inputs/{input-name}/

• Output directory at /valohai/outputs/ (write your results here)

• Docker image with your specified tools and libraries

Common Issues

Files Not Found

Problem: FileNotFoundError: /valohai/inputs/data/file.csv

Solution: Check your input paths. Inputs are directories, not files:

# Wrong
data = pd.read_csv('/valohai/inputs/dataset.csv')

# Correct
data = pd.read_csv('/valohai/inputs/dataset/data.csv')

Outputs Not Saved

Problem: Generated files don't appear in the web UI.

Solutions:

• Write files to /valohai/outputs/ only

• Ensure files exist when your script completes

Permission Errors

Problem: PermissionError: [Errno 13] Permission denied

Solution: Don't write to /valohai/inputs/ it's read-only. Use /valohai/outputs/ instead.

Next Steps

• Monitor progress in the web UI or with vh exec logs

• Download outputs once execution completes

• Compare results across multiple executions

• Scale up by running multiple executions with different parameters

🎓 Want a visual walkthrough? Complete Module 3 on Valohai Academy