# Time Limits

Time limits help you control costs and prevent runaway executions. Valohai provides two timeout mechanisms:

* **Time Limit** — Maximum total execution duration
* **No Output Timeout** — Stop if execution produces no logs for a period

Both can be configured in the web UI or in your `valohai.yaml`.

## Time Limit

Set a maximum duration for your execution. When the time limit is reached, Valohai terminates the execution.

**Use cases:**

* Prevent forgotten executions from running indefinitely
* Enforce budget constraints on expensive GPU instances
* Ensure batch jobs complete within a scheduling window

### Set in Web UI

1. Create a new execution
2. Scroll to the **Runtime** section
3. Check **Set a Time Limit**
4. Enter the maximum duration in hours and minutes

> Not setting a time limit allows the execution to run indefinitely. This is the default behavior.

### Set in valohai.yaml

Define a default time limit for a step:

```yaml
- step:
    name: train-model
    image: tensorflow/tensorflow:2.13.0-gpu
    time-limit: 1h  # 1 hour
    command:
      - python train.py {parameters}
```

The `time-limit` value supports human-readable formats like `1h 30m 5s`, or you can specify seconds as an integer (e.g., `3600`).

***

## No Output Timeout

Stop executions that become unresponsive. If your execution produces no logs or output for the specified duration, Valohai terminates it.

**Use cases:**

* Detect and stop hung processes
* Catch infinite loops that produce no output
* Identify network or I/O blocking issues

### Set in Web UI

1. Create a new execution
2. Scroll to the **Runtime** section
3. Check **Set a No Output Timeout**
4. Enter the timeout duration in hours and minutes

> Not setting this will default to about 8 hours.

### Set in valohai.yaml

Define a default no-output timeout for a step:

```yaml
- step:
    name: train-model
    image: tensorflow/tensorflow:2.13.0-gpu
    no-output-timeout: 30m  # 30 minutes
    command:
      - python train.py {parameters}
```

The `no-output-timeout` value supports human-readable formats like `1h 30m 5s`, or you can specify seconds as an integer (e.g., `1800`).

***

## Example: Complete Step Configuration

Combine time limits with other step settings:

```yaml
- step:
    name: train-model
    image: tensorflow/tensorflow:2.13.0-gpu
    time-limit: 4h              # 4 hours max
    no-output-timeout: 30m      # 30 min no-output timeout
    command:
      - pip install -r requirements.txt
      - python train.py {parameters}
    parameters:
      - name: epochs
        default: 100
        type: integer
```

***

## Best Practices

**Set reasonable defaults in YAML.** Define time limits in your `valohai.yaml` so all team members use consistent settings. Override in the UI when needed.

**Use no-output timeout to catch hangs.** Long-running jobs should periodically log progress. If your training loop runs for hours without output, it may be stuck.

**Account for setup time.** Time limits include dependency installation, data download, and model initialization. Give enough buffer for these steps.

**Combine with early stopping.** For training jobs, consider using [early stopping](/tasks/early-stopping.md) based on metrics in addition to time limits.

***

## Troubleshooting

### Execution stopped unexpectedly

Check the execution logs for timeout messages. Common causes:

* **Time limit reached** — Increase the limit or optimize your code
* **No output timeout** — Add periodic logging to your training loop

***

## Related

* [Early Stopping](/tasks/early-stopping.md) — Stop based on metadata conditions
* [Spot Instances](/executions/advanced-features/spot-instances.md) — Handle interruptions for cost savings
* [Run Basic Execution](/executions/run-basic-execution.md) — Creating and running executions


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