> For the complete documentation index, see [llms.txt](https://docs.valohai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.valohai.com/tasks/grid-search.md).
# Grid Search
Grid Search tests all possible combinations of your specified parameter values.
It's the most thorough approach, every combination gets evaluated. This works well when you have a manageable parameter space and want exhaustive results.
> 💡 **Tip:** For large parameter spaces, consider [Random Search](/tasks/random-search.md) or [Bayesian Optimization](/tasks/bayesian-optimization.md) to reduce execution count.
### Before you start
You need at least one step with parameters defined in your `valohai.yaml`:
```yaml
- step:
name: train-model
image: tensorflow/tensorflow:2.6.0
command:
- python train.py {parameters}
parameters:
- name: learning_rate
default: 0.001
type: float
- name: batch_size
default: 32
type: integer
```
You can also launch a Task from a completed execution without editing your YAML.
### Parameter types
When creating a Grid Search Task, you can define parameters using these types:
**Single:** Use one specific value for this parameter across all executions.
**Multiple:** Provide a list of discrete values. Valohai will test every combination with other parameters.
**Linear:** Generate evenly spaced values between a minimum and maximum (e.g., `0.001, 0.002, 0.003`).
**Logspace**: Generates values that are evenly spaced on a logarithmic scale, which means each step multiplies by a constant factor rather than adding a constant amount.
### Create a Grid Search from Git
1. Open your project in Valohai
2. Go to the **Tasks** tab
3. Click **Create Task**
4. Select the step that contains your parameters
5. Scroll to **Parameters**
6. Confirm **Grid search** is selected as the Task type (this is the default)
7. For each parameter:
* Choose the type (Single, Multiple, Linear, Logspace)
* Enter the values:
* **Multiple:** One value per line
* **Linear/Logspace:** Min, max, and count
8. Click **Create task**
Valohai will queue executions for every parameter combination and start running them in parallel.
### Create a Grid Search from an execution
If you've already run an execution and want to explore variations:
1. Open your project in Valohai
2. Go to the **Executions** tab and open a completed execution
3. Click the **Task** button in the top-right corner
4. Scroll to **Parameters**
5. Confirm **Grid search** is selected
6. Configure parameter values as described above
7. Click **Create task**
### Example: Grid Search Task
This configuration creates 12 executions (3 learning rates × 4 batch sizes):
| **Parameter** | **Type** | **Values** |
| -------------- | -------- | ---------------- |
| learning\_rate | Multiple | 0.001, 0.01, 0.1 |
| batch\_size | Multiple | 16, 32, 64, 128 |
Each execution will train with one unique combination, and you can compare results in the Task view.
### Next steps
* [Bayesian Optimization](/tasks/bayesian-optimization.md) for intelligent hyperparameter search
* [Manual Sweeps](/tasks/manual-sweeps.md) to specify exact parameter combinations
* [Task Blueprints](/tasks/task-blueprints.md) to define Tasks in your YAML
---
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