> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> Integrate W&B with Hydra to manage complex configurations for ML experiments and log hyperparameters automatically.

# Hydra

> [Hydra](https://hydra.cc) is an open-source Python framework that simplifies the development of research and other complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line.

You can continue to use Hydra for configuration management while taking advantage of the power of W\&B.

## Track metrics

Track your metrics as normal with `wandb.init()` and `wandb.Run.log()` . Here, `wandb.entity` and `wandb.project` are defined within a hydra configuration file.

```python theme={null}
import wandb


@hydra.main(config_path="configs/", config_name="defaults")
def run_experiment(cfg):

    with wandb.init(entity=cfg.wandb.entity, project=cfg.wandb.project) as run:
      run.log({"loss": loss})
```

## Track hyperparameters

Hydra uses [omegaconf](https://omegaconf.readthedocs.io/en/2.1_branch/) as the default way to interface with configuration dictionaries. `OmegaConf`'s dictionary are not a subclass of primitive dictionaries so directly passing Hydra's `Config` to `wandb.Run.config` leads to unexpected results on the dashboard. It's necessary to convert `omegaconf.DictConfig` to the primitive `dict` type before passing to `wandb.Run.config`.

```python theme={null}
@hydra.main(config_path="configs/", config_name="defaults")
def run_experiment(cfg):
  with wandb.init(entity=cfg.wandb.entity, project=cfg.wandb.project) as run:
    run.config = omegaconf.OmegaConf.to_container(
        cfg, resolve=True, throw_on_missing=True
    )
    run = wandb.init(entity=cfg.wandb.entity, project=cfg.wandb.project)
    run.log({"loss": loss})
    model = Model(**run.config.model.configs)
```

## Troubleshoot multiprocessing

If your process hangs when started, this may be caused by [this known issue](/models/track/log/distributed-training). To solve this, try to changing wandb's multiprocessing protocol either by adding an extra settings parameter to `wandb.init()` as:

```python theme={null}
wandb.init(settings=wandb.Settings(start_method="thread"))
```

or by setting a global environment variable from your shell:

```bash theme={null}
$ export WANDB_START_METHOD=thread
```

## Optimize hyperparameters

[W\&B Sweeps](/models/sweeps) is a highly scalable hyperparameter search platform, which provides interesting insights and visualization about W\&B experiments with minimal requirements code real-estate. Sweeps integrates seamlessly with Hydra projects with no-coding requirements. The only thing needed is a configuration file describing the various parameters to sweep over as normal.

A simple example `sweep.yaml` file would be:

```yaml theme={null}
program: main.py
method: bayes
metric:
  goal: maximize
  name: test/accuracy
parameters:
  dataset:
    values: [mnist, cifar10]

command:
  - ${env}
  - python
  - ${program}
  - ${args_no_hyphens}
```

Invoke the sweep:

```bash theme={null}
wandb sweep sweep.yaml
```

W\&B automatically creates a sweep inside your project and returns a `wandb agent` command for you to run on each machine you want to run your sweep.

### Pass parameters not present in Hydra defaults

<a id="pitfall-3-sweep-passing-parameters-not-present-in-defaults" aria-label="Pass parameters not present in Hydra defaults" />

Hydra supports passing extra parameters through the command line which aren't present in the default configuration file, by using a `+` before command. For example, you can pass an extra parameter with some value by simply calling:

```bash theme={null}
$ python program.py +experiment=some_experiment
```

You cannot sweep over such `+` configurations similar to what one does while configuring [Hydra Experiments](https://hydra.cc/docs/patterns/configuring_experiments/). To work around this, you can initialize the experiment parameter with a default empty file and use W\&B Sweep to override those empty configs on each call. For more information, read [this W\&B Report](https://wandb.ai/adrishd/hydra-example/reports/Configuring-W-B-Projects-with-Hydra--VmlldzoxNTA2MzQw?galleryTag=posts\&utm_source=fully_connected\&utm_medium=blog\&utm_campaign=hydra).