# wandb.config

[![](https://www.tensorflow.org/images/GitHub-Mark-32px.png)View source on GitHub](https://www.github.com/wandb/client/tree/v0.10.31.dev1/wandb/sdk/wandb_config.py#L23-L239)

Config object

```python
config() -> None
```

Config objects are intended to hold all of the hyperparameters associated with a wandb run and are saved with the run object when `wandb.init` is called.

We recommend setting `wandb.config` once at the top of your training experiment or setting the config as a parameter to init, ie. `wandb.init(config=my_config_dict)`

You can create a file called `config-defaults.yaml`, and it will automatically be loaded into `wandb.config`. See <https://docs.wandb.com/library/config#file-based-configs>.

You can also load a config YAML file with your custom name and pass the filename into `wandb.init(config="special_config.yaml")`. See <https://docs.wandb.com/library/config#file-based-configs>.

## Examples:

Basic usage

```python
wandb.config.epochs = 4
wandb.init()
for x in range(wandb.config.epochs):
    # train
```

Using wandb.init to set config

```python
wandb.init(config={"epochs": 4, "batch_size": 32})
for x in range(wandb.config.epochs):
    # train
```

Nested configs

```python
wandb.config['train']['epochs] = 4
wandb.init()
for x in range(wandb.config['train']['epochs']):
    # train
```

Using absl flags

```python
flags.DEFINE_string(‘model’, None, ‘model to run’) # name, default, help
wandb.config.update(flags.FLAGS) # adds all absl flags to config
```

Argparse flags

```python
wandb.init()
wandb.config.epochs = 4

parser = argparse.ArgumentParser()
parser.add_argument('-b', '--batch-size', type=int, default=8, metavar='N',
                    help='input batch size for training (default: 8)')
args = parser.parse_args()
wandb.config.update(args)
```

Using TensorFlow flags (deprecated in tensorflow v2)

```python
flags = tf.app.flags
flags.DEFINE_string('data_dir', '/tmp/data')
flags.DEFINE_integer('batch_size', 128, 'Batch size.')
wandb.config.update(flags.FLAGS)  # adds all of the tensorflow flags to config
```