### Install mlconfig Python package

Source: https://github.com/narumiruna/mlconfig/blob/main/README.md

This command installs the mlconfig library using pip, the standard package installer for Python. It should be run in a shell or terminal environment.

```shell
$ pip install mlconfig
```

--------------------------------

### Instantiate PyTorch components from mlconfig YAML in Python

Source: https://github.com/narumiruna/mlconfig/blob/main/README.md

This Python script (`main.py`) demonstrates how to use `mlconfig` to load a YAML configuration file and instantiate PyTorch models and optimizers. It includes the definition of a custom `LeNet` neural network module and shows how to register classes for `mlconfig` to find and instantiate them dynamically.

```python
from torch import nn
from torch import optim

from mlconfig import instantiate
from mlconfig import load
from mlconfig import register

register(optim.Adam)


@register
class LeNet(nn.Module):

    def __init__(self, num_classes):
        super(LeNet, self).__init__()
        self.num_classes = num_classes

        self.features = nn.Sequential(
            nn.Conv2d(1, 6, 5, bias=False),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(6, 16, 5, bias=False),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),
        )

        self.classifier = nn.Sequential(
            nn.Linear(16 * 5 * 5, 120),
            nn.ReLU(inplace=True),
            nn.Linear(120, 84),
            nn.ReLU(inplace=True),
            nn.Linear(84, self.num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x


def main():
    config = load('conf.yaml')

    model = instantiate(config.model)
    optimizer = instantiate(config.optimizer, model.parameters())


if __name__ == '__main__':
    main()
```

--------------------------------

### Define PyTorch model and optimizer configuration in YAML

Source: https://github.com/narumiruna/mlconfig/blob/main/README.md

This YAML file (`conf.yaml`) specifies the configuration for a machine learning model (LeNet) and an optimizer (Adam). It demonstrates how to define parameters, including a shared variable `${num_classes}`, which mlconfig can resolve during instantiation.

```yaml
num_classes: 50

model:
  name: LeNet
  num_classes: ${num_classes}

optimizer:
  name: Adam
  lr: 1.e-3
  weight_decay: 1.e-4
```

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