https://github.com/seungjaeryanlee/torchconfig

TorchConfig is a Python package that simplifies configuring PyTorch.
https://github.com/seungjaeryanlee/torchconfig

configuration python pytorch

Last synced: 7 days ago
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TorchConfig is a Python package that simplifies configuring PyTorch.

• Host: GitHub
• URL: https://github.com/seungjaeryanlee/torchconfig
• Owner: seungjaeryanlee
• Created: 2020-05-23T16:18:38.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-05-27T13:24:38.000Z (over 4 years ago)
• Last Synced: 2025-01-16T09:18:03.779Z (11 days ago)
• Topics: configuration, python, pytorch
• Language: Python
• Homepage: https://pypi.org/project/torchconfig/
• Size: 13.7 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# TorchConfig

**TorchConfig** is a Python package that simplifies configuring PyTorch.

Suppose that you want to test multiple optimizers to find which optimizer works best with your model. Here is one way you could achieve this:

```python

if CONFIG["optimizer_name"] == "SGD":

    optimizer = optim.SGD(

        net.parameters(),

        lr=CONFIG["optimizer_lr"],

        momentum=CONFIG["optimizer_momentum"],

        dampening=CONFIG["optimizer_dampening"],

        weight_decay=CONFIG["optimizer_weight_decay"],

        nesterov=CONFIG["optimizer_nesterov"],

    )

...

elif CONFIG["optimizer_name"] == "Adam":

    optimizer = optim.Adam(

        net.parameters(),

        lr=CONFIG["optimizer_lr"],

        betas=CONFIG["optimizer_betas"],

        eps=CONFIG["optimizer_eps"],

        weight_decay=CONFIG["optimizer_weight_decay"],

        amsgrad=CONFIG["optimizer_amsgrad"],

    )

}

```

With TorchConfig, this is just one line!

```python

optimizer = torchconfig.get_optimizer_from_dict(net.parameters(), CONFIG)

```

## Installation

```

pip install torchconfig

```

## How to Use

You can specify any `optimizer` or `lr_scheduler` by specifying its name through a dictionary key-value pair or an argument.

```python

optimizer_config = {"name": "SGD", "lr": 0.1 }

optimizer = torchconfig.get_optimizer_from_args(net.parameters(), name="SGD", lr=0.1)

# or

optimizer = torchconfig.get_optimizer_from_args(net.parameters(), **optimizer_config)

# or

optimizer = torchconfig.get_optimizer_from_dict(net.parameters(), optimizer_config)

```

```python

lr_scheduler_config = { "name": "CyclicLR", "base_lr": 0.01, "max_lr": 1 }

lr_scheduler = torchconfig.get_lr_scheduler_from_args(optimizer, **CONFIG["lr_scheduler"])

# or

lr_scheduler = torchconfig.get_lr_scheduler_from_args(optimizer, name="CyclicLR", base_lr=0.01, max_lr=1)

# or

lr_scheduler = torchconfig.get_lr_scheduler_from_dict(optimizer, CONFIG["lr_scheduler"])

```