Metadata-Version: 2.1
Name: enchanter
Version: 0.6.0rc1
Summary: Enchanter is a library for machine learning tasks for comet.ml users.
Home-page: https://github.com/khirotaka/enchanter
Author: Hirotaka Kawashima
Author-email: 
License: Apache-2.0
Description: # Enchanter
        
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        [![Using PyTorch](https://img.shields.io/badge/PyTorch-red.svg?labelColor=f3f4f7&logo=data:image/svg+xml;base64,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)](https://pytorch.org/)
        
        Enchanter is a library for machine learning tasks for comet.ml users.
        
        ## Installation
        ```shell script
        pip install enchanter
        ```
        or
        ```shell script
        pip install git+https://github.com/khirotaka/enchanter.git
        ```
        
        ## Documentation
        *   [API Reference](https://enchanter.readthedocs.io/ja/latest/)
        *   [Tutorial](https://enchanter.readthedocs.io/ja/latest/tutorial/modules.html)
        
        ## Getting Started
        Try your first Enchanter Program
        
        ### Training Neural Network
        
        ```python
        from comet_ml import Experiment
        import torch
        import enchanter
        
        model = torch.nn.Linear(6, 10)
        optimizer = torch.optim.Adam(model.parameters())
        
        runner = enchanter.wrappers.ClassificationRunner(
            model, 
            optimizer,
            criterion=torch.nn.CrossEntropyLoss(),
            experiment=Experiment()
        )
        
        runner.add_loader("train", train_loader)
        runner.train_config(epochs=10)
        runner.run()
        ```
        
        ### Hyper parameter searching using Comet.ml
        
        ```python
        from comet_ml import Optimizer
        
        import torch
        import torch.nn as nn
        import torch.optim as optim
        from sklearn.datasets import load_iris
        
        import enchanter.wrappers as wrappers
        import enchanter.addons as addons
        import enchanter.addons.layers as layers
        from enchanter.utils import comet
        
        
        config = comet.TunerConfigGenerator(
            algorithm="bayes",
            metric="train_avg_loss",
            objective="minimize",
            seed=0,
            trials=5
        )
        
        config.suggest_categorical("activation", ["addons.mish", "torch.relu", "torch.sigmoid"])
        opt = Optimizer(config.generate())
        
        x, y = load_iris(return_X_y=True)
        x = x.astype("float32")
        y = y.astype("int64")
        
        
        for experiment in opt.get_experiments():
            model = layers.MLP([4, 512, 128, 3], eval(experiment.get_parameter("activation")))
            optimizer = optim.Adam(model.parameters())
            runner = wrappers.ClassificationRunner(
                model, optimizer=optimizer, criterion=nn.CrossEntropyLoss(), experiment=experiment
            )
        
            runner.fit(x, y, epochs=1, batch_size=32)
        ```
        
        ## License
        [Apache License 2.0](LICENSE)
        
Keywords: pytorch comet_ml
Platform: UNKNOWN
Description-Content-Type: text/markdown
