my-pytorch-kit 0.3.0

My toolkit for pytorch model development

My Pytorch Toolkit

Userful pytorch toolkit for training models, replacing boilerplate code. Provides functions for training, modeling and evaluating models. Also provides several architecture implementations.

Installation

From PyPI

pip install my-pytorch-kit

From source

Clone this repo and run pip install ..
Then, you can import the module my_pytorch_kit.

Usage

This package revolves around the BaseModel, Trainer and Evaluator classes, which are extended to model, train and evaluate a model respectively.

graph TD
    subgraph "Usage Workflow"
        A["<b>Define Model</b><br/>(extends BaseModel)"]
        B["<b>Define Data</b><br/>(Dataset / DataLoader)"]
        G["<b>Extras</b><br/>(Optimizer, Tensorboard)"]
        C["<b>Initialize Trainer</b><br/>(extends Trainer)"]
        D["<b>Initialize Evaluator</b><br/>(extends Evaluator)"]
        E["trainer.train()"]
        F["evaluator.evaluate()"]
        G["<b>Intitialize Tuner</b><br/>(Hyperparameter Tuning)"]
        H["tuner.tune()"]
        I["<b>Provided Architectures</b><br/>(Classifier, AE, VAE, ...)"]

    end

    %% Define node relationships
    A --> C
    B --> C
    I --> A
    A --> D
    B --> D
    C --> E
    D --> F
    C --> G
    G --> H

    %% Style the nodes
    style A fill:#fbe,stroke:#333,stroke-width:2px
    style B fill:#fbe,stroke:#333,stroke-width:2px
    style G fill:#ffc,stroke:#333,stroke-width:2px
    style C fill:#cde,stroke:#333,stroke-width:2px
    style D fill:#cde,stroke:#333,stroke-width:2px
    style E fill:#cfc,stroke:#333,stroke-width:2px
    style F fill:#cfc,stroke:#333,stroke-width:2px
    style G fill:#cde,stroke:#333,stroke-width:2px
    style H fill:#cfc,stroke:#333,stroke-width:2px

Furthermore, this package provides architecture implementations and modelling utilities. Currently implemented architectures include:

• Classifier
• Autoencoder
• Variational Autoencoder

Lastly the Tuner class provides hyperparameter tuning using grid, random and random dynamic search.

For an initial guide, see the examples/mnist/classifier/example.py file.

Development

Clone this repo and run pip install -e .[dev].
Run pytest in the root directory to run tests.