torchflare 0.2.3

TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

TorchFlare

TorchFlare is a simple, beginner-friendly and an easy-to-use PyTorch Framework train your models without much effort. It provides an almost Keras-like experience for training your models with all the callbacks, metrics, etc

Features

• A high-level module for Keras-like training.
• Flexibility to write custom training and validation loops for advanced use cases.
• Off-the-shelf Pytorch style Datasets/Dataloaders for standard tasks such as Image classification, Image segmentation, Text Classification, etc
• Callbacks for model checkpoints, early stopping, and much more!
• Metrics and much more.
• Reduction of the boiler plate code required for training your models.

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Currently, TorchFlare supports CPU and GPU training. DDP and TPU support will be coming soon!

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Installation

pip install torchflare

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Documentation

The Documentation is available here

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Getting Started

The core idea around TorchFlare is the Experiment class. It handles all the internal stuff like boiler plate code for training, calling callbacks,metrics,etc. The only thing you need to focus on is creating you PyTorch Model.

Also, there are off-the-shelf pytorch style datasets/dataloaders available for standard tasks, so that you don't have to worry about creating Pytorch Datasets/Dataloaders.

Here is an easy-to-understand example to show how Experiment class works.

import torch
import torch.nn as nn
from torchflare.experiments import Experiment, ModelConfig
import torchflare.callbacks as cbs
import torchflare.metrics as metrics

#Some dummy dataloaders
train_dl = SomeTrainingDataloader()
valid_dl = SomeValidationDataloader()
test_dl = SomeTestingDataloader()

Create a pytorch Model

class Net(nn.Module):
       def __init__(self, n_classes, p_dropout):
            super().__init__()
            self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
            self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
            self.conv2_drop = nn.Dropout2d(p=p_dropout)
            self.fc1 = nn.Linear(320, 50)
            self.fc2 = nn.Linear(50, n_classes)

       def forward(self, x):
            x = F.relu(F.max_pool2d(self.conv1(x), 2))
            x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
            x = x.view(-1, 320)
            x = F.relu(self.fc1(x))
            x = F.dropout(x, training=self.training)
            x = self.fc2(x)
            return x

Define callbacks and metrics

metric_list = [metrics.Accuracy(num_classes=num_classes, multilabel=False),
                metrics.F1Score(num_classes=num_classes, multilabel=False)]

callbacks = [cbs.EarlyStopping(monitor="val_accuracy", mode="max"), cbs.ModelCheckpoint(monitor="val_accuracy"),
            cbs.ReduceLROnPlateau(mode="max" , patience = 2)]

Define your experiment

# Set some constants for training
exp = Experiment(
    num_epochs=5,
    fp16=False,
    device="cuda",
    seed=42,
)

# Compile your experiment with model, optimizer, schedulers, etc
config = ModelConfig(nn_module = Net,
                          module_params = {"n_classes" : 10 , "p_dropout" : 0.3},
                          optimizer = "Adam"
                          optimizer_params = {"lr" : 3e-4},
                          criterion = "cross_entropy")

exp.compile_experiment(model_config = config,
                       callbacks = callbacks,
                       metrics = metric_list,
                       main_metrics = "accuracy")
# Run your experiment with training dataloader and validation dataloader.
exp.fit_loader(train_dl=train_dl, valid_dl= valid_dl)

For inference, you can use infer method, which yields output per batch. You can use it as follows

outputs = []

for op in exp.predict_on_loader(test_loader=test_dl , path_to_model='./models/model.bin' , device = 'cuda'):
    op = some_post_process_function(op)
    outputs.extend(op)

If you want to access your experiments history or get as a dataframe. You can do it as follows.

history = exp.history # This will return a dict
exp.get_logs() #This will return a dataframe constructed from model-history.

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Examples

• Image Classification on CIFAR-10 using TorchFlare.
• Text Classification on IMDB data.
• Tutorial on using Hydra and TorchFlare for efficient workflow and parameter management.
• Tutorial on fit methods and how to dispatch training progress to your personal discord channel.
• Tutorial on how to train Variational Autoencoders using torchflare on MNIST Dataset.
• Tutorial to train GAN's with custom training loop.

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Current Contributors

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Stability

The library isn't mature or stable for production use yet.

The best of the library currently would be for non production use and rapid prototyping.

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Contribution

Contributions are always welcome, it would be great to have people use and contribute to this project to help users understand and benefit from the library.

How to contribute

• Create an issue: If you have a new feature in mind, feel free to open an issue and add some short description on what that feature could be.
• Create a PR: If you have a bug fix, enhancement or new feature addition, create a Pull Request and the maintainers of the repo, would review and merge them.

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Author

• Atharva Phatak - Atharva-Phatak