How to Extend PyTorch Lightning with Deep Learning Components Using Lightning Bolts

October 19, 2020

Are you ready to enhance your PyTorch Lightning projects? Welcome to the world of Lightning Bolts, where we introduce essential components for accelerating and improving your deep learning models! This guide will walk you through the installation and utilization of various features provided by Lightning Bolts.

Getting Started

To kick things off, you’ll need to install Lightning Bolts. Follow the steps below based on your package manager preference.

Using pip:
```
pip install lightning-bolts
```
Using conda:
```
bashpip install lightning-bolts
```

Install bleeding-edge (no guarantees):

pip install https://github.com/Lightning-Universe/lightning-bolts/archive/refs/heads/master.zip

To install all optional dependencies:
```
pip install lightning-bolts[extra]
```

What is Bolts?

The Bolts package provides numerous components to extend PyTorch Lightning for applied research and production, including callbacks and datasets. Let’s dive into two interesting examples of how you can use these components.

Example 1: Accelerate Lightning Training with the Torch ORT Callback

The Torch ORT callback is like a turbocharger for your model—it converts your neural network into an optimized ONNX graph that speeds up training and inference, especially when using NVIDIA or AMD GPUs. Here’s how you can implement it:

from pytorch_lightning import LightningModule, Trainer
import torchvision.models as models
from pl_bolts.callbacks import ORTCallback

class VisionModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.model = models.vgg19_bn(pretrained=True)

model = VisionModel()
trainer = Trainer(gpus=1, callbacks=ORTCallback())
trainer.fit(model)

Example 2: Introduce Sparsity to Accelerate Inference

In this example, we utilize the SparseML callback to introduce sparsity during model fine-tuning. Think of it as pruning a plant to help it grow better—removing unnecessary parts while focusing on performance. This can enhance inference time with the DeepSparse engine.

from pytorch_lightning import LightningModule, Trainer
import torchvision.models as models
from pl_bolts.callbacks import SparseMLCallback

class VisionModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.model = models.vgg19_bn(pretrained=True)

model = VisionModel()
trainer = Trainer(gpus=1, callbacks=SparseMLCallback(recipe_path='recipe.yaml'))
trainer.fit(model)

Are Specific Research Implementations Supported?

Absolutely! Lightning Bolts welcomes contributions from users that can help a broad range of problems. Whether you’re developing a callback for SSL models or something domain-specific, your contributions are encouraged!

For state-of-the-art models and applied research, we recommend checking out Lightning Flash for training, predicting, and serving models. Additionally, explore our VISSL integration for SSL-related tasks.

Troubleshooting Ideas

If you encounter any issues while using Lightning Bolts, here are a few troubleshooting tips:

Ensure your Python environment is updated and compatible with the necessary packages.
Consult the Latest Docs for comprehensive guidance.
If errors arise during code execution, review the error messages for hints and check if any dependencies are missing.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By leveraging the power of Lightning Bolts, you can significantly enhance your deep learning workflows with ease. From optimizing training speeds to introducing sparsity for better performance, the components in Bolts offer remarkable extensions to PyTorch Lightning.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.