![]() As a result, the majority of lightning never leaves a cloud system. This is by far the most common form of lightning, accounting for roughly three-quarters of all lightning strikes. Intracloud lightning occurs when an electrical discharge occurs between the positive and negative poles within a single cloud once the atmosphere can no longer act as an insulator between the two charges. While describing the lightning strikes that are characteristic of each main category, we will also take a look at a few of the more significant and noteworthy subcategories. Some of these three main types of lightning groups contain a variety of subcategories. Cloud-To-Cloud (CC or Inter-Cloud) Lightning.However, all lightning bolts that are the result of an electrical discharge in a cloud can be classified into three main categories: Types Of Lightningĭepending on what you read or who you talk to, there can be a long list of different types of lightning that occur under various circumstances. For all intents and purposes, all these occurrences are lightning strikes. This simply means that when we talk about thunderstorms or a thunder cloud, technically we are referring to a byproduct of a lightning bolt. The thunder we hear during a lightning storm is nothing more than the sound generated by the discharge of electrical energy during a lightning strike. When people talk about a thunder and lightning storm, they are actually talking about the same occurrence. This temperature is roughly five times hotter than the surface of the sun. In fact, a lightning bolt can heat up the surrounding air by up to 30 000° Celsius (54 000° Fahrenheit). The electrical discharge is not only powerful but also generates a lot of heat. This will explain why a lightning strike is able to light up the sky, even during the middle of the day. To put this in practical terms, this is enough to power a 100-watt lightbulb for three months. In addition the Lightning framework is Patent Pending.One lightning bolt alone can discharge up to one billion volts of electrical energy. Please observe the Apache 2.0 license that is listed in this repository. Join our Slack and/or read our CONTRIBUTING guidelines to get help becoming a contributor! Contribute!īolts is supported by the PyTorch Lightning team and the PyTorch Lightning community! We suggest looking at our VISSL Flash integration for SSL based tasks. Use Lightning Flash to train, predict and serve state-of-the-art models for applied research. We'd like to encourage users to contribute general components that will help a broad range of problems, however components that help specifics domains will also be welcomed!įor example a callback to help train SSL models would be a great contribution, however the next greatest SSL model from your latest paper would be a good contribution to Lightning Flash. fit ( model ) Are specific research implementations supported? model = VisionModel () trainer = Trainer ( gpus = 1, callbacks = SparseMLCallback ( recipe_path = "recipe.yaml" )) trainer. from pytorch_lightning import LightningModule, Trainer import torchvision.models as models from pl_bolts.callbacks import SparseMLCallback class VisionModel ( LightningModule ): def _init_ ( self ): super (). We can introduce sparsity during fine-tuning with SparseML, which ultimately allows us to leverage the DeepSparse engine to see performance improvements at inference time. fit ( model ) Example 2: Introduce Sparsity with the SparseMLCallback to Accelerate Inference model = VisionModel () trainer = Trainer ( gpus = 1, callbacks = ORTCallback ()) trainer. from pytorch_lightning import LightningModule, Trainer import torchvision.models as models from pl_bolts.callbacks import ORTCallback class VisionModel ( LightningModule ): def _init_ ( self ): super (). Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. Aug 26: Fine-tune Transformers Faster with Lightning Flash and Torch ORTĮxample 1: Accelerate Lightning Training with the Torch ORT Callback.Sept 22: Leverage Sparsity for Faster Inference with Lightning Flash and SparseML.To install all optional dependencies pip install lightning-bolts What is Boltsīolts provides a variety of components to extend PyTorch Lightning such as callbacks & datasets, for applied research and production. Install bleeding-edge (no guarantees) pip install -upgrade Deep Learning components for extending PyTorch Lightning
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