The actual diameter of the lightning channel current is one to two inches, surrounded by a region of charged particles. We see lightning flicker when the process rapidly repeats itself several times along the same path. A negative CG flash consists of one or perhaps as many as 20 return strokes. This return stroke current of bright luminosity travels about 60,000 miles per second back towards the cloud. When the oppositely-charged leader and streamer connect, a powerful electrical current begins flowing. As it nears the ground, the negatively charged stepped leader causes streamer channels of positive charge to reach upward, normally from taller objects in the area, such as a tree, house, or telephone pole. This stepped leader is invisible to the human eye, and shoots to the ground in less time than it takes to blink. In the most common type of cloud-to-ground lightning (CG), a channel of negative charge, called a stepped leader, will zigzag downward in roughly 50-yard segments in a forked pattern. If the plane is below the cloud, then a CG flash could result. Lightning can also be triggered by aircraft flying through strong electric fields. Upward triggered lightning usually occurs in response to a natural lightning flash, but on rare occasions can be “self-triggered”-usually in winter storms with strong winds. ![]() Triggered lightning starts at the “ground,” which in this case may mean the top of a tower, and travels upward into the cloud, while “natural” lightning starts in the cloud and travels to ground. Artificially initiated lightning is associated with things like very tall structures, rockets and towers. In addition the Lightning framework is Patent Pending.Charge distribution in a typical storm cloudĭoes lightning go up or down? There are two ways that flashes can strike ground: naturally downward (those that occur because of normal electrification in the environment), and artificially initiated or triggered upward. 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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