本章“从Pytorch到Lightning: 入门到脊椎康复(1)”主要讲述了: PyTorch Lightning区别于Pytorch的主要特征及其优势 辅以简单案例进行解释,从LightningModule和Trainer两个方面展开对比分析 在之后的章节中,还会陆续了解: Metrics DataModule Restructuring Callbacks(非常重要!) Logging Profiler Multi-GPU等内容编辑...
# in lightning, forward defines the prediction(预测)/inference(推理) actions embedding = self.encoder(x) return embedding def training_step(self, batch, batch_idx): # training_step defined the train loop. # It is independent of forward x, y = batch x = x.view(x.size(0), -1) z =...
When running under a distributed strategy, Lightning handles the distributed sampler for you by default. 当在分布式策略下运行时,Lightning默认为你处理分布式采样器。 也就是说,当在默认情况下,如果你使用的是DDP strategy,那么pyTorch Lightning会默认给你的数据集类包装一个DistributedSampler。 在官方文档中有具...
PyTorch Lightningis a very light-weight structure for PyTorch — it’s more of a style guide than a framework. But once you structure your code, we give you free GPU, TPU, 16-bit precision support and much more! Lightning is just structured PyTorch Metrics This release has a major new p...
请注意,目前量化仅支持 CPU,因此在本教程中我们将不使用 GPU/CUDA。通过本教程,您将看到 PyTorch 中的量化如何导致模型尺寸显著减小,同时提高速度。此外,您将看到如何轻松应用一些高级量化技术,使您的量化模型比以往更少地准确性下降。警告:我们从其他 PyTorch 存储库中使用了大量样板代码,例如定义MobileNetV2模型架构...
Original. Reposted with permission. Related: PyTorch Multi-GPU Metrics Library and More in New PyTorch Lightning Release Pytorch Lightning vs PyTorch Ignite vs Fast.ai 9 Tips For Training Lightning-Fast Neural Networks In Pytorch
Migrating from Legacy TLT to TAO Migrating to the TAO API from TAO 5.3.0 Working With the Containers Invoking the Containers Directly Running Multi-Node Training Running Training on a Multi-Instance GPU Running without Elevated User Privileges Getting Started Getting Started ...
Linear(128, 28 * 28)) def forward(self, x): # in lightning, forward defines the prediction/inference actions embedding = self.encoder(x) return embedding def training_step(self, batch, batch_idx): # training_step defined the train loop. It is independent of forward x, y = batch x =...
CloudCompute("gpu")) def run(self): self.train.run("CPU machine 1") self.analyze.run("GPU machine 2") app = L.LightningApp(WorkflowOrchestrator())Run on the cloud or locally# run on the cloud lightning run app app.py --setup --cloud # run locally lightning run app app.py...
Multi-GPU trainingsupport TPU support 16-bit trainingsupport Predict or Deploy 当结束训练后,有3个选项去使用你训练好的LightningModule Option 1: Sub-models 使用系统内任何模型去预测 # ---# to use as embedding extractor# ---autoencoder=LitAutoEncoder.load_from_checkpoint('path/to/checkpoint_file....