PyTorch 1.10.0, Lightning 1.6.5PyTorch 1.9.0, Lightning 1.6.0PyTorch 1.8.0, Lightning 1.5.0PyTorch 1.7.0, Lightning 1.4.0CompatiblePerformancePerformance vs Compatibility 我们还需进行详细的兼容性分析,包括硬件配置和依赖版本对比。 硬件配置 依赖版本对比代码 importtorchimportpytorch_lightningasplprint(tor...
PyTorch Lightning- 采用模块化设计,通过LightningModule统一管理模型逻辑- 预定义接口减少样板代码- 强制实施良好的代码组织实践 Ignite- 基于事件系统的灵活架构- 完全自定义的训练流程- 更接近底层PyTorch实现 2. 分布式训练支持 PyTorch Lightning# 简洁的分布式配置 trainer=pl.Trainer( accelerator="gpu", devices=4,...
PyTorch Lightning- 采用模块化设计,通过LightningModule统一管理模型逻辑- 预定义接口减少样板代码- 强制实施良好的代码组织实践 Ignite- 基于事件系统的灵活架构- 完全自定义的训练流程- 更接近底层PyTorch实现 2. 分布式训练支持 PyTorch Lightning# 简洁的分布式配置 trainer=pl.Trainer( accelerator="gpu", devices=4,...
生态系统扩展# 未来可能的统一接口示例 classUnifiedTrainer: def__init__(self, framework="lightning"): self.framework=framework defcreate_trainer(self): ifself.framework=="lightning": returnpl.Trainer() else: returnEngine(self._train_step) deftrain(self, model, dataloader): trainer=self.create_tr...
There is a compatibility matrix here: https://lightning.ai/docs/pytorch/stable/versioning.html#compatibility-matrix For torch 1.10, you should be able to install pytorch_lightning from 1.5 up to 1.9. Let me know if that helps.awaelchli added question waiting on author and removed bug needs tr...
Lightning Studios Ecosystem Feature Projects See all Projects Explore a rich ecosystem of libraries, tools, and more to support development. Captum Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch. ...
PyTorch Lightning的DDP实现 importpytorch_lightningaspl# 模型定义(假设已完成)model=LightningModel()# DDP配置trainer=pl.Trainer(accelerator="gpu",devices=4,# GPU数量配置strategy="ddp"# 分布式策略设置)trainer.fit(model,train_dataloader,val_dataloader) ...
参考https://lightning.ai/docs/pytorch/stable/versioning.html#compatibility-matrix中Compatibility matrix的部分,可以找到PyTorch对应的Python版本号。例如我的版本2.5.x不在列表中,这说明该版本可能还不够稳定,我们选择1个稳定版本2.4.0,看到其对应的Python版本是3.9<=ver<=3.12,w.l.o.g,我们选择3.10版本,在conda...
PyTorch provides a user-friendly interface for mixed-precision training, enhancing performance on GPUs equipped withTensor Cores. While PyTorch has improved its compatibility withcustom hardware, including Google’s TPUs, it does not match the native support that TensorFlow offers for these devices. ...
Enable PyTorch 1.7 compatibility (#3541) Added LightningModule.to_torchscript to support exporting as ScriptModule (#3258) Added warning when dropping unpicklable hparams (#2874) Added EMB similarity (#3349) Added ModelCheckpoint.to_yaml method (#3048) Allow ModelCheckpoint monitor to be None, me...