Lightning supports a variety of plugins to further speed up distributed GPU training. Most notably: DDPStrategy. DDPShardedStrategy. DeepSpeedStrategy. # run on 1 gpu trainer = Trainer(gpus=1) # train on 8 gpus, using the DDP strategy trainer = Trainer(gpus=8, strategy="ddp") # train on multiple GPUs across nodes (uses 8 gpus in total) trainer ...
Trainer — PyTorch Lightning 1.5.0 documentation Trainer Once you’ve organized your PyTorch code into a LightningModule, the Trainer automates everything else. This abstraction achieves the following: You maintain control over all aspects via …
Trainer. Once you've organized your PyTorch code into a LightningModule, ... Under the hood, the Lightning Trainer handles the training loop details for you ...
It will configure a default ModelCheckpoint callback if there is no user-defined ModelCheckpoint in:paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`. check_val_every_n_epoch: Check val every n train epochs. default_root_dir: Default path for logs and weights when no logger/ckpt_callback passed. Default: ``os.getcwd()``.
PyTorch Lightning trainers In this tutorial, we demonstrate TorchGeo trainers to train and test a model. Specifically, we use the Tropical Cyclone dataset and train models to predict cyclone windspeed given imagery of the cyclone. It’s recommended to run this notebook on Google Colab if you don’t have your own GPU.
It will configure a default ModelCheckpoint callback if there is no user-defined ModelCheckpoint in:paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`. check_val_every_n_epoch: Check val every n train epochs. default_root_dir: Default path for logs and weights when no logger/ckpt_callback passed. Default: ``os.getcwd()``.
property checkpoint_callback: Optional[pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint] ¶ The first ModelCheckpoint callback in the Trainer.callbacks list, or None if it doesn’t exist. Return type. Optional [ModelCheckpoint] property checkpoint_callbacks: …
Some features such as distributed training using multiple GPUs are meant for power users. PyTorch lightning is a wrapper around PyTorch and is aimed at giving PyTorch a Keras-like interface without taking away any of the flexibility. If you already use PyTorch as your daily driver, PyTorch-lightning can be a good addition to your toolset ...
Once you add your plugin to the PyTorch Lightning Trainer, you can parallelize training to all the cores in your laptop, or across a massive multi-node, ...
By default, Lightning uses PyTorch TensorBoard logging under the hood, and stores the logs to a directory (by default in lightning_logs/ ). from pytorch_lightning import Trainer # Automatically logs to a directory # (by default ``lightning_logs/``) trainer = Trainer() To see your logs: tensorboard --logdir = lightning_logs/
You can perform an evaluation epoch over the validation set, outside of the training loop, using pytorch_lightning.trainer.trainer.Trainer.validate(). This might be useful if you want to collect new metrics from a model right at its initialization or after it has already been trained.
Note that you need to use zero-indexed epoch keys here trainer = Trainer(accumulate_grad_batches={0: 8, 4: 4, 8: 1}) Or, you can create custom GradientAccumulationScheduler. from pytorch_lightning.callbacks import GradientAccumulationScheduler # till 5th epoch, it will accumulate every 8 batches.
To use a different key set a string instead of True with the key name. auto_scale_batch_size ( Union [ str, bool ]) – If set to True, will initially run a batch size finder trying to find the largest batch size that fits into memory. The result will be stored in self.batch_size in the LightningModule.