The result will be stored in self.batch_size in the LightningModule.Īdditionally, can be set to either power that estimates the batch size throughĪ power search or binsearch that estimates the batch size through a binary search.Īuto_select_gpus ¶ ( bool) – If enabled and gpus or devices is an integer, pick available To use a different key set a string instead of True with the key name.Īuto_scale_batch_size ¶ ( Union) – If set to True, will initially run a batch sizeįinder trying to find the largest batch size that fits into memory. Set the suggested learning rate in self.lr or self.learning_rate in the LightningModule. Trying to optimize initial learning for faster convergence. By default it will be set to “O2”Īuto_lr_find ¶ ( Union) – If set to True, will make trainer.tune() run a learning rate finder, Please use the strategy argument instead.Īccumulate_grad_batches ¶ ( Union, None]) – Accumulates grads every k batches or as set up in the dict.Īmp_backend ¶ ( str) – The mixed precision backend to use (“native” or “apex”).Īmp_level ¶ ( Optional) – The optimization level to use (O1, O2, etc…). Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”)ĭeprecated since version v1.5: Passing training strategies (e.g., ‘ddp’) to accelerator has been deprecated in v1.5.0Īnd will be removed in v1.7.0. _init_ ( logger = True, enable_checkpointing = True, callbacks = None, default_root_dir = None, gradient_clip_val = None, gradient_clip_algorithm = None, num_nodes = 1, num_processes = None, devices = None, gpus = None, auto_select_gpus = False, tpu_cores = None, ipus = None, enable_progress_bar = True, overfit_batches = 0.0, track_grad_norm = - 1, check_val_every_n_epoch = 1, fast_dev_run = False, accumulate_grad_batches = None, max_epochs = None, min_epochs = None, max_steps = - 1, min_steps = None, max_time = None, limit_train_batches = None, limit_val_batches = None, limit_test_batches = None, limit_predict_batches = None, val_check_interval = None, log_every_n_steps = 50, accelerator = None, strategy = None, sync_batchnorm = False, precision = 32, enable_model_summary = True, weights_save_path = None, num_sanity_val_steps = 2, resume_from_checkpoint = None, profiler = None, benchmark = None, deterministic = None, reload_dataloaders_every_n_epochs = 0, auto_lr_find = False, replace_sampler_ddp = True, detect_anomaly = False, auto_scale_batch_size = False, plugins = None, amp_backend = 'native', amp_level = None, move_metrics_to_cpu = False, multiple_trainloader_mode = 'max_size_cycle' ) Ĭustomize every aspect of training via flags. Trainer class API ¶ Methods ¶ init ¶ Trainer. # default used by the Trainer trainer = Trainer ( enable_model_summary = True ) # disable summarization trainer = Trainer ( enable_model_summary = False ) # enable custom summarization from pytorch_lightning.callbacks import ModelSummary trainer = Trainer ( enable_model_summary = True, callbacks = ) If using the CLI, the configuration file is not saved. This argument is different from limit_batches to 1 or the number passed. ) # runs 7 predict batches and program ends trainer = Trainer ( fast_dev_run = 7 ) trainer. # default used by the Trainer trainer = Trainer ( fast_dev_run = False ) # runs only 1 training and 1 validation batch and the program ends trainer = Trainer ( fast_dev_run = True ) trainer. Multi-agent Reinforcement Learning With WarpDrive.Finetune Transformers Models with PyTorch Lightning.PyTorch Lightning CIFAR10 ~94% Baseline Tutorial.GPU and batched data augmentation with Kornia and PyTorch-Lightning.Tutorial 13: Self-Supervised Contrastive Learning with SimCLR.Tutorial 12: Meta-Learning - Learning to Learn.
Tutorial 10: Autoregressive Image Modeling.Tutorial 9: Normalizing Flows for Image Modeling.Tutorial 7: Deep Energy-Based Generative Models.Tutorial 6: Basics of Graph Neural Networks.Tutorial 5: Transformers and Multi-Head Attention.Tutorial 4: Inception, ResNet and DenseNet.Tutorial 3: Initialization and Optimization.LightningLite (Stepping Stone to Lightning).Organize existing PyTorch into Lightning.
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