- class pytorch_lightning.strategies.DeepSpeedStrategy(accelerator=None, zero_optimization=True, stage=2, remote_device='cpu', offload_optimizer=False, offload_parameters=False, offload_params_device='cpu', nvme_path='/local_nvme', params_buffer_count=5, params_buffer_size=100000000, max_in_cpu=1000000000, offload_optimizer_device='cpu', optimizer_buffer_count=4, block_size=1048576, queue_depth=8, single_submit=False, overlap_events=True, thread_count=1, pin_memory=False, sub_group_size=1000000000000, contiguous_gradients=True, overlap_comm=True, allgather_partitions=True, reduce_scatter=True, allgather_bucket_size=200000000, reduce_bucket_size=200000000, zero_allow_untested_optimizer=True, logging_batch_size_per_gpu='auto', config=None, logging_level=30, parallel_devices=None, cluster_environment=None, loss_scale=0, initial_scale_power=16, loss_scale_window=1000, hysteresis=2, min_loss_scale=1, partition_activations=False, cpu_checkpointing=False, contiguous_memory_optimization=False, synchronize_checkpoint_boundary=False, load_full_weights=False, precision_plugin=None, process_group_backend=None)¶
Provides capabilities to run training using the DeepSpeed library, with training optimizations for large billion parameter models. For more information: https://pytorch- lightning.readthedocs.io/en/stable/advanced/model_parallel.html#deepspeed.
DeepSpeedStrategyis in beta and subject to change.
Defaults have been set to enable ZeRO-Offload and some have been taken from the link below. These defaults have been set generally, but may require tuning for optimum performance based on your model size. For more information: https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training.
int) – Different stages of the ZeRO Optimizer. 0 is disabled, 1 is optimizer state partitioning, 2 is optimizer+gradient state partitioning, 3 is optimizer+gradient_parameter partitioning using the infinity engine.
int) – Number of buffers in buffer pool for optimizer state offloading when
offload_optimizer_deviceis set to to
nvme. This should be at least the number of states maintained per parameter by the optimizer. For example, Adam optimizer has 4 states (parameter, gradient, momentum, and variance).
int]) – Config used in DeepSpeed to calculate verbose timing for logging on a per sample per second basis (only displayed if logging=logging.INFO). If set to “auto”, the plugin tries to infer this from the train DataLoader’s BatchSampler, else defaults to 1. To obtain accurate logs when using datasets that do not support batch samplers, set this to the actual per gpu batch size (trainer.batch_size).
None]) – Pass in a deepspeed formatted config dict, or path to a deepspeed config: https://www.deepspeed.ai/docs/config-json. All defaults will be ignored if a config is passed in.
bool) – Enables partition activation when used with ZeRO stage 3 and model parallelism. Still requires you to wrap your forward functions in deepspeed.checkpointing.checkpoint. See deepspeed tutorial.
bool) – True when loading a single checkpoint file containing the model state dict when using ZeRO Stage 3. This differs from the DeepSpeed checkpoint which contains shards per worker.
- batch_to_device(batch, device=None, dataloader_idx=0)¶
Moves the batch to the correct device.
The returned batch is of the same type as the input batch, just having all tensors on the correct device.
Provide hook to create modules in a distributed aware context. This is useful for when we’d like to shard the model instantly, which is useful for extremely large models which can save memory and initialization time.
Returns: Model parallel context.
- predict_step(*args, **kwargs)¶
The actual predict step.
predict_step()for more details
- save_checkpoint(checkpoint, filepath, storage_options=None)¶
Save model/training states as a checkpoint file through state-dump and file-write.
Setup plugins for the trainer fit and creates optimizers.
Creates optimizers and schedulers.
- test_step(*args, **kwargs)¶
The actual test step.
test_step()for more details
- validation_step(*args, **kwargs)¶
The actual validation step.
validation_step()for more details
- property handles_gradient_accumulation: bool¶
Whether the plugin handles gradient accumulation internally.
- Return type
- property lightning_restore_optimizer: bool¶
Override to disable Lightning restoring optimizers/schedulers.
This is useful for plugins which manage restoring optimizers/schedulers.
- Return type