IPUStrategy

class pytorch_lightning.strategies.IPUStrategy(accelerator=None, device_iterations=1, autoreport=False, autoreport_dir=None, parallel_devices=None, cluster_environment=None, checkpoint_io=None, precision_plugin=None, training_opts=None, inference_opts=None)

Bases: pytorch_lightning.strategies.parallel.ParallelStrategy

Plugin for training on IPU devices.

Parameters:

• device_iterations – Number of iterations to run on device at once before returning to host. This can be used as an optimization to speed up training. https://docs.graphcore.ai/projects/poptorch-user-guide/en/latest/batching.html

• autoreport – Enable auto-reporting for IPUs using PopVision https://docs.graphcore.ai/projects/graphcore-popvision-user-guide/en/latest/graph/graph.html

• autoreport_dir – Optional directory to store autoReport output.

• training_opts – Optional poptorch.Options to override the default created options for training.

• inference_opts – Optional poptorch.Options to override the default created options for validation/testing and predicting.

all_gather(tensor, group=None, sync_grads=False)

Perform a all_gather on all processes.

Return type:

Tensor

barrier(name=None)

Synchronizes all processes which blocks processes until the whole group enters this function.

Parameters:

name (Optional[str]) – an optional name to pass into barrier.

Return type:

None

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.

Parameters:

• batch (Any) – The batch of samples to move to the correct device

• device (Optional[device]) – The target device

• dataloader_idx (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

broadcast(obj, src=0)

Broadcasts an object to all processes.

Parameters:

• obj (TypeVar(TBroadcast)) – the object to broadcast

• src (int) – source rank

Return type:

TypeVar(TBroadcast)

model_to_device()

Moves the model to the correct device.

Return type:

None

on_predict_end()

Called when predict ends.

Return type:

None

on_predict_start()

Called when predict begins.

Return type:

None

on_test_end()

Called when test end.

Return type:

None

on_test_start()

Called when test begins.

Return type:

None

on_train_batch_start(batch, batch_idx)

Called in the training loop before anything happens for that batch.

Return type:

None

on_train_end()

Called when train ends.

Return type:

None

on_train_start()

Called when train begins.

Return type:

None

on_validation_end()

Called when validation ends.

Return type:

None

on_validation_start()

Called when validation begins.

Return type:

None

predict_step(*args, **kwargs)

The actual predict step.

See predict_step() for more details

Return type:

Union[Tensor, Dict[str, Any]]

reduce(tensor, *args, **kwargs)

Reduces the given tensor (e.g. across GPUs/processes).

Parameters:

• tensor (Union[Tensor, Any]) – the tensor to sync and reduce

• group – the process group to reduce

• reduce_op – the reduction operation. Defaults to ‘mean’. Can also be a string ‘sum’ or ReduceOp.

Return type:

Union[Tensor, Any]

setup(trainer)

Setup plugins for the trainer fit and creates optimizers.

Parameters:

trainer (Trainer) – the trainer instance

Return type:

None

setup_optimizers(trainer)

Creates optimizers and schedulers.

Parameters:

trainer (Trainer) – the Trainer, these optimizers should be connected to

Return type:

None

teardown()

This method is called to teardown the training process.

It is the right place to release memory and free other resources.

Return type:

None

test_step(*args, **kwargs)

The actual test step.

See test_step() for more details

Return type:

Union[Tensor, Dict[str, Any], None]

training_step(*args, **kwargs)

The actual training step.

See training_step() for more details

Return type:

Union[Tensor, Dict[str, Any]]

validation_step(*args, **kwargs)

The actual validation step.

See validation_step() for more details

Return type:

Union[Tensor, Dict[str, Any], None]

property is_global_zero: bool

Whether the current process is the rank zero process not only on the local node, but for all nodes.

property root_device: torch.device

Return the root device.