cerebras_pytorch package

Backend related functions

Top-level functions for creating and retrieving the Cerebras backend being used in the current run

backend

cerebras_pytorch.backend(backend_type: str, *args, **kwargs)

Instantiates a backend with the given type

Supported backend types include:

CSX

Parameters

• artifact_dir (str) – The directory at which to store any Cerebras specific artifacts generated by the backend. Default: $cwd/cerebras_logs

• compile_dir (str) – The directory at which to store any compile related artifacts. These compile artifacts are used to cache the compile to avoid recompilation. Default: /opt/cerebras/cached_compile

• compile_only (bool) – If True, then configure the CSX backend only for compilation. This means that all parameter data is immediately dropped as it isn’t required for compilation. Further, the data executor will not send an execution request to the wafer scale cluster. This mode is intended to verify that the model is able to be compiled. As such, no system is required in this mode. Default: False

• validate_only (bool) – If True, then configure the CSX backend only for validation. This means that all parameter data is immediately dropped as it isn’t required for validation. Further, the data executor will not send compile and execute requests to the wafer scale cluster. This mode is intended to verify that the model is able to be traced. As such, no system is required in this mode. Default: False

• drop_data (bool) – If True, all parameter data is immediately dropped even if in a non-compile-only run. In this case, a checkpoint containing values for all stateful tensors must be loaded in order to be able to run. Default: False

• max_checkpoints (int) – If provided, cstorch.save will automatically only keep the newest max_checkpoints checkpoints, removing the oldest when the number of checkpoints exceeds the specified number. Default: None

• log_initialization (bool) – If True, print logs during weight initialization to keep the users updated on the current progress. Default: True

• use_cs_grad_accum (bool) – If True, configure the compilation to use gradient accumulation internally. Default: False

• retrace_every_iteration (bool) – If True, retrace the entire training/evaluation step every iteration. This provides the benefit of being able to check whether the graph changes or not every iteration. But tracing overhead can hurt performance for certain models. Default: False

CPU

Parameters

• artifact_dir (str) – The directory at which to store any Cerebras specific artifacts generated by the backend. Default: $cwd/cerebras_logs

• max_checkpoints (int) – If provided, cstorch.save will automatically only keep the newest max_checkpoints checkpoints, removing the oldest when the number of checkpoints exceeds the specified number. Default: None

• mixed_precision (bool) – If True, use the autocast context manager during the forward pass. Default: None

GPU

Parameters

• artifact_dir (str) – The directory at which to store any Cerebras specific artifacts generated by the backend. Default: $cwd/cerebras_logs

• enable_distributed (bool) – If True, configure the run to use torch.distributed. Note, the run must have been triggered using torchrun. Default: False

• main_process_id (int) – The rank of the main process. Default: 0

• dist_backend (str) – The distributed backend to use to initialize the process group. Default: "nccl"

• init_method (str) – The method to use to initialize the process group. Default: None

• sync_batchnorm (bool) – If True, wraps the model with convert_sync_batchnorm. Default: False

current_backend

cerebras_pytorch.current_backend(raise_exception: bool = True)

Gets instance of the current backend

Parameters

raise_exception – If True, raise an exception if no backend has been instantiated. Otherwise return None

current_torch_device

cerebras_pytorch.current_torch_device()

Gets the torch device of the current backend.

Returns torch.device(‘cpu’) if no backend has been initialized yet

use_cs

cerebras_pytorch.use_cs()

Returns True if the active device is a CSX device

Compile related functions

compile

cerebras_pytorch.compile(model: torch.nn.Module, backend: Optional[Union[str, cerebras_pytorch.backend.Backend]] = None)

Prepares the PyTorch module for tracing.

This method prepares the module by moving it to the device so that it can be compiled after the first trace. Note that parameter initialization must be done before calling this method since post this call, the parameters are moved to the device.

Parameters

• model – The PyTorch module to be compiled.

• backend – The Cerebras backend to use to compile. If None, the current backend is used. If not current backend is set, the CPU backend is initialized and used. Defaults to None.

Returns

A pseudo-module that almost acts like the original module but does not have any of the property accessor or private methods of the original module. It can be called module(*args, **kwargs) to run the forward pass, similar to the original module.

trace

cerebras_pytorch.trace(step_fn: callable) → callable

A decorator that wraps the training/evaluation step function for tracing.

Any operation that is meant to be executed on the Cerebras Wafer-Scale Cluster must be wrapped with this decorator. This includes the forward pass, backward pass, optimizer steps, and more.

For example, the following code snippet shows how to wrap a training step that does the forward and backward pass and optimizer step:

@cstorch.trace
def training_step(batch, model, optimizer, loss_fn):
    features, labels = batch
    outputs = model(features)
    loss = loss_fn(outputs, labels)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    return loss

Parameters

step_fn – The training/evaluation step function to be wrapped.

Returns

The wrapped training/evaluation step function.

In addition, no tensor value may be eagerly evaluated at any point inside this training step. This means, no tensor is allowed to be printed, fetched via a debugger, or used as part of a python conditional. Any operation that requires knowing the value of tensor inside the training step will result in an error stating that it is not allowed to read a tensor’s contents outside of a step_closure

Creation Ops

Can be used to lazily initialize tensors with known shape, dtype and value to avoid have them unnecessarily take up memory.

full

cerebras_pytorch.full(shape, value: float, dtype=None)

Returns an lazily initialized tensor filled with the provided value

Parameters

• shape – The shape of the tensor.

• value – The value to fill the tensor with.

• dtype – The dtype of the tensor.

full_like

cerebras_pytorch.full_like(other: torch.Tensor, value: float, dtype=None)

Returns an lazily initialized full tensor with the same properties as the provided tensor

Parameters

• other – The tensor to copy the properties from

• value – The value to fill the tensor with

• dtype – The dtype of the tensor. If not provided, the dtype of the other tensor is used

ones

cerebras_pytorch.ones(shape, dtype=None)

Returns an lazily initialized tensor filled with ones

Parameters

• shape – The shape of the tensor

• dtype – The dtype of the tensor

ones_like

cerebras_pytorch.ones_like(other: torch.Tensor, dtype=None)

Returns an lazily initialized tensor full of ones with the same properties as the provided tensor

Parameters

• other – The tensor to copy the properties from

• dtype – The dtype of the tensor. If not provided, the dtype of the other tensor is used

zeros

cerebras_pytorch.zeros(shape, dtype=None)

Returns an lazily initialized tensor filled with zeros

Parameters

• shape – The shape of the tensor

• dtype – The dtype of the tensor

zeros_like

cerebras_pytorch.zeros_like(other: torch.Tensor, dtype=None)

Returns an lazily initialized tensor full of zeros with the same properties as the provided tensor

Parameters

• other – The tensor to copy the properties from

• dtype – The dtype of the tensor. If not provided, the dtype of the other tensor is used

Checkpoint Saving/Loading utilities

cerebras_pytorch.save(obj: dict, checkpoint_file: str) → None

Save a PyTorch state dict to the given file.

Parameters

• obj – The object to save.

• checkpoint_file – The path to save the object to.

cerebras_pytorch.load(checkpoint_file: Union[cerebras_appliance.utils.file.StrPath, IO], map_location: Optional[Union[str, torch.device, Callable, dict]] = None, **kwargs) → Any

Load a PyTorch checkpoint from a file.

Parameters

• checkpoint_file – The path to the checkpoint to load.

• map_location – A mapping of where to load the checkpoint content to. If the map_location is None, then the tensors will be lazily loaded from the checkpoint file every single time the tensor is accessed. If the map_location is “cache”, then the tensors will be cached once they are lazily loaded from the checkpoint file. If the map location is “cpu”, then the tensors will be eagerly loaded into memory from the checkpoint file.

• **kwargs – Additional keyword arguments to pass to the vanilla torch checkpoint loader. These are ignored if the checkpoint is a Cerebras HDF5 checkpoint.

Returns

The loaded checkpoint file.

Raises

RuntimeError – If the checkpoint file does not exist or checkpoint is not a valid HDF5 or vanilla torch checkpoint.

Data Utilities

utils.data.DataLoader

class cerebras_pytorch.utils.data.DataLoader

Wrapper around torch.utils.data.DataLoader that facilitates moving data generated by the dataloader to a Cerebras system

Parameters

• input_fn – A callable that returns a torch.utils.data.DataLoader instance or an iterable that returns a structure containing torch tensors.

• *args – Any other positional or keyword arguments are passed into the input_fn when each worker instantiates their respective dataloaders

• **kwargs –

  Any other positional or keyword arguments are passed into the input_fn when each worker instantiates their respective dataloaders

__init__(*args: Any, **kwargs: Any) → None

disable_dataloader_checkpointing()

load_state_dict()

serialize_state_dict()

state_dict()

Each worker will call this input function to construct their own dataloader object. This means that some data sharding scheme is required if the intent is for each worker to stream in a unique set of data.

utils.data.SyntheticDataset

class cerebras_pytorch.utils.data.SyntheticDataset

A synthetic dataset that generates samples from a SampleSpec.

Constructs a SyntheticDataset instance.

A synthetic dataset can be used to generate samples on the fly with an expected dtype/shape but without needing to create a full-blown dataset. This is especially useful for compile validation.

Parameters

• sample_spec –

  Specification of the samples to generate. This can be a nested structure of one of the following types:

  • torch.Tensor: A tensor to be cloned.

  • Callable: A callable that takes the sample index and

    returns a tensor.

  Supported data structures for holding the above leaf nodes are list, tuple, dict, OrderedDict, and NamedTuple.

• num_samples – Total size of the dataset. If None, the dataset will generate samples indefinitely.

__init__(sample_spec: Union[torch.Tensor, Callable[[int], torch.Tensor], List[Union[torch.Tensor, Callable[[int], torch.Tensor], List[SampleSpecT], Tuple[SampleSpecT, ...], Dict[str, SampleSpecT], OrderedDict[str, SampleSpecT], NamedTuple]], Tuple[Union[torch.Tensor, Callable[[int], torch.Tensor], List[SampleSpecT], Tuple[SampleSpecT, ...], Dict[str, SampleSpecT], OrderedDict[str, SampleSpecT], NamedTuple], ...], Dict[str, Union[torch.Tensor, Callable[[int], torch.Tensor], List[SampleSpecT], Tuple[SampleSpecT, ...], Dict[str, SampleSpecT], OrderedDict[str, SampleSpecT], NamedTuple]], OrderedDict[str, Union[torch.Tensor, Callable[[int], torch.Tensor], List[SampleSpecT], Tuple[SampleSpecT, ...], Dict[str, SampleSpecT], OrderedDict[str, SampleSpecT], NamedTuple]], NamedTuple], num_samples: Optional[int] = None)

Constructs a SyntheticDataset instance.

A synthetic dataset can be used to generate samples on the fly with an expected dtype/shape but without needing to create a full-blown dataset. This is especially useful for compile validation.

Parameters

• sample_spec –

  Specification of the samples to generate. This can be a nested structure of one of the following types:

  • torch.Tensor: A tensor to be cloned.

  • Callable: A callable that takes the sample index and

    returns a tensor.

  Supported data structures for holding the above leaf nodes are list, tuple, dict, OrderedDict, and NamedTuple.

• num_samples – Total size of the dataset. If None, the dataset will generate samples indefinitely.

utils.data.DataExecutor

class cerebras_pytorch.utils.data.DataExecutor

Defines a single execution run on a Cerebras wafer scale cluster

Parameters

• dataloader – the dataloader to use for the run

• num_steps – the number of steps to run. Defaults to 1 if the backend was configured for compile or validate only

• checkpoint_steps – the interval at which to schedule fetching checkpoints from the cluster

• activation_steps – the interval at which to schedule fetching activations from the cluster

• cs_config – optionally, a CSConfig object can be passed in to configure the cerebras wafer-scale cluster. if none provided the default configuration values will be used.

• writer – The summary writer to be used to write any summarized scalars or tensors to tensorboard

• profiler_activities – The list of activities to profile. By default the total samples, the client side rate and global rate are tracked and accessible via the profiler attribute

__init__(*args: Any, **kwargs: Any) → None

Note

As of Cerebras Release 2.0, we don’t officially support multiple CS runs in a single process. This means that the above executor can only be run/iterated once. Any runs with different configurations must be run in separate processes.

utils.data.RestartableDataLoader

class cerebras_pytorch.utils.data.RestartableDataLoader

Defines interface for the restartable dataloader protocol.

__init__(*args, **kwargs)

aggregate_state_dict(worker_states: List[Dict[str, Any]]) → Dict[str, Any]

Use this method to specify how to combine the list of CSX Worker state dicts. Each CSX Worker state in the worker_states list is to be specified in state_dict

Returns

The consolidated state dict that will be saved in a checkpoint.

Usage:

def aggregate_state_dict(self, worker_states):
    return {
        "worker_0": worker_states[0],
        "worker_1": worker_states[1]
    }

deaggregate_state_dict(aggregated_state_dict: Dict[str, Any]) → Dict[str, Any]

Use this method to specify how to load an individual CSX Worker state given a consolidated list of state dicts, as specified in aggregate_state_dict.

Returns

The state dict will be passed to the above-defined load_state_dict method.

Usage:

def deaggregate_state_dict(self, aggregated_state_dict):
    return {
        "worker_0": aggregated_state_dict.get("worker_0", {})
    }

load_state_dict(state_dict: Dict[str, Any]) → None

Use this method to load CSX Worker state for the dataloader instance, as captured from a previous run.

Parameters

state_dict – dict holding worker state info, specified in deaggregate_state_dict

Usage:

def load_state_dict(self, state_dict):
    wrk_state_dict = state_dict.get("worker_0", {})

    worker_step = wrk_state_dict.get("worker_step", 0)
    worker_id = wrk_state_dict.get("worker_id")

    print(f"WRK {worker_id} loaded step: {worker_step}")

state_dict() → Dict[str, Any]

Use this method to specify what state information should be saved by each CSX Worker.

Returns

dict holding state information for the CSX Worker

In order to access Cerebras internal data checkpoint info per CSX Worker at some checkpoint step, follow the steps in the example below. Cerebras internal data checkpoint format is recorded in the DataLoaderCheckpoint dataclass.

Usage:

import cerebras_pytorch as cstorch
...
def state_dict(self) -> Dict[str, Any]:
    worker_state = cstorch.distributed.get_worker_state()
    state_dict = {}
    if worker_state:
        state_dict["worker_step"] = worker_state.worker_step
        state_dict["worker_id"] = worker_state.global_worker_id
    return state_dict

Note

The call to get_worker_state is well-defined only inside of the state_dict method; using this anywhere else will result in a RuntimeError exception. See linked docs for more details.

utils.data.DataLoaderCheckpoint

class cerebras_pytorch.utils.data.DataLoaderCheckpoint

Dataclass representing the Cerebras internal dataloader checkpoint format. Each CSX Worker captures its state information via this class at a checkpoint step.

Parameters

• global_worker_id (int) – ID of this worker amongst all other workers across all boxes

• local_worker_id (int) – ID of this worker amongst all other workers across the same box

• total_num_workers (int) – The total number of workers for the run across all boxes

• num_workers_per_csx (int) – The total number of workers per box for the run

• num_csx (int) – The total number of CSXs (boxes) for the run

• wse_id (int) – ID of the Wafer-Scale Engine (CSX) to which this worker streams data

• appliance_step (int) – The appliance step at which this checkpoint state info is captured

• worker_step (int) – The worker step at which this state info is captured. Note that this is simply equal to appliance_step if num_workers_per_csx = 1; for the multi-worker scenario, the appliance step is distributed across workers on a single box in a round-robin fashion based on the local worker id

• samples_streamed (int) – The total number of samples streamed by this worker at checkpoint step. This is simply worker_step * per_box_batch_size

Note

appliance_step, worker_step and samples_streamed are the attributes that vary across different steps; whereas the other attributes provide constant state information for the current run.

get_worker_state

cerebras_pytorch.distributed.get_worker_state()

API exposing internal state info captured by each CSX Worker for the current run at a checkpoint step. This state info is represented in the DataLoaderCheckpoint dataclass format:

Returns

DataLoaderCheckpoint instance holding worker state information at the checkpoint step

Note

• This method may only be called inside of a custom implementation of state_dict for

dataloaders conforming to the RestartableDataLoader protocol, since state_dict is well-defined only at a checkpoint step. - Use this method to save any of the aforementioned state info recorded by each worker when defining state_dict for custom implementations of restartable dataloaders. - This state info captured by each worker is for the current run only, i.e. if you pause and restart a run, the counters gathering information returned by this function will be reset.

utils.CSConfig

class cerebras_pytorch.utils.CSConfig

Contains config details for the Cerebras Wafer Scale Cluster

Parameters

• mgmt_address (Optional[str]) – Address to connect to appliance. If not provided, query the cluster management node for it. Default: None.

• credentials_path (Optional[str]) – Credentials for connecting to appliance. If not provided, query the cluster management node for it. Default: None.

• num_csx (int) – Number of Cerebras Systems to run on. Default: 1.

• max_wgt_servers (int) – Number of weight servers to support run. Default: 24.

• max_act_per_csx (int) – Number of activation servers per system. Default: 1.

• num_workers_per_csx (int) – Number of streaming workers per system. Default: 1.

• transfer_processes (int) – Number of processes to transfer data to/from appliance. Default: 5.

• job_time_sec (int) – Time limit for the appliance jobs, not including the queue time. Default: None.

• mount_dirs (List[str]) – Local storage to mount to appliance (ex. training data). Default: None.

• python_paths (List[str]) – A list of path that worker pods respect as PYTHONPATH in addition to the PYTHONPATH set in the container image. Default: None.

• job_labels (List[str]) – A list of equal-sign-separated key-value pairs that get applied as part of job metadata. Default: None.

• debug_args (DebugArgs) – Optional debugging arguments object. Default: None.

• precision_opt_level (int) – The precision optimization level. Default: 1.

numpy utilities

from_numpy

cerebras_pytorch.from_numpy(array: numpy.ndarray) → torch.Tensor

Converts a numpy array to a torch tensor.

to_numpy

cerebras_pytorch.to_numpy(tensor: torch.Tensor) → numpy.ndarray

Converts a torch tensor to a numpy array.

Step Closure related utilities

step_closure

cerebras_pytorch.step_closure(closure: Callable) → Callable

Decorator to automatically wrap a function call in a step closure.

Step closures are queued and all run at the end of each step. This is to ensure that the tensor arguments to the closures are computed and are retrieved before they are used.

Usage:

@step_closure
def closure(...):
    ...
...
closure(...)

Parameters

closure – The function to wrap in a step closure.

Returns

The wrapped function.

checkpoint_closure

cerebras_pytorch.checkpoint_closure(closure: Callable) → Callable

Decorator to wrap function so it is only ever called on checkpoint steps.

With this decorator, the closure may be called at any time. But it will only ever run if on a checkpoint step, as configured by setting checkpoint_steps when creating a DataExecutor. Note that if checkpoint_steps is non-zero, the closure will also run on the last step, even if it is not a checkpoint step.

An example of a function that would benefit from using this decorator is a function that saves the checkpoint. It ensures that checkpoints are only saved on steps on which the checkpoint is available to be retrieved from the Cerebras wafer-scale cluster.

Example Usage:

@checkpoint_closure
def save_checkpoint(...):
    ...
    cstorch.save(...)
...
executor = cstorch.utils.data.DataExecutor(..., checkpoint_steps=100)
for batch in executor:
    ...
    # Always call save checkpoint
    # But save checkpoint only actually runs on checkpoint steps
    save_checkpoint(...)

Parameters

closure – The function to wrap in a step closure that only runs on checkpoint steps.

Returns

The wrapped function.

Tensorboard utilities

cerebras_pytorch.summarize_scalar(name: str, scalar: Union[int, float, torch.Tensor])

Save the scalar to the event file of the writer specified in the data executor

Parameters

• name – the key to save the scalar in the event file

• scalar – the scalar value to summarize. Note, if a torch.Tensor is provided, it must be a scalar tensor for which scalar.item() can be called

Note

Scalars summarized using this API are only visible in Tensorboard if a SummaryWriter was passed to the DataExecutor object.

cerebras_pytorch.summarize_tensor(name: str, tensor: torch.Tensor)

Save the tensor to the event file of the writer specified in the data executor

Parameters

• name – the key to save the tensor in the event file

• tensor – the torch.Tensor to summarize

Note

Tensors summarized using this API are only visible if a SummaryWriter was passed to the DataExecutor object.

class cerebras_pytorch.utils.tensorboard.SummaryWriter

Thin wrapper around torch.utils.tensorboard.SummaryWriter

Additional features include the ability to add a tensor summary

Parameters

• base_step – The base step to use in summarize_{scalar,tensor} functions

• *args – Any other positional and keyword arguments are forwarded directly to the base class

• **kwargs –

  Any other positional and keyword arguments are forwarded directly to the base class

__init__(*args: Any, **kwargs: Any) → None

add_tensor()

class cerebras_pytorch.utils.tensorboard.SummaryReader

Class for reading summaries saved using the SummaryWriter

Parameters

• log_dir – The directory at which the event files can be found

• kwargs – The remaining keyword arguments are forwarded to the internal EventAccumulator object

__init__(*args: Any, **kwargs: Any) → None

read_scalar()

read_tensor()

reload()

scalar_names()

tensor_names()

Dataloader benchmark utilities

cerebras_pytorch.utils.benchmark.benchmark_dataloader(input_fn: Callable[[...], Iterable], num_epochs: Optional[int] = None, steps_per_epoch: Optional[int] = None, sampling_frequency: Optional[int] = None, profile_activities: Optional[List[str]] = None, print_metrics: bool = True) → cerebras_pytorch.utils.benchmark.utils.dataloader.Metrics

Utility to benchmark a dataloader.

Parameters

• input_fn – Function that creates and returns a dataloader.

• num_epochs – Number of epochs to iterate over the dataloader. If None, the dataloader is only iterated for one epoch.

• steps_per_epoch – Number of steps to iterate over the dataloader in each epoch. If None, the dataloader is iterated in its entirety.

• sampling_frequency – Frequency at which to sample metrics. If None, a default value of 100 (i.e. every 100 steps) is used. First step of each epoch is always sampled.

• profile_activities – List of optional activities to profile. If None, no extra activities are profiled. Note that these may incur additional overhead and could affect overall performance of the dataloader, especially if the sampling frequency is high.

• print_metrics – Whether to pretty print the final metrics to console.

Returns

Metrics for the dataloader experiment.

class cerebras_pytorch.utils.benchmark.utils.dataloader.Metrics

Metrics for a single dataloader experiment.

Parameters

• dataloader_build_time – Time to build the dataloader.

• epoch_metrics – List of metrics for each epoch.

• batch_specs – Mapping between unique batch specs found and their occurences.

• total_time – Total time to iterate through all epochs.

• global_rate – Overall global rate in steps/second.

• is_partial – Whether the metrics are partial. This can happen if the benchmark is interrupted in the middle of execution.

• start_time_ns – Time at which the experiment started.

• end_time_ns – Time at which the experiment ended.

batch_specs: Dict[cerebras_pytorch.utils.benchmark.utils.dataloader.BatchSpec, cerebras_pytorch.utils.benchmark.utils.dataloader.BatchSpecOccurence]

dataloader_build_time: numpy.timedelta64

end_time_ns: int = 0

epoch_metrics: List[cerebras_pytorch.utils.benchmark.utils.dataloader.EpochMetrics]

global_rate: float = 0.0

property global_sample_rate: Optional[float]

Returns the overall global rate in samples/second.

Note that this value only exists if all batches have the exact same structure, dtypes, and shapes. Otherwise, this value is None.

is_partial: bool = True

start_time_ns: int

property total_steps: int

Returns the total number of steps across all epochs.

total_time: numpy.timedelta64

class cerebras_pytorch.utils.benchmark.utils.dataloader.EpochMetrics

Metrics for a single epoch of a dataloader experiment.

Parameters

• iterator_creation – Time to create the dataloader iterator.

• iteration_time – Time to iterate the entire epoch excluding the creation of the iterator.

• total_steps – Total number of steps in the epoch.

• batch_metrics – List of metrics for batches generated in the epoch.

• start_time_ns – Time at which the epoch started.

• end_time_ns – Time at which the epoch ended.

batch_metrics: List[cerebras_pytorch.utils.benchmark.utils.dataloader.BatchMetrics]

end_time_ns: int = 0

iteration_time: numpy.timedelta64

iterator_creation: numpy.timedelta64

start_time_ns: int

total_steps: int = 0

property total_time: numpy.timedelta64

Returns the total time to create and iterate the epoch.

class cerebras_pytorch.utils.benchmark.utils.dataloader.BatchMetrics

Metrics for a single batch of a dataloader experiment.

Parameters

• epoch_step – Epoch step at which the batch was generated.

• global_step – Global step at which the batch was generated.

• local_rate – Local rate (in steps/second) at the sampling frequency. This is the instantaneous rate (relative to previous batch) at which the batch was generated.

• global_rate – global rate (in steps/second) at the sampling frequency. This is the global rate since the start of the iterating epochs.

• profile_activities – Dictionary of profile activities and their values.

• sampling_time_ns – Time at which the batch was sampled.

epoch_step: int

global_rate: float

global_step: int

local_rate: float

profile_activities: Dict[str, Any]

sampling_time_ns: int

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