credit.datasets.les_singlestep

Content:

• LESDataset

• LESPredict

Classes

LESDataset	LES model Pytorch Dataset class
LESPredict	An iterable Dataset.

Module Contents

class credit.datasets.les_singlestep.LESDataset(param_interior, transform=None, seed=42)

Bases: torch.utils.data.Dataset

LES model Pytorch Dataset class

list_upper_ds

list_surf_ds = []

list_dyn_forcing_ds = []

list_diag_ds = []

history_len

forecast_len

total_seq_len

LES_file_indices

filename_forcing

filename_static

transform = None

size_list

size_full

rng

total_len = 0

__post_init__()

__len__()

__getitem__(index)

class credit.datasets.les_singlestep.LESPredict(param_interior, data_lookup, rank, world_size, transform=None)

Bases: torch.utils.data.IterableDataset

An iterable Dataset.

All datasets that represent an iterable of data samples should subclass it. Such form of datasets is particularly useful when data come from a stream.

All subclasses should overwrite __iter__(), which would return an iterator of samples in this dataset.

When a subclass is used with DataLoader, each item in the dataset will be yielded from the DataLoader iterator. When num_workers > 0, each worker process will have a different copy of the dataset object, so it is often desired to configure each copy independently to avoid having duplicate data returned from the workers. get_worker_info(), when called in a worker process, returns information about the worker. It can be used in either the dataset’s __iter__() method or the DataLoader ‘s worker_init_fn option to modify each copy’s behavior.

Example 1: splitting workload across all workers in __iter__():

>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER)
>>> # xdoctest: +SKIP("Fails on MacOS12")
>>> class MyIterableDataset(torch.utils.data.IterableDataset):
...     def __init__(self, start, end):
...         super(MyIterableDataset).__init__()
...         assert end > start, "this example only works with end >= start"
...         self.start = start
...         self.end = end
...
...     def __iter__(self):
...         worker_info = torch.utils.data.get_worker_info()
...         if worker_info is None:  # single-process data loading, return the full iterator
...             iter_start = self.start
...             iter_end = self.end
...         else:  # in a worker process
...             # split workload
...             per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))
...             worker_id = worker_info.id
...             iter_start = self.start + worker_id * per_worker
...             iter_end = min(iter_start + per_worker, self.end)
...         return iter(range(iter_start, iter_end))
...
>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
>>> ds = MyIterableDataset(start=3, end=7)

>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[tensor([3]), tensor([4]), tensor([5]), tensor([6])]

>>> # xdoctest: +REQUIRES(POSIX)
>>> # Multi-process loading with two worker processes
>>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
>>> # xdoctest: +IGNORE_WANT("non deterministic")
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[tensor([3]), tensor([5]), tensor([4]), tensor([6])]

>>> # With even more workers
>>> # xdoctest: +IGNORE_WANT("non deterministic")
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=12)))
[tensor([3]), tensor([5]), tensor([4]), tensor([6])]

Example 2: splitting workload across all workers using worker_init_fn:

>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER)
>>> class MyIterableDataset(torch.utils.data.IterableDataset):
...     def __init__(self, start, end):
...         super(MyIterableDataset).__init__()
...         assert end > start, "this example only works with end >= start"
...         self.start = start
...         self.end = end
...
...     def __iter__(self):
...         return iter(range(self.start, self.end))
...
>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
>>> ds = MyIterableDataset(start=3, end=7)

>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[3, 4, 5, 6]
>>>
>>> # Directly doing multi-process loading yields duplicate data
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[3, 3, 4, 4, 5, 5, 6, 6]

>>> # Define a `worker_init_fn` that configures each dataset copy differently
>>> def worker_init_fn(worker_id):
...     worker_info = torch.utils.data.get_worker_info()
...     dataset = worker_info.dataset  # the dataset copy in this worker process
...     overall_start = dataset.start
...     overall_end = dataset.end
...     # configure the dataset to only process the split workload
...     per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))
...     worker_id = worker_info.id
...     dataset.start = overall_start + worker_id * per_worker
...     dataset.end = min(dataset.start + per_worker, overall_end)
...

>>> # Mult-process loading with the custom `worker_init_fn`
>>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))
[3, 5, 4, 6]

>>> # With even more workers
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=12, worker_init_fn=worker_init_fn)))
[3, 4, 5, 6]

list_upper_ds

list_surf_ds = []

list_dyn_forcing_ds = []

list_diag_ds = []

filenames

filename_surface

filename_dyn_forcing

filename_forcing

filename_static

filename_diagnostic

rank

world_size

transform = None

history_len

data_lookup

load_zarr_as_input(i_file, i_init_start, i_init_end, mode='input')

__len__()

__iter__()