def __init__(
self,
base_iterable: Iterable[Callable[[], Dataset[T]]],
stages: List[Callable[[Dataset[Any]], Dataset[Any]]] = None,
length: int = None,
progress_bars: bool = progress_bar._enabled,
_executed: List[bool] = None,
):
"""Construct a DatasetPipeline (internal API).
The constructor is not part of the DatasetPipeline API. Use the
``Dataset.repeat()``, ``Dataset.window()``, or
``DatasetPipeline.from_iterable()`` methods to construct a pipeline.
"""
self._base_iterable = base_iterable
self._stages = stages or []
self._optimized_stages = None
self._length = length
self._progress_bars = progress_bars
self._uuid = None # For testing only.
# Whether the pipeline execution has started.
# This variable is shared across all pipelines descending from this.
self._executed = _executed or [False]
self._dataset_iter = None
self._first_dataset = None
self._schema = None
self._stats = DatasetPipelineStats()
class DatasetPipeline(Generic[T]):
"""Implements a pipeline of Datasets.
Unlike Datasets, which execute all transformations synchronously,
DatasetPipelines implement pipelined execution. This allows for the
overlapped execution of data input (e.g., reading files), computation
(e.g. feature preprocessing), and output (e.g., distributed ML training).
A DatasetPipeline can be created by either repeating a Dataset
(``ds.repeat(times=None)``), by turning a single Dataset into a pipeline
(``ds.window(blocks_per_window=10)``), or defined explicitly using
``DatasetPipeline.from_iterable()``.
DatasetPipeline supports the all the per-record transforms of Datasets
(e.g., map, flat_map, filter), holistic transforms (e.g., repartition),
and output methods (e.g., iter_rows, to_tf, to_torch, write_datasource).
"""
def __init__(
self,
base_iterable: Iterable[Callable[[], Dataset[T]]],
stages: List[Callable[[Dataset[Any]], Dataset[Any]]] = None,
length: int = None,
progress_bars: bool = progress_bar._enabled,
_executed: List[bool] = None,
):
"""Construct a DatasetPipeline (internal API).
The constructor is not part of the DatasetPipeline API. Use the
``Dataset.repeat()``, ``Dataset.window()``, or
``DatasetPipeline.from_iterable()`` methods to construct a pipeline.
"""
self._base_iterable = base_iterable
self._stages = stages or []
self._optimized_stages = None
self._length = length
self._progress_bars = progress_bars
self._uuid = None # For testing only.
# Whether the pipeline execution has started.
# This variable is shared across all pipelines descending from this.
self._executed = _executed or [False]
self._dataset_iter = None
self._first_dataset = None
self._schema = None
self._stats = DatasetPipelineStats()
def iter_rows(self, *, prefetch_blocks: int = 0) -> Iterator[Union[T, TableRow]]:
"""Return a local row iterator over the data in the pipeline.
If the dataset is a tabular dataset (Arrow/Pandas blocks), dict-like mappings
:py:class:`~ray.data.row.TableRow` are yielded for each row by the iterator.
If the dataset is not tabular, the raw row is yielded.
Examples:
>>> for i in ray.data.range(1000000).repeat(5).iter_rows():
... print(i)
Time complexity: O(1)
Args:
prefetch_blocks: The number of blocks to prefetch ahead of the
current block during the scan.
Returns:
A local iterator over the records in the pipeline.
"""
def gen_rows() -> Iterator[Union[T, TableRow]]:
time_start = time.perf_counter()
for ds in self.iter_datasets():
wait_start = time.perf_counter()
for row in ds.iter_rows(prefetch_blocks=prefetch_blocks):
self._stats.iter_wait_s.add(time.perf_counter() - wait_start)
with self._stats.iter_user_s.timer():
yield row
wait_start = time.perf_counter()
self._stats.iter_total_s.add(time.perf_counter() - time_start)
return gen_rows()
def iter_batches(
self,
*,
prefetch_blocks: int = 0,
batch_size: int = None,
batch_format: str = "native",
drop_last: bool = False,
) -> Iterator[BatchType]:
"""Return a local batched iterator over the data in the pipeline.
Examples:
>>> for pandas_df in ray.data.range(1000000).repeat(5).iter_batches():
... print(pandas_df)
Time complexity: O(1)
Args:
prefetch_blocks: The number of blocks to prefetch ahead of the
current block during the scan.
batch_size: Record batch size, or None to let the system pick.
batch_format: The format in which to return each batch.
Specify "native" to use the current block format (promoting
Arrow to pandas automatically), "pandas" to
select ``pandas.DataFrame`` or "pyarrow" to select
``pyarrow.Table``. Default is "native".
drop_last: Whether to drop the last batch if it's incomplete.
Returns:
An iterator over record batches.
"""
time_start = time.perf_counter()
yield from batch_blocks(
self._iter_blocks(),
self._stats,
prefetch_blocks=prefetch_blocks,
batch_size=batch_size,
batch_format=batch_format,
drop_last=drop_last,
)
self._stats.iter_total_s.add(time.perf_counter() - time_start)
def _iter_blocks(self) -> Iterator[Block]:
ds_wait_start = time.perf_counter()
for ds in self.iter_datasets():
self._stats.iter_ds_wait_s.add(time.perf_counter() - ds_wait_start)
yield from ds._plan.execute().iter_blocks()
ds_wait_start = time.perf_counter()
def split(
self, n: int, *, equal: bool = False, locality_hints: List[Any] = None
) -> List["DatasetPipeline[T]"]:
"""Split the pipeline into ``n`` disjoint pipeline shards.
This returns a list of sub-pipelines that can be passed to Ray tasks
and actors and used to read the pipeline records in parallel.
Examples:
>>> # Split up a pipeline to process over `n` worker actors.
>>> shards = pipe.split(len(workers), locality_hints=workers)
>>> for shard, worker in zip(shards, workers):
... worker.consume.remote(shard)
Time complexity: O(1)
Implementation detail: this launches a coordinator actor that is used
to execute the pipeline and push data blocks to each pipeline shard.
Reading from an individual shard will be blocked if other shards are
falling behind. A warning will be printed if a shard has been blocked
on read for more than 10 seconds.
Args:
n: Number of child pipelines to return.
equal: Whether to guarantee each split has an equal
number of records. This may drop records if they cannot be
divided equally among the splits.
locality_hints: A list of Ray actor handles of size ``n``. The
system will try to co-locate the blocks of the ith pipeline
shard with the ith actor to maximize data locality.
Returns:
A list of ``n`` disjoint pipeline splits.
"""
return self._split(
n,
lambda ds, equal=equal: ds.split(
n, equal=equal, locality_hints=locality_hints
),
)
def split_at_indices(self, indices: List[int]) -> List["DatasetPipeline[T]"]:
"""Split the datasets within the pipeline at the given indices
(like np.split).
This will split each dataset contained within this pipeline, thereby
producing len(indices) + 1 pipelines with the first pipeline containing
the [0, indices[0]) slice from each dataset, the second pipeline
containing the [indices[0], indices[1]) slice from each dataset, and so
on, with the final pipeline will containing the
[indices[-1], self.count()) slice from each dataset.
Examples:
>>> p1, p2, p3 = ray.data.range(
8).repeat(2).split_at_indices([2, 5])
>>> p1.take()
[0, 1, 0, 1]
>>> p2.take()
[2, 3, 4, 2, 3, 4]
>>> p3.take()
[5, 6, 7, 5, 6, 7]
Time complexity: O(num splits)
See also: ``DatasetPipeline.split``
Args:
indices: List of sorted integers which indicate where the pipeline
will be split. If an index exceeds the length of the pipeline,
an empty pipeline will be returned.
Returns:
The pipeline splits.
"""
if len(indices) < 1:
raise ValueError("indices must be at least of length 1")
if sorted(indices) != indices:
raise ValueError("indices must be sorted")
if indices[0] < 0:
raise ValueError("indices must be positive")
return self._split(len(indices) + 1, lambda ds: ds.split_at_indices(indices))
def _split(self, n: int, splitter: Callable[[Dataset], "DatasetPipeline[T]"]):
# Pin the coordinator (and any child actors) to the local node to avoid
# errors during node failures. If the local node dies, then the driver
# will fate-share with the coordinator anyway.
local_node_resource = "node:{}".format(ray.util.get_node_ip_address())
coordinator = PipelineSplitExecutorCoordinator.options(
resources={local_node_resource: 0.0001},
placement_group=None,
).remote(self, n, splitter, DatasetContext.get_current())
if self._executed[0]:
raise RuntimeError("Pipeline cannot be read multiple times.")
self._executed[0] = True
class SplitIterator:
def __init__(self, split_index, coordinator):
self.split_index = split_index
self.coordinator = coordinator
self.warn_threshold = 100
self.wait_delay_s = 0.1
def __iter__(self):
return self
def __next__(self):
ds = None
tries = 0
while ds is None:
ds = ray.get(
self.coordinator.next_dataset_if_ready.remote(self.split_index)
)
# Wait for other shards to catch up reading.
if not ds:
time.sleep(self.wait_delay_s)
tries += 1
if tries > self.warn_threshold:
print(
"Warning: reader on shard {} of the pipeline "
"has been blocked more than {}s waiting for "
"other readers to catch up. All pipeline shards "
"must be read from concurrently.".format(
self.split_index,
self.wait_delay_s * self.warn_threshold,
)
)
self.warn_threshold *= 2
return lambda: ds
return [
# Disable progress bars for the split readers since they would
# overwhelm the console.
DatasetPipeline(
SplitIterator(idx, coordinator),
length=self._length,
progress_bars=False,
)
for idx in range(n)
]
def rewindow(
self, *, blocks_per_window: int, preserve_epoch: bool = True
) -> "DatasetPipeline[T]":
"""Change the windowing (blocks per dataset) of this pipeline.
Changes the windowing of this pipeline to the specified size. For
example, if the current pipeline has two blocks per dataset, and
`.rewindow(blocks_per_window=4)` is requested, adjacent datasets will
be merged until each dataset is 4 blocks. If
`.rewindow(blocks_per_window)` was requested the datasets will be
split into smaller windows.
Args:
blocks_per_window: The new target blocks per window.
preserve_epoch: Whether to preserve epoch boundaries. If set to
False, then windows can contain data from two adjacent epochs.
"""
class WindowIterator:
def __init__(self, original_iter):
self._original_iter = original_iter
self._buffer: Optional[Dataset[T]] = None
def __next__(self) -> Dataset[T]:
try:
# Merge windows until we meet the requested window size.
if self._buffer is None:
self._buffer = next(self._original_iter)
while self._buffer.num_blocks() < blocks_per_window:
next_ds = next(self._original_iter)
if (
preserve_epoch
and self._buffer._get_epoch() != next_ds._get_epoch()
):
partial_window = self._buffer
self._buffer = next_ds
return lambda: partial_window
else:
self._buffer = self._buffer.union(next_ds)
# Slice off the left-most chunk and return it.
res, self._buffer = self._buffer._divide(blocks_per_window)
assert res.num_blocks() <= blocks_per_window, res
if self._buffer.num_blocks() == 0:
self._buffer = None
return lambda: res
except StopIteration:
# Return the left-over data as a single window.
if self._buffer and self._buffer.num_blocks() > 0:
res = self._buffer
assert res.num_blocks() <= blocks_per_window, res
self._buffer = None
return lambda: res
else:
raise
class WindowIterable:
def __init__(self, original_iter):
self._original_iter = original_iter
def __iter__(self):
return WindowIterator(self._original_iter)
if self._length == float("inf"):
length = float("inf")
else:
length = None
return DatasetPipeline(WindowIterable(self.iter_datasets()), length=length)
def repeat(self, times: int = None) -> "DatasetPipeline[T]":
"""Repeat this pipeline a given number or times, or indefinitely.
This operation is only allowed for pipelines of a finite length. An
error will be raised for pipelines of infinite length.
Note that every repeat of the pipeline is considered an "epoch" for
the purposes of ``iter_epochs()``. If there are multiple repeat calls,
the latest repeat takes precedence for the purpose of defining epochs.
Args:
times: The number of times to loop over this pipeline, or None
to repeat indefinitely.
"""
if self._length == float("inf"):
raise ValueError("Cannot repeat a pipeline of infinite length.")
class RepeatIterator:
def __init__(self, original_iter):
self._original_iter = original_iter
# Holds results to repeat.
self._results = []
# Incrementing cursor over results.
self._i = 0
# This is calculated later.
self._max_i = None
def __next__(self) -> Dataset[T]:
# Still going through the original pipeline.
if self._original_iter:
try:
make_ds = next(self._original_iter)
self._results.append(make_ds)
def gen():
res = make_ds()
res._set_epoch(0)
return res
return gen
except StopIteration:
self._original_iter = None
# Calculate the cursor limit.
if times:
self._max_i = len(self._results) * (times - 1)
else:
self._max_i = float("inf")
# Going through a repeat of the pipeline.
if self._i < self._max_i:
make_ds = self._results[self._i % len(self._results)]
epoch = 1 + self._i // len(self._results)
def gen():
res = make_ds()
res._set_epoch(epoch)
return res
self._i += 1
return gen
else:
raise StopIteration
class RepeatIterable:
def __init__(self, original_iter):
self._original_iter = original_iter
def __iter__(self):
return RepeatIterator(self._original_iter)
if not times:
length = float("inf")
elif times and self._length:
length = times * self._length
else:
length = None
return DatasetPipeline(
RepeatIterable(iter(self._base_iterable)),
stages=self._stages.copy(),
length=length,
)
def schema(
self, fetch_if_missing: bool = False
) -> Union[type, "pyarrow.lib.Schema"]:
"""Return the schema of the dataset pipeline.
For datasets of Arrow records, this will return the Arrow schema.
For dataset of Python objects, this returns their Python type.
Note: This is intended to be a method for peeking schema before
the execution of DatasetPipeline. If execution has already started,
it will simply return the cached schema from the previous call.
Time complexity: O(1)
Args:
fetch_if_missing: If True, synchronously fetch the schema if it's
not known. Default is False, where None is returned if the
schema is not known.
Returns:
The Python type or Arrow schema of the records, or None if the
schema is not known.
"""
if not self._executed[0]:
self._schema = self._peek().schema(fetch_if_missing)
return self._schema
def count(self) -> int:
"""Count the number of records in the dataset pipeline.
This blocks until the entire pipeline is fully executed.
Time complexity: O(dataset size / parallelism)
Returns:
The number of records in the dataset pipeline.
"""
if self._length == float("inf"):
raise ValueError("Cannot count a pipeline of infinite length.")
pipe = self.map_batches(lambda batch: [len(batch)])
total = 0
for elem in pipe.iter_rows():
total += elem
return total
def sum(self) -> int:
"""Sum the records in the dataset pipeline.
This blocks until the entire pipeline is fully executed.
Time complexity: O(dataset size / parallelism)
Returns:
The sum of the records in the dataset pipeline.
"""
if self._length == float("inf"):
raise ValueError("Cannot sum a pipeline of infinite length.")
pipe = self.map_batches(lambda batch: [batch.sum()[0]], batch_format="pandas")
total = 0
for elem in pipe.iter_rows():
total += elem
return total
def show_windows(self, limit_per_dataset: int = 10) -> None:
"""Print up to the given number of records from each window/dataset.
This is helpful as a debugging tool for understanding the structure of
dataset pipelines.
Args:
limit_per_dataset: Rows to print per window/dataset.
"""
epoch = None
for i, ds in enumerate(self.iter_datasets()):
if ds._get_epoch() != epoch:
epoch = ds._get_epoch()
print("------ Epoch {} ------".format(epoch))
print("=== Window {} ===".format(i))
ds.show(limit_per_dataset)
def iter_epochs(self) -> Iterator["DatasetPipeline[T]"]:
"""Split this pipeline up by epoch.
This allows reading of data per-epoch for repeated Datasets, which is
useful for ML training. For example, ``ray.data.range(10).repeat(50)``
generates a pipeline with 500 rows total split across 50 epochs. This
method allows iterating over the data individually per epoch
(repetition) of the original data.
Examples:
>>> epochs = ray.data.range(10).repeat(50).iter_epochs()
>>> for i, epoch in enumerate(epochs):
... print("Epoch", i)
... for row in epoch.iter_rows():
... print(row)
Returns:
Iterator over epoch objects, where each epoch is a DatasetPipeline
containing data from that epoch only.
"""
class Peekable:
def __init__(self, base_iter: Iterator[T]):
self._iter = base_iter
self._buffer = None
def _fill_buffer_if_possible(self):
if self._buffer is None:
try:
self._buffer = next(self._iter)
assert self._buffer is not None
except StopIteration:
pass
def peek(self) -> T:
self._fill_buffer_if_possible()
if self._buffer is None:
raise StopIteration
return self._buffer
def __next__(self) -> T:
self._fill_buffer_if_possible()
if self._buffer is None:
raise StopIteration
item = self._buffer
self._buffer = None
return item
class SingleEpochIterator:
def __init__(self, peekable_iter: Iterator[Dataset[T]], epoch: int):
self._iter = peekable_iter
self._epoch = epoch
def __next__(self) -> Dataset[T]:
if self._iter.peek()._get_epoch() > self._epoch:
raise StopIteration
ds = next(self._iter)
return lambda: ds
def __iter__(self):
return self
class EpochDelimitedIterator:
def __init__(self, pipe):
self._iter = Peekable(pipe.iter_datasets())
self._cur_epoch = None
def __next__(self) -> "DatasetPipeline[T]":
if self._cur_epoch is None:
self._cur_epoch = self._iter.peek()._get_epoch()
else:
self._cur_epoch += 1
warned = False
while self._iter.peek()._get_epoch() < self._cur_epoch:
if not warned:
warned = True
logger.warn(
"Data from epoch {} was not fully read, "
"skipping to next epoch.".format(self._cur_epoch - 1)
)
next(self._iter)
epoch_pipe = DatasetPipeline.from_iterable(
SingleEpochIterator(self._iter, epoch=self._cur_epoch)
)
return epoch_pipe
def __iter__(self):
return self
return EpochDelimitedIterator(self)
@DeveloperAPI
def iter_datasets(self) -> Iterator[Dataset[T]]:
"""Iterate over the output datasets of this pipeline.
Returns:
Iterator over the datasets outputted from this pipeline.
"""
if self._executed[0]:
raise RuntimeError("Pipeline cannot be read multiple times.")
self._executed[0] = True
if self._first_dataset is None:
self._peek()
iter = itertools.chain([self._first_dataset], self._dataset_iter)
self._first_dataset = None
self._dataset_iter = None
return iter
@DeveloperAPI
def foreach_window(
self, fn: Callable[[Dataset[T]], Dataset[U]]
) -> "DatasetPipeline[U]":
"""Apply a transform to each dataset/window in this pipeline.
Args:
fn: The function to transform each dataset with.
Returns:
The transformed DatasetPipeline.
"""
if self._executed[0]:
raise RuntimeError("Pipeline cannot be read multiple times.")
return DatasetPipeline(
self._base_iterable,
self._stages + [fn],
self._length,
self._progress_bars,
_executed=self._executed,
)
def stats(self, exclude_first_window: bool = True) -> str:
"""Returns a string containing execution timing information.
Args:
exclude_first_window: Whether to exclude the first window from
the pipeline time breakdown. This is generally a good idea
since there is always a stall waiting for the first window to
be initially computed, which can be misleading in the stats.
"""
return self._stats.summary_string(exclude_first_window)
@staticmethod
def from_iterable(
iterable: Iterable[Callable[[], Dataset[T]]],
) -> "DatasetPipeline[T]":
"""Create a pipeline from an sequence of Dataset producing functions.
Args:
iterable: A finite or infinite-length sequence of functions that
each produce a Dataset when called.
"""
if hasattr(iterable, "__len__"):
length = len(iterable)
else:
length = None
return DatasetPipeline(iterable, length=length)
def __repr__(self) -> str:
return "DatasetPipeline(num_windows={}, num_stages={})".format(
self._length, 1 + len(self._stages)
)
def __str__(self) -> str:
return repr(self)
def _get_uuid(self) -> str:
return self._uuid
def _set_uuid(self, uuid: str) -> None:
self._uuid = uuid
def _optimize_stages(self):
"""Optimize this pipeline, fusing stages together as possible."""
context = DatasetContext.get_current()
if not context.optimize_fuse_stages:
self._optimized_stages = self._stages
return
dummy_ds = Dataset(
ExecutionPlan(BlockList([], []), DatasetStats(stages={}, parent=None)),
0,
True,
)
for stage in self._stages:
dummy_ds = stage(dummy_ds)
dummy_ds._plan._optimize()
optimized_stages = []
for stage in dummy_ds._plan._stages:
optimized_stages.append(
lambda ds, stage=stage: Dataset(
ds._plan.with_stage(stage), ds._epoch, True
)
)
self._optimized_stages = optimized_stages
def _peek(self) -> Dataset[T]:
if self._first_dataset is None:
self._optimize_stages()
self._dataset_iter = PipelineExecutor(self)
self._first_dataset = next(self._dataset_iter)
return self._first_dataset