import collections
import copy
import html
import itertools
import logging
import time
import warnings
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Generic,
Iterable,
Iterator,
List,
Literal,
Mapping,
Optional,
Tuple,
TypeVar,
Union,
)
import numpy as np
import ray
import ray.cloudpickle as pickle
from ray._private.thirdparty.tabulate.tabulate import tabulate
from ray._private.usage import usage_lib
from ray.air.util.tensor_extensions.arrow import (
ArrowTensorTypeV2,
get_arrow_extension_fixed_shape_tensor_types,
)
from ray.air.util.tensor_extensions.utils import _create_possibly_ragged_ndarray
from ray.data._internal.aggregate import Max, Mean, Min, Std, Sum
from ray.data._internal.compute import ComputeStrategy
from ray.data._internal.datasource.bigquery_datasink import BigQueryDatasink
from ray.data._internal.datasource.csv_datasink import CSVDatasink
from ray.data._internal.datasource.image_datasink import ImageDatasink
from ray.data._internal.datasource.json_datasink import JSONDatasink
from ray.data._internal.datasource.mongo_datasink import MongoDatasink
from ray.data._internal.datasource.numpy_datasink import NumpyDatasink
from ray.data._internal.datasource.parquet_datasink import ParquetDatasink
from ray.data._internal.datasource.sql_datasink import SQLDatasink
from ray.data._internal.datasource.tfrecords_datasink import TFRecordDatasink
from ray.data._internal.datasource.webdataset_datasink import WebDatasetDatasink
from ray.data._internal.equalize import _equalize
from ray.data._internal.execution.interfaces import RefBundle
from ray.data._internal.execution.interfaces.ref_bundle import (
_ref_bundles_iterator_to_block_refs_list,
)
from ray.data._internal.iterator.iterator_impl import DataIteratorImpl
from ray.data._internal.iterator.stream_split_iterator import StreamSplitDataIterator
from ray.data._internal.logical.operators.all_to_all_operator import (
RandomizeBlocks,
RandomShuffle,
Repartition,
Sort,
)
from ray.data._internal.logical.operators.count_operator import Count
from ray.data._internal.logical.operators.input_data_operator import InputData
from ray.data._internal.logical.operators.map_operator import (
Filter,
FlatMap,
MapBatches,
MapRows,
Project,
)
from ray.data._internal.logical.operators.n_ary_operator import (
Union as UnionLogicalOperator,
)
from ray.data._internal.logical.operators.n_ary_operator import Zip
from ray.data._internal.logical.operators.one_to_one_operator import Limit
from ray.data._internal.logical.operators.write_operator import Write
from ray.data._internal.logical.optimizers import LogicalPlan
from ray.data._internal.pandas_block import PandasBlockBuilder, PandasBlockSchema
from ray.data._internal.plan import ExecutionPlan
from ray.data._internal.planner.exchange.sort_task_spec import SortKey
from ray.data._internal.remote_fn import cached_remote_fn
from ray.data._internal.split import _get_num_rows, _split_at_indices
from ray.data._internal.stats import DatasetStats, DatasetStatsSummary, StatsManager
from ray.data._internal.util import AllToAllAPI, ConsumptionAPI, get_compute_strategy
from ray.data.aggregate import AggregateFn
from ray.data.block import (
VALID_BATCH_FORMATS,
Block,
BlockAccessor,
DataBatch,
DataBatchColumn,
T,
U,
UserDefinedFunction,
_apply_batch_format,
_apply_batch_size,
)
from ray.data.context import DataContext
from ray.data.datasource import Connection, Datasink, FilenameProvider
from ray.data.iterator import DataIterator
from ray.data.random_access_dataset import RandomAccessDataset
from ray.types import ObjectRef
from ray.util.annotations import Deprecated, DeveloperAPI, PublicAPI
from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy
from ray.widgets import Template
from ray.widgets.util import repr_with_fallback
if TYPE_CHECKING:
import dask
import mars
import modin
import pandas
import pyarrow
import pyspark
import tensorflow as tf
import torch
import torch.utils.data
from tensorflow_metadata.proto.v0 import schema_pb2
from ray.data._internal.execution.interfaces import Executor, NodeIdStr
from ray.data.grouped_data import GroupedData
logger = logging.getLogger(__name__)
TensorflowFeatureTypeSpec = Union[
"tf.TypeSpec", List["tf.TypeSpec"], Dict[str, "tf.TypeSpec"]
]
TensorFlowTensorBatchType = Union["tf.Tensor", Dict[str, "tf.Tensor"]]
CollatedData = TypeVar("CollatedData")
TorchBatchType = Union[Dict[str, "torch.Tensor"], CollatedData]
BT_API_GROUP = "Basic Transformations"
SSR_API_GROUP = "Sorting, Shuffling and Repartitioning"
SMD_API_GROUP = "Splitting and Merging datasets"
GGA_API_GROUP = "Grouped and Global aggregations"
CD_API_GROUP = "Consuming Data"
IOC_API_GROUP = "I/O and Conversion"
IM_API_GROUP = "Inspecting Metadata"
E_API_GROUP = "Execution"
[docs]
@PublicAPI
class Dataset:
"""A Dataset is a distributed data collection for data loading and processing.
Datasets are distributed pipelines that produce ``ObjectRef[Block]`` outputs,
where each block holds data in Arrow format, representing a shard of the overall
data collection. The block also determines the unit of parallelism. For more
details, see :ref:`Ray Data Internals <dataset_concept>`.
Datasets can be created in multiple ways: from synthetic data via ``range_*()``
APIs, from existing memory data via ``from_*()`` APIs (this creates a subclass
of Dataset called ``MaterializedDataset``), or from external storage
systems such as local disk, S3, HDFS etc. via the ``read_*()`` APIs. The
(potentially processed) Dataset can be saved back to external storage systems
via the ``write_*()`` APIs.
Examples:
.. testcode::
:skipif: True
import ray
# Create dataset from synthetic data.
ds = ray.data.range(1000)
# Create dataset from in-memory data.
ds = ray.data.from_items(
[{"col1": i, "col2": i * 2} for i in range(1000)]
)
# Create dataset from external storage system.
ds = ray.data.read_parquet("s3://bucket/path")
# Save dataset back to external storage system.
ds.write_csv("s3://bucket/output")
Dataset has two kinds of operations: transformation, which takes in Dataset
and outputs a new Dataset (e.g. :py:meth:`.map_batches()`); and consumption,
which produces values (not a data stream) as output
(e.g. :meth:`.iter_batches()`).
Dataset transformations are lazy, with execution of the transformations being
triggered by downstream consumption.
Dataset supports parallel processing at scale: transformations such as
:py:meth:`.map_batches()`, aggregations such as
:py:meth:`.min()`/:py:meth:`.max()`/:py:meth:`.mean()`, grouping via
:py:meth:`.groupby()`, shuffling operations such as :py:meth:`.sort()`,
:py:meth:`.random_shuffle()`, and :py:meth:`.repartition()`.
Examples:
>>> import ray
>>> ds = ray.data.range(1000)
>>> # Transform batches (Dict[str, np.ndarray]) with map_batches().
>>> ds.map_batches(lambda batch: {"id": batch["id"] * 2}) # doctest: +ELLIPSIS
MapBatches(<lambda>)
+- Dataset(num_rows=1000, schema={id: int64})
>>> # Compute the maximum.
>>> ds.max("id")
999
>>> # Shuffle this dataset randomly.
>>> ds.random_shuffle() # doctest: +ELLIPSIS
RandomShuffle
+- Dataset(num_rows=1000, schema={id: int64})
>>> # Sort it back in order.
>>> ds.sort("id") # doctest: +ELLIPSIS
Sort
+- Dataset(num_rows=1000, schema={id: int64})
Both unexecuted and materialized Datasets can be passed between Ray tasks and
actors without incurring a copy. Dataset supports conversion to/from several
more featureful dataframe libraries (e.g., Spark, Dask, Modin, MARS), and are also
compatible with distributed TensorFlow / PyTorch.
"""
def __init__(
self,
plan: ExecutionPlan,
logical_plan: LogicalPlan,
):
"""Construct a Dataset (internal API).
The constructor is not part of the Dataset API. Use the ``ray.data.*``
read methods to construct a dataset.
"""
assert isinstance(plan, ExecutionPlan), type(plan)
usage_lib.record_library_usage("dataset") # Legacy telemetry name.
self._plan = plan
self._logical_plan = logical_plan
self._plan.link_logical_plan(logical_plan)
# Handle to currently running executor for this dataset.
self._current_executor: Optional["Executor"] = None
self._write_ds = None
self._set_uuid(StatsManager.get_dataset_id_from_stats_actor())
@staticmethod
def copy(
ds: "Dataset", _deep_copy: bool = False, _as: Optional[type] = None
) -> "Dataset":
if not _as:
_as = type(ds)
if _deep_copy:
return _as(ds._plan.deep_copy(), ds._logical_plan)
else:
return _as(ds._plan.copy(), ds._logical_plan)
[docs]
@PublicAPI(api_group=BT_API_GROUP)
def map(
self,
fn: UserDefinedFunction[Dict[str, Any], Dict[str, Any]],
*,
compute: Optional[ComputeStrategy] = None,
fn_args: Optional[Iterable[Any]] = None,
fn_kwargs: Optional[Dict[str, Any]] = None,
fn_constructor_args: Optional[Iterable[Any]] = None,
fn_constructor_kwargs: Optional[Dict[str, Any]] = None,
num_cpus: Optional[float] = None,
num_gpus: Optional[float] = None,
concurrency: Optional[Union[int, Tuple[int, int]]] = None,
ray_remote_args_fn: Optional[Callable[[], Dict[str, Any]]] = None,
**ray_remote_args,
) -> "Dataset":
"""Apply the given function to each row of this dataset.
Use this method to transform your data. To learn more, see
:ref:`Transforming rows <transforming_rows>`.
You can use either a function or a callable class to perform the transformation.
For functions, Ray Data uses stateless Ray tasks. For classes, Ray Data uses
stateful Ray actors. For more information, see
:ref:`Stateful Transforms <stateful_transforms>`.
.. tip::
If your transformation is vectorized like most NumPy or pandas operations,
:meth:`~Dataset.map_batches` might be faster.
.. warning::
Specifying both ``num_cpus`` and ``num_gpus`` for map tasks is experimental,
and may result in scheduling or stability issues. Please
`report any issues <https://github.com/ray-project/ray/issues/new/choose>`_
to the Ray team.
Examples:
.. testcode::
import os
from typing import Any, Dict
import ray
def parse_filename(row: Dict[str, Any]) -> Dict[str, Any]:
row["filename"] = os.path.basename(row["path"])
return row
ds = (
ray.data.read_images("s3://anonymous@ray-example-data/image-datasets/simple", include_paths=True)
.map(parse_filename)
)
print(ds.schema())
.. testoutput::
Column Type
------ ----
image numpy.ndarray(shape=(32, 32, 3), dtype=uint8)
path string
filename string
Time complexity: O(dataset size / parallelism)
Args:
fn: The function to apply to each row, or a class type
that can be instantiated to create such a callable.
compute: This argument is deprecated. Use ``concurrency`` argument.
fn_args: Positional arguments to pass to ``fn`` after the first argument.
These arguments are top-level arguments to the underlying Ray task.
fn_kwargs: Keyword arguments to pass to ``fn``. These arguments are
top-level arguments to the underlying Ray task.
fn_constructor_args: Positional arguments to pass to ``fn``'s constructor.
You can only provide this if ``fn`` is a callable class. These arguments
are top-level arguments in the underlying Ray actor construction task.
fn_constructor_kwargs: Keyword arguments to pass to ``fn``'s constructor.
This can only be provided if ``fn`` is a callable class. These arguments
are top-level arguments in the underlying Ray actor construction task.
num_cpus: The number of CPUs to reserve for each parallel map worker.
num_gpus: The number of GPUs to reserve for each parallel map worker. For
example, specify `num_gpus=1` to request 1 GPU for each parallel map
worker.
concurrency: The number of Ray workers to use concurrently. For a fixed-sized
worker pool of size ``n``, specify ``concurrency=n``. For an autoscaling
worker pool from ``m`` to ``n`` workers, specify ``concurrency=(m, n)``.
ray_remote_args_fn: A function that returns a dictionary of remote args
passed to each map worker. The purpose of this argument is to generate
dynamic arguments for each actor/task, and will be called each time prior
to initializing the worker. Args returned from this dict will always
override the args in ``ray_remote_args``. Note: this is an advanced,
experimental feature.
ray_remote_args: Additional resource requirements to request from
Ray for each map worker.
.. seealso::
:meth:`~Dataset.flat_map`
Call this method to create new rows from existing ones. Unlike
:meth:`~Dataset.map`, a function passed to
:meth:`~Dataset.flat_map` can return multiple rows.
:meth:`~Dataset.map_batches`
Call this method to transform batches of data.
""" # noqa: E501
compute = get_compute_strategy(
fn,
fn_constructor_args=fn_constructor_args,
compute=compute,
concurrency=concurrency,
)
if num_cpus is not None:
ray_remote_args["num_cpus"] = num_cpus
if num_gpus is not None:
ray_remote_args["num_gpus"] = num_gpus
plan = self._plan.copy()
map_op = MapRows(
self._logical_plan.dag,
fn,
fn_args=fn_args,
fn_kwargs=fn_kwargs,
fn_constructor_args=fn_constructor_args,
fn_constructor_kwargs=fn_constructor_kwargs,
compute=compute,
ray_remote_args_fn=ray_remote_args_fn,
ray_remote_args=ray_remote_args,
)
logical_plan = LogicalPlan(map_op, self.context)
return Dataset(plan, logical_plan)
def _set_name(self, name: Optional[str]):
"""Set the name of the dataset.
Used as a prefix for metrics tags.
"""
self._plan._dataset_name = name
@property
def _name(self) -> Optional[str]:
"""Returns the dataset name"""
return self._plan._dataset_name
[docs]
@PublicAPI(api_group=BT_API_GROUP)
def map_batches(
self,
fn: UserDefinedFunction[DataBatch, DataBatch],
*,
batch_size: Union[int, None, Literal["default"]] = "default",
compute: Optional[ComputeStrategy] = None,
batch_format: Optional[str] = "default",
zero_copy_batch: bool = False,
fn_args: Optional[Iterable[Any]] = None,
fn_kwargs: Optional[Dict[str, Any]] = None,
fn_constructor_args: Optional[Iterable[Any]] = None,
fn_constructor_kwargs: Optional[Dict[str, Any]] = None,
num_cpus: Optional[float] = None,
num_gpus: Optional[float] = None,
concurrency: Optional[Union[int, Tuple[int, int]]] = None,
ray_remote_args_fn: Optional[Callable[[], Dict[str, Any]]] = None,
**ray_remote_args,
) -> "Dataset":
"""Apply the given function to batches of data.
This method is useful for preprocessing data and performing inference. To learn
more, see :ref:`Transforming batches <transforming_batches>`.
You can use either a function or a callable class to perform the transformation.
For functions, Ray Data uses stateless Ray tasks. For classes, Ray Data uses
stateful Ray actors. For more information, see
:ref:`Stateful Transforms <stateful_transforms>`.
.. tip::
To understand the format of the input to ``fn``, call :meth:`~Dataset.take_batch`
on the dataset to get a batch in the same format as will be passed to ``fn``.
.. tip::
If ``fn`` doesn't mutate its input, set ``zero_copy_batch=True`` to improve
performance and decrease memory utilization.
.. warning::
Specifying both ``num_cpus`` and ``num_gpus`` for map tasks is experimental,
and may result in scheduling or stability issues. Please
`report any issues <https://github.com/ray-project/ray/issues/new/choose>`_
to the Ray team.
Examples:
Call :meth:`~Dataset.map_batches` to transform your data.
.. testcode::
from typing import Dict
import numpy as np
import ray
def add_dog_years(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
batch["age_in_dog_years"] = 7 * batch["age"]
return batch
ds = (
ray.data.from_items([
{"name": "Luna", "age": 4},
{"name": "Rory", "age": 14},
{"name": "Scout", "age": 9},
])
.map_batches(add_dog_years)
)
ds.show()
.. testoutput::
{'name': 'Luna', 'age': 4, 'age_in_dog_years': 28}
{'name': 'Rory', 'age': 14, 'age_in_dog_years': 98}
{'name': 'Scout', 'age': 9, 'age_in_dog_years': 63}
If your function returns large objects, yield outputs in chunks.
.. testcode::
from typing import Dict
import ray
import numpy as np
def map_fn_with_large_output(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
for i in range(3):
yield {"large_output": np.ones((100, 1000))}
ds = (
ray.data.from_items([1])
.map_batches(map_fn_with_large_output)
)
If you require stateful transfomation,
use Python callable class. Here is an example showing how to use stateful transforms to create model inference workers, without having to reload the model on each call.
.. testcode::
from typing import Dict
import numpy as np
import torch
import ray
class TorchPredictor:
def __init__(self):
self.model = torch.nn.Identity().cuda()
self.model.eval()
def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
inputs = torch.as_tensor(batch["data"], dtype=torch.float32).cuda()
with torch.inference_mode():
batch["output"] = self.model(inputs).detach().cpu().numpy()
return batch
ds = (
ray.data.from_numpy(np.ones((32, 100)))
.map_batches(
TorchPredictor,
# Two workers with one GPU each
concurrency=2,
# Batch size is required if you're using GPUs.
batch_size=4,
num_gpus=1
)
)
To learn more, see
:ref:`End-to-end: Offline Batch Inference <batch_inference_home>`.
Args:
fn: The function or generator to apply to a record batch, or a class type
that can be instantiated to create such a callable. Note ``fn`` must be
pickle-able.
batch_size: The desired number of rows in each batch, or ``None`` to use
entire blocks as batches (blocks may contain different numbers of rows).
The actual size of the batch provided to ``fn`` may be smaller than
``batch_size`` if ``batch_size`` doesn't evenly divide the block(s) sent
to a given map task. Default batch_size is 1024 with "default".
compute: This argument is deprecated. Use ``concurrency`` argument.
batch_format: If ``"default"`` or ``"numpy"``, batches are
``Dict[str, numpy.ndarray]``. If ``"pandas"``, batches are
``pandas.DataFrame``. If ``"pyarrow"``, batches are
``pyarrow.Table``.
zero_copy_batch: Whether ``fn`` should be provided zero-copy, read-only
batches. If this is ``True`` and no copy is required for the
``batch_format`` conversion, the batch is a zero-copy, read-only
view on data in Ray's object store, which can decrease memory
utilization and improve performance. If this is ``False``, the batch
is writable, which requires an extra copy to guarantee.
If ``fn`` mutates its input, this needs to be ``False`` in order to
avoid "assignment destination is read-only" or "buffer source array is
read-only" errors. Default is ``False``.
fn_args: Positional arguments to pass to ``fn`` after the first argument.
These arguments are top-level arguments to the underlying Ray task.
fn_kwargs: Keyword arguments to pass to ``fn``. These arguments are
top-level arguments to the underlying Ray task.
fn_constructor_args: Positional arguments to pass to ``fn``'s constructor.
You can only provide this if ``fn`` is a callable class. These arguments
are top-level arguments in the underlying Ray actor construction task.
fn_constructor_kwargs: Keyword arguments to pass to ``fn``'s constructor.
This can only be provided if ``fn`` is a callable class. These arguments
are top-level arguments in the underlying Ray actor construction task.
num_cpus: The number of CPUs to reserve for each parallel map worker.
num_gpus: The number of GPUs to reserve for each parallel map worker. For
example, specify `num_gpus=1` to request 1 GPU for each parallel map worker.
concurrency: The number of Ray workers to use concurrently. For a fixed-sized
worker pool of size ``n``, specify ``concurrency=n``. For an autoscaling
worker pool from ``m`` to ``n`` workers, specify ``concurrency=(m, n)``.
ray_remote_args_fn: A function that returns a dictionary of remote args
passed to each map worker. The purpose of this argument is to generate
dynamic arguments for each actor/task, and will be called each time prior
to initializing the worker. Args returned from this dict will always
override the args in ``ray_remote_args``. Note: this is an advanced,
experimental feature.
ray_remote_args: Additional resource requirements to request from
ray for each map worker.
.. note::
The size of the batches provided to ``fn`` might be smaller than the
specified ``batch_size`` if ``batch_size`` doesn't evenly divide the
block(s) sent to a given map task.
If ``batch_size`` is set and each input block is smaller than the
``batch_size``, Ray Data will bundle up many blocks as the input for one
task, until their total size is equal to or greater than the given
``batch_size``.
If ``batch_size`` is not set, the bundling will not be performed. Each task
will receive only one input block.
.. seealso::
:meth:`~Dataset.iter_batches`
Call this function to iterate over batches of data.
:meth:`~Dataset.take_batch`
Call this function to get a batch of data from the dataset
in the same format as will be passed to the `fn` function of
:meth:`~Dataset.map_batches`.
:meth:`~Dataset.flat_map`
Call this method to create new records from existing ones. Unlike
:meth:`~Dataset.map`, a function passed to :meth:`~Dataset.flat_map`
can return multiple records.
:meth:`~Dataset.map`
Call this method to transform one record at time.
""" # noqa: E501
use_gpus = num_gpus is not None and num_gpus > 0
if use_gpus and (batch_size is None or batch_size == "default"):
raise ValueError(
"You must provide `batch_size` to `map_batches` when requesting GPUs. "
"The optimal batch size depends on the model, data, and GPU used. "
"We recommend using the largest batch size that doesn't result "
"in your GPU device running out of memory. You can view the GPU memory "
"usage via the Ray dashboard."
)
if isinstance(batch_size, int) and batch_size < 1:
raise ValueError("Batch size can't be negative or 0")
return self._map_batches_without_batch_size_validation(
fn,
batch_size=batch_size,
compute=compute,
batch_format=batch_format,
zero_copy_batch=zero_copy_batch,
fn_args=fn_args,
fn_kwargs=fn_kwargs,
fn_constructor_args=fn_constructor_args,
fn_constructor_kwargs=fn_constructor_kwargs,
num_cpus=num_cpus,
num_gpus=num_gpus,
concurrency=concurrency,
ray_remote_args_fn=ray_remote_args_fn,
**ray_remote_args,
)
def _map_batches_without_batch_size_validation(
self,
fn: UserDefinedFunction[DataBatch, DataBatch],
*,
batch_size: Union[int, None, Literal["default"]],
compute: Optional[ComputeStrategy],
batch_format: Optional[str],
zero_copy_batch: bool,
fn_args: Optional[Iterable[Any]],
fn_kwargs: Optional[Dict[str, Any]],
fn_constructor_args: Optional[Iterable[Any]],
fn_constructor_kwargs: Optional[Dict[str, Any]],
num_cpus: Optional[float],
num_gpus: Optional[float],
concurrency: Optional[Union[int, Tuple[int, int]]],
ray_remote_args_fn: Optional[Callable[[], Dict[str, Any]]],
**ray_remote_args,
):
# NOTE: The `map_groups` implementation calls `map_batches` with
# `batch_size=None`. The issue is that if you request GPUs with
# `batch_size=None`, then `map_batches` raises a value error. So, to allow users
# to call `map_groups` with GPUs, we need a separate method that doesn't
# perform batch size validation.
compute = get_compute_strategy(
fn,
fn_constructor_args=fn_constructor_args,
compute=compute,
concurrency=concurrency,
)
if num_cpus is not None:
ray_remote_args["num_cpus"] = num_cpus
if num_gpus is not None:
ray_remote_args["num_gpus"] = num_gpus
batch_format = _apply_batch_format(batch_format)
min_rows_per_bundled_input = None
if batch_size is not None and batch_size != "default":
# Enable blocks bundling when batch_size is specified by caller.
min_rows_per_bundled_input = batch_size
batch_size = _apply_batch_size(batch_size)
if batch_format not in VALID_BATCH_FORMATS:
raise ValueError(
f"The batch format must be one of {VALID_BATCH_FORMATS}, got: "
f"{batch_format}"
)
plan = self._plan.copy()
map_batches_op = MapBatches(
self._logical_plan.dag,
fn,
batch_size=batch_size,
batch_format=batch_format,
zero_copy_batch=zero_copy_batch,
min_rows_per_bundled_input=min_rows_per_bundled_input,
fn_args=fn_args,
fn_kwargs=fn_kwargs,
fn_constructor_args=fn_constructor_args,
fn_constructor_kwargs=fn_constructor_kwargs,
compute=compute,
ray_remote_args_fn=ray_remote_args_fn,
ray_remote_args=ray_remote_args,
)
logical_plan = LogicalPlan(map_batches_op, self.context)
return Dataset(plan, logical_plan)
[docs]
@PublicAPI(api_group=BT_API_GROUP)
def add_column(
self,
col: str,
fn: Callable[
[DataBatch],
DataBatchColumn,
],
*,
batch_format: Optional[str] = "pandas",
compute: Optional[str] = None,
concurrency: Optional[Union[int, Tuple[int, int]]] = None,
**ray_remote_args,
) -> "Dataset":
"""Add the given column to the dataset.
A function generating the new column values given the batch in pyarrow or pandas
format must be specified. This function must operate on batches of
`batch_format`.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.schema()
Column Type
------ ----
id int64
Add a new column equal to ``id * 2``.
>>> ds.add_column("new_id", lambda df: df["id"] * 2).schema()
Column Type
------ ----
id int64
new_id int64
Time complexity: O(dataset size / parallelism)
Args:
col: Name of the column to add. If the name already exists, the
column is overwritten.
fn: Map function generating the column values given a batch of
records in pandas format.
batch_format: If ``"default"`` or ``"numpy"``, batches are
``Dict[str, numpy.ndarray]``. If ``"pandas"``, batches are
``pandas.DataFrame``. If ``"pyarrow"``, batches are
``pyarrow.Table``. If ``"numpy"``, batches are
``Dict[str, numpy.ndarray]``.
compute: This argument is deprecated. Use ``concurrency`` argument.
concurrency: The number of Ray workers to use concurrently. For a
fixed-sized worker pool of size ``n``, specify ``concurrency=n``. For
an autoscaling worker pool from ``m`` to ``n`` workers, specify
``concurrency=(m, n)``.
ray_remote_args: Additional resource requirements to request from
ray (e.g., num_gpus=1 to request GPUs for the map tasks).
"""
# Check that batch_format
accepted_batch_formats = ["pandas", "pyarrow", "numpy"]
if batch_format not in accepted_batch_formats:
raise ValueError(
f"batch_format argument must be on of {accepted_batch_formats}, "
f"got: {batch_format}"
)
def add_column(batch: DataBatch) -> DataBatch:
column = fn(batch)
if batch_format == "pandas":
import pandas as pd
assert isinstance(column, pd.Series), (
f"For pandas batch format, the function must return a pandas "
f"Series, got: {type(column)}"
)
if col in batch:
raise ValueError(
f"Trying to add an existing column with name" f" {col}"
)
batch.loc[:, col] = column
return batch
elif batch_format == "pyarrow":
import pyarrow as pa
assert isinstance(column, (pa.Array, pa.ChunkedArray)), (
f"For pyarrow batch format, the function must return a pyarrow "
f"Array, got: {type(column)}"
)
# Historically, this method was written for pandas batch format.
# To resolve https://github.com/ray-project/ray/issues/48090,
# we also allow pyarrow batch format which is preferred but would be
# a breaking change to enforce.
# For pyarrow, the index of the column will be -1 if it is missing in
# which case we'll want to append it
column_idx = batch.schema.get_field_index(col)
if column_idx == -1:
# Append the column to the table
return batch.append_column(col, column)
else:
raise ValueError(
f"Trying to add an existing column with name {col}"
)
else:
# batch format is assumed to be numpy since we checked at the
# beginning of the add_column function
assert isinstance(column, np.ndarray), (
f"For numpy batch format, the function must return a "
f"numpy.ndarray, got: {type(column)}"
)
if col in batch:
raise ValueError(
f"Trying to add an existing column with name" f" {col}"
)
batch[col] = column
return batch
if not callable(fn):
raise ValueError("`fn` must be callable, got {}".format(fn))
return self.map_batches(
add_column,
batch_format=batch_format,
compute=compute,
concurrency=concurrency,
zero_copy_batch=False,
**ray_remote_args,
)
[docs]
@PublicAPI(api_group=BT_API_GROUP)
def drop_columns(
self,
cols: List[str],
*,
compute: Optional[str] = None,
concurrency: Optional[Union[int, Tuple[int, int]]] = None,
**ray_remote_args,
) -> "Dataset":
"""Drop one or more columns from the dataset.
Examples:
>>> import ray
>>> ds = ray.data.read_parquet("s3://anonymous@ray-example-data/iris.parquet")
>>> ds.schema()
Column Type
------ ----
sepal.length double
sepal.width double
petal.length double
petal.width double
variety string
>>> ds.drop_columns(["variety"]).schema()
Column Type
------ ----
sepal.length double
sepal.width double
petal.length double
petal.width double
Time complexity: O(dataset size / parallelism)
Args:
cols: Names of the columns to drop. If any name does not exist,
an exception is raised. Column names must be unique.
compute: This argument is deprecated. Use ``concurrency`` argument.
concurrency: The number of Ray workers to use concurrently. For a fixed-sized
worker pool of size ``n``, specify ``concurrency=n``. For an autoscaling
worker pool from ``m`` to ``n`` workers, specify ``concurrency=(m, n)``.
ray_remote_args: Additional resource requirements to request from
ray (e.g., num_gpus=1 to request GPUs for the map tasks).
""" # noqa: E501
if len(cols) != len(set(cols)):
raise ValueError(f"drop_columns expects unique column names, got: {cols}")
def drop_columns(batch):
return batch.drop(cols)
return self.map_batches(
drop_columns,
batch_format="pyarrow",
zero_copy_batch=True,
compute=compute,
concurrency=concurrency,
**ray_remote_args,
)
[docs]
@PublicAPI(api_group=BT_API_GROUP)
def select_columns(
self,
cols: List[str],
*,
compute: Union[str, ComputeStrategy] = None,
concurrency: Optional[Union[int, Tuple[int, int]]] = None,
**ray_remote_args,
) -> "Dataset":
"""Select one or more columns from the dataset.
Specified columns must be in the dataset schema.
.. tip::
If you're reading parquet files with :meth:`ray.data.read_parquet`,
you might be able to speed it up by using projection pushdown; see
:ref:`Parquet column pruning <parquet_column_pruning>` for details.
Examples:
>>> import ray
>>> ds = ray.data.read_parquet("s3://anonymous@ray-example-data/iris.parquet")
>>> ds.schema()
Column Type
------ ----
sepal.length double
sepal.width double
petal.length double
petal.width double
variety string
>>> ds.select_columns(["sepal.length", "sepal.width"]).schema()
Column Type
------ ----
sepal.length double
sepal.width double
Time complexity: O(dataset size / parallelism)
Args:
cols: Names of the columns to select. If a name isn't in the
dataset schema, an exception is raised. Columns also should be unique.
compute: This argument is deprecated. Use ``concurrency`` argument.
concurrency: The number of Ray workers to use concurrently. For a fixed-sized
worker pool of size ``n``, specify ``concurrency=n``. For an autoscaling
worker pool from ``m`` to ``n`` workers, specify ``concurrency=(m, n)``.
ray_remote_args: Additional resource requirements to request from
ray (e.g., num_gpus=1 to request GPUs for the map tasks).
""" # noqa: E501
if not isinstance(cols, list):
raise ValueError(
"select_columns expected List[str], "
f"got {type(cols)} for input '{cols}'"
)
bad_input = [col for col in cols if not isinstance(col, str)]
if bad_input:
raise ValueError(
"select_columns expected List[str], "
f"got input type: {type(bad_input[0])} "
f"for input {cols}"
)
if len(cols) != len(set(cols)):
raise ValueError(
"select_columns expected unique column names, "
f"got duplicate column names: {cols}"
)
# Don't feel like we really need this
from ray.data._internal.compute import TaskPoolStrategy
compute = TaskPoolStrategy(size=concurrency)
plan = self._plan.copy()
select_op = Project(
self._logical_plan.dag,
cols=cols,
compute=compute,
ray_remote_args=ray_remote_args,
)
logical_plan = LogicalPlan(select_op, self.context)
return Dataset(plan, logical_plan)
[docs]
@PublicAPI(api_group=BT_API_GROUP)
def rename_columns(
self,
names: Union[List[str], Dict[str, str]],
*,
concurrency: Optional[Union[int, Tuple[int, int]]] = None,
**ray_remote_args,
):
"""Rename columns in the dataset.
Examples:
>>> import ray
>>> ds = ray.data.read_parquet("s3://anonymous@ray-example-data/iris.parquet")
>>> ds.schema()
Column Type
------ ----
sepal.length double
sepal.width double
petal.length double
petal.width double
variety string
You can pass a dictionary mapping old column names to new column names.
>>> ds.rename_columns({"variety": "category"}).schema()
Column Type
------ ----
sepal.length double
sepal.width double
petal.length double
petal.width double
category string
Or you can pass a list of new column names.
>>> ds.rename_columns(
... ["sepal_length", "sepal_width", "petal_length", "petal_width", "variety"]
... ).schema()
Column Type
------ ----
sepal_length double
sepal_width double
petal_length double
petal_width double
variety string
Args:
mapper: A dictionary that maps old column names to new column names, or a
list of new column names.
concurrency: The maximum number of Ray workers to use concurrently.
ray_remote_args: Additional resource requirements to request from
ray (e.g., num_gpus=1 to request GPUs for the map tasks).
""" # noqa: E501
if concurrency is not None and not isinstance(concurrency, int):
raise ValueError(
"Expected `concurrency` to be an integer or `None`, but got "
f"{concurrency}."
)
def rename_columns(batch: "pyarrow.Table") -> "pyarrow.Table":
# Versions of PyArrow before 17 don't support renaming columns with a dict.
if isinstance(names, dict):
column_names_list = batch.column_names
for i, column_name in enumerate(column_names_list):
if column_name in names:
column_names_list[i] = names[column_name]
else:
column_names_list = names
return batch.rename_columns(column_names_list)
return self.map_batches(
rename_columns,
batch_format="pyarrow",
zero_copy_batch=True,
concurrency=concurrency,
**ray_remote_args,
)
[docs]
@PublicAPI(api_group=BT_API_GROUP)
def flat_map(
self,
fn: UserDefinedFunction[Dict[str, Any], List[Dict[str, Any]]],
*,
compute: Optional[ComputeStrategy] = None,
fn_args: Optional[Iterable[Any]] = None,
fn_kwargs: Optional[Dict[str, Any]] = None,
fn_constructor_args: Optional[Iterable[Any]] = None,
fn_constructor_kwargs: Optional[Dict[str, Any]] = None,
num_cpus: Optional[float] = None,
num_gpus: Optional[float] = None,
concurrency: Optional[Union[int, Tuple[int, int]]] = None,
ray_remote_args_fn: Optional[Callable[[], Dict[str, Any]]] = None,
**ray_remote_args,
) -> "Dataset":
"""Apply the given function to each row and then flatten results.
Use this method if your transformation returns multiple rows for each input
row.
You can use either a function or a callable class to perform the transformation.
For functions, Ray Data uses stateless Ray tasks. For classes, Ray Data uses
stateful Ray actors. For more information, see
:ref:`Stateful Transforms <stateful_transforms>`.
.. tip::
:meth:`~Dataset.map_batches` can also modify the number of rows. If your
transformation is vectorized like most NumPy and pandas operations,
it might be faster.
.. warning::
Specifying both ``num_cpus`` and ``num_gpus`` for map tasks is experimental,
and may result in scheduling or stability issues. Please
`report any issues <https://github.com/ray-project/ray/issues/new/choose>`_
to the Ray team.
Examples:
.. testcode::
from typing import Any, Dict, List
import ray
def duplicate_row(row: Dict[str, Any]) -> List[Dict[str, Any]]:
return [row] * 2
print(
ray.data.range(3)
.flat_map(duplicate_row)
.take_all()
)
.. testoutput::
[{'id': 0}, {'id': 0}, {'id': 1}, {'id': 1}, {'id': 2}, {'id': 2}]
Time complexity: O(dataset size / parallelism)
Args:
fn: The function or generator to apply to each record, or a class type
that can be instantiated to create such a callable.
compute: This argument is deprecated. Use ``concurrency`` argument.
fn_args: Positional arguments to pass to ``fn`` after the first argument.
These arguments are top-level arguments to the underlying Ray task.
fn_kwargs: Keyword arguments to pass to ``fn``. These arguments are
top-level arguments to the underlying Ray task.
fn_constructor_args: Positional arguments to pass to ``fn``'s constructor.
You can only provide this if ``fn`` is a callable class. These arguments
are top-level arguments in the underlying Ray actor construction task.
fn_constructor_kwargs: Keyword arguments to pass to ``fn``'s constructor.
This can only be provided if ``fn`` is a callable class. These arguments
are top-level arguments in the underlying Ray actor construction task.
num_cpus: The number of CPUs to reserve for each parallel map worker.
num_gpus: The number of GPUs to reserve for each parallel map worker. For
example, specify `num_gpus=1` to request 1 GPU for each parallel map
worker.
concurrency: The number of Ray workers to use concurrently. For a
fixed-sized worker pool of size ``n``, specify ``concurrency=n``.
For an autoscaling worker pool from ``m`` to ``n`` workers, specify
``concurrency=(m, n)``.
ray_remote_args_fn: A function that returns a dictionary of remote args
passed to each map worker. The purpose of this argument is to generate
dynamic arguments for each actor/task, and will be called each time
prior to initializing the worker. Args returned from this dict will
always override the args in ``ray_remote_args``. Note: this is an
advanced, experimental feature.
ray_remote_args: Additional resource requirements to request from
ray for each map worker.
.. seealso::
:meth:`~Dataset.map_batches`
Call this method to transform batches of data.
:meth:`~Dataset.map`
Call this method to transform one row at time.
"""
compute = get_compute_strategy(
fn,
fn_constructor_args=fn_constructor_args,
compute=compute,
concurrency=concurrency,
)
if num_cpus is not None:
ray_remote_args["num_cpus"] = num_cpus
if num_gpus is not None:
ray_remote_args["num_gpus"] = num_gpus
plan = self._plan.copy()
op = FlatMap(
input_op=self._logical_plan.dag,
fn=fn,
fn_args=fn_args,
fn_kwargs=fn_kwargs,
fn_constructor_args=fn_constructor_args,
fn_constructor_kwargs=fn_constructor_kwargs,
compute=compute,
ray_remote_args_fn=ray_remote_args_fn,
ray_remote_args=ray_remote_args,
)
logical_plan = LogicalPlan(op, self.context)
return Dataset(plan, logical_plan)
[docs]
@PublicAPI(api_group=BT_API_GROUP)
def filter(
self,
fn: UserDefinedFunction[Dict[str, Any], bool],
*,
compute: Union[str, ComputeStrategy] = None,
concurrency: Optional[Union[int, Tuple[int, int]]] = None,
ray_remote_args_fn: Optional[Callable[[], Dict[str, Any]]] = None,
**ray_remote_args,
) -> "Dataset":
"""Filter out rows that don't satisfy the given predicate.
You can use either a function or a callable class to perform the transformation.
For functions, Ray Data uses stateless Ray tasks. For classes, Ray Data uses
stateful Ray actors. For more information, see
:ref:`Stateful Transforms <stateful_transforms>`.
.. tip::
If you can represent your predicate with NumPy or pandas operations,
:meth:`Dataset.map_batches` might be faster. You can implement filter by
dropping rows.
.. tip::
If you're reading parquet files with :meth:`ray.data.read_parquet`,
and the filter is a simple predicate, you might
be able to speed it up by using filter pushdown; see
:ref:`Parquet row pruning <parquet_row_pruning>` for details.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.filter(lambda row: row["id"] % 2 == 0).take_all()
[{'id': 0}, {'id': 2}, {'id': 4}, ...]
Time complexity: O(dataset size / parallelism)
Args:
fn: The predicate to apply to each row, or a class type
that can be instantiated to create such a callable.
compute: This argument is deprecated. Use ``concurrency`` argument.
concurrency: The number of Ray workers to use concurrently. For a
fixed-sized worker pool of size ``n``, specify ``concurrency=n``.
For an autoscaling worker pool from ``m`` to ``n`` workers, specify
``concurrency=(m, n)``.
ray_remote_args_fn: A function that returns a dictionary of remote args
passed to each map worker. The purpose of this argument is to generate
dynamic arguments for each actor/task, and will be called each time
prior to initializing the worker. Args returned from this dict will
always override the args in ``ray_remote_args``. Note: this is an
advanced, experimental feature.
ray_remote_args: Additional resource requirements to request from
ray (e.g., num_gpus=1 to request GPUs for the map tasks).
"""
compute = get_compute_strategy(
fn,
compute=compute,
concurrency=concurrency,
)
plan = self._plan.copy()
op = Filter(
input_op=self._logical_plan.dag,
fn=fn,
compute=compute,
ray_remote_args_fn=ray_remote_args_fn,
ray_remote_args=ray_remote_args,
)
logical_plan = LogicalPlan(op, self.context)
return Dataset(plan, logical_plan)
[docs]
@AllToAllAPI
@PublicAPI(api_group=SSR_API_GROUP)
def repartition(
self,
num_blocks: int,
*,
shuffle: bool = False,
) -> "Dataset":
"""Repartition the :class:`Dataset` into exactly this number of :ref:`blocks <dataset_concept>`.
This method can be useful to tune the performance of your pipeline. To learn
more, see :ref:`Advanced: Performance Tips and Tuning <data_performance_tips>`.
If you're writing data to files, you can also use this method to change the
number of output files. To learn more, see
:ref:`Changing the number of output files <changing-number-output-files>`.
.. note::
Repartition has two modes. If ``shuffle=False``, Ray Data performs the
minimal data movement needed to equalize block sizes. Otherwise, Ray Data
performs a full distributed shuffle.
.. image:: /data/images/dataset-shuffle.svg
:align: center
..
https://docs.google.com/drawings/d/132jhE3KXZsf29ho1yUdPrCHB9uheHBWHJhDQMXqIVPA/edit
Examples:
>>> import ray
>>> ds = ray.data.range(100).repartition(10).materialize()
>>> ds.num_blocks()
10
Time complexity: O(dataset size / parallelism)
Args:
num_blocks: The number of blocks.
shuffle: Whether to perform a distributed shuffle during the
repartition. When shuffle is enabled, each output block
contains a subset of data rows from each input block, which
requires all-to-all data movement. When shuffle is disabled,
output blocks are created from adjacent input blocks,
minimizing data movement.
Returns:
The repartitioned :class:`Dataset`.
""" # noqa: E501
plan = self._plan.copy()
op = Repartition(
self._logical_plan.dag,
num_outputs=num_blocks,
shuffle=shuffle,
)
logical_plan = LogicalPlan(op, self.context)
return Dataset(plan, logical_plan)
[docs]
@AllToAllAPI
@PublicAPI(api_group=SSR_API_GROUP)
def random_shuffle(
self,
*,
seed: Optional[int] = None,
num_blocks: Optional[int] = None,
**ray_remote_args,
) -> "Dataset":
"""Randomly shuffle the rows of this :class:`Dataset`.
.. tip::
This method can be slow. For better performance, try
:ref:`Iterating over batches with shuffling <iterating-over-batches-with-shuffling>`.
Also, see :ref:`Optimizing shuffles <optimizing_shuffles>`.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.random_shuffle().take(3) # doctest: +SKIP
{'id': 41}, {'id': 21}, {'id': 92}]
>>> ds.random_shuffle(seed=42).take(3) # doctest: +SKIP
{'id': 77}, {'id': 21}, {'id': 63}]
Time complexity: O(dataset size / parallelism)
Args:
seed: Fix the random seed to use, otherwise one is chosen
based on system randomness.
Returns:
The shuffled :class:`Dataset`.
""" # noqa: E501
if num_blocks is not None:
raise DeprecationWarning(
"`num_blocks` parameter is deprecated in Ray 2.9. random_shuffle() "
"does not support to change the number of output blocks. Use "
"repartition() instead.", # noqa: E501
)
plan = self._plan.copy()
op = RandomShuffle(
self._logical_plan.dag,
seed=seed,
ray_remote_args=ray_remote_args,
)
logical_plan = LogicalPlan(op, self.context)
return Dataset(plan, logical_plan)
[docs]
@AllToAllAPI
@PublicAPI(api_group=SSR_API_GROUP)
def randomize_block_order(
self,
*,
seed: Optional[int] = None,
) -> "Dataset":
"""Randomly shuffle the :ref:`blocks <dataset_concept>` of this :class:`Dataset`.
This method is useful if you :meth:`~Dataset.split` your dataset into shards and
want to randomize the data in each shard without performing a full
:meth:`~Dataset.random_shuffle`.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.take(5)
[{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}]
>>> ds.randomize_block_order().take(5) # doctest: +SKIP
{'id': 15}, {'id': 16}, {'id': 17}, {'id': 18}, {'id': 19}]
Args:
seed: Fix the random seed to use, otherwise one is chosen
based on system randomness.
Returns:
The block-shuffled :class:`Dataset`.
""" # noqa: E501
plan = self._plan.copy()
op = RandomizeBlocks(
self._logical_plan.dag,
seed=seed,
)
logical_plan = LogicalPlan(op, self.context)
return Dataset(plan, logical_plan)
[docs]
@PublicAPI(api_group=BT_API_GROUP)
def random_sample(
self, fraction: float, *, seed: Optional[int] = None
) -> "Dataset":
"""Returns a new :class:`Dataset` containing a random fraction of the rows.
.. note::
This method returns roughly ``fraction * total_rows`` rows. An exact number
of rows isn't guaranteed.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.random_sample(0.1).count() # doctest: +SKIP
10
Args:
fraction: The fraction of elements to sample.
seed: Seeds the python random pRNG generator.
Returns:
Returns a :class:`Dataset` containing the sampled rows.
"""
import random
import pandas as pd
import pyarrow as pa
if self._plan.initial_num_blocks() == 0:
raise ValueError("Cannot sample from an empty Dataset.")
if fraction < 0 or fraction > 1:
raise ValueError("Fraction must be between 0 and 1.")
if seed is not None:
random.seed(seed)
def random_sample(batch):
if isinstance(batch, list):
return [row for row in batch if random.random() <= fraction]
if isinstance(batch, pa.Table):
# Lets the item pass if weight generated for that item <= fraction
return batch.filter(
pa.array(random.random() <= fraction for _ in range(len(batch)))
)
if isinstance(batch, pd.DataFrame):
return batch.sample(frac=fraction)
if isinstance(batch, np.ndarray):
return _create_possibly_ragged_ndarray(
[row for row in batch if random.random() <= fraction]
)
raise ValueError(f"Unsupported batch type: {type(batch)}")
return self.map_batches(random_sample, batch_format=None)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=SMD_API_GROUP)
def streaming_split(
self,
n: int,
*,
equal: bool = False,
locality_hints: Optional[List["NodeIdStr"]] = None,
) -> List[DataIterator]:
"""Returns ``n`` :class:`DataIterators <ray.data.DataIterator>` that can
be used to read disjoint subsets of the dataset in parallel.
This method is the recommended way to consume :class:`Datasets <Dataset>` for
distributed training.
Streaming split works by delegating the execution of this :class:`Dataset` to a
coordinator actor. The coordinator pulls block references from the executed
stream, and divides those blocks among ``n`` output iterators. Iterators pull
blocks from the coordinator actor to return to their caller on ``next``.
The returned iterators are also repeatable; each iteration will trigger a
new execution of the Dataset. There is an implicit barrier at the start of
each iteration, which means that `next` must be called on all iterators before
the iteration starts.
.. warning::
Because iterators are pulling blocks from the same :class:`Dataset`
execution, if one iterator falls behind, other iterators may be stalled.
Examples:
.. testcode::
import ray
ds = ray.data.range(100)
it1, it2 = ds.streaming_split(2, equal=True)
Consume data from iterators in parallel.
.. testcode::
@ray.remote
def consume(it):
for batch in it.iter_batches():
pass
ray.get([consume.remote(it1), consume.remote(it2)])
You can loop over the iterators multiple times (multiple epochs).
.. testcode::
@ray.remote
def train(it):
NUM_EPOCHS = 2
for _ in range(NUM_EPOCHS):
for batch in it.iter_batches():
pass
ray.get([train.remote(it1), train.remote(it2)])
The following remote function call blocks waiting for a read on ``it2`` to
start.
.. testcode::
:skipif: True
ray.get(train.remote(it1))
Args:
n: Number of output iterators to return.
equal: If ``True``, each output iterator sees an exactly equal number
of rows, dropping data if necessary. If ``False``, some iterators may
see slightly more or less rows than others, but no data is dropped.
locality_hints: Specify the node ids corresponding to each iterator
location. Dataset will try to minimize data movement based on the
iterator output locations. This list must have length ``n``. You can
get the current node id of a task or actor by calling
``ray.get_runtime_context().get_node_id()``.
Returns:
The output iterator splits. These iterators are Ray-serializable and can
be freely passed to any Ray task or actor.
.. seealso::
:meth:`Dataset.split`
Unlike :meth:`~Dataset.streaming_split`, :meth:`~Dataset.split`
materializes the dataset in memory.
"""
return StreamSplitDataIterator.create(self, n, equal, locality_hints)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=SMD_API_GROUP)
def split(
self, n: int, *, equal: bool = False, locality_hints: Optional[List[Any]] = None
) -> List["MaterializedDataset"]:
"""Materialize and split the dataset into ``n`` disjoint pieces.
This method returns a list of ``MaterializedDataset`` that can be passed to Ray
Tasks and Actors and used to read the dataset rows in parallel.
Examples:
.. testcode::
@ray.remote
class Worker:
def train(self, data_iterator):
for batch in data_iterator.iter_batches(batch_size=8):
pass
workers = [Worker.remote() for _ in range(4)]
shards = ray.data.range(100).split(n=4, equal=True)
ray.get([w.train.remote(s) for w, s in zip(workers, shards)])
Time complexity: O(1)
Args:
n: Number of child datasets to return.
equal: Whether to guarantee each split has an equal
number of records. This might drop records if the rows can't be
divided equally among the splits.
locality_hints: [Experimental] A list of Ray actor handles of size ``n``.
The system tries to co-locate the blocks of the i-th dataset
with the i-th actor to maximize data locality.
Returns:
A list of ``n`` disjoint dataset splits.
.. seealso::
:meth:`Dataset.split_at_indices`
Unlike :meth:`~Dataset.split`, which splits a dataset into approximately
equal splits, :meth:`Dataset.split_proportionately` lets you split a
dataset into different sizes.
:meth:`Dataset.split_proportionately`
This method is equivalent to :meth:`Dataset.split_at_indices` if
you compute indices manually.
:meth:`Dataset.streaming_split`.
Unlike :meth:`~Dataset.split`, :meth:`~Dataset.streaming_split`
doesn't materialize the dataset in memory.
"""
if n <= 0:
raise ValueError(f"The number of splits {n} is not positive.")
# fallback to split_at_indices for equal split without locality hints.
# simple benchmarks shows spilit_at_indices yields more stable performance.
# https://github.com/ray-project/ray/pull/26641 for more context.
if equal and locality_hints is None:
count = self.count()
split_index = count // n
# we are creating n split_indices which will generate
# n + 1 splits; the last split will at most contains (n - 1)
# rows, which could be safely dropped.
split_indices = [split_index * i for i in range(1, n + 1)]
shards = self.split_at_indices(split_indices)
return shards[:n]
if locality_hints and len(locality_hints) != n:
raise ValueError(
f"The length of locality_hints {len(locality_hints)} "
f"doesn't equal the number of splits {n}."
)
bundle = self._plan.execute()
# We should not free blocks since we will materialize the Datasets.
owned_by_consumer = False
stats = self._plan.stats()
block_refs, metadata = zip(*bundle.blocks)
if locality_hints is None:
block_refs_splits = np.array_split(block_refs, n)
metadata_splits = np.array_split(metadata, n)
split_datasets = []
for block_refs_split, metadata_split in zip(
block_refs_splits, metadata_splits
):
ref_bundles = [
RefBundle([(b, m)], owns_blocks=owned_by_consumer)
for b, m in zip(block_refs_split, metadata_split)
]
logical_plan = LogicalPlan(
InputData(input_data=ref_bundles), self.context
)
split_datasets.append(
MaterializedDataset(
ExecutionPlan(stats),
logical_plan,
)
)
return split_datasets
metadata_mapping = dict(zip(block_refs, metadata))
# If the locality_hints is set, we use a two-round greedy algorithm
# to co-locate the blocks with the actors based on block
# and actor's location (node_id).
#
# The split algorithm tries to allocate equally-sized blocks regardless
# of locality. Thus we first calculate the expected number of blocks
# for each split.
#
# In the first round, for each actor, we look for all blocks that
# match the actor's node_id, then allocate those matched blocks to
# this actor until we reach the limit(expected number).
#
# In the second round: fill each actor's allocation with
# remaining unallocated blocks until we reach the limit.
def build_allocation_size_map(
num_blocks: int, actors: List[Any]
) -> Dict[Any, int]:
"""Given the total number of blocks and a list of actors, calcuate
the expected number of blocks to allocate for each actor.
"""
num_actors = len(actors)
num_blocks_per_actor = num_blocks // num_actors
num_blocks_left = num_blocks - num_blocks_per_actor * n
num_blocks_by_actor = {}
for i, actor in enumerate(actors):
num_blocks_by_actor[actor] = num_blocks_per_actor
if i < num_blocks_left:
num_blocks_by_actor[actor] += 1
return num_blocks_by_actor
def build_block_refs_by_node_id(
blocks: List[ObjectRef[Block]],
) -> Dict[str, List[ObjectRef[Block]]]:
"""Build the reverse index from node_id to block_refs. For
simplicity, if the block is stored on multiple nodes we
only pick the first one.
"""
block_ref_locations = ray.experimental.get_object_locations(blocks)
block_refs_by_node_id = collections.defaultdict(list)
for block_ref in blocks:
node_ids = block_ref_locations.get(block_ref, {}).get("node_ids", [])
node_id = node_ids[0] if node_ids else None
block_refs_by_node_id[node_id].append(block_ref)
return block_refs_by_node_id
def build_node_id_by_actor(actors: List[Any]) -> Dict[Any, str]:
"""Build a map from a actor to its node_id."""
actors_state = ray._private.state.actors()
return {
actor: actors_state.get(actor._actor_id.hex(), {})
.get("Address", {})
.get("NodeID")
for actor in actors
}
# expected number of blocks to be allocated for each actor
expected_block_count_by_actor = build_allocation_size_map(
len(block_refs), locality_hints
)
# the reverse index from node_id to block_refs
block_refs_by_node_id = build_block_refs_by_node_id(block_refs)
# the map from actor to its node_id
node_id_by_actor = build_node_id_by_actor(locality_hints)
allocation_per_actor = collections.defaultdict(list)
# In the first round, for each actor, we look for all blocks that
# match the actor's node_id, then allocate those matched blocks to
# this actor until we reach the limit(expected number)
for actor in locality_hints:
node_id = node_id_by_actor[actor]
matching_blocks = block_refs_by_node_id[node_id]
expected_block_count = expected_block_count_by_actor[actor]
allocation = []
while matching_blocks and len(allocation) < expected_block_count:
allocation.append(matching_blocks.pop())
allocation_per_actor[actor] = allocation
# In the second round: fill each actor's allocation with
# remaining unallocated blocks until we reach the limit
remaining_block_refs = list(
itertools.chain.from_iterable(block_refs_by_node_id.values())
)
for actor in locality_hints:
while (
len(allocation_per_actor[actor]) < expected_block_count_by_actor[actor]
):
allocation_per_actor[actor].append(remaining_block_refs.pop())
assert len(remaining_block_refs) == 0, len(remaining_block_refs)
per_split_bundles = []
for actor in locality_hints:
blocks = allocation_per_actor[actor]
metadata = [metadata_mapping[b] for b in blocks]
bundle = RefBundle(
tuple(zip(blocks, metadata)), owns_blocks=owned_by_consumer
)
per_split_bundles.append(bundle)
if equal:
# equalize the splits
per_split_bundles = _equalize(per_split_bundles, owned_by_consumer)
split_datasets = []
for bundle in per_split_bundles:
logical_plan = LogicalPlan(InputData(input_data=[bundle]), self.context)
split_datasets.append(
MaterializedDataset(
ExecutionPlan(stats),
logical_plan,
)
)
return split_datasets
[docs]
@ConsumptionAPI
@PublicAPI(api_group=SMD_API_GROUP)
def split_at_indices(self, indices: List[int]) -> List["MaterializedDataset"]:
"""Materialize and split the dataset at the given indices (like ``np.split``).
Examples:
>>> import ray
>>> ds = ray.data.range(10)
>>> d1, d2, d3 = ds.split_at_indices([2, 5])
>>> d1.take_batch()
{'id': array([0, 1])}
>>> d2.take_batch()
{'id': array([2, 3, 4])}
>>> d3.take_batch()
{'id': array([5, 6, 7, 8, 9])}
Time complexity: O(num splits)
Args:
indices: List of sorted integers which indicate where the dataset
are split. If an index exceeds the length of the dataset,
an empty dataset is returned.
Returns:
The dataset splits.
.. seealso::
:meth:`Dataset.split`
Unlike :meth:`~Dataset.split_at_indices`, which lets you split a
dataset into different sizes, :meth:`Dataset.split` splits a dataset
into approximately equal splits.
:meth:`Dataset.split_proportionately`
This method is equivalent to :meth:`Dataset.split_at_indices` if
you compute indices manually.
:meth:`Dataset.streaming_split`.
Unlike :meth:`~Dataset.split`, :meth:`~Dataset.streaming_split`
doesn't materialize the dataset in memory.
"""
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")
start_time = time.perf_counter()
bundle = self._plan.execute()
blocks, metadata = _split_at_indices(
bundle.blocks,
indices,
False,
)
split_duration = time.perf_counter() - start_time
parent_stats = self._plan.stats()
splits = []
for bs, ms in zip(blocks, metadata):
stats = DatasetStats(metadata={"Split": ms}, parent=parent_stats)
stats.time_total_s = split_duration
ref_bundles = [
RefBundle([(b, m)], owns_blocks=False) for b, m in zip(bs, ms)
]
logical_plan = LogicalPlan(InputData(input_data=ref_bundles), self.context)
splits.append(
MaterializedDataset(
ExecutionPlan(stats),
logical_plan,
)
)
return splits
[docs]
@ConsumptionAPI
@PublicAPI(api_group=SMD_API_GROUP)
def split_proportionately(
self, proportions: List[float]
) -> List["MaterializedDataset"]:
"""Materialize and split the dataset using proportions.
A common use case for this is splitting the dataset into train
and test sets (equivalent to eg. scikit-learn's ``train_test_split``).
For a higher level abstraction, see :meth:`Dataset.train_test_split`.
This method splits datasets so that all splits
always contains at least one row. If that isn't possible,
an exception is raised.
This is equivalent to caulculating the indices manually and calling
:meth:`Dataset.split_at_indices`.
Examples:
>>> import ray
>>> ds = ray.data.range(10)
>>> d1, d2, d3 = ds.split_proportionately([0.2, 0.5])
>>> d1.take_batch()
{'id': array([0, 1])}
>>> d2.take_batch()
{'id': array([2, 3, 4, 5, 6])}
>>> d3.take_batch()
{'id': array([7, 8, 9])}
Time complexity: O(num splits)
Args:
proportions: List of proportions to split the dataset according to.
Must sum up to less than 1, and each proportion must be bigger
than 0.
Returns:
The dataset splits.
.. seealso::
:meth:`Dataset.split`
Unlike :meth:`~Dataset.split_proportionately`, which lets you split a
dataset into different sizes, :meth:`Dataset.split` splits a dataset
into approximately equal splits.
:meth:`Dataset.split_at_indices`
:meth:`Dataset.split_proportionately` uses this method under the hood.
:meth:`Dataset.streaming_split`.
Unlike :meth:`~Dataset.split`, :meth:`~Dataset.streaming_split`
doesn't materialize the dataset in memory.
"""
if len(proportions) < 1:
raise ValueError("proportions must be at least of length 1")
if sum(proportions) >= 1:
raise ValueError("proportions must sum to less than 1")
if any(p <= 0 for p in proportions):
raise ValueError("proportions must be bigger than 0")
dataset_length = self.count()
cumulative_proportions = np.cumsum(proportions)
split_indices = [
int(dataset_length * proportion) for proportion in cumulative_proportions
]
# Ensure each split has at least one element
subtract = 0
for i in range(len(split_indices) - 2, -1, -1):
split_indices[i] -= subtract
if split_indices[i] == split_indices[i + 1]:
subtract += 1
split_indices[i] -= 1
if any(i <= 0 for i in split_indices):
raise ValueError(
"Couldn't create non-empty splits with the given proportions."
)
return self.split_at_indices(split_indices)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=SMD_API_GROUP)
def train_test_split(
self,
test_size: Union[int, float],
*,
shuffle: bool = False,
seed: Optional[int] = None,
) -> Tuple["MaterializedDataset", "MaterializedDataset"]:
"""Materialize and split the dataset into train and test subsets.
Examples:
>>> import ray
>>> ds = ray.data.range(8)
>>> train, test = ds.train_test_split(test_size=0.25)
>>> train.take_batch()
{'id': array([0, 1, 2, 3, 4, 5])}
>>> test.take_batch()
{'id': array([6, 7])}
Args:
test_size: If float, should be between 0.0 and 1.0 and represent the
proportion of the dataset to include in the test split. If int,
represents the absolute number of test samples. The train split
always complements the test split.
shuffle: Whether or not to globally shuffle the dataset before splitting.
Defaults to ``False``. This may be a very expensive operation with a
large dataset.
seed: Fix the random seed to use for shuffle, otherwise one is chosen
based on system randomness. Ignored if ``shuffle=False``.
Returns:
Train and test subsets as two ``MaterializedDatasets``.
.. seealso::
:meth:`Dataset.split_proportionately`
"""
ds = self
if shuffle:
ds = ds.random_shuffle(seed=seed)
if not isinstance(test_size, (int, float)):
raise TypeError(f"`test_size` must be int or float got {type(test_size)}.")
if isinstance(test_size, float):
if test_size <= 0 or test_size >= 1:
raise ValueError(
"If `test_size` is a float, it must be bigger than 0 and smaller "
f"than 1. Got {test_size}."
)
return ds.split_proportionately([1 - test_size])
else:
ds_length = ds.count()
if test_size <= 0 or test_size >= ds_length:
raise ValueError(
"If `test_size` is an int, it must be bigger than 0 and smaller "
f"than the size of the dataset ({ds_length}). "
f"Got {test_size}."
)
return ds.split_at_indices([ds_length - test_size])
[docs]
@PublicAPI(api_group=SMD_API_GROUP)
def union(self, *other: List["Dataset"]) -> "Dataset":
"""Concatenate :class:`Datasets <ray.data.Dataset>` across rows.
The order of the blocks in the datasets is preserved, as is the
relative ordering between the datasets passed in the argument list.
.. caution::
Unioned datasets aren't lineage-serializable. As a result, they can't be
used as a tunable hyperparameter in Ray Tune.
Examples:
>>> import ray
>>> ds1 = ray.data.range(2)
>>> ds2 = ray.data.range(3)
>>> ds1.union(ds2).take_all()
[{'id': 0}, {'id': 1}, {'id': 0}, {'id': 1}, {'id': 2}]
Args:
other: List of datasets to combine with this one. The datasets
must have the same schema as this dataset, otherwise the
behavior is undefined.
Returns:
A new dataset holding the rows of the input datasets.
"""
start_time = time.perf_counter()
datasets = [self] + list(other)
logical_plans = [union_ds._plan._logical_plan for union_ds in datasets]
op = UnionLogicalOperator(
*[plan.dag for plan in logical_plans],
)
logical_plan = LogicalPlan(op, self.context)
stats = DatasetStats(
metadata={"Union": []},
parent=[d._plan.stats() for d in datasets],
)
stats.time_total_s = time.perf_counter() - start_time
return Dataset(
ExecutionPlan(stats),
logical_plan,
)
[docs]
@AllToAllAPI
@PublicAPI(api_group=GGA_API_GROUP)
def groupby(
self,
key: Union[str, List[str], None],
) -> "GroupedData":
"""Group rows of a :class:`Dataset` according to a column.
Use this method to transform data based on a
categorical variable.
Examples:
.. testcode::
import pandas as pd
import ray
def normalize_variety(group: pd.DataFrame) -> pd.DataFrame:
for feature in group.drop("variety").columns:
group[feature] = group[feature] / group[feature].abs().max()
return group
ds = (
ray.data.read_parquet("s3://anonymous@ray-example-data/iris.parquet")
.groupby("variety")
.map_groups(normalize_variety, batch_format="pandas")
)
Time complexity: O(dataset size * log(dataset size / parallelism))
Args:
key: A column name or list of column names.
If this is ``None``, place all rows in a single group.
Returns:
A lazy :class:`~ray.data.grouped_data.GroupedData`.
.. seealso::
:meth:`~ray.data.grouped_data.GroupedData.map_groups`
Call this method to transform groups of data.
"""
from ray.data.grouped_data import GroupedData
# Always allow None since groupby interprets that as grouping all
# records into a single global group.
if key is not None:
# Fetching the schema can trigger execution, so don't fetch it for
# input validation.
SortKey(key).validate_schema(self.schema(fetch_if_missing=False))
return GroupedData(self, key)
[docs]
@AllToAllAPI
@PublicAPI(api_group=GGA_API_GROUP)
def unique(self, column: str) -> List[Any]:
"""List the unique elements in a given column.
Examples:
>>> import ray
>>> ds = ray.data.from_items([1, 2, 3, 2, 3])
>>> ds.unique("item")
[1, 2, 3]
This function is very useful for computing labels
in a machine learning dataset:
>>> import ray
>>> ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
>>> ds.unique("target")
[0, 1, 2]
One common use case is to convert the class labels
into integers for training and inference:
>>> classes = {0: 'Setosa', 1: 'Versicolor', 2: 'Virginica'}
>>> def preprocessor(df, classes):
... df["variety"] = df["target"].map(classes)
... return df
>>> train_ds = ds.map_batches(
... preprocessor, fn_kwargs={"classes": classes}, batch_format="pandas")
>>> train_ds.sort("sepal length (cm)").take(1) # Sort to make it deterministic
[{'sepal length (cm)': 4.3, ..., 'variety': 'Setosa'}]
Time complexity: O(dataset size * log(dataset size / parallelism))
Args:
column: The column to collect unique elements over.
Returns:
A list with unique elements in the given column.
""" # noqa: E501
ds = self.select_columns([column]).groupby(column).count()
return [item[column] for item in ds.take_all()]
[docs]
@AllToAllAPI
@ConsumptionAPI
@PublicAPI(api_group=GGA_API_GROUP)
def aggregate(self, *aggs: AggregateFn) -> Union[Any, Dict[str, Any]]:
"""Aggregate values using one or more functions.
Use this method to compute metrics like the product of a column.
Examples:
.. testcode::
import ray
from ray.data.aggregate import AggregateFn
ds = ray.data.from_items([{"number": i} for i in range(1, 10)])
aggregation = AggregateFn(
init=lambda column: 1,
# Apply this to each row to produce a partial aggregate result
accumulate_row=lambda a, row: a * row["number"],
# Apply this to merge partial aggregate results into a final result
merge=lambda a1, a2: a1 * a2,
name="prod"
)
print(ds.aggregate(aggregation))
.. testoutput::
{'prod': 362880}
Time complexity: O(dataset size / parallelism)
Args:
*aggs: :class:`Aggregations <ray.data.aggregate.AggregateFn>` to perform.
Returns:
A ``dict`` where each each value is an aggregation for a given column.
"""
ret = self.groupby(None).aggregate(*aggs).take(1)
return ret[0] if len(ret) > 0 else None
[docs]
@AllToAllAPI
@ConsumptionAPI
@PublicAPI(api_group=GGA_API_GROUP)
def sum(
self, on: Optional[Union[str, List[str]]] = None, ignore_nulls: bool = True
) -> Union[Any, Dict[str, Any]]:
"""Compute the sum of one or more columns.
Examples:
>>> import ray
>>> ray.data.range(100).sum("id")
4950
>>> ray.data.from_items([
... {"A": i, "B": i**2}
... for i in range(100)
... ]).sum(["A", "B"])
{'sum(A)': 4950, 'sum(B)': 328350}
Args:
on: a column name or a list of column names to aggregate.
ignore_nulls: Whether to ignore null values. If ``True``, null
values are ignored when computing the sum. If ``False``,
when a null value is encountered, the output is ``None``.
Ray Data considers ``np.nan``, ``None``, and ``pd.NaT`` to be null
values. Default is ``True``.
Returns:
The sum result.
For different values of ``on``, the return varies:
- ``on=None``: a dict containing the column-wise sum of all
columns,
- ``on="col"``: a scalar representing the sum of all items in
column ``"col"``,
- ``on=["col_1", ..., "col_n"]``: an n-column ``dict``
containing the column-wise sum of the provided columns.
If the dataset is empty, all values are null. If ``ignore_nulls`` is
``False`` and any value is null, then the output is ``None``.
"""
ret = self._aggregate_on(Sum, on, ignore_nulls)
return self._aggregate_result(ret)
[docs]
@AllToAllAPI
@ConsumptionAPI
@PublicAPI(api_group=GGA_API_GROUP)
def min(
self, on: Optional[Union[str, List[str]]] = None, ignore_nulls: bool = True
) -> Union[Any, Dict[str, Any]]:
"""Return the minimum of one or more columns.
Examples:
>>> import ray
>>> ray.data.range(100).min("id")
0
>>> ray.data.from_items([
... {"A": i, "B": i**2}
... for i in range(100)
... ]).min(["A", "B"])
{'min(A)': 0, 'min(B)': 0}
Args:
on: a column name or a list of column names to aggregate.
ignore_nulls: Whether to ignore null values. If ``True``, null
values are ignored when computing the min; if ``False``,
when a null value is encountered, the output is ``None``.
This method considers ``np.nan``, ``None``, and ``pd.NaT`` to be null
values. Default is ``True``.
Returns:
The min result.
For different values of ``on``, the return varies:
- ``on=None``: an dict containing the column-wise min of
all columns,
- ``on="col"``: a scalar representing the min of all items in
column ``"col"``,
- ``on=["col_1", ..., "col_n"]``: an n-column dict
containing the column-wise min of the provided columns.
If the dataset is empty, all values are null. If ``ignore_nulls`` is
``False`` and any value is null, then the output is ``None``.
"""
ret = self._aggregate_on(Min, on, ignore_nulls)
return self._aggregate_result(ret)
[docs]
@AllToAllAPI
@ConsumptionAPI
@PublicAPI(api_group=GGA_API_GROUP)
def max(
self, on: Optional[Union[str, List[str]]] = None, ignore_nulls: bool = True
) -> Union[Any, Dict[str, Any]]:
"""Return the maximum of one or more columns.
Examples:
>>> import ray
>>> ray.data.range(100).max("id")
99
>>> ray.data.from_items([
... {"A": i, "B": i**2}
... for i in range(100)
... ]).max(["A", "B"])
{'max(A)': 99, 'max(B)': 9801}
Args:
on: a column name or a list of column names to aggregate.
ignore_nulls: Whether to ignore null values. If ``True``, null
values are ignored when computing the max; if ``False``,
when a null value is encountered, the output is ``None``.
This method considers ``np.nan``, ``None``, and ``pd.NaT`` to be null
values. Default is ``True``.
Returns:
The max result.
For different values of ``on``, the return varies:
- ``on=None``: an dict containing the column-wise max of
all columns,
- ``on="col"``: a scalar representing the max of all items in
column ``"col"``,
- ``on=["col_1", ..., "col_n"]``: an n-column dict
containing the column-wise max of the provided columns.
If the dataset is empty, all values are null. If ``ignore_nulls`` is
``False`` and any value is null, then the output is ``None``.
"""
ret = self._aggregate_on(Max, on, ignore_nulls)
return self._aggregate_result(ret)
[docs]
@AllToAllAPI
@ConsumptionAPI
@PublicAPI(api_group=GGA_API_GROUP)
def mean(
self, on: Optional[Union[str, List[str]]] = None, ignore_nulls: bool = True
) -> Union[Any, Dict[str, Any]]:
"""Compute the mean of one or more columns.
Examples:
>>> import ray
>>> ray.data.range(100).mean("id")
49.5
>>> ray.data.from_items([
... {"A": i, "B": i**2}
... for i in range(100)
... ]).mean(["A", "B"])
{'mean(A)': 49.5, 'mean(B)': 3283.5}
Args:
on: a column name or a list of column names to aggregate.
ignore_nulls: Whether to ignore null values. If ``True``, null
values are ignored when computing the mean; if ``False``,
when a null value is encountered, the output is ``None``.
This method considers ``np.nan``, ``None``, and ``pd.NaT`` to be null
values. Default is ``True``.
Returns:
The mean result.
For different values of ``on``, the return varies:
- ``on=None``: an dict containing the column-wise mean of
all columns,
- ``on="col"``: a scalar representing the mean of all items in
column ``"col"``,
- ``on=["col_1", ..., "col_n"]``: an n-column dict
containing the column-wise mean of the provided columns.
If the dataset is empty, all values are null. If ``ignore_nulls`` is
``False`` and any value is null, then the output is ``None``.
"""
ret = self._aggregate_on(Mean, on, ignore_nulls)
return self._aggregate_result(ret)
[docs]
@AllToAllAPI
@ConsumptionAPI
@PublicAPI(api_group=GGA_API_GROUP)
def std(
self,
on: Optional[Union[str, List[str]]] = None,
ddof: int = 1,
ignore_nulls: bool = True,
) -> Union[Any, Dict[str, Any]]:
"""Compute the standard deviation of one or more columns.
.. note::
This method uses Welford's online method for an accumulator-style
computation of the standard deviation. This method has
numerical stability, and is computable in a single pass. This may give
different (but more accurate) results than NumPy, Pandas, and sklearn, which
use a less numerically stable two-pass algorithm.
To learn more, see
`the Wikapedia article <https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm>`_.
Examples:
>>> import ray
>>> round(ray.data.range(100).std("id", ddof=0), 5)
28.86607
>>> ray.data.from_items([
... {"A": i, "B": i**2}
... for i in range(100)
... ]).std(["A", "B"])
{'std(A)': 29.011491975882016, 'std(B)': 2968.1748039269296}
Args:
on: a column name or a list of column names to aggregate.
ddof: Delta Degrees of Freedom. The divisor used in calculations
is ``N - ddof``, where ``N`` represents the number of elements.
ignore_nulls: Whether to ignore null values. If ``True``, null
values are ignored when computing the std; if ``False``,
when a null value is encountered, the output is ``None``.
This method considers ``np.nan``, ``None``, and ``pd.NaT`` to be null
values. Default is ``True``.
Returns:
The standard deviation result.
For different values of ``on``, the return varies:
- ``on=None``: an dict containing the column-wise std of
all columns,
- ``on="col"``: a scalar representing the std of all items in
column ``"col"``,
- ``on=["col_1", ..., "col_n"]``: an n-column dict
containing the column-wise std of the provided columns.
If the dataset is empty, all values are null. If ``ignore_nulls`` is
``False`` and any value is null, then the output is ``None``.
""" # noqa: E501
ret = self._aggregate_on(Std, on, ignore_nulls, ddof=ddof)
return self._aggregate_result(ret)
[docs]
@AllToAllAPI
@PublicAPI(api_group=SSR_API_GROUP)
def sort(
self,
key: Union[str, List[str], None] = None,
descending: Union[bool, List[bool]] = False,
boundaries: List[Union[int, float]] = None,
) -> "Dataset":
"""Sort the dataset by the specified key column or key function.
.. note::
The `descending` parameter must be a boolean, or a list of booleans.
If it is a list, all items in the list must share the same direction.
Multi-directional sort is not supported yet.
Examples:
>>> import ray
>>> ds = ray.data.range(15)
>>> ds = ds.sort("id", descending=False, boundaries=[5, 10])
>>> for df in ray.get(ds.to_pandas_refs()):
... print(df)
id
0 0
1 1
2 2
3 3
4 4
id
0 5
1 6
2 7
3 8
4 9
id
0 10
1 11
2 12
3 13
4 14
Time complexity: O(dataset size * log(dataset size / parallelism))
Args:
key: The column or a list of columns to sort by.
descending: Whether to sort in descending order. Must be a boolean or a list
of booleans matching the number of the columns.
boundaries: The list of values based on which to repartition the dataset.
For example, if the input boundary is [10,20], rows with values less
than 10 will be divided into the first block, rows with values greater
than or equal to 10 and less than 20 will be divided into the
second block, and rows with values greater than or equal to 20
will be divided into the third block. If not provided, the
boundaries will be sampled from the input blocks. This feature
only supports numeric columns right now.
Returns:
A new, sorted :class:`Dataset`.
"""
sort_key = SortKey(key, descending, boundaries)
plan = self._plan.copy()
op = Sort(
self._logical_plan.dag,
sort_key=sort_key,
)
logical_plan = LogicalPlan(op, self.context)
return Dataset(plan, logical_plan)
[docs]
@PublicAPI(api_group=SMD_API_GROUP)
def zip(self, other: "Dataset") -> "Dataset":
"""Zip the columns of this dataset with the columns of another.
The datasets must have the same number of rows. Their column sets are
merged, and any duplicate column names are disambiguated with suffixes like
``"_1"``.
.. note::
The smaller of the two datasets is repartitioned to align the number
of rows per block with the larger dataset.
.. note::
Zipped datasets aren't lineage-serializable. As a result, they can't be used
as a tunable hyperparameter in Ray Tune.
Examples:
>>> import ray
>>> ds1 = ray.data.range(5)
>>> ds2 = ray.data.range(5)
>>> ds1.zip(ds2).take_batch()
{'id': array([0, 1, 2, 3, 4]), 'id_1': array([0, 1, 2, 3, 4])}
Args:
other: The dataset to zip with on the right hand side.
Returns:
A :class:`Dataset` containing the columns of the second dataset
concatenated horizontally with the columns of the first dataset,
with duplicate column names disambiguated with suffixes like ``"_1"``.
"""
plan = self._plan.copy()
op = Zip(self._logical_plan.dag, other._logical_plan.dag)
logical_plan = LogicalPlan(op, self.context)
return Dataset(plan, logical_plan)
[docs]
@PublicAPI(api_group=BT_API_GROUP)
def limit(self, limit: int) -> "Dataset":
"""Truncate the dataset to the first ``limit`` rows.
Unlike :meth:`~Dataset.take`, this method doesn't move data to the caller's
machine. Instead, it returns a new :class:`Dataset` pointing to the truncated
distributed data.
Examples:
>>> import ray
>>> ds = ray.data.range(1000)
>>> ds.limit(5).count()
5
Time complexity: O(limit specified)
Args:
limit: The size of the dataset to truncate to.
Returns:
The truncated dataset.
"""
plan = self._plan.copy()
op = Limit(self._logical_plan.dag, limit=limit)
logical_plan = LogicalPlan(op, self.context)
return Dataset(plan, logical_plan)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=CD_API_GROUP)
def take_batch(
self, batch_size: int = 20, *, batch_format: Optional[str] = "default"
) -> DataBatch:
"""Return up to ``batch_size`` rows from the :class:`Dataset` in a batch.
Ray Data represents batches as NumPy arrays or pandas DataFrames. You can
configure the batch type by specifying ``batch_format``.
This method is useful for inspecting inputs to :meth:`~Dataset.map_batches`.
.. warning::
:meth:`~Dataset.take_batch` moves up to ``batch_size`` rows to the caller's
machine. If ``batch_size`` is large, this method can cause an `
``OutOfMemory`` error on the caller.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.take_batch(5)
{'id': array([0, 1, 2, 3, 4])}
Time complexity: O(batch_size specified)
Args:
batch_size: The maximum number of rows to return.
batch_format: If ``"default"`` or ``"numpy"``, batches are
``Dict[str, numpy.ndarray]``. If ``"pandas"``, batches are
``pandas.DataFrame``.
Returns:
A batch of up to ``batch_size`` rows from the dataset.
Raises:
``ValueError``: if the dataset is empty.
"""
batch_format = _apply_batch_format(batch_format)
limited_ds = self.limit(batch_size)
try:
res = next(
iter(
limited_ds.iter_batches(
batch_size=batch_size,
prefetch_batches=0,
batch_format=batch_format,
)
)
)
except StopIteration:
raise ValueError("The dataset is empty.")
self._synchronize_progress_bar()
# Save the computed stats to the original dataset.
self._plan._snapshot_stats = limited_ds._plan.stats()
return res
[docs]
@ConsumptionAPI
@PublicAPI(api_group=CD_API_GROUP)
def take(self, limit: int = 20) -> List[Dict[str, Any]]:
"""Return up to ``limit`` rows from the :class:`Dataset`.
This method is useful for inspecting data.
.. warning::
:meth:`~Dataset.take` moves up to ``limit`` rows to the caller's machine. If
``limit`` is large, this method can cause an ``OutOfMemory`` error on the
caller.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.take(3)
[{'id': 0}, {'id': 1}, {'id': 2}]
Time complexity: O(limit specified)
Args:
limit: The maximum number of rows to return.
Returns:
A list of up to ``limit`` rows from the dataset.
.. seealso::
:meth:`~Dataset.take_all`
Call this method to return all rows.
"""
if ray.util.log_once("dataset_take"):
logger.info(
"Tip: Use `take_batch()` instead of `take() / show()` to return "
"records in pandas or numpy batch format."
)
output = []
limited_ds = self.limit(limit)
for row in limited_ds.iter_rows():
output.append(row)
if len(output) >= limit:
break
self._synchronize_progress_bar()
# Save the computed stats to the original dataset.
self._plan._snapshot_stats = limited_ds._plan.stats()
return output
[docs]
@ConsumptionAPI
@PublicAPI(api_group=CD_API_GROUP)
def take_all(self, limit: Optional[int] = None) -> List[Dict[str, Any]]:
"""Return all of the rows in this :class:`Dataset`.
This method is useful for inspecting small datasets.
.. warning::
:meth:`~Dataset.take_all` moves the entire dataset to the caller's
machine. If the dataset is large, this method can cause an
``OutOfMemory`` error on the caller.
Examples:
>>> import ray
>>> ds = ray.data.range(5)
>>> ds.take_all()
[{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}]
Time complexity: O(dataset size)
Args:
limit: Raise an error if the size exceeds the specified limit.
Returns:
A list of all the rows in the dataset.
.. seealso::
:meth:`~Dataset.take`
Call this method to return a specific number of rows.
"""
output = []
for row in self.iter_rows():
output.append(row)
if limit is not None and len(output) > limit:
raise ValueError(
f"The dataset has more than the given limit of {limit} records."
)
self._synchronize_progress_bar()
return output
[docs]
@ConsumptionAPI
@PublicAPI(api_group=CD_API_GROUP)
def show(self, limit: int = 20) -> None:
"""Print up to the given number of rows from the :class:`Dataset`.
This method is useful for inspecting data.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.show(3)
{'id': 0}
{'id': 1}
{'id': 2}
Time complexity: O(limit specified)
Args:
limit: The maximum number of row to print.
.. seealso::
:meth:`~Dataset.take`
Call this method to get (not print) a given number of rows.
"""
for row in self.take(limit):
print(row)
[docs]
@ConsumptionAPI(
if_more_than_read=True,
datasource_metadata="row count",
pattern="Examples:",
)
@PublicAPI(api_group=IM_API_GROUP)
def count(self) -> int:
"""Count the number of rows in the dataset.
For Datasets which only read Parquet files (created with
:meth:`~ray.data.read_parquet`), this method reads the file metadata to
efficiently count the number of rows without reading in the entire data.
Examples:
>>> import ray
>>> ds = ray.data.range(10)
>>> ds.count()
10
Returns:
The number of records in the dataset.
"""
# Handle empty dataset.
if self._plan.initial_num_blocks() == 0:
return 0
# For parquet, we can return the count directly from metadata.
meta_count = self._meta_count()
if meta_count is not None:
return meta_count
plan = self._plan.copy()
count_op = Count([self._logical_plan.dag])
logical_plan = LogicalPlan(count_op, self.context)
count_ds = Dataset(plan, logical_plan)
count = 0
for batch in count_ds.iter_batches(batch_size=None):
assert Count.COLUMN_NAME in batch, (
"Outputs from the 'Count' logical operator should contain a column "
f"named '{Count.COLUMN_NAME}'"
)
count += batch[Count.COLUMN_NAME].sum()
# Explicitly cast to int to avoid returning `np.int64`, which is the result
# from calculating `sum()` from numpy batches.
return int(count)
[docs]
@ConsumptionAPI(
if_more_than_read=True,
datasource_metadata="schema",
extra_condition="or if ``fetch_if_missing=True`` (the default)",
pattern="Time complexity:",
)
@PublicAPI(api_group=IM_API_GROUP)
def schema(self, fetch_if_missing: bool = True) -> Optional["Schema"]:
"""Return the schema of the dataset.
Examples:
>>> import ray
>>> ds = ray.data.range(10)
>>> ds.schema()
Column Type
------ ----
id int64
Time complexity: O(1)
Args:
fetch_if_missing: If True, synchronously fetch the schema if it's
not known. If False, None is returned if the schema is not known.
Default is True.
Returns:
The :class:`ray.data.Schema` class of the records, or None if the
schema is not known and fetch_if_missing is False.
"""
context = self._plan._context
# First check if the schema is already known from materialized blocks.
base_schema = self._plan.schema(fetch_if_missing=False)
if base_schema is not None:
return Schema(base_schema, data_context=context)
# Lazily execute only the first block to minimize computation. We achieve this
# by appending a Limit[1] operation to a copy of this Dataset, which we then
# execute to get its schema.
base_schema = self.limit(1)._plan.schema(fetch_if_missing=fetch_if_missing)
if base_schema is not None:
self._plan.cache_schema(base_schema)
return Schema(base_schema, data_context=context)
else:
return None
[docs]
@ConsumptionAPI(
if_more_than_read=True,
datasource_metadata="schema",
extra_condition="or if ``fetch_if_missing=True`` (the default)",
pattern="Time complexity:",
)
@PublicAPI(api_group=IM_API_GROUP)
def columns(self, fetch_if_missing: bool = True) -> Optional[List[str]]:
"""Returns the columns of this Dataset.
Time complexity: O(1)
Example:
>>> import ray
>>> # Create dataset from synthetic data.
>>> ds = ray.data.range(1000)
>>> ds.columns()
['id']
Args:
fetch_if_missing: If True, synchronously fetch the column names from the
schema if it's not known. If False, None is returned if the schema is
not known. Default is True.
Returns:
A list of the column names for this Dataset or None if schema is not known
and `fetch_if_missing` is False.
"""
schema = self.schema(fetch_if_missing=fetch_if_missing)
if schema is not None:
return schema.names
return None
[docs]
@PublicAPI(api_group=IM_API_GROUP)
def num_blocks(self) -> int:
"""Return the number of blocks of this :class:`Dataset`.
This method is only implemented for :class:`~ray.data.MaterializedDataset`,
since the number of blocks may dynamically change during execution.
For instance, during read and transform operations, Ray Data may dynamically
adjust the number of blocks to respect memory limits, increasing the
number of blocks at runtime.
Returns:
The number of blocks of this :class:`Dataset`.
"""
raise NotImplementedError(
"Number of blocks is only available for `MaterializedDataset`,"
"because the number of blocks may dynamically change during execution."
"Call `ds.materialize()` to get a `MaterializedDataset`."
)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=IM_API_GROUP)
def size_bytes(self) -> int:
"""Return the in-memory size of the dataset.
Examples:
>>> import ray
>>> ds = ray.data.range(10)
>>> ds.size_bytes()
80
Returns:
The in-memory size of the dataset in bytes, or None if the
in-memory size is not known.
"""
# If the size is known from metadata, return it.
if self._logical_plan.dag.aggregate_output_metadata().size_bytes is not None:
return self._logical_plan.dag.aggregate_output_metadata().size_bytes
metadata = self._plan.execute().metadata
if not metadata or metadata[0].size_bytes is None:
return None
return sum(m.size_bytes for m in metadata)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=IOC_API_GROUP)
def write_parquet(
self,
path: str,
*,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
try_create_dir: bool = True,
arrow_open_stream_args: Optional[Dict[str, Any]] = None,
filename_provider: Optional[FilenameProvider] = None,
arrow_parquet_args_fn: Optional[Callable[[], Dict[str, Any]]] = None,
num_rows_per_file: Optional[int] = None,
ray_remote_args: Dict[str, Any] = None,
concurrency: Optional[int] = None,
**arrow_parquet_args,
) -> None:
"""Writes the :class:`~ray.data.Dataset` to parquet files under the provided ``path``.
The number of files is determined by the number of blocks in the dataset.
To control the number of number of blocks, call
:meth:`~ray.data.Dataset.repartition`.
If pyarrow can't represent your data, this method errors.
By default, the format of the output files is ``{uuid}_{block_idx}.parquet``,
where ``uuid`` is a unique id for the dataset. To modify this behavior,
implement a custom :class:`~ray.data.datasource.FilenameProvider` and pass it in
as the ``filename_provider`` argument.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.write_parquet("local:///tmp/data/")
Time complexity: O(dataset size / parallelism)
Args:
path: The path to the destination root directory, where
parquet files are written to.
filesystem: The pyarrow filesystem implementation to write to.
These filesystems are specified in the
`pyarrow docs <https://arrow.apache.org/docs\
/python/api/filesystems.html#filesystem-implementations>`_.
Specify this if you need to provide specific configurations to the
filesystem. By default, the filesystem is automatically selected based
on the scheme of the paths. For example, if the path begins with
``s3://``, the ``S3FileSystem`` is used.
try_create_dir: If ``True``, attempts to create all directories in the
destination path. Does nothing if all directories already
exist. Defaults to ``True``.
arrow_open_stream_args: kwargs passed to
`pyarrow.fs.FileSystem.open_output_stream <https://arrow.apache.org\
/docs/python/generated/pyarrow.fs.FileSystem.html\
#pyarrow.fs.FileSystem.open_output_stream>`_, which is used when
opening the file to write to.
filename_provider: A :class:`~ray.data.datasource.FilenameProvider`
implementation. Use this parameter to customize what your filenames
look like.
arrow_parquet_args_fn: Callable that returns a dictionary of write
arguments that are provided to `pyarrow.parquet.write_table() <https:/\
/arrow.apache.org/docs/python/generated/\
pyarrow.parquet.write_table.html#pyarrow.parquet.write_table>`_
when writing each block to a file. Overrides
any duplicate keys from ``arrow_parquet_args``. Use this argument
instead of ``arrow_parquet_args`` if any of your write arguments
can't pickled, or if you'd like to lazily resolve the write
arguments for each dataset block.
num_rows_per_file: [Experimental] The target number of rows to write to each
file. If ``None``, Ray Data writes a system-chosen number of rows to
each file. The specified value is a hint, not a strict limit. Ray Data
might write more or fewer rows to each file. In specific, if the number
of rows per block is larger than the specified value, Ray Data writes
the number of rows per block to each file.
ray_remote_args: Kwargs passed to :meth:`~ray.remote` in the write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
arrow_parquet_args: Options to pass to
`pyarrow.parquet.write_table() <https://arrow.apache.org/docs/python\
/generated/pyarrow.parquet.write_table.html\
#pyarrow.parquet.write_table>`_, which is used to write out each
block to a file.
""" # noqa: E501
if arrow_parquet_args_fn is None:
arrow_parquet_args_fn = lambda: {} # noqa: E731
datasink = ParquetDatasink(
path,
arrow_parquet_args_fn=arrow_parquet_args_fn,
arrow_parquet_args=arrow_parquet_args,
num_rows_per_file=num_rows_per_file,
filesystem=filesystem,
try_create_dir=try_create_dir,
open_stream_args=arrow_open_stream_args,
filename_provider=filename_provider,
dataset_uuid=self._uuid,
)
self.write_datasink(
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=IOC_API_GROUP)
def write_json(
self,
path: str,
*,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
try_create_dir: bool = True,
arrow_open_stream_args: Optional[Dict[str, Any]] = None,
filename_provider: Optional[FilenameProvider] = None,
pandas_json_args_fn: Optional[Callable[[], Dict[str, Any]]] = None,
num_rows_per_file: Optional[int] = None,
ray_remote_args: Dict[str, Any] = None,
concurrency: Optional[int] = None,
**pandas_json_args,
) -> None:
"""Writes the :class:`~ray.data.Dataset` to JSON and JSONL files.
The number of files is determined by the number of blocks in the dataset.
To control the number of number of blocks, call
:meth:`~ray.data.Dataset.repartition`.
This method is only supported for datasets with records that are convertible to
pandas dataframes.
By default, the format of the output files is ``{uuid}_{block_idx}.json``,
where ``uuid`` is a unique id for the dataset. To modify this behavior,
implement a custom :class:`~ray.data.datasource.FilenameProvider` and pass it in
as the ``filename_provider`` argument.
Examples:
Write the dataset as JSON file to a local directory.
>>> import ray
>>> import pandas as pd
>>> ds = ray.data.from_pandas([pd.DataFrame({"one": [1], "two": ["a"]})])
>>> ds.write_json("local:///tmp/data")
Write the dataset as JSONL files to a local directory.
>>> ds = ray.data.read_json("s3://anonymous@ray-example-data/train.jsonl")
>>> ds.write_json("local:///tmp/data")
Time complexity: O(dataset size / parallelism)
Args:
path: The path to the destination root directory, where
the JSON files are written to.
filesystem: The pyarrow filesystem implementation to write to.
These filesystems are specified in the
`pyarrow docs <https://arrow.apache.org/docs\
/python/api/filesystems.html#filesystem-implementations>`_.
Specify this if you need to provide specific configurations to the
filesystem. By default, the filesystem is automatically selected based
on the scheme of the paths. For example, if the path begins with
``s3://``, the ``S3FileSystem`` is used.
try_create_dir: If ``True``, attempts to create all directories in the
destination path. Does nothing if all directories already
exist. Defaults to ``True``.
arrow_open_stream_args: kwargs passed to
`pyarrow.fs.FileSystem.open_output_stream <https://arrow.apache.org\
/docs/python/generated/pyarrow.fs.FileSystem.html\
#pyarrow.fs.FileSystem.open_output_stream>`_, which is used when
opening the file to write to.
filename_provider: A :class:`~ray.data.datasource.FilenameProvider`
implementation. Use this parameter to customize what your filenames
look like.
pandas_json_args_fn: Callable that returns a dictionary of write
arguments that are provided to
`pandas.DataFrame.to_json() <https://pandas.pydata.org/docs/reference/\
api/pandas.DataFrame.to_json.html>`_
when writing each block to a file. Overrides
any duplicate keys from ``pandas_json_args``. Use this parameter
instead of ``pandas_json_args`` if any of your write arguments
can't be pickled, or if you'd like to lazily resolve the write
arguments for each dataset block.
num_rows_per_file: [Experimental] The target number of rows to write to each
file. If ``None``, Ray Data writes a system-chosen number of rows to
each file. The specified value is a hint, not a strict limit. Ray Data
might write more or fewer rows to each file. In specific, if the number
of rows per block is larger than the specified value, Ray Data writes
the number of rows per block to each file.
ray_remote_args: kwargs passed to :meth:`~ray.remote` in the write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
pandas_json_args: These args are passed to
`pandas.DataFrame.to_json() <https://pandas.pydata.org/docs/reference/\
api/pandas.DataFrame.to_json.html>`_,
which is used under the hood to write out each
:class:`~ray.data.Dataset` block. These
are dict(orient="records", lines=True) by default.
"""
if pandas_json_args_fn is None:
pandas_json_args_fn = lambda: {} # noqa: E731
datasink = JSONDatasink(
path,
pandas_json_args_fn=pandas_json_args_fn,
pandas_json_args=pandas_json_args,
num_rows_per_file=num_rows_per_file,
filesystem=filesystem,
try_create_dir=try_create_dir,
open_stream_args=arrow_open_stream_args,
filename_provider=filename_provider,
dataset_uuid=self._uuid,
)
self.write_datasink(
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
[docs]
@PublicAPI(stability="alpha", api_group=IOC_API_GROUP)
@ConsumptionAPI
def write_images(
self,
path: str,
column: str,
file_format: str = "png",
*,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
try_create_dir: bool = True,
arrow_open_stream_args: Optional[Dict[str, Any]] = None,
filename_provider: Optional[FilenameProvider] = None,
ray_remote_args: Dict[str, Any] = None,
concurrency: Optional[int] = None,
) -> None:
"""Writes the :class:`~ray.data.Dataset` to images.
Examples:
>>> import ray
>>> ds = ray.data.read_images("s3://anonymous@ray-example-data/image-datasets/simple")
>>> ds.write_images("local:///tmp/images", column="image")
Time complexity: O(dataset size / parallelism)
Args:
path: The path to the destination root directory, where
the images are written to.
column: The column containing the data you want to write to images.
file_format: The image file format to write with. For available options,
see `Image file formats <https://pillow.readthedocs.io/en/latest\
/handbook/image-file-formats.html>`_.
filesystem: The pyarrow filesystem implementation to write to.
These filesystems are specified in the
`pyarrow docs <https://arrow.apache.org/docs\
/python/api/filesystems.html#filesystem-implementations>`_.
Specify this if you need to provide specific configurations to the
filesystem. By default, the filesystem is automatically selected based
on the scheme of the paths. For example, if the path begins with
``s3://``, the ``S3FileSystem`` is used.
try_create_dir: If ``True``, attempts to create all directories in the
destination path. Does nothing if all directories already
exist. Defaults to ``True``.
arrow_open_stream_args: kwargs passed to
`pyarrow.fs.FileSystem.open_output_stream <https://arrow.apache.org\
/docs/python/generated/pyarrow.fs.FileSystem.html\
#pyarrow.fs.FileSystem.open_output_stream>`_, which is used when
opening the file to write to.
filename_provider: A :class:`~ray.data.datasource.FilenameProvider`
implementation. Use this parameter to customize what your filenames
look like.
ray_remote_args: kwargs passed to :meth:`~ray.remote` in the write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
""" # noqa: E501
datasink = ImageDatasink(
path,
column,
file_format,
filesystem=filesystem,
try_create_dir=try_create_dir,
open_stream_args=arrow_open_stream_args,
filename_provider=filename_provider,
dataset_uuid=self._uuid,
)
self.write_datasink(
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=IOC_API_GROUP)
def write_csv(
self,
path: str,
*,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
try_create_dir: bool = True,
arrow_open_stream_args: Optional[Dict[str, Any]] = None,
filename_provider: Optional[FilenameProvider] = None,
arrow_csv_args_fn: Optional[Callable[[], Dict[str, Any]]] = None,
num_rows_per_file: Optional[int] = None,
ray_remote_args: Dict[str, Any] = None,
concurrency: Optional[int] = None,
**arrow_csv_args,
) -> None:
"""Writes the :class:`~ray.data.Dataset` to CSV files.
The number of files is determined by the number of blocks in the dataset.
To control the number of number of blocks, call
:meth:`~ray.data.Dataset.repartition`.
This method is only supported for datasets with records that are convertible to
pyarrow tables.
By default, the format of the output files is ``{uuid}_{block_idx}.csv``,
where ``uuid`` is a unique id for the dataset. To modify this behavior,
implement a custom :class:`~ray.data.datasource.FilenameProvider`
and pass it in as the ``filename_provider`` argument.
Examples:
Write the dataset as CSV files to a local directory.
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.write_csv("local:///tmp/data")
Write the dataset as CSV files to S3.
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.write_csv("s3://bucket/folder/) # doctest: +SKIP
Time complexity: O(dataset size / parallelism)
Args:
path: The path to the destination root directory, where
the CSV files are written to.
filesystem: The pyarrow filesystem implementation to write to.
These filesystems are specified in the
`pyarrow docs <https://arrow.apache.org/docs\
/python/api/filesystems.html#filesystem-implementations>`_.
Specify this if you need to provide specific configurations to the
filesystem. By default, the filesystem is automatically selected based
on the scheme of the paths. For example, if the path begins with
``s3://``, the ``S3FileSystem`` is used.
try_create_dir: If ``True``, attempts to create all directories in the
destination path if ``True``. Does nothing if all directories already
exist. Defaults to ``True``.
arrow_open_stream_args: kwargs passed to
`pyarrow.fs.FileSystem.open_output_stream <https://arrow.apache.org\
/docs/python/generated/pyarrow.fs.FileSystem.html\
#pyarrow.fs.FileSystem.open_output_stream>`_, which is used when
opening the file to write to.
filename_provider: A :class:`~ray.data.datasource.FilenameProvider`
implementation. Use this parameter to customize what your filenames
look like.
arrow_csv_args_fn: Callable that returns a dictionary of write
arguments that are provided to `pyarrow.write.write_csv <https://\
arrow.apache.org/docs/python/generated/\
pyarrow.csv.write_csv.html#pyarrow.csv.write_csv>`_ when writing each
block to a file. Overrides any duplicate keys from ``arrow_csv_args``.
Use this argument instead of ``arrow_csv_args`` if any of your write
arguments cannot be pickled, or if you'd like to lazily resolve the
write arguments for each dataset block.
num_rows_per_file: [Experimental] The target number of rows to write to each
file. If ``None``, Ray Data writes a system-chosen number of rows to
each file. The specified value is a hint, not a strict limit. Ray Data
might write more or fewer rows to each file. In specific, if the number
of rows per block is larger than the specified value, Ray Data writes
the number of rows per block to each file.
ray_remote_args: kwargs passed to :meth:`~ray.remote` in the write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
arrow_csv_args: Options to pass to `pyarrow.write.write_csv <https://\
arrow.apache.org/docs/python/generated/pyarrow.csv.write_csv.html\
#pyarrow.csv.write_csv>`_
when writing each block to a file.
"""
if arrow_csv_args_fn is None:
arrow_csv_args_fn = lambda: {} # noqa: E731
datasink = CSVDatasink(
path,
arrow_csv_args_fn=arrow_csv_args_fn,
arrow_csv_args=arrow_csv_args,
num_rows_per_file=num_rows_per_file,
filesystem=filesystem,
try_create_dir=try_create_dir,
open_stream_args=arrow_open_stream_args,
filename_provider=filename_provider,
dataset_uuid=self._uuid,
)
self.write_datasink(
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=IOC_API_GROUP)
def write_tfrecords(
self,
path: str,
*,
tf_schema: Optional["schema_pb2.Schema"] = None,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
try_create_dir: bool = True,
arrow_open_stream_args: Optional[Dict[str, Any]] = None,
filename_provider: Optional[FilenameProvider] = None,
num_rows_per_file: Optional[int] = None,
ray_remote_args: Dict[str, Any] = None,
concurrency: Optional[int] = None,
) -> None:
"""Write the :class:`~ray.data.Dataset` to TFRecord files.
The `TFRecord <https://www.tensorflow.org/tutorials/load_data/tfrecord>`_
files contain
`tf.train.Example <https://www.tensorflow.org/api_docs/python/tf/train/\
Example>`_
records, with one Example record for each row in the dataset.
.. warning::
tf.train.Feature only natively stores ints, floats, and bytes,
so this function only supports datasets with these data types,
and will error if the dataset contains unsupported types.
The number of files is determined by the number of blocks in the dataset.
To control the number of number of blocks, call
:meth:`~ray.data.Dataset.repartition`.
This method is only supported for datasets with records that are convertible to
pyarrow tables.
By default, the format of the output files is ``{uuid}_{block_idx}.tfrecords``,
where ``uuid`` is a unique id for the dataset. To modify this behavior,
implement a custom :class:`~ray.data.datasource.FilenameProvider`
and pass it in as the ``filename_provider`` argument.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.write_tfrecords("local:///tmp/data/")
Time complexity: O(dataset size / parallelism)
Args:
path: The path to the destination root directory, where tfrecords
files are written to.
filesystem: The pyarrow filesystem implementation to write to.
These filesystems are specified in the
`pyarrow docs <https://arrow.apache.org/docs\
/python/api/filesystems.html#filesystem-implementations>`_.
Specify this if you need to provide specific configurations to the
filesystem. By default, the filesystem is automatically selected based
on the scheme of the paths. For example, if the path begins with
``s3://``, the ``S3FileSystem`` is used.
try_create_dir: If ``True``, attempts to create all directories in the
destination path. Does nothing if all directories already
exist. Defaults to ``True``.
arrow_open_stream_args: kwargs passed to
`pyarrow.fs.FileSystem.open_output_stream <https://arrow.apache.org\
/docs/python/generated/pyarrow.fs.FileSystem.html\
#pyarrow.fs.FileSystem.open_output_stream>`_, which is used when
opening the file to write to.
filename_provider: A :class:`~ray.data.datasource.FilenameProvider`
implementation. Use this parameter to customize what your filenames
look like.
num_rows_per_file: [Experimental] The target number of rows to write to each
file. If ``None``, Ray Data writes a system-chosen number of rows to
each file. The specified value is a hint, not a strict limit. Ray Data
might write more or fewer rows to each file. In specific, if the number
of rows per block is larger than the specified value, Ray Data writes
the number of rows per block to each file.
ray_remote_args: kwargs passed to :meth:`~ray.remote` in the write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
"""
datasink = TFRecordDatasink(
path=path,
tf_schema=tf_schema,
num_rows_per_file=num_rows_per_file,
filesystem=filesystem,
try_create_dir=try_create_dir,
open_stream_args=arrow_open_stream_args,
filename_provider=filename_provider,
dataset_uuid=self._uuid,
)
self.write_datasink(
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
[docs]
@ConsumptionAPI
@PublicAPI(stability="alpha", api_group=IOC_API_GROUP)
def write_webdataset(
self,
path: str,
*,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
try_create_dir: bool = True,
arrow_open_stream_args: Optional[Dict[str, Any]] = None,
filename_provider: Optional[FilenameProvider] = None,
num_rows_per_file: Optional[int] = None,
ray_remote_args: Dict[str, Any] = None,
encoder: Optional[Union[bool, str, callable, list]] = True,
concurrency: Optional[int] = None,
) -> None:
"""Writes the dataset to `WebDataset <https://webdataset.github.io/webdataset/>`_ files.
The `TFRecord <https://www.tensorflow.org/tutorials/load_data/tfrecord>`_
files will contain
`tf.train.Example <https://www.tensorflow.org/api_docs/python/tf/train/Example>`_ # noqa: E501
records, with one Example record for each row in the dataset.
.. warning::
tf.train.Feature only natively stores ints, floats, and bytes,
so this function only supports datasets with these data types,
and will error if the dataset contains unsupported types.
This is only supported for datasets convertible to Arrow records.
To control the number of files, use :meth:`Dataset.repartition`.
Unless a custom filename provider is given, the format of the output
files is ``{uuid}_{block_idx}.tfrecords``, where ``uuid`` is a unique id
for the dataset.
Examples:
.. testcode::
:skipif: True
import ray
ds = ray.data.range(100)
ds.write_webdataset("s3://bucket/folder/")
Time complexity: O(dataset size / parallelism)
Args:
path: The path to the destination root directory, where tfrecords
files are written to.
filesystem: The filesystem implementation to write to.
try_create_dir: If ``True``, attempts to create all
directories in the destination path. Does nothing if all directories
already exist. Defaults to ``True``.
arrow_open_stream_args: kwargs passed to
``pyarrow.fs.FileSystem.open_output_stream``
filename_provider: A :class:`~ray.data.datasource.FilenameProvider`
implementation. Use this parameter to customize what your filenames
look like.
num_rows_per_file: [Experimental] The target number of rows to write to each
file. If ``None``, Ray Data writes a system-chosen number of rows to
each file. The specified value is a hint, not a strict limit. Ray Data
might write more or fewer rows to each file. In specific, if the number
of rows per block is larger than the specified value, Ray Data writes
the number of rows per block to each file.
ray_remote_args: Kwargs passed to ``ray.remote`` in the write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
"""
datasink = WebDatasetDatasink(
path,
encoder=encoder,
num_rows_per_file=num_rows_per_file,
filesystem=filesystem,
try_create_dir=try_create_dir,
open_stream_args=arrow_open_stream_args,
filename_provider=filename_provider,
dataset_uuid=self._uuid,
)
self.write_datasink(
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=IOC_API_GROUP)
def write_numpy(
self,
path: str,
*,
column: str,
filesystem: Optional["pyarrow.fs.FileSystem"] = None,
try_create_dir: bool = True,
arrow_open_stream_args: Optional[Dict[str, Any]] = None,
filename_provider: Optional[FilenameProvider] = None,
num_rows_per_file: Optional[int] = None,
ray_remote_args: Dict[str, Any] = None,
concurrency: Optional[int] = None,
) -> None:
"""Writes a column of the :class:`~ray.data.Dataset` to .npy files.
This is only supported for columns in the datasets that can be converted to
NumPy arrays.
The number of files is determined by the number of blocks in the dataset.
To control the number of number of blocks, call
:meth:`~ray.data.Dataset.repartition`.
By default, the format of the output files is ``{uuid}_{block_idx}.npy``,
where ``uuid`` is a unique id for the dataset. To modify this behavior,
implement a custom :class:`~ray.data.datasource.FilenameProvider`
and pass it in as the ``filename_provider`` argument.
Examples:
>>> import ray
>>> ds = ray.data.range(100)
>>> ds.write_numpy("local:///tmp/data/", column="id")
Time complexity: O(dataset size / parallelism)
Args:
path: The path to the destination root directory, where
the npy files are written to.
column: The name of the column that contains the data to
be written.
filesystem: The pyarrow filesystem implementation to write to.
These filesystems are specified in the
`pyarrow docs <https://arrow.apache.org/docs\
/python/api/filesystems.html#filesystem-implementations>`_.
Specify this if you need to provide specific configurations to the
filesystem. By default, the filesystem is automatically selected based
on the scheme of the paths. For example, if the path begins with
``s3://``, the ``S3FileSystem`` is used.
try_create_dir: If ``True``, attempts to create all directories in
destination path. Does nothing if all directories already
exist. Defaults to ``True``.
arrow_open_stream_args: kwargs passed to
`pyarrow.fs.FileSystem.open_output_stream <https://arrow.apache.org\
/docs/python/generated/pyarrow.fs.FileSystem.html\
#pyarrow.fs.FileSystem.open_output_stream>`_, which is used when
opening the file to write to.
filename_provider: A :class:`~ray.data.datasource.FilenameProvider`
implementation. Use this parameter to customize what your filenames
look like.
num_rows_per_file: [Experimental] The target number of rows to write to each
file. If ``None``, Ray Data writes a system-chosen number of rows to
each file. The specified value is a hint, not a strict limit. Ray Data
might write more or fewer rows to each file. In specific, if the number
of rows per block is larger than the specified value, Ray Data writes
the number of rows per block to each file.
ray_remote_args: kwargs passed to :meth:`~ray.remote` in the write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
"""
datasink = NumpyDatasink(
path,
column,
num_rows_per_file=num_rows_per_file,
filesystem=filesystem,
try_create_dir=try_create_dir,
open_stream_args=arrow_open_stream_args,
filename_provider=filename_provider,
dataset_uuid=self._uuid,
)
self.write_datasink(
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
[docs]
@ConsumptionAPI
def write_sql(
self,
sql: str,
connection_factory: Callable[[], Connection],
ray_remote_args: Optional[Dict[str, Any]] = None,
concurrency: Optional[int] = None,
) -> None:
"""Write to a database that provides a
`Python DB API2-compliant <https://peps.python.org/pep-0249/>`_ connector.
.. note::
This method writes data in parallel using the DB API2 ``executemany``
method. To learn more about this method, see
`PEP 249 <https://peps.python.org/pep-0249/#executemany>`_.
Examples:
.. testcode::
import sqlite3
import ray
connection = sqlite3.connect("example.db")
connection.cursor().execute("CREATE TABLE movie(title, year, score)")
dataset = ray.data.from_items([
{"title": "Monty Python and the Holy Grail", "year": 1975, "score": 8.2},
{"title": "And Now for Something Completely Different", "year": 1971, "score": 7.5}
])
dataset.write_sql(
"INSERT INTO movie VALUES(?, ?, ?)", lambda: sqlite3.connect("example.db")
)
result = connection.cursor().execute("SELECT * FROM movie ORDER BY year")
print(result.fetchall())
.. testoutput::
[('And Now for Something Completely Different', 1971, 7.5), ('Monty Python and the Holy Grail', 1975, 8.2)]
.. testcode::
:hide:
import os
os.remove("example.db")
Arguments:
sql: An ``INSERT INTO`` statement that specifies the table to write to. The
number of parameters must match the number of columns in the table.
connection_factory: A function that takes no arguments and returns a
Python DB API2
`Connection object <https://peps.python.org/pep-0249/#connection-objects>`_.
ray_remote_args: Keyword arguments passed to :meth:`~ray.remote` in the
write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
""" # noqa: E501
datasink = SQLDatasink(sql=sql, connection_factory=connection_factory)
self.write_datasink(
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
[docs]
@PublicAPI(stability="alpha", api_group=IOC_API_GROUP)
@ConsumptionAPI
def write_mongo(
self,
uri: str,
database: str,
collection: str,
ray_remote_args: Dict[str, Any] = None,
concurrency: Optional[int] = None,
) -> None:
"""Writes the :class:`~ray.data.Dataset` to a MongoDB database.
This method is only supported for datasets convertible to pyarrow tables.
The number of parallel writes is determined by the number of blocks in the
dataset. To control the number of number of blocks, call
:meth:`~ray.data.Dataset.repartition`.
.. warning::
This method supports only a subset of the PyArrow's types, due to the
limitation of pymongoarrow which is used underneath. Writing unsupported
types fails on type checking. See all the supported types at:
https://mongo-arrow.readthedocs.io/en/latest/data_types.html.
.. note::
The records are inserted into MongoDB as new documents. If a record has
the _id field, this _id must be non-existent in MongoDB, otherwise the write
is rejected and fail (hence preexisting documents are protected from
being mutated). It's fine to not have _id field in record and MongoDB will
auto generate one at insertion.
Examples:
.. testcode::
:skipif: True
import ray
ds = ray.data.range(100)
ds.write_mongo(
uri="mongodb://username:[email protected]:27017/?authSource=admin",
database="my_db",
collection="my_collection"
)
Args:
uri: The URI to the destination MongoDB where the dataset is
written to. For the URI format, see details in the
`MongoDB docs <https://www.mongodb.com/docs/manual/reference\
/connection-string/>`_.
database: The name of the database. This database must exist otherwise
a ValueError is raised.
collection: The name of the collection in the database. This collection
must exist otherwise a ValueError is raised.
ray_remote_args: kwargs passed to :meth:`~ray.remote` in the write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
Raises:
ValueError: if ``database`` doesn't exist.
ValueError: if ``collection`` doesn't exist.
"""
datasink = MongoDatasink(
uri=uri,
database=database,
collection=collection,
)
self.write_datasink(
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
[docs]
@ConsumptionAPI
def write_bigquery(
self,
project_id: str,
dataset: str,
max_retry_cnt: int = 10,
overwrite_table: Optional[bool] = True,
ray_remote_args: Dict[str, Any] = None,
concurrency: Optional[int] = None,
) -> None:
"""Write the dataset to a BigQuery dataset table.
To control the number of parallel write tasks, use ``.repartition()``
before calling this method.
Examples:
.. testcode::
:skipif: True
import ray
import pandas as pd
docs = [{"title": "BigQuery Datasource test"} for key in range(4)]
ds = ray.data.from_pandas(pd.DataFrame(docs))
ds.write_bigquery(
project_id="my_project_id",
dataset="my_dataset_table",
overwrite_table=True
)
Args:
project_id: The name of the associated Google Cloud Project that hosts
the dataset to read. For more information, see details in
`Creating and managing projects <https://cloud.google.com/resource-manager/docs/creating-managing-projects>`_.
dataset: The name of the dataset in the format of ``dataset_id.table_id``.
The dataset is created if it doesn't already exist.
max_retry_cnt: The maximum number of retries that an individual block write
is retried due to BigQuery rate limiting errors. This isn't
related to Ray fault tolerance retries. The default number of retries
is 10.
overwrite_table: Whether the write will overwrite the table if it already
exists. The default behavior is to overwrite the table.
``overwrite_table=False`` will append to the table if it exists.
ray_remote_args: Kwargs passed to ray.remote in the write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
""" # noqa: E501
if ray_remote_args is None:
ray_remote_args = {}
# Each write task will launch individual remote tasks to write each block
# To avoid duplicate block writes, the write task should not be retried
if ray_remote_args.get("max_retries", 0) != 0:
warnings.warn(
"The max_retries of a BigQuery Write Task should be set to 0"
" to avoid duplicate writes."
)
else:
ray_remote_args["max_retries"] = 0
datasink = BigQueryDatasink(
project_id=project_id,
dataset=dataset,
max_retry_cnt=max_retry_cnt,
overwrite_table=overwrite_table,
)
self.write_datasink(
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
[docs]
@ConsumptionAPI(pattern="Time complexity:")
def write_datasink(
self,
datasink: Datasink,
*,
ray_remote_args: Dict[str, Any] = None,
concurrency: Optional[int] = None,
) -> None:
"""Writes the dataset to a custom :class:`~ray.data.Datasink`.
Time complexity: O(dataset size / parallelism)
Args:
datasink: The :class:`~ray.data.Datasink` to write to.
ray_remote_args: Kwargs passed to ``ray.remote`` in the write tasks.
concurrency: The maximum number of Ray tasks to run concurrently. Set this
to control number of tasks to run concurrently. This doesn't change the
total number of tasks run. By default, concurrency is dynamically
decided based on the available resources.
""" # noqa: E501
if ray_remote_args is None:
ray_remote_args = {}
if not datasink.supports_distributed_writes:
if ray.util.client.ray.is_connected():
raise ValueError(
"If you're using Ray Client, Ray Data won't schedule write tasks "
"on the driver's node."
)
ray_remote_args["scheduling_strategy"] = NodeAffinitySchedulingStrategy(
ray.get_runtime_context().get_node_id(),
soft=False,
)
plan = self._plan.copy()
write_op = Write(
self._logical_plan.dag,
datasink,
ray_remote_args=ray_remote_args,
concurrency=concurrency,
)
logical_plan = LogicalPlan(write_op, self.context)
try:
import pandas as pd
datasink.on_write_start()
self._write_ds = Dataset(plan, logical_plan).materialize()
# TODO: Get and handle the blocks with an iterator instead of getting
# everything in a blocking way, so some blocks can be freed earlier.
raw_write_results = ray.get(self._write_ds._plan.execute().block_refs)
assert all(
isinstance(block, pd.DataFrame) and len(block) == 1
for block in raw_write_results
)
datasink.on_write_complete(raw_write_results)
except Exception as e:
datasink.on_write_failed(e)
raise
[docs]
@ConsumptionAPI(
delegate=(
"Calling any of the consumption methods on the returned ``DataIterator``"
),
pattern="Returns:",
)
@PublicAPI(api_group=CD_API_GROUP)
def iterator(self) -> DataIterator:
"""Return a :class:`~ray.data.DataIterator` over this dataset.
Don't call this method directly. Use it internally.
Returns:
A :class:`~ray.data.DataIterator` over this dataset.
"""
return DataIteratorImpl(self)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=CD_API_GROUP)
def iter_rows(self) -> Iterable[Dict[str, Any]]:
"""Return an iterable over the rows in this dataset.
Examples:
>>> import ray
>>> for row in ray.data.range(3).iter_rows():
... print(row)
{'id': 0}
{'id': 1}
{'id': 2}
Time complexity: O(1)
Returns:
An iterable over the rows in this dataset.
"""
return self.iterator().iter_rows()
[docs]
@ConsumptionAPI
@PublicAPI(api_group=CD_API_GROUP)
def iter_batches(
self,
*,
prefetch_batches: int = 1,
batch_size: Optional[int] = 256,
batch_format: Optional[str] = "default",
drop_last: bool = False,
local_shuffle_buffer_size: Optional[int] = None,
local_shuffle_seed: Optional[int] = None,
_collate_fn: Optional[Callable[[DataBatch], CollatedData]] = None,
) -> Iterable[DataBatch]:
"""Return an iterable over batches of data.
This method is useful for model training.
Examples:
.. testcode::
import ray
ds = ray.data.read_images("example://image-datasets/simple")
for batch in ds.iter_batches(batch_size=2, batch_format="numpy"):
print(batch)
.. testoutput::
:options: +MOCK
{'image': array([[[[...]]]], dtype=uint8)}
...
{'image': array([[[[...]]]], dtype=uint8)}
Time complexity: O(1)
Args:
prefetch_batches: The number of batches to fetch ahead of the current batch
to fetch. If set to greater than 0, a separate threadpool is used
to fetch the objects to the local node and format the batches. Defaults
to 1.
batch_size: The number of rows in each batch, or ``None`` to use entire
blocks as batches (blocks may contain different numbers of rows).
The final batch may include fewer than ``batch_size`` rows if
``drop_last`` is ``False``. Defaults to 256.
batch_format: If ``"default"`` or ``"numpy"``, batches are
``Dict[str, numpy.ndarray]``. If ``"pandas"``, batches are
``pandas.DataFrame``.
drop_last: Whether to drop the last batch if it's incomplete.
local_shuffle_buffer_size: If not ``None``, the data is randomly shuffled
using a local in-memory shuffle buffer, and this value serves as the
minimum number of rows that must be in the local in-memory shuffle
buffer in order to yield a batch. When there are no more rows to add to
the buffer, the remaining rows in the buffer are drained.
local_shuffle_seed: The seed to use for the local random shuffle.
Returns:
An iterable over batches of data.
"""
batch_format = _apply_batch_format(batch_format)
return self.iterator().iter_batches(
prefetch_batches=prefetch_batches,
batch_size=batch_size,
batch_format=batch_format,
drop_last=drop_last,
local_shuffle_buffer_size=local_shuffle_buffer_size,
local_shuffle_seed=local_shuffle_seed,
_collate_fn=_collate_fn,
)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=CD_API_GROUP)
def iter_torch_batches(
self,
*,
prefetch_batches: int = 1,
batch_size: Optional[int] = 256,
dtypes: Optional[Union["torch.dtype", Dict[str, "torch.dtype"]]] = None,
device: str = "auto",
collate_fn: Optional[Callable[[Dict[str, np.ndarray]], CollatedData]] = None,
drop_last: bool = False,
local_shuffle_buffer_size: Optional[int] = None,
local_shuffle_seed: Optional[int] = None,
) -> Iterable[TorchBatchType]:
"""Return an iterable over batches of data represented as Torch tensors.
This iterable yields batches of type ``Dict[str, torch.Tensor]``.
For more flexibility, call :meth:`~Dataset.iter_batches` and manually convert
your data to Torch tensors.
Examples:
>>> import ray
>>> for batch in ray.data.range(
... 12,
... ).iter_torch_batches(batch_size=4):
... print(batch)
{'id': tensor([0, 1, 2, 3])}
{'id': tensor([4, 5, 6, 7])}
{'id': tensor([ 8, 9, 10, 11])}
Use the ``collate_fn`` to customize how the tensor batch is created.
>>> from typing import Any, Dict
>>> import torch
>>> import numpy as np
>>> import ray
>>> def collate_fn(batch: Dict[str, np.ndarray]) -> Any:
... return torch.stack(
... [torch.as_tensor(array) for array in batch.values()],
... axis=1
... )
>>> dataset = ray.data.from_items([
... {"col_1": 1, "col_2": 2},
... {"col_1": 3, "col_2": 4}])
>>> for batch in dataset.iter_torch_batches(collate_fn=collate_fn):
... print(batch)
tensor([[1, 2],
[3, 4]])
Time complexity: O(1)
Args:
prefetch_batches: The number of batches to fetch ahead of the current batch
to fetch. If set to greater than 0, a separate threadpool is used
to fetch the objects to the local node, format the batches, and apply
the ``collate_fn``. Defaults to 1.
batch_size: The number of rows in each batch, or ``None`` to use entire
blocks as batches (blocks may contain different number of rows).
The final batch may include fewer than ``batch_size`` rows if
``drop_last`` is ``False``. Defaults to 256.
dtypes: The Torch dtype(s) for the created tensor(s); if ``None``, the dtype
is inferred from the tensor data. You can't use this parameter with
``collate_fn``.
device: The device on which the tensor should be placed. Defaults to
"auto" which moves the tensors to the appropriate device when the
Dataset is passed to Ray Train and ``collate_fn`` is not provided.
Otherwise, defaults to CPU. You can't use this parameter with
``collate_fn``.
collate_fn: A function to convert a Numpy batch to a PyTorch tensor batch.
When this parameter is specified, the user should manually handle the
host to device data transfer outside of collate_fn.
This is useful for further processing the data after it has been
batched. Potential use cases include collating along a dimension other
than the first, padding sequences of various lengths, or generally
handling batches of different length tensors. If not provided, the
default collate function is used which simply converts the batch of
numpy arrays to a batch of PyTorch tensors. This API is still
experimental and is subject to change. You can't use this parameter in
conjunction with ``dtypes`` or ``device``.
drop_last: Whether to drop the last batch if it's incomplete.
local_shuffle_buffer_size: If not ``None``, the data is randomly shuffled
using a local in-memory shuffle buffer, and this value serves as the
minimum number of rows that must be in the local in-memory shuffle
buffer in order to yield a batch. When there are no more rows to add to
the buffer, the remaining rows in the buffer are drained.
``batch_size`` must also be specified when using local shuffling.
local_shuffle_seed: The seed to use for the local random shuffle.
Returns:
An iterable over Torch Tensor batches.
.. seealso::
:meth:`Dataset.iter_batches`
Call this method to manually convert your data to Torch tensors.
""" # noqa: E501
return self.iterator().iter_torch_batches(
prefetch_batches=prefetch_batches,
batch_size=batch_size,
dtypes=dtypes,
device=device,
collate_fn=collate_fn,
drop_last=drop_last,
local_shuffle_buffer_size=local_shuffle_buffer_size,
local_shuffle_seed=local_shuffle_seed,
)
[docs]
@ConsumptionAPI
@Deprecated
def iter_tf_batches(
self,
*,
prefetch_batches: int = 1,
batch_size: Optional[int] = 256,
dtypes: Optional[Union["tf.dtypes.DType", Dict[str, "tf.dtypes.DType"]]] = None,
drop_last: bool = False,
local_shuffle_buffer_size: Optional[int] = None,
local_shuffle_seed: Optional[int] = None,
) -> Iterable[TensorFlowTensorBatchType]:
"""Return an iterable over batches of data represented as TensorFlow tensors.
This iterable yields batches of type ``Dict[str, tf.Tensor]``.
For more flexibility, call :meth:`~Dataset.iter_batches` and manually convert
your data to TensorFlow tensors.
.. tip::
If you don't need the additional flexibility provided by this method,
consider using :meth:`~ray.data.Dataset.to_tf` instead. It's easier
to use.
Examples:
.. testcode::
import ray
ds = ray.data.read_csv("s3://anonymous@air-example-data/iris.csv")
tf_dataset = ds.to_tf(
feature_columns="sepal length (cm)",
label_columns="target",
batch_size=2
)
for features, labels in tf_dataset:
print(features, labels)
.. testoutput::
tf.Tensor([5.1 4.9], shape=(2,), dtype=float64) tf.Tensor([0 0], shape=(2,), dtype=int64)
...
tf.Tensor([6.2 5.9], shape=(2,), dtype=float64) tf.Tensor([2 2], shape=(2,), dtype=int64)
Time complexity: O(1)
Args:
prefetch_batches: The number of batches to fetch ahead of the current batch
to fetch. If set to greater than 0, a separate threadpool is used
to fetch the objects to the local node, format the batches, and apply
the ``collate_fn``. Defaults to 1.
batch_size: The number of rows in each batch, or ``None`` to use entire
blocks as batches (blocks may contain different numbers of rows).
The final batch may include fewer than ``batch_size`` rows if
``drop_last`` is ``False``. Defaults to 256.
dtypes: The TensorFlow dtype(s) for the created tensor(s); if ``None``, the
dtype is inferred from the tensor data.
drop_last: Whether to drop the last batch if it's incomplete.
local_shuffle_buffer_size: If not ``None``, the data is randomly shuffled
using a local in-memory shuffle buffer, and this value serves as the
minimum number of rows that must be in the local in-memory shuffle
buffer in order to yield a batch. When there are no more rows to add to
the buffer, the remaining rows in the buffer are drained.
``batch_size`` must also be specified when using local shuffling.
local_shuffle_seed: The seed to use for the local random shuffle.
Returns:
An iterable over TensorFlow Tensor batches.
.. seealso::
:meth:`Dataset.iter_batches`
Call this method to manually convert your data to TensorFlow tensors.
""" # noqa: E501
warnings.warn(
"`iter_tf_batches` is deprecated and will be removed after May 2025. Use "
"`to_tf` instead.",
DeprecationWarning,
)
return self.iterator().iter_tf_batches(
prefetch_batches=prefetch_batches,
batch_size=batch_size,
dtypes=dtypes,
drop_last=drop_last,
local_shuffle_buffer_size=local_shuffle_buffer_size,
local_shuffle_seed=local_shuffle_seed,
)
[docs]
@ConsumptionAPI(pattern="Time complexity:")
@Deprecated
def to_torch(
self,
*,
label_column: Optional[str] = None,
feature_columns: Optional[
Union[List[str], List[List[str]], Dict[str, List[str]]]
] = None,
label_column_dtype: Optional["torch.dtype"] = None,
feature_column_dtypes: Optional[
Union["torch.dtype", List["torch.dtype"], Dict[str, "torch.dtype"]]
] = None,
batch_size: int = 1,
prefetch_batches: int = 1,
drop_last: bool = False,
local_shuffle_buffer_size: Optional[int] = None,
local_shuffle_seed: Optional[int] = None,
unsqueeze_label_tensor: bool = True,
unsqueeze_feature_tensors: bool = True,
) -> "torch.utils.data.IterableDataset":
"""Return a
`Torch IterableDataset <https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset>`_
over this :class:`~ray.data.Dataset`.
This is only supported for datasets convertible to Arrow records.
It is recommended to use the returned ``IterableDataset`` directly
instead of passing it into a torch ``DataLoader``.
Each element in ``IterableDataset`` is a tuple consisting of 2
elements. The first item contains the feature tensor(s), and the
second item is the label tensor. Those can take on different
forms, depending on the specified arguments.
For the features tensor (N is the ``batch_size`` and n, m, k
are the number of features per tensor):
* If ``feature_columns`` is a ``List[str]``, the features is
a tensor of shape (N, n), with columns corresponding to
``feature_columns``
* If ``feature_columns`` is a ``List[List[str]]``, the features is
a list of tensors of shape [(N, m),...,(N, k)], with columns of each
tensor corresponding to the elements of ``feature_columns``
* If ``feature_columns`` is a ``Dict[str, List[str]]``, the features
is a dict of key-tensor pairs of shape
{key1: (N, m),..., keyN: (N, k)}, with columns of each
tensor corresponding to the value of ``feature_columns`` under the
key.
If ``unsqueeze_label_tensor=True`` (default), the label tensor is
of shape (N, 1). Otherwise, it is of shape (N,).
If ``label_column`` is specified as ``None``, then no column from the
``Dataset`` is treated as the label, and the output label tensor
is ``None``.
Note that you probably want to call :meth:`Dataset.split` on this dataset if
there are to be multiple Torch workers consuming the data.
Time complexity: O(1)
Args:
label_column: The name of the column used as the
label (second element of the output list). Can be None for
prediction, in which case the second element of returned
tuple will also be None.
feature_columns: The names of the columns
to use as the features. Can be a list of lists or
a dict of string-list pairs for multi-tensor output.
If ``None``, then use all columns except the label column as
the features.
label_column_dtype: The torch dtype to
use for the label column. If ``None``, then automatically infer
the dtype.
feature_column_dtypes: The dtypes to use for the feature
tensors. This should match the format of ``feature_columns``,
or be a single dtype, in which case it is applied to
all tensors. If ``None``, then automatically infer the dtype.
batch_size: How many samples per batch to yield at a time.
Defaults to 1.
prefetch_batches: The number of batches to fetch ahead of the current batch
to fetch. If set to greater than 0, a separate threadpool is used
to fetch the objects to the local node, format the batches, and apply
the collate_fn. Defaults to 1.
drop_last: Set to True to drop the last incomplete batch,
if the dataset size is not divisible by the batch size. If
False and the size of the stream is not divisible by the batch
size, then the last batch is smaller. Defaults to False.
local_shuffle_buffer_size: If non-None, the data is randomly shuffled
using a local in-memory shuffle buffer, and this value will serve as the
minimum number of rows that must be in the local in-memory shuffle
buffer in order to yield a batch. When there are no more rows to add to
the buffer, the remaining rows in the buffer is drained. This
buffer size must be greater than or equal to ``batch_size``, and
therefore ``batch_size`` must also be specified when using local
shuffling.
local_shuffle_seed: The seed to use for the local random shuffle.
unsqueeze_label_tensor: If set to True, the label tensor
is unsqueezed (reshaped to (N, 1)). Otherwise, it will
be left as is, that is (N, ). In general, regression loss
functions expect an unsqueezed tensor, while classification
loss functions expect a squeezed one. Defaults to True.
unsqueeze_feature_tensors: If set to True, the features tensors
are unsqueezed (reshaped to (N, 1)) before being concatenated into
the final features tensor. Otherwise, they are left as is, that is
(N, ). Defaults to True.
Returns:
A `Torch IterableDataset`_.
""" # noqa: E501
warnings.warn(
"`to_torch` is deprecated and will be removed after May 2025. Use "
"`iter_torch_batches` instead.",
DeprecationWarning,
)
return self.iterator().to_torch(
label_column=label_column,
feature_columns=feature_columns,
label_column_dtype=label_column_dtype,
feature_column_dtypes=feature_column_dtypes,
batch_size=batch_size,
prefetch_batches=prefetch_batches,
drop_last=drop_last,
local_shuffle_buffer_size=local_shuffle_buffer_size,
local_shuffle_seed=local_shuffle_seed,
unsqueeze_label_tensor=unsqueeze_label_tensor,
unsqueeze_feature_tensors=unsqueeze_feature_tensors,
)
[docs]
@ConsumptionAPI
@PublicAPI(api_group=IOC_API_GROUP)
def to_tf(
self,
feature_columns: Union[str, List[str]],
label_columns: Union[str, List[str]],
*,
additional_columns: Union[str, List[str]] = None,
prefetch_batches: int = 1,
batch_size: int = 1,
drop_last: bool = False,
local_shuffle_buffer_size: Optional[int] = None,
local_shuffle_seed: Optional[int] = None,
feature_type_spec: Union["tf.TypeSpec", Dict[str, "tf.TypeSpec"]] = None,
label_type_spec: Union["tf.TypeSpec", Dict[str, "tf.TypeSpec"]] = None,
additional_type_spec: Union["tf.TypeSpec", Dict[str, "tf.TypeSpec"]] = None,
) -> "tf.data.Dataset":
"""Return a `TensorFlow Dataset <https://www.tensorflow.org/api_docs/python/tf/data/Dataset/>`_
over this :class:`~ray.data.Dataset`.
.. warning::
If your :class:`~ray.data.Dataset` contains ragged tensors, this method errors.
To prevent errors, :ref:`resize your tensors <transforming_tensors>`.
Examples:
>>> import ray
>>> ds = ray.data.read_csv("s3://anonymous@air-example-data/iris.csv")
>>> ds
Dataset(
num_rows=?,
schema={
sepal length (cm): double,
sepal width (cm): double,
petal length (cm): double,
petal width (cm): double,
target: int64
}
)
If your model accepts a single tensor as input, specify a single feature column.
>>> ds.to_tf(feature_columns="sepal length (cm)", label_columns="target")
<_OptionsDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.float64, name='sepal length (cm)'), TensorSpec(shape=(None,), dtype=tf.int64, name='target'))>
If your model accepts a dictionary as input, specify a list of feature columns.
>>> ds.to_tf(["sepal length (cm)", "sepal width (cm)"], "target")
<_OptionsDataset element_spec=({'sepal length (cm)': TensorSpec(shape=(None,), dtype=tf.float64, name='sepal length (cm)'), 'sepal width (cm)': TensorSpec(shape=(None,), dtype=tf.float64, name='sepal width (cm)')}, TensorSpec(shape=(None,), dtype=tf.int64, name='target'))>
If your dataset contains multiple features but your model accepts a single
tensor as input, combine features with
:class:`~ray.data.preprocessors.Concatenator`.
>>> from ray.data.preprocessors import Concatenator
>>> columns_to_concat = ["sepal length (cm)", "sepal width (cm)", "petal length (cm)", "petal width (cm)"]
>>> preprocessor = Concatenator(columns=columns_to_concat, output_column_name="features")
>>> ds = preprocessor.transform(ds)
>>> ds
Concatenator
+- Dataset(
num_rows=?,
schema={
sepal length (cm): double,
sepal width (cm): double,
petal length (cm): double,
petal width (cm): double,
target: int64
}
)
>>> ds.to_tf("features", "target")
<_OptionsDataset element_spec=(TensorSpec(shape=(None, 4), dtype=tf.float64, name='features'), TensorSpec(shape=(None,), dtype=tf.int64, name='target'))>
If your model accepts different types, shapes, or names of tensors as input, specify the type spec.
If type specs are not specified, they are automatically inferred from the schema of the dataset.
>>> import tensorflow as tf
>>> ds.to_tf(
... feature_columns="features",
... label_columns="target",
... feature_type_spec=tf.TensorSpec(shape=(None, 4), dtype=tf.float32, name="features"),
... label_type_spec=tf.TensorSpec(shape=(None,), dtype=tf.float32, name="label")
... )
<_OptionsDataset element_spec=(TensorSpec(shape=(None, 4), dtype=tf.float32, name='features'), TensorSpec(shape=(None,), dtype=tf.float32, name='label'))>
If your model accepts additional metadata aside from features and label, specify a single additional column or a list of additional columns.
A common use case is to include sample weights in the data samples and train a ``tf.keras.Model`` with ``tf.keras.Model.fit``.
>>> import pandas as pd
>>> ds = ds.add_column("sample weights", lambda df: pd.Series([1] * len(df)))
>>> ds.to_tf(feature_columns="features", label_columns="target", additional_columns="sample weights")
<_OptionsDataset element_spec=(TensorSpec(shape=(None, 4), dtype=tf.float64, name='features'), TensorSpec(shape=(None,), dtype=tf.int64, name='target'), TensorSpec(shape=(None,), dtype=tf.int64, name='sample weights'))>
If your model accepts different types, shapes, or names for the additional metadata, specify the type spec of the additional column.
>>> ds.to_tf(
... feature_columns="features",
... label_columns="target",
... additional_columns="sample weights",
... additional_type_spec=tf.TensorSpec(shape=(None,), dtype=tf.float32, name="weight")
... )
<_OptionsDataset element_spec=(TensorSpec(shape=(None, 4), dtype=tf.float64, name='features'), TensorSpec(shape=(None,), dtype=tf.int64, name='target'), TensorSpec(shape=(None,), dtype=tf.float32, name='weight'))>
Args:
feature_columns: Columns that correspond to model inputs. If this is a
string, the input data is a tensor. If this is a list, the input data
is a ``dict`` that maps column names to their tensor representation.
label_columns: Columns that correspond to model targets. If this is a
string, the target data is a tensor. If this is a list, the target data
is a ``dict`` that maps column names to their tensor representation.
additional_columns: Columns that correspond to sample weights or other metadata.
If this is a string, the weight data is a tensor. If this is a list, the
weight data is a ``dict`` that maps column names to their tensor representation.
prefetch_batches: The number of batches to fetch ahead of the current batch
to fetch. If set to greater than 0, a separate threadpool is used
to fetch the objects to the local node, format the batches, and apply
the collate_fn. Defaults to 1.
batch_size: Record batch size. Defaults to 1.
drop_last: Set to True to drop the last incomplete batch,
if the dataset size is not divisible by the batch size. If
False and the size of the stream is not divisible by the batch
size, then the last batch is smaller. Defaults to False.
local_shuffle_buffer_size: If non-None, the data is randomly shuffled
using a local in-memory shuffle buffer, and this value will serve as the
minimum number of rows that must be in the local in-memory shuffle
buffer in order to yield a batch. When there are no more rows to add to
the buffer, the remaining rows in the buffer is drained. This
buffer size must be greater than or equal to ``batch_size``, and
therefore ``batch_size`` must also be specified when using local
shuffling.
local_shuffle_seed: The seed to use for the local random shuffle.
feature_type_spec: The `tf.TypeSpec` of `feature_columns`. If there is
only one column, specify a `tf.TypeSpec`. If there are multiple columns,
specify a ``dict`` that maps column names to their `tf.TypeSpec`.
Default is `None` to automatically infer the type of each column.
label_type_spec: The `tf.TypeSpec` of `label_columns`. If there is
only one column, specify a `tf.TypeSpec`. If there are multiple columns,
specify a ``dict`` that maps column names to their `tf.TypeSpec`.
Default is `None` to automatically infer the type of each column.
additional_type_spec: The `tf.TypeSpec` of `additional_columns`. If there
is only one column, specify a `tf.TypeSpec`. If there are multiple
columns, specify a ``dict`` that maps column names to their `tf.TypeSpec`.
Default is `None` to automatically infer the type of each column.
Returns:
A `TensorFlow Dataset`_ that yields inputs and targets.
.. seealso::
:meth:`~ray.data.Dataset.iter_tf_batches`
Call this method if you need more flexibility.
""" # noqa: E501
return self.iterator().to_tf(
feature_columns=feature_columns,
label_columns=label_columns,
additional_columns=additional_columns,
prefetch_batches=prefetch_batches,
drop_last=drop_last,
batch_size=batch_size,
local_shuffle_buffer_size=local_shuffle_buffer_size,
local_shuffle_seed=local_shuffle_seed,
feature_type_spec=feature_type_spec,
label_type_spec=label_type_spec,
additional_type_spec=additional_type_spec,
)
[docs]
@ConsumptionAPI(pattern="Time complexity:")
@PublicAPI(api_group=IOC_API_GROUP)
def to_dask(
self,
meta: Union[
"pandas.DataFrame",
"pandas.Series",
Dict[str, Any],
Iterable[Any],
Tuple[Any],
None,
] = None,
verify_meta: bool = True,
) -> "dask.dataframe.DataFrame":
"""Convert this :class:`~ray.data.Dataset` into a
`Dask DataFrame <https://docs.dask.org/en/stable/generated/dask.dataframe.DataFrame.html#dask.dataframe.DataFrame>`_.
This is only supported for datasets convertible to Arrow records.
Note that this function will set the Dask scheduler to Dask-on-Ray
globally, via the config.
Time complexity: O(dataset size / parallelism)
Args:
meta: An empty `pandas DataFrame`_ or `Series`_ that matches the dtypes and column
names of the stream. This metadata is necessary for many algorithms in
dask dataframe to work. For ease of use, some alternative inputs are
also available. Instead of a DataFrame, a dict of ``{name: dtype}`` or
iterable of ``(name, dtype)`` can be provided (note that the order of
the names should match the order of the columns). Instead of a series, a
tuple of ``(name, dtype)`` can be used.
By default, this is inferred from the underlying Dataset schema,
with this argument supplying an optional override.
verify_meta: If True, Dask will check that the partitions have consistent
metadata. Defaults to True.
Returns:
A `Dask DataFrame`_ created from this dataset.
.. _pandas DataFrame: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html
.. _Series: https://pandas.pydata.org/docs/reference/api/pandas.Series.html
""" # noqa: E501
import dask
import dask.dataframe as dd
import pandas as pd
try:
import pyarrow as pa
except Exception:
pa = None
from ray.data._internal.pandas_block import PandasBlockSchema
from ray.util.client.common import ClientObjectRef
from ray.util.dask import ray_dask_get
dask.config.set(scheduler=ray_dask_get)
@dask.delayed
def block_to_df(block_ref: ObjectRef[Block]) -> pd.DataFrame:
if isinstance(block_ref, (ray.ObjectRef, ClientObjectRef)):
raise ValueError(
"Dataset.to_dask() must be used with Dask-on-Ray, please "
"set the Dask scheduler to ray_dask_get (located in "
"ray.util.dask)."
)
return _block_to_df(block_ref)
if meta is None:
from ray.data.extensions import TensorDtype
# Infer Dask metadata from Dataset schema.
schema = self.schema(fetch_if_missing=True)
if isinstance(schema, PandasBlockSchema):
meta = pd.DataFrame(
{
col: pd.Series(
dtype=(
dtype
if not isinstance(dtype, TensorDtype)
else np.object_
)
)
for col, dtype in zip(schema.names, schema.types)
}
)
elif pa is not None and isinstance(schema, pa.Schema):
arrow_tensor_ext_types = get_arrow_extension_fixed_shape_tensor_types()
if any(
isinstance(type_, arrow_tensor_ext_types) for type_ in schema.types
):
meta = pd.DataFrame(
{
col: pd.Series(
dtype=(
dtype.to_pandas_dtype()
if not isinstance(dtype, arrow_tensor_ext_types)
else np.object_
)
)
for col, dtype in zip(schema.names, schema.types)
}
)
else:
meta = schema.empty_table().to_pandas()
dfs = []
for ref_bundle in self.iter_internal_ref_bundles():
for block_ref in ref_bundle.block_refs:
dfs.append(block_to_df(block_ref))
ddf = dd.from_delayed(
dfs,
meta=meta,
verify_meta=verify_meta,
)
return ddf
[docs]
@ConsumptionAPI(pattern="Time complexity:")
@PublicAPI(api_group=IOC_API_GROUP)
def to_mars(self) -> "mars.dataframe.DataFrame":
"""Convert this :class:`~ray.data.Dataset` into a
`Mars DataFrame <https://mars-project.readthedocs.io/en/latest/reference/dataframe/index.html>`_.
Time complexity: O(dataset size / parallelism)
Returns:
A `Mars DataFrame`_ created from this dataset.
""" # noqa: E501
import pandas as pd
import pyarrow as pa
from mars.dataframe.datasource.read_raydataset import DataFrameReadRayDataset
from mars.dataframe.utils import parse_index
from ray.data._internal.pandas_block import PandasBlockSchema
refs = self.to_pandas_refs()
# remove this when https://github.com/mars-project/mars/issues/2945 got fixed
schema = self.schema()
if isinstance(schema, Schema):
schema = schema.base_schema
if isinstance(schema, PandasBlockSchema):
dtypes = pd.Series(schema.types, index=schema.names)
elif isinstance(schema, pa.Schema):
dtypes = schema.empty_table().to_pandas().dtypes
else:
raise NotImplementedError(f"Unsupported format of schema {schema}")
index_value = parse_index(pd.RangeIndex(-1))
columns_value = parse_index(dtypes.index, store_data=True)
op = DataFrameReadRayDataset(refs=refs)
return op(index_value=index_value, columns_value=columns_value, dtypes=dtypes)
[docs]
@ConsumptionAPI(pattern="Time complexity:")
@PublicAPI(api_group=IOC_API_GROUP)
def to_modin(self) -> "modin.pandas.dataframe.DataFrame":
"""Convert this :class:`~ray.data.Dataset` into a
`Modin DataFrame <https://modin.readthedocs.io/en/stable/flow/modin/pandas/dataframe.html>`_.
This works by first converting this dataset into a distributed set of
Pandas DataFrames (using :meth:`Dataset.to_pandas_refs`).
See caveats there. Then the individual DataFrames are used to
create the Modin DataFrame using
``modin.distributed.dataframe.pandas.partitions.from_partitions()``.
This is only supported for datasets convertible to Arrow records.
This function induces a copy of the data. For zero-copy access to the
underlying data, consider using :meth:`.to_arrow_refs` or
:meth:`.iter_internal_ref_bundles`.
Time complexity: O(dataset size / parallelism)
Returns:
A `Modin DataFrame`_ created from this dataset.
""" # noqa: E501
from modin.distributed.dataframe.pandas.partitions import from_partitions
pd_objs = self.to_pandas_refs()
return from_partitions(pd_objs, axis=0)
[docs]
@ConsumptionAPI(pattern="Time complexity:")
@PublicAPI(api_group=IOC_API_GROUP)
def to_spark(self, spark: "pyspark.sql.SparkSession") -> "pyspark.sql.DataFrame":
"""Convert this :class:`~ray.data.Dataset` into a
`Spark DataFrame <https://spark.apache.org/docs/3.1.1/api/python/reference/api/pyspark.sql.DataFrame.html>`_.
Time complexity: O(dataset size / parallelism)
Args:
spark: A `SparkSession`_, which must be created by RayDP (Spark-on-Ray).
Returns:
A `Spark DataFrame`_ created from this dataset.
.. _SparkSession: https://spark.apache.org/docs/3.1.1/api/python/reference/api/pyspark.sql.SparkSession.html
""" # noqa: E501
import raydp
schema = self.schema()
if isinstance(schema, Schema):
schema = schema.base_schema
ref_bundles = self.iter_internal_ref_bundles()
block_refs = _ref_bundles_iterator_to_block_refs_list(ref_bundles)
return raydp.spark.ray_dataset_to_spark_dataframe(spark, schema, block_refs)
[docs]
@ConsumptionAPI(pattern="Time complexity:")
@PublicAPI(api_group=IOC_API_GROUP)
def to_pandas(self, limit: int = None) -> "pandas.DataFrame":
"""Convert this :class:`~ray.data.Dataset` to a single pandas DataFrame.
This method errors if the number of rows exceeds the provided ``limit``.
To truncate the dataset beforehand, call :meth:`.limit`.
Examples:
>>> import ray
>>> ds = ray.data.from_items([{"a": i} for i in range(3)])
>>> ds.to_pandas()
a
0 0
1 1
2 2
Time complexity: O(dataset size)
Args:
limit: The maximum number of rows to return. An error is
raised if the dataset has more rows than this limit. Defaults to
``None``, which means no limit.
Returns:
A pandas DataFrame created from this dataset, containing a limited
number of rows.
Raises:
ValueError: if the number of rows in the :class:`~ray.data.Dataset` exceeds
``limit``.
"""
if limit is not None:
count = self.count()
if count > limit:
raise ValueError(
f"the dataset has more than the given limit of {limit} "
f"rows: {count}. If you are sure that a DataFrame with "
f"{count} rows will fit in local memory, set "
"ds.to_pandas(limit=None) to disable limits."
)
builder = PandasBlockBuilder()
for batch in self.iter_batches(batch_format="pandas", batch_size=None):
builder.add_block(batch)
block = builder.build()
# `PandasBlockBuilder` creates a dataframe with internal extension types like
# 'TensorDtype'. We use the `to_pandas` method to convert these extension
# types to regular types.
return BlockAccessor.for_block(block).to_pandas()
[docs]
@ConsumptionAPI(pattern="Time complexity:")
@DeveloperAPI
def to_pandas_refs(self) -> List[ObjectRef["pandas.DataFrame"]]:
"""Converts this :class:`~ray.data.Dataset` into a distributed set of Pandas
dataframes.
One DataFrame is created for each block in this Dataset.
This function induces a copy of the data. For zero-copy access to the
underlying data, consider using :meth:`Dataset.to_arrow_refs` or
:meth:`Dataset.iter_internal_ref_bundles`.
Examples:
>>> import ray
>>> ds = ray.data.range(10, override_num_blocks=2)
>>> refs = ds.to_pandas_refs()
>>> len(refs)
2
Time complexity: O(dataset size / parallelism)
Returns:
A list of remote pandas DataFrames created from this dataset.
"""
block_to_df = cached_remote_fn(_block_to_df)
pandas_refs = []
for bundle in self.iter_internal_ref_bundles():
for block_ref in bundle.block_refs:
pandas_refs.append(block_to_df.remote(block_ref))
return pandas_refs
[docs]
@DeveloperAPI
def to_numpy_refs(
self, *, column: Optional[str] = None
) -> List[ObjectRef[np.ndarray]]:
"""Converts this :class:`~ray.data.Dataset` into a distributed set of NumPy
ndarrays or dictionary of NumPy ndarrays.
This is only supported for datasets convertible to NumPy ndarrays.
This function induces a copy of the data. For zero-copy access to the
underlying data, consider using :meth:`Dataset.to_arrow_refs` or
:meth:`Dataset.iter_internal_ref_bundles`.
Examples:
>>> import ray
>>> ds = ray.data.range(10, override_num_blocks=2)
>>> refs = ds.to_numpy_refs()
>>> len(refs)
2
Time complexity: O(dataset size / parallelism)
Args:
column: The name of the column to convert to numpy. If ``None``, all columns
are used. If multiple columns are specified, each returned
future represents a dict of ndarrays. Defaults to None.
Returns:
A list of remote NumPy ndarrays created from this dataset.
"""
block_to_ndarray = cached_remote_fn(_block_to_ndarray)
numpy_refs = []
for bundle in self.iter_internal_ref_bundles():
for block_ref in bundle.block_refs:
numpy_refs.append(block_to_ndarray.remote(block_ref, column=column))
return numpy_refs
[docs]
@ConsumptionAPI(pattern="Time complexity:")
@DeveloperAPI
def to_arrow_refs(self) -> List[ObjectRef["pyarrow.Table"]]:
"""Convert this :class:`~ray.data.Dataset` into a distributed set of PyArrow
tables.
One PyArrow table is created for each block in this Dataset.
This method is only supported for datasets convertible to PyArrow tables.
This function is zero-copy if the existing data is already in PyArrow
format. Otherwise, the data is converted to PyArrow format.
Examples:
>>> import ray
>>> ds = ray.data.range(10, override_num_blocks=2)
>>> refs = ds.to_arrow_refs()
>>> len(refs)
2
Time complexity: O(1) unless conversion is required.
Returns:
A list of remote PyArrow tables created from this dataset.
"""
import pyarrow as pa
ref_bundles: Iterator[RefBundle] = self.iter_internal_ref_bundles()
block_refs: List[
ObjectRef["pyarrow.Table"]
] = _ref_bundles_iterator_to_block_refs_list(ref_bundles)
# Schema is safe to call since we have already triggered execution with
# iter_internal_ref_bundles.
schema = self.schema(fetch_if_missing=True)
if isinstance(schema, Schema):
schema = schema.base_schema
if isinstance(schema, pa.Schema):
# Zero-copy path.
return block_refs
block_to_arrow = cached_remote_fn(_block_to_arrow)
return [block_to_arrow.remote(block) for block in block_refs]
@ConsumptionAPI(pattern="Args:")
def to_random_access_dataset(
self,
key: str,
num_workers: Optional[int] = None,
) -> RandomAccessDataset:
"""Convert this dataset into a distributed RandomAccessDataset (EXPERIMENTAL).
RandomAccessDataset partitions the dataset across the cluster by the given
sort key, providing efficient random access to records via binary search. A
number of worker actors are created, each of which has zero-copy access to the
underlying sorted data blocks of the dataset.
Note that the key must be unique in the dataset. If there are duplicate keys,
an arbitrary value is returned.
This is only supported for Arrow-format datasets.
Args:
key: The key column over which records can be queried.
num_workers: The number of actors to use to serve random access queries.
By default, this is determined by multiplying the number of Ray nodes
in the cluster by four. As a rule of thumb, you can expect each worker
to provide ~3000 records / second via ``get_async()``, and
~10000 records / second via ``multiget()``.
"""
if num_workers is None:
num_workers = 4 * len(ray.nodes())
return RandomAccessDataset(self, key, num_workers=num_workers)
[docs]
@ConsumptionAPI(pattern="store memory.", insert_after=True)
@PublicAPI(api_group=E_API_GROUP)
def materialize(self) -> "MaterializedDataset":
"""Execute and materialize this dataset into object store memory.
This can be used to read all blocks into memory. By default, Dataset
doesn't read blocks from the datasource until the first transform.
Note that this does not mutate the original Dataset. Only the blocks of the
returned MaterializedDataset class are pinned in memory.
Examples:
>>> import ray
>>> ds = ray.data.range(10)
>>> materialized_ds = ds.materialize()
>>> materialized_ds
MaterializedDataset(num_blocks=..., num_rows=10, schema={id: int64})
Returns:
A MaterializedDataset holding the materialized data blocks.
"""
copy = Dataset.copy(self, _deep_copy=True, _as=MaterializedDataset)
copy._plan.execute()
bundle = copy._plan._snapshot_bundle
blocks_with_metadata = bundle.blocks
# TODO(hchen): Here we generate the same number of blocks as
# the original Dataset. Because the old code path does this, and
# some unit tests implicily depend on this behavior.
# After we remove the old code path, we should consider merging
# some blocks for better perf.
ref_bundles = [
RefBundle(
blocks=[block_with_metadata],
owns_blocks=False,
)
for block_with_metadata in blocks_with_metadata
]
logical_plan = LogicalPlan(InputData(input_data=ref_bundles), self.context)
output = MaterializedDataset(
ExecutionPlan(copy._plan.stats()),
logical_plan,
)
# Metrics are tagged with `copy`s uuid, update the output uuid with
# this so the user can access the metrics label.
output._set_name(copy._name)
output._set_uuid(copy._get_uuid())
output._plan.execute() # No-op that marks the plan as fully executed.
return output
[docs]
@PublicAPI(api_group=IM_API_GROUP)
def stats(self) -> str:
"""Returns a string containing execution timing information.
Note that this does not trigger execution, so if the dataset has not yet
executed, an empty string is returned.
Examples:
.. testcode::
import ray
ds = ray.data.range(10)
assert ds.stats() == ""
ds = ds.materialize()
print(ds.stats())
.. testoutput::
:options: +MOCK
Operator 0 Read: 1 tasks executed, 5 blocks produced in 0s
* Remote wall time: 16.29us min, 7.29ms max, 1.21ms mean, 24.17ms total
* Remote cpu time: 16.0us min, 2.54ms max, 810.45us mean, 16.21ms total
* Peak heap memory usage (MiB): 137968.75 min, 142734.38 max, 139846 mean
* Output num rows: 0 min, 1 max, 0 mean, 10 total
* Output size bytes: 0 min, 8 max, 4 mean, 80 total
* Tasks per node: 20 min, 20 max, 20 mean; 1 nodes used
"""
if self._current_executor:
return self._current_executor.get_stats().to_summary().to_string()
elif self._write_ds is not None and self._write_ds._plan.has_computed_output():
return self._write_ds.stats()
return self._get_stats_summary().to_string()
def _get_stats_summary(self) -> DatasetStatsSummary:
return self._plan.stats().to_summary()
[docs]
@ConsumptionAPI(pattern="Examples:")
@DeveloperAPI
def iter_internal_ref_bundles(self) -> Iterator[RefBundle]:
"""Get an iterator over ``RefBundles``
belonging to this Dataset. Calling this function doesn't keep
the data materialized in-memory.
Examples:
>>> import ray
>>> ds = ray.data.range(1)
>>> for ref_bundle in ds.iter_internal_ref_bundles():
... for block_ref, block_md in ref_bundle.blocks:
... block = ray.get(block_ref)
Returns:
An iterator over this Dataset's ``RefBundles``.
"""
iter_ref_bundles, _, _ = self._plan.execute_to_iterator()
self._synchronize_progress_bar()
return iter_ref_bundles
@Deprecated
@ConsumptionAPI(pattern="Examples:")
def get_internal_block_refs(self) -> List[ObjectRef[Block]]:
"""Get a list of references to the underlying blocks of this dataset.
This function can be used for zero-copy access to the data. It blocks
until the underlying blocks are computed.
Examples:
>>> import ray
>>> ds = ray.data.range(1)
>>> ds.get_internal_block_refs()
[ObjectRef(...)]
Returns:
A list of references to this dataset's blocks.
"""
logger.warning(
"`Dataset.get_internal_block_refs()` is deprecated. Use "
"`Dataset.iter_internal_ref_bundles()` instead.",
)
block_refs = self._plan.execute().block_refs
self._synchronize_progress_bar()
return block_refs
@DeveloperAPI
def has_serializable_lineage(self) -> bool:
"""Whether this dataset's lineage is able to be serialized for storage and
later deserialized, possibly on a different cluster.
Only datasets that are created from data that we know will still exist at
deserialization time, e.g. data external to this Ray cluster such as persistent
cloud object stores, support lineage-based serialization. All of the
ray.data.read_*() APIs support lineage-based serialization.
Examples:
>>> import ray
>>> ray.data.from_items(list(range(10))).has_serializable_lineage()
False
>>> ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv").has_serializable_lineage()
True
""" # noqa: E501
return all(
op.is_lineage_serializable()
for op in self._logical_plan.dag.post_order_iter()
)
@DeveloperAPI
def serialize_lineage(self) -> bytes:
"""
Serialize this dataset's lineage, not the actual data or the existing data
futures, to bytes that can be stored and later deserialized, possibly on a
different cluster.
Note that this uses pickle and will drop all computed data, and that everything
is recomputed from scratch after deserialization.
Use :py:meth:`Dataset.deserialize_lineage` to deserialize the serialized
bytes returned from this method into a Dataset.
.. note::
Unioned and zipped datasets, produced by :py:meth`Dataset.union` and
:py:meth:`Dataset.zip`, are not lineage-serializable.
Examples:
.. testcode::
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
serialized_ds = ds.serialize_lineage()
ds = ray.data.Dataset.deserialize_lineage(serialized_ds)
print(ds)
.. testoutput::
Dataset(
num_rows=?,
schema={
sepal length (cm): double,
sepal width (cm): double,
petal length (cm): double,
petal width (cm): double,
target: int64
}
)
Returns:
Serialized bytes containing the lineage of this dataset.
"""
if not self.has_serializable_lineage():
raise ValueError(
"Lineage-based serialization is not supported for this stream, which "
"means that it cannot be used as a tunable hyperparameter. "
"Lineage-based serialization is explicitly NOT supported for unioned "
"or zipped datasets (see docstrings for those methods), and is only "
"supported for datasets created from data that we know will still "
"exist at deserialization time, e.g. external data in persistent cloud "
"object stores or in-memory data from long-lived clusters. Concretely, "
"all ray.data.read_*() APIs should support lineage-based "
"serialization, while all of the ray.data.from_*() APIs do not. To "
"allow this stream to be serialized to storage, write the data to an "
"external store (such as AWS S3, GCS, or Azure Blob Storage) using the "
"Dataset.write_*() APIs, and serialize a new dataset reading "
"from the external store using the ray.data.read_*() APIs."
)
# Copy Dataset and clear the blocks from the execution plan so only the
# Dataset's lineage is serialized.
plan_copy = self._plan.deep_copy()
logical_plan_copy = copy.copy(self._plan._logical_plan)
ds = Dataset(plan_copy, logical_plan_copy)
ds._plan.clear_snapshot()
ds._set_uuid(self._get_uuid())
def _reduce_remote_fn(rf: ray.remote_function.RemoteFunction):
# Custom reducer for Ray remote function handles that allows for
# cross-cluster serialization.
# This manually unsets the last export session and job to force re-exporting
# of the function when the handle is deserialized on a new cluster.
# TODO(Clark): Fix this in core Ray, see issue:
# https://github.com/ray-project/ray/issues/24152.
reconstructor, args, state = rf.__reduce__()
state["_last_export_session_and_job"] = None
return reconstructor, args, state
context = ray._private.worker.global_worker.get_serialization_context()
try:
context._register_cloudpickle_reducer(
ray.remote_function.RemoteFunction, _reduce_remote_fn
)
serialized = pickle.dumps(ds)
finally:
context._unregister_cloudpickle_reducer(ray.remote_function.RemoteFunction)
return serialized
@staticmethod
@DeveloperAPI
def deserialize_lineage(serialized_ds: bytes) -> "Dataset":
"""
Deserialize the provided lineage-serialized Dataset.
This uses pickle, and assumes that the provided serialized bytes were
serialized using :py:meth:`Dataset.serialize_lineage`.
Examples:
.. testcode::
import ray
ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
serialized_ds = ds.serialize_lineage()
ds = ray.data.Dataset.deserialize_lineage(serialized_ds)
print(ds)
.. testoutput::
Dataset(
num_rows=?,
schema={
sepal length (cm): double,
sepal width (cm): double,
petal length (cm): double,
petal width (cm): double,
target: int64
}
)
Args:
serialized_ds: The serialized Dataset that we wish to deserialize.
Returns:
A deserialized ``Dataset`` instance.
"""
return pickle.loads(serialized_ds)
@property
@DeveloperAPI
def context(self) -> DataContext:
"""Return the DataContext used to create this Dataset."""
return self._plan._context
def _aggregate_on(
self, agg_cls: type, on: Optional[Union[str, List[str]]], *args, **kwargs
):
"""Helper for aggregating on a particular subset of the dataset.
This validates the `on` argument, and converts a list of column names
or lambdas to a multi-aggregation. A null `on` results in a
multi-aggregation on all columns for an Arrow Dataset, and a single
aggregation on the entire row for a simple Dataset.
"""
aggs = self._build_multicolumn_aggs(agg_cls, on, *args, **kwargs)
return self.aggregate(*aggs)
def _build_multicolumn_aggs(
self,
agg_cls: type,
on: Optional[Union[str, List[str]]],
ignore_nulls: bool,
*args,
skip_cols: Optional[List[str]] = None,
**kwargs,
):
"""Build set of aggregations for applying a single aggregation to
multiple columns.
"""
# Expand None into an aggregation for each column.
if on is None:
schema = self.schema(fetch_if_missing=True)
if schema is not None and not isinstance(schema, type):
if not skip_cols:
skip_cols = []
if len(schema.names) > 0:
on = [col for col in schema.names if col not in skip_cols]
if not isinstance(on, list):
on = [on]
return [agg_cls(on_, *args, ignore_nulls=ignore_nulls, **kwargs) for on_ in on]
def _aggregate_result(self, result: Union[Tuple, Mapping]) -> U:
if result is not None and len(result) == 1:
if isinstance(result, tuple):
return result[0]
else:
# NOTE (kfstorm): We cannot call `result[0]` directly on
# `PandasRow` because indexing a column with position is not
# supported by pandas.
return list(result.values())[0]
else:
return result
@repr_with_fallback(["ipywidgets", "8"])
def _repr_mimebundle_(self, **kwargs):
"""Return a mimebundle with an ipywidget repr and a simple text repr.
Depending on the frontend where the data is being displayed,
different mimetypes are used from this bundle.
See https://ipython.readthedocs.io/en/stable/config/integrating.html
for information about this method, and
https://ipywidgets.readthedocs.io/en/latest/embedding.html
for more information about the jupyter widget mimetype.
Returns:
A mimebundle containing an ipywidget repr and a simple text repr.
"""
import ipywidgets
title = ipywidgets.HTML(f"<h2>{self.__class__.__name__}</h2>")
tab = self._tab_repr_()
widget = ipywidgets.VBox([title, tab], layout=ipywidgets.Layout(width="100%"))
# Get the widget mime bundle, but replace the plaintext
# with the Datastream repr
bundle = widget._repr_mimebundle_(**kwargs)
bundle.update(
{
"text/plain": repr(self),
}
)
return bundle
def _tab_repr_(self):
from ipywidgets import HTML, Tab
metadata = {
"num_blocks": self._plan.initial_num_blocks(),
"num_rows": self._meta_count(),
}
# Show metadata if available, but don't trigger execution.
schema = self.schema(fetch_if_missing=False)
if schema is None:
schema_repr = Template("rendered_html_common.html.j2").render(
content="<h5>Unknown schema</h5>"
)
elif isinstance(schema, type):
schema_repr = Template("rendered_html_common.html.j2").render(
content=f"<h5>Data type: <code>{html.escape(str(schema))}</code></h5>"
)
else:
schema_data = {}
for sname, stype in zip(schema.names, schema.types):
schema_data[sname] = getattr(stype, "__name__", str(stype))
schema_repr = Template("scrollableTable.html.j2").render(
table=tabulate(
tabular_data=schema_data.items(),
tablefmt="html",
showindex=False,
headers=["Name", "Type"],
),
max_height="300px",
)
children = []
children.append(
HTML(
Template("scrollableTable.html.j2").render(
table=tabulate(
tabular_data=metadata.items(),
tablefmt="html",
showindex=False,
headers=["Field", "Value"],
),
max_height="300px",
)
)
)
children.append(HTML(schema_repr))
return Tab(children, titles=["Metadata", "Schema"])
def __repr__(self) -> str:
return self._plan.get_plan_as_string(self.__class__)
def __str__(self) -> str:
return repr(self)
def __bool__(self) -> bool:
# Prevents `__len__` from being called to check if it is None
# see: issue #25152
return True
def __len__(self) -> int:
raise AttributeError(
"Use `ds.count()` to compute the length of a distributed Dataset. "
"This may be an expensive operation."
)
def __iter__(self):
raise TypeError(
"`Dataset` objects aren't iterable. To iterate records, call "
"`ds.iter_rows()` or `ds.iter_batches()`. For more information, read "
"https://docs.ray.io/en/latest/data/iterating-over-data.html."
)
def _block_num_rows(self) -> List[int]:
get_num_rows = cached_remote_fn(_get_num_rows)
num_rows = []
for ref_bundle in self.iter_internal_ref_bundles():
for block_ref in ref_bundle.block_refs:
num_rows.append(get_num_rows.remote(block_ref))
return ray.get(num_rows)
def _meta_count(self) -> Optional[int]:
return self._plan.meta_count()
def _get_uuid(self) -> str:
return self._uuid
def _set_uuid(self, uuid: str) -> None:
self._uuid = uuid
self._plan._dataset_uuid = uuid
self._plan._in_stats.dataset_uuid = uuid
def _synchronize_progress_bar(self):
"""Flush progress bar output by shutting down the current executor.
This should be called at the end of all blocking APIs (e.g., `take`), but not
async APIs (e.g., `iter_batches`).
The streaming executor runs in a separate generator / thread, so it is
possible the shutdown logic runs even after a call to retrieve rows from the
stream has finished. Explicit shutdown avoids this, which can clobber console
output (https://github.com/ray-project/ray/issues/32414).
"""
if self._current_executor:
self._current_executor.shutdown()
self._current_executor = None
def __getstate__(self):
# Note: excludes _current_executor which is not serializable.
return {
"plan": self._plan,
"uuid": self._uuid,
"logical_plan": self._logical_plan,
}
def __setstate__(self, state):
self._plan = state["plan"]
self._uuid = state["uuid"]
self._logical_plan = state["logical_plan"]
self._current_executor = None
def __del__(self):
if not self._current_executor:
return
# When Python shuts down, `ray` might evaluate to `<module None from None>`.
# This value is truthy and not `None`, so we use a try-catch in addition to
# `if ray is not None`. For more information, see #42382.
try:
if ray is not None and ray.is_initialized():
self._current_executor.shutdown()
except TypeError:
pass
@PublicAPI
class MaterializedDataset(Dataset, Generic[T]):
"""A Dataset materialized in Ray memory, e.g., via `.materialize()`.
The blocks of a MaterializedDataset object are materialized into Ray object store
memory, which means that this class can be shared or iterated over by multiple Ray
tasks without re-executing the underlying computations for producing the stream.
"""
def num_blocks(self) -> int:
"""Return the number of blocks of this :class:`MaterializedDataset`.
Examples:
>>> import ray
>>> ds = ray.data.range(100).repartition(10).materialize()
>>> ds.num_blocks()
10
Time complexity: O(1)
Returns:
The number of blocks of this :class:`Dataset`.
"""
return self._plan.initial_num_blocks()
[docs]
@PublicAPI(stability="beta")
class Schema:
"""Dataset schema.
Attributes:
base_schema: The underlying Arrow or Pandas schema.
"""
def __init__(
self,
base_schema: Union["pyarrow.lib.Schema", "PandasBlockSchema"],
*,
data_context: Optional[DataContext] = None,
):
self.base_schema = base_schema
# Snapshot the current context, so that the config of Datasets is always
# determined by the config at the time it was created.
self._context = data_context or copy.deepcopy(DataContext.get_current())
@property
def names(self) -> List[str]:
"""Lists the columns of this Dataset."""
return self.base_schema.names
@property
def types(self) -> List[Union[type[object], "pyarrow.lib.DataType"]]:
"""Lists the types of this Dataset in Arrow format
For non-Arrow compatible types, we return "object".
"""
import pyarrow as pa
from ray.data.extensions import ArrowTensorType, TensorDtype
if isinstance(self.base_schema, pa.lib.Schema):
return list(self.base_schema.types)
arrow_types = []
for dtype in self.base_schema.types:
if isinstance(dtype, TensorDtype):
if self._context.use_arrow_tensor_v2:
pa_tensor_type_class = ArrowTensorTypeV2
else:
pa_tensor_type_class = ArrowTensorType
# Manually convert our Pandas tensor extension type to Arrow.
arrow_types.append(
pa_tensor_type_class(
shape=dtype._shape, dtype=pa.from_numpy_dtype(dtype._dtype)
)
)
else:
try:
arrow_types.append(pa.from_numpy_dtype(dtype))
except pa.ArrowNotImplementedError:
arrow_types.append(object)
except Exception:
logger.exception(f"Error converting dtype {dtype} to Arrow.")
arrow_types.append(None)
return arrow_types
def __eq__(self, other):
return (
isinstance(other, Schema)
and other.types == self.types
and other.names == self.names
)
def __repr__(self):
column_width = max([len(name) for name in self.names] + [len("Column")])
padding = 2
output = "Column"
output += " " * ((column_width + padding) - len("Column"))
output += "Type\n"
output += "-" * len("Column")
output += " " * ((column_width + padding) - len("Column"))
output += "-" * len("Type") + "\n"
for name, type in zip(self.names, self.types):
output += name
output += " " * ((column_width + padding) - len(name))
output += f"{type}\n"
output = output.rstrip()
return output
def _block_to_df(block: Block) -> "pandas.DataFrame":
block = BlockAccessor.for_block(block)
return block.to_pandas()
def _block_to_ndarray(block: Block, column: Optional[str]):
block = BlockAccessor.for_block(block)
return block.to_numpy(column)
def _block_to_arrow(block: Block):
block = BlockAccessor.for_block(block)
return block.to_arrow()