Source code for ray.data.read_api

import collections
import logging
import os
import warnings
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    List,
    Literal,
    Optional,
    Tuple,
    TypeVar,
    Union,
)

import numpy as np

import ray
from ray._private.auto_init_hook import wrap_auto_init
from ray.air.util.tensor_extensions.utils import _create_possibly_ragged_ndarray
from ray.data._internal.block_list import BlockList
from ray.data._internal.delegating_block_builder import DelegatingBlockBuilder
from ray.data._internal.lazy_block_list import LazyBlockList
from ray.data._internal.logical.operators.from_operators import (
    FromArrow,
    FromItems,
    FromNumpy,
    FromPandas,
)
from ray.data._internal.logical.operators.read_operator import Read
from ray.data._internal.logical.optimizers import LogicalPlan
from ray.data._internal.plan import ExecutionPlan
from ray.data._internal.remote_fn import cached_remote_fn
from ray.data._internal.stats import DatasetStats
from ray.data._internal.util import (
    _autodetect_parallelism,
    _lazy_import_pyarrow_dataset,
    get_table_block_metadata,
    ndarray_to_block,
    pandas_df_to_arrow_block,
)
from ray.data.block import Block, BlockAccessor, BlockExecStats, BlockMetadata
from ray.data.context import DataContext
from ray.data.dataset import Dataset, MaterializedDataset
from ray.data.datasource import (
    AvroDatasource,
    BaseFileMetadataProvider,
    BigQueryDatasource,
    BinaryDatasource,
    Connection,
    CSVDatasource,
    Datasource,
    ImageDatasource,
    JSONDatasource,
    MongoDatasource,
    NumpyDatasource,
    ParquetBaseDatasource,
    ParquetDatasource,
    ParquetMetadataProvider,
    PathPartitionFilter,
    RangeDatasource,
    SQLDatasource,
    TextDatasource,
    TFRecordDatasource,
    TorchDatasource,
    WebDatasetDatasource,
)
from ray.data.datasource._default_metadata_providers import (
    get_generic_metadata_provider,
    get_image_metadata_provider,
    get_parquet_bulk_metadata_provider,
    get_parquet_metadata_provider,
)
from ray.data.datasource.datasource import Reader
from ray.data.datasource.file_based_datasource import (
    _unwrap_arrow_serialization_workaround,
    _wrap_arrow_serialization_workaround,
)
from ray.data.datasource.partitioning import Partitioning
from ray.data.datasource.tfrecords_datasource import TFXReadOptions
from ray.types import ObjectRef
from ray.util.annotations import DeveloperAPI, PublicAPI
from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy

if TYPE_CHECKING:
    import dask
    import datasets
    import mars
    import modin
    import pandas
    import pyarrow
    import pymongoarrow.api
    import pyspark
    import tensorflow as tf
    import torch
    from tensorflow_metadata.proto.v0 import schema_pb2


T = TypeVar("T")

logger = logging.getLogger(__name__)


[docs]@PublicAPI def from_items( items: List[Any], *, parallelism: int = -1, override_num_blocks: Optional[int] = None, ) -> MaterializedDataset: """Create a :class:`~ray.data.Dataset` from a list of local Python objects. Use this method to create small datasets from data that fits in memory. Examples: >>> import ray >>> ds = ray.data.from_items([1, 2, 3, 4, 5]) >>> ds MaterializedDataset(num_blocks=..., num_rows=5, schema={item: int64}) >>> ds.schema() Column Type ------ ---- item int64 Args: items: List of local Python objects. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: A :class:`~ray.data.Dataset` holding the items. """ import builtins parallelism = _get_num_output_blocks(parallelism, override_num_blocks) if parallelism == 0: raise ValueError(f"parallelism must be -1 or > 0, got: {parallelism}") detected_parallelism, _, _ = _autodetect_parallelism( parallelism, ray.util.get_current_placement_group(), DataContext.get_current(), ) # Truncate parallelism to number of items to avoid empty blocks. detected_parallelism = min(len(items), detected_parallelism) if detected_parallelism > 0: block_size, remainder = divmod(len(items), detected_parallelism) else: block_size, remainder = 0, 0 # NOTE: We need to explicitly use the builtins range since we override range below, # with the definition of ray.data.range. blocks: List[ObjectRef[Block]] = [] metadata: List[BlockMetadata] = [] for i in builtins.range(detected_parallelism): stats = BlockExecStats.builder() builder = DelegatingBlockBuilder() # Evenly distribute remainder across block slices while preserving record order. block_start = i * block_size + min(i, remainder) block_end = (i + 1) * block_size + min(i + 1, remainder) for j in builtins.range(block_start, block_end): item = items[j] if not isinstance(item, collections.abc.Mapping): item = {"item": item} builder.add(item) block = builder.build() blocks.append(ray.put(block)) metadata.append( BlockAccessor.for_block(block).get_metadata( input_files=None, exec_stats=stats.build() ) ) from_items_op = FromItems(blocks, metadata) logical_plan = LogicalPlan(from_items_op) return MaterializedDataset( ExecutionPlan( BlockList(blocks, metadata, owned_by_consumer=False), DatasetStats(metadata={"FromItems": metadata}, parent=None), run_by_consumer=False, ), logical_plan, )
[docs]@PublicAPI def range( n: int, *, parallelism: int = -1, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Dataset: """Creates a :class:`~ray.data.Dataset` from a range of integers [0..n). This function allows for easy creation of synthetic datasets for testing or benchmarking :ref:`Ray Data <data>`. Examples: >>> import ray >>> ds = ray.data.range(10000) >>> ds Dataset(num_rows=10000, schema={id: int64}) >>> ds.map(lambda row: {"id": row["id"] * 2}).take(4) [{'id': 0}, {'id': 2}, {'id': 4}, {'id': 6}] Args: n: The upper bound of the range of integers. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: A :class:`~ray.data.Dataset` producing the integers from the range 0 to n. .. seealso:: :meth:`~ray.data.range_tensor` Call this method for creating synthetic datasets of tensor data. """ datasource = RangeDatasource(n=n, block_format="arrow", column_name="id") return read_datasource( datasource, parallelism=parallelism, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI def range_tensor( n: int, *, shape: Tuple = (1,), parallelism: int = -1, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Dataset: """Creates a :class:`~ray.data.Dataset` tensors of the provided shape from range [0...n]. This function allows for easy creation of synthetic tensor datasets for testing or benchmarking :ref:`Ray Data <data>`. Examples: >>> import ray >>> ds = ray.data.range_tensor(1000, shape=(2, 2)) >>> ds Dataset(num_rows=1000, schema={data: numpy.ndarray(shape=(2, 2), dtype=int64)}) >>> ds.map_batches(lambda row: {"data": row["data"] * 2}).take(2) [{'data': array([[0, 0], [0, 0]])}, {'data': array([[2, 2], [2, 2]])}] Args: n: The upper bound of the range of tensor records. shape: The shape of each tensor in the dataset. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: A :class:`~ray.data.Dataset` producing the tensor data from range 0 to n. .. seealso:: :meth:`~ray.data.range` Call this method to create synthetic datasets of integer data. """ datasource = RangeDatasource( n=n, block_format="tensor", column_name="data", tensor_shape=tuple(shape) ) return read_datasource( datasource, parallelism=parallelism, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI @wrap_auto_init def read_datasource( datasource: Datasource, *, parallelism: int = -1, ray_remote_args: Dict[str, Any] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, **read_args, ) -> Dataset: """Read a stream from a custom :class:`~ray.data.Datasource`. Args: datasource: The :class:`~ray.data.Datasource` to read data from. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to :meth:`ray.remote` in the read 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. read_args: Additional kwargs to pass to the :class:`~ray.data.Datasource` implementation. Returns: :class:`~ray.data.Dataset` that reads data from the :class:`~ray.data.Datasource`. """ # noqa: E501 parallelism = _get_num_output_blocks(parallelism, override_num_blocks) ctx = DataContext.get_current() if ray_remote_args is None: ray_remote_args = {} if not datasource.supports_distributed_reads: ray_remote_args["scheduling_strategy"] = NodeAffinitySchedulingStrategy( ray.get_runtime_context().get_node_id(), soft=False, ) if "scheduling_strategy" not in ray_remote_args: ray_remote_args["scheduling_strategy"] = ctx.scheduling_strategy force_local = False pa_ds = _lazy_import_pyarrow_dataset() if pa_ds: partitioning = read_args.get("dataset_kwargs", {}).get("partitioning", None) if isinstance(partitioning, pa_ds.Partitioning): logger.info( "Forcing local metadata resolution since the provided partitioning " f"{partitioning} is not serializable." ) force_local = True if force_local: datasource_or_legacy_reader = _get_datasource_or_legacy_reader( datasource, ctx, read_args, ) else: # Prepare read in a remote task at same node. # NOTE: in Ray client mode, this is expected to be run on head node. # So we aren't attempting metadata resolution from the client machine. scheduling_strategy = NodeAffinitySchedulingStrategy( ray.get_runtime_context().get_node_id(), soft=False, ) get_datasource_or_legacy_reader = cached_remote_fn( _get_datasource_or_legacy_reader, retry_exceptions=False, num_cpus=0 ).options(scheduling_strategy=scheduling_strategy) datasource_or_legacy_reader = ray.get( get_datasource_or_legacy_reader.remote( datasource, ctx, _wrap_arrow_serialization_workaround(read_args), ) ) cur_pg = ray.util.get_current_placement_group() requested_parallelism, _, inmemory_size = _autodetect_parallelism( parallelism, ctx.target_max_block_size, DataContext.get_current(), datasource_or_legacy_reader, placement_group=cur_pg, ) # TODO(hchen/chengsu): Remove the duplicated get_read_tasks call here after # removing LazyBlockList code path. read_tasks = datasource_or_legacy_reader.get_read_tasks(requested_parallelism) read_op_name = f"Read{datasource.get_name()}" block_list = LazyBlockList( read_tasks, read_op_name=read_op_name, ray_remote_args=ray_remote_args, owned_by_consumer=False, ) block_list._estimated_num_blocks = len(read_tasks) if read_tasks else 0 read_op = Read( datasource, datasource_or_legacy_reader, parallelism, inmemory_size, block_list._estimated_num_blocks, ray_remote_args, concurrency, ) logical_plan = LogicalPlan(read_op) return Dataset( plan=ExecutionPlan(block_list, block_list.stats(), run_by_consumer=False), logical_plan=logical_plan, )
[docs]@PublicAPI(stability="alpha") def read_mongo( uri: str, database: str, collection: str, *, pipeline: Optional[List[Dict]] = None, schema: Optional["pymongoarrow.api.Schema"] = None, parallelism: int = -1, ray_remote_args: Dict[str, Any] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, **mongo_args, ) -> Dataset: """Create a :class:`~ray.data.Dataset` from a MongoDB database. The data to read from is specified via the ``uri``, ``database`` and ``collection`` of the MongoDB. The dataset is created from the results of executing ``pipeline`` against the ``collection``. If ``pipeline`` is None, the entire ``collection`` is read. .. tip:: For more details about these MongoDB concepts, see the following: - URI: https://www.mongodb.com/docs/manual/reference/connection-string/ - Database and Collection: https://www.mongodb.com/docs/manual/core/databases-and-collections/ - Pipeline: https://www.mongodb.com/docs/manual/core/aggregation-pipeline/ To read the MongoDB in parallel, the execution of the pipeline is run on partitions of the collection, with a Ray read task to handle a partition. Partitions are created in an attempt to evenly distribute the documents into the specified number of partitions. The number of partitions is determined by ``parallelism`` which can be requested from this interface or automatically chosen if unspecified (see the ``parallelism`` arg below). Examples: >>> import ray >>> from pymongoarrow.api import Schema # doctest: +SKIP >>> ds = ray.data.read_mongo( # doctest: +SKIP ... uri="mongodb://username:[email protected]:27017/?authSource=admin", # noqa: E501 ... database="my_db", ... collection="my_collection", ... pipeline=[{"$match": {"col2": {"$gte": 0, "$lt": 100}}}, {"$sort": "sort_field"}], # noqa: E501 ... schema=Schema({"col1": pa.string(), "col2": pa.int64()}), ... override_num_blocks=10, ... ) Args: uri: The URI of the source MongoDB where the dataset is read from. 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 hosted in the MongoDB. This database must exist otherwise ValueError is raised. collection: The name of the collection in the database. This collection must exist otherwise ValueError is raised. pipeline: A `MongoDB pipeline <https://www.mongodb.com/docs/manual/core\ /aggregation-pipeline/>`_, which is executed on the given collection with results used to create Dataset. If None, the entire collection will be read. schema: The schema used to read the collection. If None, it'll be inferred from the results of pipeline. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. mongo_args: kwargs passed to `aggregate_arrow_all() <https://mongo-arrow\ .readthedocs.io/en/latest/api/api.html#pymongoarrow.api\ aggregate_arrow_all>`_ in pymongoarrow in producing Arrow-formatted results. Returns: :class:`~ray.data.Dataset` producing rows from the results of executing the pipeline on the specified MongoDB collection. Raises: ValueError: if ``database`` doesn't exist. ValueError: if ``collection`` doesn't exist. """ datasource = MongoDatasource( uri=uri, database=database, collection=collection, pipeline=pipeline, schema=schema, **mongo_args, ) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI(stability="alpha") def read_bigquery( project_id: str, dataset: Optional[str] = None, query: Optional[str] = None, *, parallelism: int = -1, ray_remote_args: Dict[str, Any] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Dataset: """Create a dataset from BigQuery. The data to read from is specified via the ``project_id``, ``dataset`` and/or ``query`` parameters. The dataset is created from the results of executing ``query`` if a query is provided. Otherwise, the entire ``dataset`` is read. For more information about BigQuery, see the following concepts: - Project id: `Creating and Managing Projects <https://cloud.google.com/resource-manager/docs/creating-managing-projects>`_ - Dataset: `Datasets Intro <https://cloud.google.com/bigquery/docs/datasets-intro>`_ - Query: `Query Syntax <https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax>`_ This method uses the BigQuery Storage Read API which reads in parallel, with a Ray read task to handle each stream. The number of streams is determined by ``parallelism`` which can be requested from this interface or automatically chosen if unspecified (see the ``parallelism`` arg below). .. warning:: The maximum query response size is 10GB. For more information, see `BigQuery response too large to return <https://cloud.google.com/knowledge/kb/bigquery-response-too-large-to-return-consider-setting-allowlargeresults-to-true-in-your-job-configuration-000004266>`_. Examples: .. testcode:: :skipif: True import ray # Users will need to authenticate beforehand (https://cloud.google.com/sdk/gcloud/reference/auth/login) ds = ray.data.read_bigquery( project_id="my_project", query="SELECT * FROM `bigquery-public-data.samples.gsod` LIMIT 1000", ) Args: project_id: The name of the associated Google Cloud Project that hosts the dataset to read. For more information, see `Creating and Managing Projects <https://cloud.google.com/resource-manager/docs/creating-managing-projects>`_. dataset: The name of the dataset hosted in BigQuery in the format of ``dataset_id.table_id``. Both the dataset_id and table_id must exist otherwise an exception will be raised. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to ray.remote in the read 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: Dataset producing rows from the results of executing the query (or reading the entire dataset) on the specified BigQuery dataset. """ # noqa: E501 datasource = BigQueryDatasource(project_id=project_id, dataset=dataset, query=query) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI def read_parquet( paths: Union[str, List[str]], *, filesystem: Optional["pyarrow.fs.FileSystem"] = None, columns: Optional[List[str]] = None, parallelism: int = -1, ray_remote_args: Dict[str, Any] = None, tensor_column_schema: Optional[Dict[str, Tuple[np.dtype, Tuple[int, ...]]]] = None, meta_provider: Optional[ParquetMetadataProvider] = None, partition_filter: Optional[PathPartitionFilter] = None, shuffle: Union[Literal["files"], None] = None, include_paths: bool = False, file_extensions: Optional[List[str]] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, **arrow_parquet_args, ) -> Dataset: """Creates a :class:`~ray.data.Dataset` from parquet files. Examples: Read a file in remote storage. >>> 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 Read a directory in remote storage. >>> ds = ray.data.read_parquet("s3://anonymous@ray-example-data/iris-parquet/") Read multiple local files. >>> ray.data.read_parquet( ... ["local:///path/to/file1", "local:///path/to/file2"]) # doctest: +SKIP Specify a schema for the parquet file. >>> import pyarrow as pa >>> fields = [("sepal.length", pa.float32()), ... ("sepal.width", pa.float32()), ... ("petal.length", pa.float32()), ... ("petal.width", pa.float32()), ... ("variety", pa.string())] >>> ds = ray.data.read_parquet("s3://anonymous@ray-example-data/iris.parquet", ... schema=pa.schema(fields)) >>> ds.schema() Column Type ------ ---- sepal.length float sepal.width float petal.length float petal.width float variety string The Parquet reader also supports projection and filter pushdown, allowing column selection and row filtering to be pushed down to the file scan. .. testcode:: import pyarrow as pa # Create a Dataset by reading a Parquet file, pushing column selection and # row filtering down to the file scan. ds = ray.data.read_parquet( "s3://anonymous@ray-example-data/iris.parquet", columns=["sepal.length", "variety"], filter=pa.dataset.field("sepal.length") > 5.0, ) ds.show(2) .. testoutput:: {'sepal.length': 5.1, 'variety': 'Setosa'} {'sepal.length': 5.4, 'variety': 'Setosa'} For further arguments you can pass to PyArrow as a keyword argument, see the `PyArrow API reference <https://arrow.apache.org/docs/python/generated/\ pyarrow.dataset.Scanner.html#pyarrow.dataset.Scanner.from_fragment>`_. Args: paths: A single file path or directory, or a list of file paths. Multiple directories are not supported. filesystem: The PyArrow filesystem implementation to read from. These filesystems are specified in the `pyarrow docs <https://arrow.apache.org/docs/python/api/\ filesystems.html#filesystem-implementations>`_. Specify this parameter 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. If ``None``, this function uses a system-chosen implementation. columns: A list of column names to read. Only the specified columns are read during the file scan. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read tasks. tensor_column_schema: A dict of column name to PyArrow dtype and shape mappings for converting a Parquet column containing serialized tensors (ndarrays) as their elements to PyArrow tensors. This function assumes that the tensors are serialized in the raw NumPy array format in C-contiguous order (e.g., via `arr.tobytes()`). meta_provider: A :ref:`file metadata provider <metadata_provider>`. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases you do not need to set this parameter. partition_filter: A :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use with a custom callback to read only selected partitions of a dataset. shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. arrow_parquet_args: Other parquet read options to pass to PyArrow. For the full set of arguments, see the `PyArrow API <https://arrow.apache.org/docs/\ python/generated/pyarrow.dataset.Scanner.html\ #pyarrow.dataset.Scanner.from_fragment>`_ include_paths: If ``True``, include the path to each file. File paths are stored in the ``'path'`` column. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: :class:`~ray.data.Dataset` producing records read from the specified parquet files. """ if meta_provider is None: meta_provider = get_parquet_metadata_provider() arrow_parquet_args = _resolve_parquet_args( tensor_column_schema, **arrow_parquet_args, ) dataset_kwargs = arrow_parquet_args.pop("dataset_kwargs", None) _block_udf = arrow_parquet_args.pop("_block_udf", None) schema = arrow_parquet_args.pop("schema", None) datasource = ParquetDatasource( paths, columns=columns, dataset_kwargs=dataset_kwargs, to_batch_kwargs=arrow_parquet_args, _block_udf=_block_udf, filesystem=filesystem, schema=schema, meta_provider=meta_provider, partition_filter=partition_filter, shuffle=shuffle, include_paths=include_paths, file_extensions=file_extensions, ) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI(stability="beta") def read_images( paths: Union[str, List[str]], *, filesystem: Optional["pyarrow.fs.FileSystem"] = None, parallelism: int = -1, meta_provider: Optional[BaseFileMetadataProvider] = None, ray_remote_args: Dict[str, Any] = None, arrow_open_file_args: Optional[Dict[str, Any]] = None, partition_filter: Optional[PathPartitionFilter] = None, partitioning: Partitioning = None, size: Optional[Tuple[int, int]] = None, mode: Optional[str] = None, include_paths: bool = False, ignore_missing_paths: bool = False, shuffle: Union[Literal["files"], None] = None, file_extensions: Optional[List[str]] = ImageDatasource._FILE_EXTENSIONS, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Dataset: """Creates a :class:`~ray.data.Dataset` from image files. Examples: >>> import ray >>> path = "s3://anonymous@ray-example-data/batoidea/JPEGImages/" >>> ds = ray.data.read_images(path) >>> ds.schema() Column Type ------ ---- image numpy.ndarray(shape=(32, 32, 3), dtype=uint8) If you need image file paths, set ``include_paths=True``. >>> ds = ray.data.read_images(path, include_paths=True) >>> ds.schema() Column Type ------ ---- image numpy.ndarray(shape=(32, 32, 3), dtype=uint8) path string >>> ds.take(1)[0]["path"] 'ray-example-data/batoidea/JPEGImages/1.jpeg' If your images are arranged like: .. code:: root/dog/xxx.png root/dog/xxy.png root/cat/123.png root/cat/nsdf3.png Then you can include the labels by specifying a :class:`~ray.data.datasource.partitioning.Partitioning`. >>> import ray >>> from ray.data.datasource.partitioning import Partitioning >>> root = "s3://anonymous@ray-example-data/image-datasets/dir-partitioned" >>> partitioning = Partitioning("dir", field_names=["class"], base_dir=root) >>> ds = ray.data.read_images(root, size=(224, 224), partitioning=partitioning) >>> ds.schema() Column Type ------ ---- image numpy.ndarray(shape=(224, 224, 3), dtype=uint8) class string Args: paths: A single file or directory, or a list of file or directory paths. A list of paths can contain both files and directories. filesystem: The pyarrow filesystem implementation to read from. These filesystems are specified in the `pyarrow docs <https://arrow.apache.org/docs/python/api/\ filesystems.html#filesystem-implementations>`_. Specify this parameter 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. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. meta_provider: A :ref:`file metadata provider <metadata_provider>`. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If ``None``, this function uses a system-chosen implementation. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read tasks. arrow_open_file_args: kwargs passed to `pyarrow.fs.FileSystem.open_input_file <https://arrow.apache.org/docs/\ python/generated/pyarrow.fs.FileSystem.html\ #pyarrow.fs.FileSystem.open_input_file>`_. when opening input files to read. partition_filter: A :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use with a custom callback to read only selected partitions of a dataset. By default, this filters out any file paths whose file extension does not match ``*.png``, ``*.jpg``, ``*.jpeg``, ``*.tiff``, ``*.bmp``, or ``*.gif``. partitioning: A :class:`~ray.data.datasource.partitioning.Partitioning` object that describes how paths are organized. Defaults to ``None``. size: The desired height and width of loaded images. If unspecified, images retain their original shape. mode: A `Pillow mode <https://pillow.readthedocs.io/en/stable/handbook/concepts\ .html#modes>`_ describing the desired type and depth of pixels. If unspecified, image modes are inferred by `Pillow <https://pillow.readthedocs.io/en/stable/index.html>`_. include_paths: If ``True``, include the path to each image. File paths are stored in the ``'path'`` column. ignore_missing_paths: If True, ignores any file/directory paths in ``paths`` that are not found. Defaults to False. shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: A :class:`~ray.data.Dataset` producing tensors that represent the images at the specified paths. For information on working with tensors, read the :ref:`tensor data guide <working_with_tensors>`. Raises: ValueError: if ``size`` contains non-positive numbers. ValueError: if ``mode`` is unsupported. """ if meta_provider is None: meta_provider = get_image_metadata_provider() datasource = ImageDatasource( paths, size=size, mode=mode, include_paths=include_paths, filesystem=filesystem, meta_provider=meta_provider, open_stream_args=arrow_open_file_args, partition_filter=partition_filter, partitioning=partitioning, ignore_missing_paths=ignore_missing_paths, shuffle=shuffle, file_extensions=file_extensions, ) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI def read_parquet_bulk( paths: Union[str, List[str]], *, filesystem: Optional["pyarrow.fs.FileSystem"] = None, columns: Optional[List[str]] = None, parallelism: int = -1, ray_remote_args: Dict[str, Any] = None, arrow_open_file_args: Optional[Dict[str, Any]] = None, tensor_column_schema: Optional[Dict[str, Tuple[np.dtype, Tuple[int, ...]]]] = None, meta_provider: Optional[BaseFileMetadataProvider] = None, partition_filter: Optional[PathPartitionFilter] = None, shuffle: Union[Literal["files"], None] = None, include_paths: bool = False, file_extensions: Optional[List[str]] = ParquetBaseDatasource._FILE_EXTENSIONS, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, **arrow_parquet_args, ) -> Dataset: """Create :class:`~ray.data.Dataset` from parquet files without reading metadata. Use :meth:`~ray.data.read_parquet` for most cases. Use :meth:`~ray.data.read_parquet_bulk` if all the provided paths point to files and metadata fetching using :meth:`~ray.data.read_parquet` takes too long or the parquet files do not all have a unified schema. Performance slowdowns are possible when using this method with parquet files that are very large. .. warning:: Only provide file paths as input (i.e., no directory paths). An OSError is raised if one or more paths point to directories. If your use-case requires directory paths, use :meth:`~ray.data.read_parquet` instead. Examples: Read multiple local files. You should always provide only input file paths (i.e. no directory paths) when known to minimize read latency. >>> ray.data.read_parquet_bulk( # doctest: +SKIP ... ["/path/to/file1", "/path/to/file2"]) Args: paths: A single file path or a list of file paths. filesystem: The PyArrow filesystem implementation to read from. These filesystems are specified in the `PyArrow docs <https://arrow.apache.org/docs/python/api/\ filesystems.html#filesystem-implementations>`_. Specify this parameter 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. columns: A list of column names to read. Only the specified columns are read during the file scan. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read tasks. arrow_open_file_args: kwargs passed to `pyarrow.fs.FileSystem.open_input_file <https://arrow.apache.org/docs/\ python/generated/pyarrow.fs.FileSystem.html\ #pyarrow.fs.FileSystem.open_input_file>`_. when opening input files to read. tensor_column_schema: A dict of column name to PyArrow dtype and shape mappings for converting a Parquet column containing serialized tensors (ndarrays) as their elements to PyArrow tensors. This function assumes that the tensors are serialized in the raw NumPy array format in C-contiguous order (e.g. via `arr.tobytes()`). meta_provider: A :ref:`file metadata provider <metadata_provider>`. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If ``None``, this function uses a system-chosen implementation. partition_filter: A :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use with a custom callback to read only selected partitions of a dataset. By default, this filters out any file paths whose file extension does not match "*.parquet*". shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. arrow_parquet_args: Other parquet read options to pass to PyArrow. For the full set of arguments, see the `PyArrow API <https://arrow.apache.org/docs/python/generated/\ pyarrow.dataset.Scanner.html#pyarrow.dataset.Scanner.from_fragment>`_ include_paths: If ``True``, include the path to each file. File paths are stored in the ``'path'`` column. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: :class:`~ray.data.Dataset` producing records read from the specified paths. """ if meta_provider is None: meta_provider = get_parquet_bulk_metadata_provider() read_table_args = _resolve_parquet_args( tensor_column_schema, **arrow_parquet_args, ) if columns is not None: read_table_args["columns"] = columns datasource = ParquetBaseDatasource( paths, read_table_args=read_table_args, filesystem=filesystem, open_stream_args=arrow_open_file_args, meta_provider=meta_provider, partition_filter=partition_filter, shuffle=shuffle, include_paths=include_paths, file_extensions=file_extensions, ) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI def read_json( paths: Union[str, List[str]], *, filesystem: Optional["pyarrow.fs.FileSystem"] = None, parallelism: int = -1, ray_remote_args: Dict[str, Any] = None, arrow_open_stream_args: Optional[Dict[str, Any]] = None, meta_provider: Optional[BaseFileMetadataProvider] = None, partition_filter: Optional[PathPartitionFilter] = None, partitioning: Partitioning = Partitioning("hive"), include_paths: bool = False, ignore_missing_paths: bool = False, shuffle: Union[Literal["files"], None] = None, file_extensions: Optional[List[str]] = JSONDatasource._FILE_EXTENSIONS, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, **arrow_json_args, ) -> Dataset: """Creates a :class:`~ray.data.Dataset` from JSON and JSONL files. For JSON file, the whole file is read as one row. For JSONL file, each line of file is read as separate row. Examples: Read a JSON file in remote storage. >>> import ray >>> ds = ray.data.read_json("s3://anonymous@ray-example-data/log.json") >>> ds.schema() Column Type ------ ---- timestamp timestamp[s] size int64 Read a JSONL file in remote storage. >>> ds = ray.data.read_json("s3://anonymous@ray-example-data/train.jsonl") >>> ds.schema() Column Type ------ ---- input string Read multiple local files. >>> ray.data.read_json( # doctest: +SKIP ... ["local:///path/to/file1", "local:///path/to/file2"]) Read multiple directories. >>> ray.data.read_json( # doctest: +SKIP ... ["s3://bucket/path1", "s3://bucket/path2"]) By default, :meth:`~ray.data.read_json` parses `Hive-style partitions <https://athena.guide/articles/\ hive-style-partitioning/>`_ from file paths. If your data adheres to a different partitioning scheme, set the ``partitioning`` parameter. >>> ds = ray.data.read_json("s3://anonymous@ray-example-data/year=2022/month=09/sales.json") >>> ds.take(1) [{'order_number': 10107, 'quantity': 30, 'year': '2022', 'month': '09'}] Args: paths: A single file or directory, or a list of file or directory paths. A list of paths can contain both files and directories. filesystem: The PyArrow filesystem implementation to read from. These filesystems are specified in the `PyArrow docs <https://arrow.apache.org/docs/python/api/\ filesystems.html#filesystem-implementations>`_. Specify this parameter 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. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read tasks. arrow_open_stream_args: kwargs passed to `pyarrow.fs.FileSystem.open_input_file <https://arrow.apache.org/docs/\ python/generated/pyarrow.fs.FileSystem.html\ #pyarrow.fs.FileSystem.open_input_stream>`_. when opening input files to read. meta_provider: A :ref:`file metadata provider <metadata_provider>`. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If ``None``, this function uses a system-chosen implementation. partition_filter: A :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use with a custom callback to read only selected partitions of a dataset. By default, this filters out any file paths whose file extension does not match "*.json" or "*.jsonl". partitioning: A :class:`~ray.data.datasource.partitioning.Partitioning` object that describes how paths are organized. By default, this function parses `Hive-style partitions <https://athena.guide/articles/\ hive-style-partitioning/>`_. include_paths: If ``True``, include the path to each file. File paths are stored in the ``'path'`` column. ignore_missing_paths: If True, ignores any file paths in ``paths`` that are not found. Defaults to False. shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. arrow_json_args: JSON read options to pass to `pyarrow.json.read_json <https://\ arrow.apache.org/docs/python/generated/pyarrow.json.read_json.html#pyarrow.\ json.read_json>`_. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: :class:`~ray.data.Dataset` producing records read from the specified paths. """ # noqa: E501 if meta_provider is None: meta_provider = get_generic_metadata_provider(JSONDatasource._FILE_EXTENSIONS) datasource = JSONDatasource( paths, arrow_json_args=arrow_json_args, filesystem=filesystem, open_stream_args=arrow_open_stream_args, meta_provider=meta_provider, partition_filter=partition_filter, partitioning=partitioning, ignore_missing_paths=ignore_missing_paths, shuffle=shuffle, include_paths=include_paths, file_extensions=file_extensions, ) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI def read_csv( paths: Union[str, List[str]], *, filesystem: Optional["pyarrow.fs.FileSystem"] = None, parallelism: int = -1, ray_remote_args: Dict[str, Any] = None, arrow_open_stream_args: Optional[Dict[str, Any]] = None, meta_provider: Optional[BaseFileMetadataProvider] = None, partition_filter: Optional[PathPartitionFilter] = None, partitioning: Partitioning = Partitioning("hive"), include_paths: bool = False, ignore_missing_paths: bool = False, shuffle: Union[Literal["files"], None] = None, file_extensions: Optional[List[str]] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, **arrow_csv_args, ) -> Dataset: """Creates a :class:`~ray.data.Dataset` from CSV files. Examples: Read a file in remote storage. >>> import ray >>> ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv") >>> ds.schema() Column Type ------ ---- sepal length (cm) double sepal width (cm) double petal length (cm) double petal width (cm) double target int64 Read multiple local files. >>> ray.data.read_csv( # doctest: +SKIP ... ["local:///path/to/file1", "local:///path/to/file2"]) Read a directory from remote storage. >>> ds = ray.data.read_csv("s3://anonymous@ray-example-data/iris-csv/") Read files that use a different delimiter. For more uses of ParseOptions see https://arrow.apache.org/docs/python/generated/pyarrow.csv.ParseOptions.html # noqa: #501 >>> from pyarrow import csv >>> parse_options = csv.ParseOptions(delimiter="\\t") >>> ds = ray.data.read_csv( ... "s3://anonymous@ray-example-data/iris.tsv", ... parse_options=parse_options) >>> ds.schema() Column Type ------ ---- sepal.length double sepal.width double petal.length double petal.width double variety string Convert a date column with a custom format from a CSV file. For more uses of ConvertOptions see https://arrow.apache.org/docs/python/generated/pyarrow.csv.ConvertOptions.html # noqa: #501 >>> from pyarrow import csv >>> convert_options = csv.ConvertOptions( ... timestamp_parsers=["%m/%d/%Y"]) >>> ds = ray.data.read_csv( ... "s3://anonymous@ray-example-data/dow_jones.csv", ... convert_options=convert_options) By default, :meth:`~ray.data.read_csv` parses `Hive-style partitions <https://athena.guide/\ articles/hive-style-partitioning/>`_ from file paths. If your data adheres to a different partitioning scheme, set the ``partitioning`` parameter. >>> ds = ray.data.read_csv("s3://anonymous@ray-example-data/year=2022/month=09/sales.csv") >>> ds.take(1) [{'order_number': 10107, 'quantity': 30, 'year': '2022', 'month': '09'}] By default, :meth:`~ray.data.read_csv` reads all files from file paths. If you want to filter files by file extensions, set the ``partition_filter`` parameter. Read only ``*.csv`` files from a directory. >>> ray.data.read_csv("s3://anonymous@ray-example-data/different-extensions/", ... file_extensions=["csv"]) Dataset(num_rows=1, schema={a: int64, b: int64}) Args: paths: A single file or directory, or a list of file or directory paths. A list of paths can contain both files and directories. filesystem: The PyArrow filesystem implementation to read from. These filesystems are specified in the `pyarrow docs <https://arrow.apache.org/docs/python/api/\ filesystems.html#filesystem-implementations>`_. Specify this parameter 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. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read tasks. arrow_open_stream_args: kwargs passed to `pyarrow.fs.FileSystem.open_input_file <https://arrow.apache.org/docs/\ python/generated/pyarrow.fs.FileSystem.html\ #pyarrow.fs.FileSystem.open_input_stream>`_. when opening input files to read. meta_provider: A :ref:`file metadata provider <metadata_provider>`. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If ``None``, this function uses a system-chosen implementation. partition_filter: A :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use with a custom callback to read only selected partitions of a dataset. By default, no files are filtered. partitioning: A :class:`~ray.data.datasource.partitioning.Partitioning` object that describes how paths are organized. By default, this function parses `Hive-style partitions <https://athena.guide/articles/\ hive-style-partitioning/>`_. include_paths: If ``True``, include the path to each file. File paths are stored in the ``'path'`` column. ignore_missing_paths: If True, ignores any file paths in ``paths`` that are not found. Defaults to False. shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. arrow_csv_args: CSV read options to pass to `pyarrow.csv.open_csv <https://arrow.apache.org/docs/python/generated/\ pyarrow.csv.open_csv.html#pyarrow.csv.open_csv>`_ when opening CSV files. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: :class:`~ray.data.Dataset` producing records read from the specified paths. """ if meta_provider is None: meta_provider = get_generic_metadata_provider(CSVDatasource._FILE_EXTENSIONS) datasource = CSVDatasource( paths, arrow_csv_args=arrow_csv_args, filesystem=filesystem, open_stream_args=arrow_open_stream_args, meta_provider=meta_provider, partition_filter=partition_filter, partitioning=partitioning, ignore_missing_paths=ignore_missing_paths, shuffle=shuffle, include_paths=include_paths, file_extensions=file_extensions, ) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI def read_text( paths: Union[str, List[str]], *, encoding: str = "utf-8", drop_empty_lines: bool = True, filesystem: Optional["pyarrow.fs.FileSystem"] = None, parallelism: int = -1, ray_remote_args: Optional[Dict[str, Any]] = None, arrow_open_stream_args: Optional[Dict[str, Any]] = None, meta_provider: Optional[BaseFileMetadataProvider] = None, partition_filter: Optional[PathPartitionFilter] = None, partitioning: Partitioning = None, include_paths: bool = False, ignore_missing_paths: bool = False, shuffle: Union[Literal["files"], None] = None, file_extensions: Optional[List[str]] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Dataset: """Create a :class:`~ray.data.Dataset` from lines stored in text files. Examples: Read a file in remote storage. >>> import ray >>> ds = ray.data.read_text("s3://anonymous@ray-example-data/this.txt") >>> ds.schema() Column Type ------ ---- text string Read multiple local files. >>> ray.data.read_text( # doctest: +SKIP ... ["local:///path/to/file1", "local:///path/to/file2"]) Args: paths: A single file or directory, or a list of file or directory paths. A list of paths can contain both files and directories. encoding: The encoding of the files (e.g., "utf-8" or "ascii"). filesystem: The PyArrow filesystem implementation to read from. These filesystems are specified in the `PyArrow docs <https://arrow.apache.org/docs/python/api/\ filesystems.html#filesystem-implementations>`_. Specify this parameter 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. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read tasks and in the subsequent text decoding map task. arrow_open_stream_args: kwargs passed to `pyarrow.fs.FileSystem.open_input_file <https://arrow.apache.org/docs/\ python/generated/pyarrow.fs.FileSystem.html\ #pyarrow.fs.FileSystem.open_input_stream>`_. when opening input files to read. meta_provider: A :ref:`file metadata provider <metadata_provider>`. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If ``None``, this function uses a system-chosen implementation. partition_filter: A :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use with a custom callback to read only selected partitions of a dataset. By default, no files are filtered. partitioning: A :class:`~ray.data.datasource.partitioning.Partitioning` object that describes how paths are organized. Defaults to ``None``. include_paths: If ``True``, include the path to each file. File paths are stored in the ``'path'`` column. ignore_missing_paths: If True, ignores any file paths in ``paths`` that are not found. Defaults to False. shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: :class:`~ray.data.Dataset` producing lines of text read from the specified paths. """ if meta_provider is None: meta_provider = get_generic_metadata_provider(TextDatasource._FILE_EXTENSIONS) datasource = TextDatasource( paths, drop_empty_lines=drop_empty_lines, encoding=encoding, filesystem=filesystem, open_stream_args=arrow_open_stream_args, meta_provider=meta_provider, partition_filter=partition_filter, partitioning=partitioning, ignore_missing_paths=ignore_missing_paths, shuffle=shuffle, include_paths=include_paths, file_extensions=file_extensions, ) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI def read_avro( paths: Union[str, List[str]], *, filesystem: Optional["pyarrow.fs.FileSystem"] = None, parallelism: int = -1, ray_remote_args: Optional[Dict[str, Any]] = None, arrow_open_stream_args: Optional[Dict[str, Any]] = None, meta_provider: Optional[BaseFileMetadataProvider] = None, partition_filter: Optional[PathPartitionFilter] = None, partitioning: Partitioning = None, include_paths: bool = False, ignore_missing_paths: bool = False, shuffle: Union[Literal["files"], None] = None, file_extensions: Optional[List[str]] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Dataset: """Create a :class:`~ray.data.Dataset` from records stored in Avro files. Examples: Read an Avro file in remote storage or local storage. >>> import ray >>> ds = ray.data.read_avro("s3://anonymous@ray-example-data/mnist.avro") >>> ds.schema() Column Type ------ ---- features list<item: int64> label int64 dataType string >>> ray.data.read_avro( # doctest: +SKIP ... ["local:///path/to/file1", "local:///path/to/file2"]) Args: paths: A single file or directory, or a list of file or directory paths. A list of paths can contain both files and directories. filesystem: The PyArrow filesystem implementation to read from. These filesystems are specified in the `PyArrow docs <https://arrow.apache.org/docs/python/api/\ filesystems.html#filesystem-implementations>`_. Specify this parameter 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. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read tasks and in the subsequent text decoding map task. arrow_open_stream_args: kwargs passed to `pyarrow.fs.FileSystem.open_input_file <https://arrow.apache.org/docs/\ python/generated/pyarrow.fs.FileSystem.html\ #pyarrow.fs.FileSystem.open_input_stream>`_. when opening input files to read. meta_provider: A :ref:`file metadata provider <metadata_provider>`. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If ``None``, this function uses a system-chosen implementation. partition_filter: A :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use with a custom callback to read only selected partitions of a dataset. By default, no files are filtered. partitioning: A :class:`~ray.data.datasource.partitioning.Partitioning` object that describes how paths are organized. Defaults to ``None``. include_paths: If ``True``, include the path to each file. File paths are stored in the ``'path'`` column. ignore_missing_paths: If True, ignores any file paths in ``paths`` that are not found. Defaults to False. shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: :class:`~ray.data.Dataset` holding records from the Avro files. """ if meta_provider is None: meta_provider = get_generic_metadata_provider(AvroDatasource._FILE_EXTENSIONS) datasource = AvroDatasource( paths, filesystem=filesystem, open_stream_args=arrow_open_stream_args, meta_provider=meta_provider, partition_filter=partition_filter, partitioning=partitioning, ignore_missing_paths=ignore_missing_paths, shuffle=shuffle, include_paths=include_paths, file_extensions=file_extensions, ) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI def read_numpy( paths: Union[str, List[str]], *, filesystem: Optional["pyarrow.fs.FileSystem"] = None, parallelism: int = -1, arrow_open_stream_args: Optional[Dict[str, Any]] = None, meta_provider: Optional[BaseFileMetadataProvider] = None, partition_filter: Optional[PathPartitionFilter] = None, partitioning: Partitioning = None, include_paths: bool = False, ignore_missing_paths: bool = False, shuffle: Union[Literal["files"], None] = None, file_extensions: Optional[List[str]] = NumpyDatasource._FILE_EXTENSIONS, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, **numpy_load_args, ) -> Dataset: """Create an Arrow dataset from numpy files. Examples: Read a directory of files in remote storage. >>> import ray >>> ray.data.read_numpy("s3://bucket/path") # doctest: +SKIP Read multiple local files. >>> ray.data.read_numpy(["/path/to/file1", "/path/to/file2"]) # doctest: +SKIP Read multiple directories. >>> ray.data.read_numpy( # doctest: +SKIP ... ["s3://bucket/path1", "s3://bucket/path2"]) Args: paths: A single file/directory path or a list of file/directory paths. A list of paths can contain both files and directories. filesystem: The filesystem implementation to read from. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. arrow_open_stream_args: kwargs passed to `pyarrow.fs.FileSystem.open_input_stream <https://arrow.apache.org/docs/python/generated/pyarrow.fs.FileSystem.html>`_. numpy_load_args: Other options to pass to np.load. meta_provider: File metadata provider. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. If ``None``, this function uses a system-chosen implementation. partition_filter: Path-based partition filter, if any. Can be used with a custom callback to read only selected partitions of a dataset. By default, this filters out any file paths whose file extension does not match "*.npy*". partitioning: A :class:`~ray.data.datasource.partitioning.Partitioning` object that describes how paths are organized. Defaults to ``None``. include_paths: If ``True``, include the path to each file. File paths are stored in the ``'path'`` column. ignore_missing_paths: If True, ignores any file paths in ``paths`` that are not found. Defaults to False. shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: Dataset holding Tensor records read from the specified paths. """ # noqa: E501 if meta_provider is None: meta_provider = get_generic_metadata_provider(NumpyDatasource._FILE_EXTENSIONS) datasource = NumpyDatasource( paths, numpy_load_args=numpy_load_args, filesystem=filesystem, open_stream_args=arrow_open_stream_args, meta_provider=meta_provider, partition_filter=partition_filter, partitioning=partitioning, ignore_missing_paths=ignore_missing_paths, shuffle=shuffle, include_paths=include_paths, file_extensions=file_extensions, ) return read_datasource( datasource, parallelism=parallelism, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI(stability="alpha") def read_tfrecords( paths: Union[str, List[str]], *, filesystem: Optional["pyarrow.fs.FileSystem"] = None, parallelism: int = -1, arrow_open_stream_args: Optional[Dict[str, Any]] = None, meta_provider: Optional[BaseFileMetadataProvider] = None, partition_filter: Optional[PathPartitionFilter] = None, include_paths: bool = False, ignore_missing_paths: bool = False, tf_schema: Optional["schema_pb2.Schema"] = None, shuffle: Union[Literal["files"], None] = None, file_extensions: Optional[List[str]] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, tfx_read_options: Optional[TFXReadOptions] = None, ) -> Dataset: """Create a :class:`~ray.data.Dataset` from TFRecord files that contain `tf.train.Example <https://www.tensorflow.org/api_docs/python/tf/train/Example>`_ messages. .. info: Using tfx-bsl for reading tfrecord files is prefered, When reading large datasets in production use cases. To use this implementation you should install tfx-bsl with: 1. `pip install tfx_bsl --no-dependencies` 2. Pass tfx_read_options to read_tfrecords, for example: `ds = read_tfrecords(path, ..., tfx_read_options=TFXReadOptions())` .. warning:: This function exclusively supports ``tf.train.Example`` messages. If a file contains a message that isn't of type ``tf.train.Example``, then this function fails. Examples: >>> import ray >>> ray.data.read_tfrecords("s3://anonymous@ray-example-data/iris.tfrecords") Dataset( num_rows=150, schema={...} ) We can also read compressed TFRecord files, which use one of the `compression types supported by Arrow <https://arrow.apache.org/docs/python/\ generated/pyarrow.CompressedInputStream.html>`_: >>> ray.data.read_tfrecords( ... "s3://anonymous@ray-example-data/iris.tfrecords.gz", ... arrow_open_stream_args={"compression": "gzip"}, ... ) Dataset( num_rows=150, schema={...} ) Args: paths: A single file or directory, or a list of file or directory paths. A list of paths can contain both files and directories. filesystem: The PyArrow filesystem implementation to read from. These filesystems are specified in the `PyArrow docs <https://arrow.apache.org/docs/python/api/\ filesystems.html#filesystem-implementations>`_. Specify this parameter 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. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. arrow_open_stream_args: kwargs passed to `pyarrow.fs.FileSystem.open_input_file <https://arrow.apache.org/docs/\ python/generated/pyarrow.fs.FileSystem.html\ #pyarrow.fs.FileSystem.open_input_stream>`_. when opening input files to read. To read a compressed TFRecord file, pass the corresponding compression type (e.g., for ``GZIP`` or ``ZLIB``), use ``arrow_open_stream_args={'compression_type': 'gzip'}``). meta_provider: A :ref:`file metadata provider <metadata_provider>`. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If ``None``, this function uses a system-chosen implementation. partition_filter: A :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use with a custom callback to read only selected partitions of a dataset. include_paths: If ``True``, include the path to each file. File paths are stored in the ``'path'`` column. ignore_missing_paths: If True, ignores any file paths in ``paths`` that are not found. Defaults to False. tf_schema: Optional TensorFlow Schema which is used to explicitly set the schema of the underlying Dataset. shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. tfx_read_options: Specifies read options when reading TFRecord files with TFX. When no options are provided, the default version without tfx-bsl will be used to read the tfrecords. Returns: A :class:`~ray.data.Dataset` that contains the example features. Raises: ValueError: If a file contains a message that isn't a ``tf.train.Example``. """ import platform tfx_read = False if tfx_read_options and platform.processor() != "arm": try: import tfx_bsl # noqa: F401 tfx_read = True except ModuleNotFoundError: # override the tfx_read_options given that tfx-bsl is not installed tfx_read_options = None logger.warning( "Please install tfx-bsl package with" " `pip install tfx_bsl --no-dependencies`." " This can help speed up the reading of large TFRecord files." ) if meta_provider is None: meta_provider = get_generic_metadata_provider( TFRecordDatasource._FILE_EXTENSIONS ) datasource = TFRecordDatasource( paths, tf_schema=tf_schema, filesystem=filesystem, open_stream_args=arrow_open_stream_args, meta_provider=meta_provider, partition_filter=partition_filter, ignore_missing_paths=ignore_missing_paths, shuffle=shuffle, include_paths=include_paths, file_extensions=file_extensions, tfx_read_options=tfx_read_options, ) ds = read_datasource( datasource, parallelism=parallelism, concurrency=concurrency, override_num_blocks=override_num_blocks, ) if ( tfx_read_options and tfx_read_options.auto_infer_schema and tfx_read and not tf_schema ): from ray.data.datasource.tfrecords_datasource import _infer_schema_and_transform return _infer_schema_and_transform(ds) return ds
[docs]@PublicAPI(stability="alpha") def read_webdataset( paths: Union[str, List[str]], *, filesystem: Optional["pyarrow.fs.FileSystem"] = None, parallelism: int = -1, arrow_open_stream_args: Optional[Dict[str, Any]] = None, meta_provider: Optional[BaseFileMetadataProvider] = None, partition_filter: Optional[PathPartitionFilter] = None, decoder: Optional[Union[bool, str, callable, list]] = True, fileselect: Optional[Union[list, callable]] = None, filerename: Optional[Union[list, callable]] = None, suffixes: Optional[Union[list, callable]] = None, verbose_open: bool = False, shuffle: Union[Literal["files"], None] = None, include_paths: bool = False, file_extensions: Optional[List[str]] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Dataset: """Create a :class:`~ray.data.Dataset` from `WebDataset <https://webdataset.github.io/webdataset/>`_ files. Args: paths: A single file/directory path or a list of file/directory paths. A list of paths can contain both files and directories. filesystem: The filesystem implementation to read from. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. arrow_open_stream_args: Key-word arguments passed to `pyarrow.fs.FileSystem.open_input_stream <https://arrow.apache.org/docs/python/generated/pyarrow.fs.FileSystem.html>`_. To read a compressed TFRecord file, pass the corresponding compression type (e.g. for ``GZIP`` or ``ZLIB``, use ``arrow_open_stream_args={'compression_type': 'gzip'}``). meta_provider: File metadata provider. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. If ``None``, this function uses a system-chosen implementation. partition_filter: Path-based partition filter, if any. Can be used with a custom callback to read only selected partitions of a dataset. decoder: A function or list of functions to decode the data. fileselect: A callable or list of glob patterns to select files. filerename: A function or list of tuples to rename files prior to grouping. suffixes: A function or list of suffixes to select for creating samples. verbose_open: Whether to print the file names as they are opened. shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. include_paths: If ``True``, include the path to each file. File paths are stored in the ``'path'`` column. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: A :class:`~ray.data.Dataset` that contains the example features. Raises: ValueError: If a file contains a message that isn't a `tf.train.Example`_. .. _tf.train.Example: https://www.tensorflow.org/api_docs/python/tf/train/Example """ # noqa: E501 if meta_provider is None: meta_provider = get_generic_metadata_provider( WebDatasetDatasource._FILE_EXTENSIONS ) datasource = WebDatasetDatasource( paths, decoder=decoder, fileselect=fileselect, filerename=filerename, suffixes=suffixes, verbose_open=verbose_open, filesystem=filesystem, open_stream_args=arrow_open_stream_args, meta_provider=meta_provider, partition_filter=partition_filter, shuffle=shuffle, include_paths=include_paths, file_extensions=file_extensions, ) return read_datasource( datasource, parallelism=parallelism, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI def read_binary_files( paths: Union[str, List[str]], *, include_paths: bool = False, filesystem: Optional["pyarrow.fs.FileSystem"] = None, parallelism: int = -1, ray_remote_args: Dict[str, Any] = None, arrow_open_stream_args: Optional[Dict[str, Any]] = None, meta_provider: Optional[BaseFileMetadataProvider] = None, partition_filter: Optional[PathPartitionFilter] = None, partitioning: Partitioning = None, ignore_missing_paths: bool = False, shuffle: Union[Literal["files"], None] = None, file_extensions: Optional[List[str]] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Dataset: """Create a :class:`~ray.data.Dataset` from binary files of arbitrary contents. Examples: Read a file in remote storage. >>> import ray >>> path = "s3://anonymous@ray-example-data/pdf-sample_0.pdf" >>> ds = ray.data.read_binary_files(path) >>> ds.schema() Column Type ------ ---- bytes binary Read multiple local files. >>> ray.data.read_binary_files( # doctest: +SKIP ... ["local:///path/to/file1", "local:///path/to/file2"]) Read a file with the filepaths included as a column in the dataset. >>> path = "s3://anonymous@ray-example-data/pdf-sample_0.pdf" >>> ds = ray.data.read_binary_files(path, include_paths=True) >>> ds.take(1)[0]["path"] 'ray-example-data/pdf-sample_0.pdf' Args: paths: A single file or directory, or a list of file or directory paths. A list of paths can contain both files and directories. include_paths: If ``True``, include the path to each file. File paths are stored in the ``'path'`` column. filesystem: The PyArrow filesystem implementation to read from. These filesystems are specified in the `PyArrow docs <https://arrow.apache.org/docs/python/api/\ filesystems.html#filesystem-implementations>`_. Specify this parameter 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. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read tasks. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. arrow_open_stream_args: kwargs passed to `pyarrow.fs.FileSystem.open_input_file <https://arrow.apache.org/docs/\ python/generated/pyarrow.fs.FileSystem.html\ #pyarrow.fs.FileSystem.open_input_stream>`_. meta_provider: A :ref:`file metadata provider <metadata_provider>`. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If ``None``, this function uses a system-chosen implementation. partition_filter: A :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use with a custom callback to read only selected partitions of a dataset. By default, no files are filtered. By default, this does not filter out any files. partitioning: A :class:`~ray.data.datasource.partitioning.Partitioning` object that describes how paths are organized. Defaults to ``None``. ignore_missing_paths: If True, ignores any file paths in ``paths`` that are not found. Defaults to False. shuffle: If setting to "files", randomly shuffle input files order before read. Defaults to not shuffle with ``None``. file_extensions: A list of file extensions to filter files by. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: :class:`~ray.data.Dataset` producing rows read from the specified paths. """ if meta_provider is None: meta_provider = get_generic_metadata_provider(BinaryDatasource._FILE_EXTENSIONS) datasource = BinaryDatasource( paths, include_paths=include_paths, filesystem=filesystem, open_stream_args=arrow_open_stream_args, meta_provider=meta_provider, partition_filter=partition_filter, partitioning=partitioning, ignore_missing_paths=ignore_missing_paths, shuffle=shuffle, file_extensions=file_extensions, ) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI(stability="alpha") def read_sql( sql: str, connection_factory: Callable[[], Connection], *, parallelism: int = -1, ray_remote_args: Optional[Dict[str, Any]] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Dataset: """Read from a database that provides a `Python DB API2-compliant <https://peps.python.org/pep-0249/>`_ connector. .. note:: By default, ``read_sql`` launches multiple read tasks, and each task executes a ``LIMIT`` and ``OFFSET`` to fetch a subset of the rows. However, for many databases, ``OFFSET`` is slow. As a workaround, set ``override_num_blocks=1`` to directly fetch all rows in a single task. Note that this approach requires all result rows to fit in the memory of single task. If the rows don't fit, your program may raise an out of memory error. Examples: For examples of reading from larger databases like MySQL and PostgreSQL, see :ref:`Reading from SQL Databases <reading_sql>`. .. testcode:: import sqlite3 import ray # Create a simple database connection = sqlite3.connect("example.db") connection.execute("CREATE TABLE movie(title, year, score)") connection.execute( \"\"\" INSERT INTO movie VALUES ('Monty Python and the Holy Grail', 1975, 8.2), ("Monty Python Live at the Hollywood Bowl", 1982, 7.9), ("Monty Python's Life of Brian", 1979, 8.0), ("Rocky II", 1979, 7.3) \"\"\" ) connection.commit() connection.close() def create_connection(): return sqlite3.connect("example.db") # Get all movies ds = ray.data.read_sql("SELECT * FROM movie", create_connection) # Get movies after the year 1980 ds = ray.data.read_sql( "SELECT title, score FROM movie WHERE year >= 1980", create_connection ) # Get the number of movies per year ds = ray.data.read_sql( "SELECT year, COUNT(*) FROM movie GROUP BY year", create_connection ) .. testcode:: :hide: import os os.remove("example.db") Args: sql: The SQL query to execute. connection_factory: A function that takes no arguments and returns a Python DB API2 `Connection object <https://peps.python.org/pep-0249/#connection-objects>`_. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: A :class:`Dataset` containing the queried data. """ datasource = SQLDatasource(sql=sql, connection_factory=connection_factory) return read_datasource( datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI(stability="alpha") def read_databricks_tables( *, warehouse_id: str, table: Optional[str] = None, query: Optional[str] = None, catalog: Optional[str] = None, schema: Optional[str] = None, parallelism: int = -1, ray_remote_args: Optional[Dict[str, Any]] = None, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Dataset: """Read a Databricks unity catalog table or Databricks SQL execution result. Before calling this API, set the ``DATABRICKS_TOKEN`` environment variable to your Databricks warehouse access token. .. code-block:: console export DATABRICKS_TOKEN=... If you're not running your program on the Databricks runtime, also set the ``DATABRICKS_HOST`` environment variable. .. code-block:: console export DATABRICKS_HOST=adb-<workspace-id>.<random-number>.azuredatabricks.net .. note:: This function is built on the `Databricks statement execution API <https://docs.databricks.com/api/workspace/statementexecution>`_. Examples: .. testcode:: :skipif: True import ray ds = ray.data.read_databricks_tables( warehouse_id='...', catalog='catalog_1', schema='db_1', query='select id from table_1 limit 750000', ) Args: warehouse_id: The ID of the Databricks warehouse. The query statement is executed on this warehouse. table: The name of UC table you want to read. If this argument is set, you can't set ``query`` argument, and the reader generates query of ``select * from {table_name}`` under the hood. query: The query you want to execute. If this argument is set, you can't set ``table_name`` argument. catalog: (Optional) The default catalog name used by the query. schema: (Optional) The default schema used by the query. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: A :class:`Dataset` containing the queried data. """ # noqa: E501 from ray.data.datasource.databricks_uc_datasource import DatabricksUCDatasource from ray.util.spark.utils import get_spark_session, is_in_databricks_runtime def get_dbutils(): no_dbutils_error = RuntimeError("No dbutils module found.") try: import IPython ip_shell = IPython.get_ipython() if ip_shell is None: raise no_dbutils_error return ip_shell.ns_table["user_global"]["dbutils"] except ImportError: raise no_dbutils_error except KeyError: raise no_dbutils_error token = os.environ.get("DATABRICKS_TOKEN") if not token: raise ValueError( "Please set environment variable 'DATABRICKS_TOKEN' to " "databricks workspace access token." ) host = os.environ.get("DATABRICKS_HOST") if not host: if is_in_databricks_runtime(): ctx = ( get_dbutils().notebook.entry_point.getDbutils().notebook().getContext() ) host = ctx.tags().get("browserHostName").get() else: raise ValueError( "You are not in databricks runtime, please set environment variable " "'DATABRICKS_HOST' to databricks workspace URL" '(e.g. "adb-<workspace-id>.<random-number>.azuredatabricks.net").' ) if not catalog: catalog = get_spark_session().sql("SELECT CURRENT_CATALOG()").collect()[0][0] if not schema: schema = get_spark_session().sql("SELECT CURRENT_DATABASE()").collect()[0][0] if query is not None and table is not None: raise ValueError("Only one of 'query' and 'table' arguments can be set.") if table: query = f"select * from {table}" if query is None: raise ValueError("One of 'query' and 'table' arguments should be set.") datasource = DatabricksUCDatasource( host=host, token=token, warehouse_id=warehouse_id, catalog=catalog, schema=schema, query=query, ) return read_datasource( datasource=datasource, parallelism=parallelism, ray_remote_args=ray_remote_args, concurrency=concurrency, override_num_blocks=override_num_blocks, )
[docs]@PublicAPI def from_dask(df: "dask.dataframe.DataFrame") -> MaterializedDataset: """Create a :class:`~ray.data.Dataset` from a `Dask DataFrame <https://docs.dask.org/en/stable/generated/dask.dataframe.DataFrame.html#dask.dataframe.DataFrame>`_. Args: df: A `Dask DataFrame`_. Returns: A :class:`~ray.data.MaterializedDataset` holding rows read from the DataFrame. """ # noqa: E501 import dask from ray.util.dask import ray_dask_get partitions = df.to_delayed() persisted_partitions = dask.persist(*partitions, scheduler=ray_dask_get) import pandas def to_ref(df): if isinstance(df, pandas.DataFrame): return ray.put(df) elif isinstance(df, ray.ObjectRef): return df else: raise ValueError( "Expected a Ray object ref or a Pandas DataFrame, " f"got {type(df)}" ) ds = from_pandas_refs( [to_ref(next(iter(part.dask.values()))) for part in persisted_partitions], ) return ds
[docs]@PublicAPI def from_mars(df: "mars.dataframe.DataFrame") -> MaterializedDataset: """Create a :class:`~ray.data.Dataset` from a `Mars DataFrame <https://mars-project.readthedocs.io/en/latest/reference/dataframe/index.html>`_. Args: df: A `Mars DataFrame`_, which must be executed by Mars-on-Ray. Returns: A :class:`~ray.data.MaterializedDataset` holding rows read from the DataFrame. """ # noqa: E501 import mars.dataframe as md ds: Dataset = md.to_ray_dataset(df) return ds
[docs]@PublicAPI def from_modin(df: "modin.pandas.dataframe.DataFrame") -> MaterializedDataset: """Create a :class:`~ray.data.Dataset` from a `Modin DataFrame <https://modin.readthedocs.io/en/stable/flow/modin/pandas/dataframe.html>`_. Args: df: A `Modin DataFrame`_, which must be using the Ray backend. Returns: A :class:`~ray.data.MaterializedDataset` rows read from the DataFrame. """ # noqa: E501 from modin.distributed.dataframe.pandas.partitions import unwrap_partitions parts = unwrap_partitions(df, axis=0) ds = from_pandas_refs(parts) return ds
[docs]@PublicAPI def from_pandas( dfs: Union["pandas.DataFrame", List["pandas.DataFrame"]] ) -> MaterializedDataset: """Create a :class:`~ray.data.Dataset` from a list of pandas dataframes. Examples: >>> import pandas as pd >>> import ray >>> df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) >>> ray.data.from_pandas(df) MaterializedDataset(num_blocks=1, num_rows=3, schema={a: int64, b: int64}) Create a Ray Dataset from a list of Pandas DataFrames. >>> ray.data.from_pandas([df, df]) MaterializedDataset(num_blocks=2, num_rows=6, schema={a: int64, b: int64}) Args: dfs: A pandas dataframe or a list of pandas dataframes. Returns: :class:`~ray.data.Dataset` holding data read from the dataframes. """ import pandas as pd if isinstance(dfs, pd.DataFrame): dfs = [dfs] from ray.air.util.data_batch_conversion import ( _cast_ndarray_columns_to_tensor_extension, ) context = DataContext.get_current() if context.enable_tensor_extension_casting: dfs = [_cast_ndarray_columns_to_tensor_extension(df.copy()) for df in dfs] return from_pandas_refs([ray.put(df) for df in dfs])
[docs]@DeveloperAPI def from_pandas_refs( dfs: Union[ObjectRef["pandas.DataFrame"], List[ObjectRef["pandas.DataFrame"]]], ) -> MaterializedDataset: """Create a :class:`~ray.data.Dataset` from a list of Ray object references to pandas dataframes. Examples: >>> import pandas as pd >>> import ray >>> df_ref = ray.put(pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})) >>> ray.data.from_pandas_refs(df_ref) MaterializedDataset(num_blocks=1, num_rows=3, schema={a: int64, b: int64}) Create a Ray Dataset from a list of Pandas Dataframes references. >>> ray.data.from_pandas_refs([df_ref, df_ref]) MaterializedDataset(num_blocks=2, num_rows=6, schema={a: int64, b: int64}) Args: dfs: A Ray object reference to a pandas dataframe, or a list of Ray object references to pandas dataframes. Returns: :class:`~ray.data.Dataset` holding data read from the dataframes. """ if isinstance(dfs, ray.ObjectRef): dfs = [dfs] elif isinstance(dfs, list): for df in dfs: if not isinstance(df, ray.ObjectRef): raise ValueError( "Expected list of Ray object refs, " f"got list containing {type(df)}" ) else: raise ValueError( "Expected Ray object ref or list of Ray object refs, " f"got {type(df)}" ) context = DataContext.get_current() if context.enable_pandas_block: get_metadata = cached_remote_fn(get_table_block_metadata) metadata = ray.get([get_metadata.remote(df) for df in dfs]) logical_plan = LogicalPlan(FromPandas(dfs, metadata)) return MaterializedDataset( ExecutionPlan( BlockList(dfs, metadata, owned_by_consumer=False), DatasetStats(metadata={"FromPandas": metadata}, parent=None), run_by_consumer=False, ), logical_plan, ) df_to_block = cached_remote_fn(pandas_df_to_arrow_block, num_returns=2) res = [df_to_block.remote(df) for df in dfs] blocks, metadata = map(list, zip(*res)) metadata = ray.get(metadata) logical_plan = LogicalPlan(FromPandas(blocks, metadata)) return MaterializedDataset( ExecutionPlan( BlockList(blocks, metadata, owned_by_consumer=False), DatasetStats(metadata={"FromPandas": metadata}, parent=None), run_by_consumer=False, ), logical_plan, )
[docs]@PublicAPI def from_numpy(ndarrays: Union[np.ndarray, List[np.ndarray]]) -> MaterializedDataset: """Creates a :class:`~ray.data.Dataset` from a list of NumPy ndarrays. Examples: >>> import numpy as np >>> import ray >>> arr = np.array([1]) >>> ray.data.from_numpy(arr) MaterializedDataset(num_blocks=1, num_rows=1, schema={data: int64}) Create a Ray Dataset from a list of NumPy arrays. >>> ray.data.from_numpy([arr, arr]) MaterializedDataset(num_blocks=2, num_rows=2, schema={data: int64}) Args: ndarrays: A NumPy ndarray or a list of NumPy ndarrays. Returns: :class:`~ray.data.Dataset` holding data from the given ndarrays. """ if isinstance(ndarrays, np.ndarray): ndarrays = [ndarrays] return from_numpy_refs([ray.put(ndarray) for ndarray in ndarrays])
[docs]@DeveloperAPI def from_numpy_refs( ndarrays: Union[ObjectRef[np.ndarray], List[ObjectRef[np.ndarray]]], ) -> MaterializedDataset: """Creates a :class:`~ray.data.Dataset` from a list of Ray object references to NumPy ndarrays. Examples: >>> import numpy as np >>> import ray >>> arr_ref = ray.put(np.array([1])) >>> ray.data.from_numpy_refs(arr_ref) MaterializedDataset(num_blocks=1, num_rows=1, schema={data: int64}) Create a Ray Dataset from a list of NumPy array references. >>> ray.data.from_numpy_refs([arr_ref, arr_ref]) MaterializedDataset(num_blocks=2, num_rows=2, schema={data: int64}) Args: ndarrays: A Ray object reference to a NumPy ndarray or a list of Ray object references to NumPy ndarrays. Returns: :class:`~ray.data.Dataset` holding data from the given ndarrays. """ if isinstance(ndarrays, ray.ObjectRef): ndarrays = [ndarrays] elif isinstance(ndarrays, list): for ndarray in ndarrays: if not isinstance(ndarray, ray.ObjectRef): raise ValueError( "Expected list of Ray object refs, " f"got list containing {type(ndarray)}" ) else: raise ValueError( f"Expected Ray object ref or list of Ray object refs, got {type(ndarray)}" ) ctx = DataContext.get_current() ndarray_to_block_remote = cached_remote_fn(ndarray_to_block, num_returns=2) res = [ndarray_to_block_remote.remote(ndarray, ctx) for ndarray in ndarrays] blocks, metadata = map(list, zip(*res)) metadata = ray.get(metadata) logical_plan = LogicalPlan(FromNumpy(blocks, metadata)) return MaterializedDataset( ExecutionPlan( BlockList(blocks, metadata, owned_by_consumer=False), DatasetStats(metadata={"FromNumpy": metadata}, parent=None), run_by_consumer=False, ), logical_plan, )
[docs]@PublicAPI def from_arrow( tables: Union["pyarrow.Table", bytes, List[Union["pyarrow.Table", bytes]]], ) -> MaterializedDataset: """Create a :class:`~ray.data.Dataset` from a list of PyArrow tables. Examples: >>> import pyarrow as pa >>> import ray >>> table = pa.table({"x": [1]}) >>> ray.data.from_arrow(table) MaterializedDataset(num_blocks=1, num_rows=1, schema={x: int64}) Create a Ray Dataset from a list of PyArrow tables. >>> ray.data.from_arrow([table, table]) MaterializedDataset(num_blocks=2, num_rows=2, schema={x: int64}) Args: tables: A PyArrow table, or a list of PyArrow tables, or its streaming format in bytes. Returns: :class:`~ray.data.Dataset` holding data from the PyArrow tables. """ import pyarrow as pa if isinstance(tables, (pa.Table, bytes)): tables = [tables] return from_arrow_refs([ray.put(t) for t in tables])
[docs]@DeveloperAPI def from_arrow_refs( tables: Union[ ObjectRef[Union["pyarrow.Table", bytes]], List[ObjectRef[Union["pyarrow.Table", bytes]]], ], ) -> MaterializedDataset: """Create a :class:`~ray.data.Dataset` from a list of Ray object references to PyArrow tables. Examples: >>> import pyarrow as pa >>> import ray >>> table_ref = ray.put(pa.table({"x": [1]})) >>> ray.data.from_arrow_refs(table_ref) MaterializedDataset(num_blocks=1, num_rows=1, schema={x: int64}) Create a Ray Dataset from a list of PyArrow table references >>> ray.data.from_arrow_refs([table_ref, table_ref]) MaterializedDataset(num_blocks=2, num_rows=2, schema={x: int64}) Args: tables: A Ray object reference to Arrow table, or list of Ray object references to Arrow tables, or its streaming format in bytes. Returns: :class:`~ray.data.Dataset` holding data read from the tables. """ if isinstance(tables, ray.ObjectRef): tables = [tables] get_metadata = cached_remote_fn(get_table_block_metadata) metadata = ray.get([get_metadata.remote(t) for t in tables]) logical_plan = LogicalPlan(FromArrow(tables, metadata)) return MaterializedDataset( ExecutionPlan( BlockList(tables, metadata, owned_by_consumer=False), DatasetStats(metadata={"FromArrow": metadata}, parent=None), run_by_consumer=False, ), logical_plan, )
[docs]@PublicAPI def from_spark( df: "pyspark.sql.DataFrame", *, parallelism: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> MaterializedDataset: """Create a :class:`~ray.data.Dataset` from a `Spark DataFrame <https://spark.apache.org/docs/3.1.1/api/python/reference/api/pyspark.sql.DataFrame.html>`_. Args: df: A `Spark DataFrame`_, which must be created by RayDP (Spark-on-Ray). parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: A :class:`~ray.data.MaterializedDataset` holding rows read from the DataFrame. """ # noqa: E501 import raydp parallelism = _get_num_output_blocks(parallelism, override_num_blocks) return raydp.spark.spark_dataframe_to_ray_dataset(df, parallelism)
[docs]@PublicAPI def from_huggingface( dataset: Union["datasets.Dataset", "datasets.IterableDataset"], parallelism: int = -1, concurrency: Optional[int] = None, override_num_blocks: Optional[int] = None, ) -> Union[MaterializedDataset, Dataset]: """Create a :class:`~ray.data.MaterializedDataset` from a `Hugging Face Datasets Dataset <https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset/>`_ or a :class:`~ray.data.Dataset` from a `Hugging Face Datasets IterableDataset <https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.IterableDataset/>`_. For an `IterableDataset`, we use a streaming implementation to read data. If the dataset is a public Hugging Face Dataset that is hosted on the Hugging Face Hub and no transformations have been applied, then the `hosted parquet files <https://huggingface.co/docs/datasets-server/parquet#list-parquet-files>`_ will be passed to :meth:`~ray.data.read_parquet` to perform a distributed read. All other cases will be done with a single node read. Example: .. The following `testoutput` is mocked to avoid illustrating download logs like "Downloading and preparing dataset 162.17 MiB". .. testcode:: import ray import datasets hf_dataset = datasets.load_dataset("tweet_eval", "emotion") ray_ds = ray.data.from_huggingface(hf_dataset["train"]) print(ray_ds) hf_dataset_stream = datasets.load_dataset("tweet_eval", "emotion", streaming=True) ray_ds_stream = ray.data.from_huggingface(hf_dataset_stream["train"]) print(ray_ds_stream) .. testoutput:: :options: +MOCK MaterializedDataset( num_blocks=..., num_rows=3257, schema={text: string, label: int64} ) Dataset( num_rows=3257, schema={text: string, label: int64} ) Args: dataset: A `Hugging Face Datasets Dataset`_ or `Hugging Face Datasets IterableDataset`_. `DatasetDict <https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.DatasetDict/>`_ and `IterableDatasetDict <https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.IterableDatasetDict/>`_ are not supported. parallelism: This argument is deprecated. Use ``override_num_blocks`` argument. 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 or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources. override_num_blocks: Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn't manually set this value in most cases. Returns: A :class:`~ray.data.Dataset` holding rows from the `Hugging Face Datasets Dataset`_. """ # noqa: E501 import datasets from aiohttp.client_exceptions import ClientResponseError from ray.data.datasource.huggingface_datasource import HuggingFaceDatasource if isinstance(dataset, (datasets.IterableDataset, datasets.Dataset)): try: # Attempt to read data via Hugging Face Hub parquet files. If the # returned list of files is empty, attempt read via other methods. file_urls = HuggingFaceDatasource.list_parquet_urls_from_dataset(dataset) if len(file_urls) > 0: # If file urls are returned, the parquet files are available via API # TODO: Add support for reading from http filesystem in # FileBasedDatasource. GH Issue: # https://github.com/ray-project/ray/issues/42706 import fsspec.implementations.http http = fsspec.implementations.http.HTTPFileSystem() return read_parquet( file_urls, parallelism=parallelism, filesystem=http, concurrency=concurrency, override_num_blocks=override_num_blocks, ray_remote_args={ "retry_exceptions": [FileNotFoundError, ClientResponseError] }, ) except (FileNotFoundError, ClientResponseError): logger.warning( "Distrubuted read via Hugging Face Hub parquet files failed, " "falling back on single node read." ) if isinstance(dataset, datasets.IterableDataset): # For an IterableDataset, we can use a streaming implementation to read data. return read_datasource(HuggingFaceDatasource(dataset=dataset)) if isinstance(dataset, datasets.Dataset): # To get the resulting Arrow table from a Hugging Face Dataset after # applying transformations (e.g., train_test_split(), shard(), select()), # we create a copy of the Arrow table, which applies the indices # mapping from the transformations. hf_ds_arrow = dataset.with_format("arrow") ray_ds = from_arrow(hf_ds_arrow[:]) return ray_ds elif isinstance(dataset, (datasets.DatasetDict, datasets.IterableDatasetDict)): available_keys = list(dataset.keys()) raise DeprecationWarning( "You provided a Hugging Face DatasetDict or IterableDatasetDict, " "which contains multiple datasets, but `from_huggingface` now " "only accepts a single Hugging Face Dataset. To convert just " "a single Hugging Face Dataset to a Ray Dataset, specify a split. " "For example, `ray.data.from_huggingface(my_dataset_dictionary" f"['{available_keys[0]}'])`. " f"Available splits are {available_keys}." ) else: raise TypeError( f"`dataset` must be a `datasets.Dataset`, but got {type(dataset)}" )
[docs]@PublicAPI def from_tf( dataset: "tf.data.Dataset", ) -> MaterializedDataset: """Create a :class:`~ray.data.Dataset` from a `TensorFlow Dataset <https://www.tensorflow.org/api_docs/python/tf/data/Dataset/>`_. This function is inefficient. Use it to read small datasets or prototype. .. warning:: If your dataset is large, this function may execute slowly or raise an out-of-memory error. To avoid issues, read the underyling data with a function like :meth:`~ray.data.read_images`. .. note:: This function isn't parallelized. It loads the entire dataset into the local node's memory before moving the data to the distributed object store. Examples: >>> import ray >>> import tensorflow_datasets as tfds >>> dataset, _ = tfds.load('cifar10', split=["train", "test"]) # doctest: +SKIP >>> ds = ray.data.from_tf(dataset) # doctest: +SKIP >>> ds # doctest: +SKIP MaterializedDataset( num_blocks=..., num_rows=50000, schema={ id: binary, image: numpy.ndarray(shape=(32, 32, 3), dtype=uint8), label: int64 } ) >>> ds.take(1) # doctest: +SKIP [{'id': b'train_16399', 'image': array([[[143, 96, 70], [141, 96, 72], [135, 93, 72], ..., [ 96, 37, 19], [105, 42, 18], [104, 38, 20]], ..., [[195, 161, 126], [187, 153, 123], [186, 151, 128], ..., [212, 177, 147], [219, 185, 155], [221, 187, 157]]], dtype=uint8), 'label': 7}] Args: dataset: A `TensorFlow Dataset`_. Returns: A :class:`MaterializedDataset` that contains the samples stored in the `TensorFlow Dataset`_. """ # noqa: E501 # FIXME: `as_numpy_iterator` errors if `dataset` contains ragged tensors. return from_items(list(dataset.as_numpy_iterator()))
[docs]@PublicAPI def from_torch( dataset: "torch.utils.data.Dataset", ) -> Dataset: """Create a :class:`~ray.data.Dataset` from a `Torch Dataset <https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset/>`_. .. note:: The input dataset can either be map-style or iterable-style, and can have arbitrarily large amount of data. The data will be sequentially streamed with one single read task. Examples: >>> import ray >>> from torchvision import datasets >>> dataset = datasets.MNIST("data", download=True) # doctest: +SKIP >>> ds = ray.data.from_torch(dataset) # doctest: +SKIP >>> ds # doctest: +SKIP MaterializedDataset(num_blocks=..., num_rows=60000, schema={item: object}) >>> ds.take(1) # doctest: +SKIP {"item": (<PIL.Image.Image image mode=L size=28x28 at 0x...>, 5)} Args: dataset: A `Torch Dataset`_. Returns: A :class:`~ray.data.Dataset` containing the Torch dataset samples. """ # noqa: E501 # Files may not be accessible from all nodes, run the read task on current node. ray_remote_args = { "scheduling_strategy": NodeAffinitySchedulingStrategy( ray.get_runtime_context().get_node_id(), soft=False, ) } return read_datasource( TorchDatasource(dataset=dataset), ray_remote_args=ray_remote_args, # Only non-parallel, streaming read is currently supported override_num_blocks=1, )
def _get_datasource_or_legacy_reader( ds: Datasource, ctx: DataContext, kwargs: dict, ) -> Union[Datasource, Reader]: """Generates reader. Args: ds: Datasource to read from. ctx: Dataset config to use. kwargs: Additional kwargs to pass to the legacy reader if `Datasource.create_reader` is implemented. Returns: The datasource or a generated legacy reader. """ kwargs = _unwrap_arrow_serialization_workaround(kwargs) DataContext._set_current(ctx) if ds.should_create_reader: warnings.warn( "`create_reader` has been deprecated in Ray 2.9. Instead of creating a " "`Reader`, implement `Datasource.get_read_tasks` and " "`Datasource.estimate_inmemory_data_size`.", DeprecationWarning, ) datasource_or_legacy_reader = ds.create_reader(**kwargs) else: datasource_or_legacy_reader = ds return datasource_or_legacy_reader def _resolve_parquet_args( tensor_column_schema: Optional[Dict[str, Tuple[np.dtype, Tuple[int, ...]]]] = None, **arrow_parquet_args, ) -> Dict[str, Any]: if tensor_column_schema is not None: existing_block_udf = arrow_parquet_args.pop("_block_udf", None) def _block_udf(block: "pyarrow.Table") -> "pyarrow.Table": from ray.data.extensions import ArrowTensorArray for tensor_col_name, (dtype, shape) in tensor_column_schema.items(): # NOTE(Clark): We use NumPy to consolidate these potentially # non-contiguous buffers, and to do buffer bookkeeping in # general. np_col = _create_possibly_ragged_ndarray( [ np.ndarray(shape, buffer=buf.as_buffer(), dtype=dtype) for buf in block.column(tensor_col_name) ] ) block = block.set_column( block._ensure_integer_index(tensor_col_name), tensor_col_name, ArrowTensorArray.from_numpy(np_col, tensor_col_name), ) if existing_block_udf is not None: # Apply UDF after casting the tensor columns. block = existing_block_udf(block) return block arrow_parquet_args["_block_udf"] = _block_udf return arrow_parquet_args def _get_num_output_blocks( parallelism: int = -1, override_num_blocks: Optional[int] = None, ) -> int: if parallelism != -1: logger.warning( "The argument ``parallelism`` is deprecated in Ray 2.10. Please specify " "argument ``override_num_blocks`` instead." ) elif override_num_blocks is not None: parallelism = override_num_blocks return parallelism