ray.data.read_numpy#

ray.data.read_numpy(paths: str | List[str], *, filesystem: pyarrow.fs.FileSystem | None = None, parallelism: int = -1, arrow_open_stream_args: Dict[str, Any] | None = None, meta_provider: BaseFileMetadataProvider | None = None, partition_filter: PathPartitionFilter | None = None, partitioning: Partitioning = None, include_paths: bool = False, ignore_missing_paths: bool = False, shuffle: Literal['files'] | FileShuffleConfig | None = None, file_extensions: List[str] | None = ['npy'], concurrency: int | None = None, override_num_blocks: int | None = None, **numpy_load_args) → Dataset[source]#

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")

Read multiple local files.

>>> ray.data.read_numpy(["/path/to/file1", "/path/to/file2"])

Read multiple directories.

>>> ray.data.read_numpy( 
...     ["s3://bucket/path1", "s3://bucket/path2"])
Parameters:

  • 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.

  • 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 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. if setting to FileShuffleConfig, the random seed can be passed toshuffle the input files, i.e. FileShuffleConfig(seed = 42). 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.