ray.data.Dataset.map#

Dataset.map(fn: Callable[[Dict[str, Any]], Dict[str, Any]] | Callable[[Dict[str, Any]], Iterator[Dict[str, Any]]] | _CallableClassProtocol, *, compute: ComputeStrategy | None = None, fn_args: Iterable[Any] | None = None, fn_kwargs: Dict[str, Any] | None = None, fn_constructor_args: Iterable[Any] | None = None, fn_constructor_kwargs: Dict[str, Any] | None = None, num_cpus: float | None = None, num_gpus: float | None = None, memory: float | None = None, concurrency: int | Tuple[int, int] | None = None, ray_remote_args_fn: Callable[[], Dict[str, Any]] | None = None, **ray_remote_args) → Dataset[source]#

Apply the given function to each row of this dataset.

Use this method to transform your data. To learn more, see Transforming rows.

You can use either a function or a callable class to perform the transformation. For functions, Ray Data uses stateless Ray tasks. For classes, Ray Data uses stateful Ray actors. For more information, see Stateful Transforms.

Tip

If your transformation is vectorized like most NumPy or pandas operations, map_batches() might be faster.

Warning

Specifying both num_cpus and num_gpus for map tasks is experimental, and may result in scheduling or stability issues. Please report any issues to the Ray team.

Examples

import os
from typing import Any, Dict
import ray

def parse_filename(row: Dict[str, Any]) -> Dict[str, Any]:
    row["filename"] = os.path.basename(row["path"])
    return row

ds = (
    ray.data.read_images("s3://anonymous@ray-example-data/image-datasets/simple", include_paths=True)
    .map(parse_filename)
)
print(ds.schema())

Column    Type
------    ----
image     numpy.ndarray(shape=(32, 32, 3), dtype=uint8)
path      string
filename  string

Time complexity: O(dataset size / parallelism)

Parameters:

fn – The function to apply to each row, or a class type that can be instantiated to create such a callable.
compute – This argument is deprecated. Use concurrency argument.
fn_args – Positional arguments to pass to fn after the first argument. These arguments are top-level arguments to the underlying Ray task.
fn_kwargs – Keyword arguments to pass to fn. These arguments are top-level arguments to the underlying Ray task.
fn_constructor_args – Positional arguments to pass to fn’s constructor. You can only provide this if fn is a callable class. These arguments are top-level arguments in the underlying Ray actor construction task.
fn_constructor_kwargs – Keyword arguments to pass to fn’s constructor. This can only be provided if fn is a callable class. These arguments are top-level arguments in the underlying Ray actor construction task.
num_cpus – The number of CPUs to reserve for each parallel map worker.
num_gpus – The number of GPUs to reserve for each parallel map worker. For example, specify num_gpus=1 to request 1 GPU for each parallel map worker.
memory – The heap memory in bytes to reserve for each parallel map worker.
concurrency –
The semantics of this argument depend on the type of fn:
- If fn is a function and concurrency isn’t set (default), the actual concurrency is implicitly determined by the available resources and number of input blocks.
- If fn is a function and concurrency is an int n, Ray Data launches at most n concurrent tasks.
- If fn is a class and concurrency is an int n, Ray Data uses an actor pool with exactly n workers.
- If fn is a class and concurrency is a tuple (m, n), Ray Data uses an autoscaling actor pool from m to n workers.
- If fn is a class and concurrency isn’t set (default), this method raises an error.
ray_remote_args_fn – A function that returns a dictionary of remote args passed to each map worker. The purpose of this argument is to generate dynamic arguments for each actor/task, and will be called each time prior to initializing the worker. Args returned from this dict will always override the args in ray_remote_args. Note: this is an advanced, experimental feature.
ray_remote_args – Additional resource requirements to request from Ray for each map worker. See ray.remote() for details.