ray.data.Dataset.map_batches#

Dataset.map_batches(fn: Union[Callable[[Union[List[ray.data.block.T], pyarrow.Table, pandas.DataFrame, bytes, numpy.ndarray, Dict[str, numpy.ndarray]]], Union[List[ray.data.block.T], pyarrow.Table, pandas.DataFrame, bytes, numpy.ndarray, Dict[str, numpy.ndarray]]], _CallableClassProtocol], *, batch_size: Optional[Union[int, typing_extensions.Literal[default]]] = 'default', compute: Optional[Union[str, ray.data._internal.compute.ComputeStrategy]] = None, batch_format: typing_extensions.Literal[default, pandas, pyarrow, numpy] = 'default', prefetch_batches: int = 0, zero_copy_batch: bool = False, fn_args: Optional[Iterable[Any]] = None, fn_kwargs: Optional[Dict[str, Any]] = None, fn_constructor_args: Optional[Iterable[Any]] = None, fn_constructor_kwargs: Optional[Dict[str, Any]] = None, **ray_remote_args) → Dataset[Any][source]#

Apply the given function to batches of data.

This applies the fn in parallel with map tasks, with each task handling a block or a bundle of blocks of the dataset. Each batch is executed serially at Ray level (at lower level, the processing of the batch is usually vectorized).

Batches are represented as dataframes, ndarrays, or lists. The default batch type is determined by your dataset’s schema. To determine the default batch type, call default_batch_format(). Alternatively, set the batch type with batch_format.

To learn more about writing functions for map_batches(), read writing user-defined functions.

Tip

If you’re using Ray AIR for training or batch inference, consider using BatchMapper. It’s more performant and easier to use.

Tip

For some standard operations like imputing, encoding or normalization, one may find directly using Preprocessor to be more convenient.

Tip

If you have a small number of big blocks, it may limit parallelism. You may consider increasing the number of blocks via .repartition() before applying .map_batches().

Tip

If fn does not mutate its input, set zero_copy_batch=True to elide a batch copy, which can improve performance and decrease memory utilization. fn will then receive zero-copy read-only batches. If fn mutates its input, you will need to ensure that the batch provided to fn is writable by setting zero_copy_batch=False (default). This will create an extra, mutable copy of each batch before handing it to fn.

Note

The size of the batches provided to fn may be smaller than the provided batch_size if batch_size doesn’t evenly divide the block(s) sent to a given map task. When batch_size is specified, each map task will be sent a single block if the block is equal to or larger than batch_size, and will be sent a bundle of blocks up to (but not exceeding) batch_size if blocks are smaller than batch_size.

Examples

>>> import pandas as pd
>>> import ray
>>> df = pd.DataFrame({
...     "name": ["Luna", "Rory", "Scout"],
...     "age": [4, 14, 9]
... })
>>> ds = ray.data.from_pandas(df)
>>> ds
Dataset(num_blocks=1, num_rows=3, schema={name: object, age: int64})

Call default_batch_format() to determine the default batch type.

>>> ds.default_batch_format()
<class 'pandas.core.frame.DataFrame'>

Tip

Datasets created from tabular data like Arrow tables and Parquet files yield pd.DataFrame batches.

Once you know the batch type, define a function that transforms batches of data. ds.map_batches applies the function in parallel.

>>> def map_fn(batch: pd.DataFrame) -> pd.DataFrame:
...     batch["age_in_dog_years"] = 7 * batch["age"]
...     return batch
>>> ds = ds.map_batches(map_fn)
>>> ds
MapBatches(map_fn)
+- Dataset(num_blocks=1, num_rows=3, schema={name: object, age: int64})

Your fn can return a different type than the input type. To learn more about supported output types, read user-defined function output types.

>>> from typing import List
>>> def map_fn(batch: pd.DataFrame) -> List[int]:
...     return list(batch["age_in_dog_years"])
>>> ds = ds.map_batches(map_fn)
>>> ds
MapBatches(map_fn)
+- MapBatches(map_fn)
   +- Dataset(num_blocks=1, num_rows=3, schema={name: object, age: int64})

Actors can improve the performance of some workloads. For example, you can use actors to load a model once per worker instead of once per inference.

To transform batches with actors, pass a callable type to fn and specify an ActorPoolStrategy>.

In the example below, CachedModel is called on an autoscaling pool of two to eight actors, each allocated one GPU by Ray.

>>> from ray.data import ActorPoolStrategy
>>> init_large_model = ... 
>>> class CachedModel:
...    def __init__(self):
...        self.model = init_large_model()
...    def __call__(self, item):
...        return self.model(item)
>>> ds.map_batches( 
...     CachedModel, 
...     batch_size=256, 
...     compute=ActorPoolStrategy(2, 8), 
...     num_gpus=1,
... ) 

Parameters

fn – The function to apply to each record batch, or a class type that can be instantiated to create such a callable. Callable classes are only supported for the actor compute strategy. Note fn must be pickle-able.
batch_size – The desired number of rows in each batch, or None to use entire blocks as batches (blocks may contain different number of rows). The actual size of the batch provided to fn may be smaller than batch_size if batch_size doesn’t evenly divide the block(s) sent to a given map task. Default batch_size is 4096 with “default”.
compute – The compute strategy, either "tasks" (default) to use Ray tasks, or "actors" to use an autoscaling actor pool. If you want to configure the size of the autoscaling actor pool, provide an ActorPoolStrategy instance. If you’re passing callable type to fn, you must pass an ActorPoolStrategy or "actors".
batch_format – Specify "default" to use the default block format (promotes tables to Pandas and tensors to NumPy), "pandas" to select pandas.DataFrame, “pyarrow” to select pyarrow.Table, or "numpy" to select numpy.ndarray for tensor datasets and Dict[str, numpy.ndarray] for tabular datasets. Default is “default”.
prefetch_batches – The number of batches to fetch ahead of the current batch to process. If set to greater than 0, a separate thread will be used to fetch the specified amount of formatted batches from blocks. This improves performance for non-CPU bound UDFs, allowing batch fetching compute and formatting to be overlapped with the UDF. Defaults to 0 (no prefetching enabled.) Increasing the number of batches to prefetch can result in higher throughput, at the expense of requiring more heap memory to buffer the batches.
zero_copy_batch – Whether fn should be provided zero-copy, read-only batches. If this is True and no copy is required for the batch_format conversion, the batch will be a zero-copy, read-only view on data in Ray’s object store, which can decrease memory utilization and improve performance. If this is False, the batch will be writable, which will require an extra copy to guarantee. If fn mutates its input, this will need to be False in order to avoid “assignment destination is read-only” or “buffer source array is read-only” errors. Default is False. See batch format docs for details on which format conversion always require a copy.
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.
ray_remote_args – Additional resource requirements to request from ray (e.g., num_gpus=1 to request GPUs for the map tasks).

Ray 2.3.0

ray.data.Dataset.map_batches

ray.data.Dataset.map_batches#