ray.data.Dataset.flat_map#

Dataset.flat_map(fn: Union[Callable[[Dict[str, Any]], List[Dict[str, Any]]], Callable[[Dict[str, Any]], Iterator[List[Dict[str, Any]]]], _CallableClassProtocol], *, compute: Optional[ray.data._internal.compute.ComputeStrategy] = None, num_cpus: Optional[float] = None, num_gpus: Optional[float] = None, **ray_remote_args) → Dataset[source]#

Apply the given function to each record and then flatten results.

Consider using .map_batches() for better performance (the batch size can be altered in map_batches).

Examples

>>> import ray
>>> ds = ray.data.range(1000)
>>> ds.flat_map(lambda x: [{"id": 1}, {"id": 2}, {"id": 4}])
FlatMap
+- Dataset(num_blocks=..., num_rows=1000, schema={id: int64})

Time complexity: O(dataset size / parallelism)

Parameters

  • fn – The function or generator to apply to each record, or a class type that can be instantiated to create such a callable. Callable classes are only supported for the actor compute strategy.

  • compute – The compute strategy, either “tasks” (default) to use Ray tasks, ray.data.ActorPoolStrategy(size=n) to use a fixed-size actor pool, or ray.data.ActorPoolStrategy(min_size=m, max_size=n) for an autoscaling actor pool.

  • 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.

  • ray_remote_args – Additional resource requirements to request from ray for each map worker.

See also

map_batches()

Call this method to transform batches of data. It’s faster and more flexible than map() and flat_map().

map()

Call this method to transform one record at time.

This method isn’t recommended because it’s slow; call map_batches() instead.