Transforming Data#

Transformations let you process and modify your dataset. You can compose transformations to express a chain of computations.

Note

Transformations are lazy by default. They aren’t executed until you trigger consumption of the data by iterating over the Dataset, saving the Dataset, or inspecting properties of the Dataset.

This guide shows you how to scale transformations (or user-defined functions (UDFs)) on your Ray Data dataset.

Transforming rows#

Tip

If your transformation is vectorized, call map_batches() for better performance. To learn more, see Transforming batches.

Transforming rows with map#

If your transformation returns exactly one row for each input row, call map(). This transformation is automatically parallelized across your Ray cluster.

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)
)

The user defined function passed to map() should be of type Callable[[Dict[str, Any]], Dict[str, Any]]. In other words, your function should input and output a dictionary with keys of strings and values of any type. For example:

from typing import Any, Dict

def fn(row: Dict[str, Any]) -> Dict[str, Any]:
    # access row data
    value = row["col1"]

    # add data to row
    row["col2"] = ...

    # return row
    return row

Transforming rows with flat map#

If your transformation returns multiple rows for each input row, call flat_map(). This transformation is automatically parallelized across your Ray cluster.

from typing import Any, Dict, List
import ray

def duplicate_row(row: Dict[str, Any]) -> List[Dict[str, Any]]:
    return [row] * 2

print(
    ray.data.range(3)
    .flat_map(duplicate_row)
    .take_all()
)

[{'id': 0}, {'id': 0}, {'id': 1}, {'id': 1}, {'id': 2}, {'id': 2}]

The user defined function passed to flat_map() should be of type Callable[[Dict[str, Any]], List[Dict[str, Any]]]. In other words your function should input a dictionary with keys of strings and values of any type and output a list of dictionaries that have the same type as the input, for example:

from typing import Any, Dict, List

def fn(row: Dict[str, Any]) -> List[Dict[str, Any]]:
    # access row data
    value = row["col1"]

    # add data to row
    row["col2"] = ...

    # construct output list
    output = [row, row]

    # return list of output rows
    return output

Transforming batches#

If your transformation can be vectorized using NumPy, PyArrow or Pandas operations, transforming batches is considerably more performant than transforming individual rows.

This transformation is automatically parallelized across your Ray cluster.

from typing import Dict
import numpy as np
import ray

def increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
    batch["image"] = np.clip(batch["image"] + 4, 0, 255)
    return batch

ds = (
    ray.data.read_images("s3://anonymous@ray-example-data/image-datasets/simple")
    .map_batches(increase_brightness)
)

Configuring batch format#

Ray Data represents batches as dicts of NumPy ndarrays, pandas DataFrames or Arrow Tables. By default, Ray Data represents batches as dicts of NumPy ndarrays. To configure the batch type, specify batch_format in map_batches(). You can return either format from your function, but batch_format should match the input of your function.

When applying transformations to batches of rows, Ray Data could represent these batches as either NumPy’s ndarrays, Pandas DataFrame or PyArrow Table.

When using

  • batch_format=numpy, the input to the function is a dictionary where keys correspond to column names and values to column values represented as ndarrays.

  • batch_format=pyarrow, the input to the function is a Pyarrow Table.

  • batch_format=pandas, the input to the function is a Pandas DataFrame.

from typing import Dict
import numpy as np
import ray

def increase_brightness(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
    batch["image"] = np.clip(batch["image"] + 4, 0, 255)
    return batch

ds = (
    ray.data.read_images("s3://anonymous@ray-example-data/image-datasets/simple")
    .map_batches(increase_brightness, batch_format="numpy")
)

import pandas as pd
import ray

def drop_nas(batch: pd.DataFrame) -> pd.DataFrame:
    return batch.dropna()

ds = (
    ray.data.read_csv("s3://anonymous@air-example-data/iris.csv")
    .map_batches(drop_nas, batch_format="pandas")
)

import pyarrow as pa
import pyarrow.compute as pc
import ray

def drop_nas(batch: pa.Table) -> pa.Table:
    return pc.drop_null(batch)

ds = (
    ray.data.read_csv("s3://anonymous@air-example-data/iris.csv")
    .map_batches(drop_nas, batch_format="pyarrow")
)

The user defined function can also be a Python generator that yields batches, so the function can also be of type Callable[DataBatch, Iterator[[DataBatch]], where DataBatch = Union[pd.DataFrame, Dict[str, np.ndarray], pyarrow.Table]. In this case, your function would look like:

from typing import Dict, Iterator
import numpy as np

def fn(batch: Dict[str, np.ndarray]) -> Iterator[Dict[str, np.ndarray]]:
    # yield the same batch multiple times
    for _ in range(10):
        yield batch

Choosing the right batch format#

When choosing appropriate batch format for your map_batches primary consideration is a trade-off of convenience vs performance:

  1. Batches are a sliding window into the underlying block: the UDF is invoked with a subset of rows of the underlying block that make up the current batch of specified batch_size. Specifying batch_size=None makes batch include all rows of the block in a single batch.

  2. Depending on the batch format, such view can either be a zero-copy (when batch format matches the block type of either pandas or pyarrow) or copying one (when the batch format differs from the block type).

For example, if the underlying block type is Arrow, specifying batch_format="numpy" or batch_format="pandas" might invoke a copy on the underlying data when converting it from the underlying block type.

Ray Data also strives to minimize the amount of data conversions: for example, if your map_batches operation returns Pandas batches, then these batches are combined into blocks without conversion and propagated further as Pandas blocks. Most Ray Data datasources produce Arrow blocks, so using batch format pyarrow can avoid unnecessary data conversions.

If you’d like to use a more ergonomic API for transformations but avoid performance overheads, you can consider using polars inside your map_batches operation with batch_format="pyarrow" as follows:

import pyarrow as pa

def udf(table: pa.Table):
    import polars as pl
    df = polars.from_pyarrow(table)
    df.summary()
    return df.to_arrow()

ds.map_batches(udf, batch_format="pyarrow")

Configuring batch size#

Increasing batch_size improves the performance of vectorized transformations as well as performance of model inference. However, if your batch size is too large, your program might run into out-of-memory (OOM) errors.

If you encounter an OOM errors, try decreasing your batch_size.

Stateful/Class-based Transforms#

If your transform requires expensive setup such as downloading model weights, use a callable Python class instead of a function to make the transform stateful. When a Python class is used, the __init__ method is called to perform setup exactly once on each worker. In contrast, functions are stateless, so any setup must be performed for each data item.

Internally, Ray Data uses tasks to execute functions, and uses actors to execute classes. To learn more about tasks and actors, read the Ray Core Key Concepts.

To transform data with a Python class, complete these steps:

  1. Implement a class. Perform setup in __init__ and transform data in __call__.

  2. Call map_batches(), map(), or flat_map(). Pass a ray.data.ActorPoolStrategy(...) object to the compute argument to control how many workers Ray uses. Each worker transforms a partition of data in parallel.

from typing import Dict
import numpy as np
import torch
import ray

class TorchPredictor:

    def __init__(self):
        self.model = torch.nn.Identity()
        self.model.eval()

    def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
        inputs = torch.as_tensor(batch["data"], dtype=torch.float32)
        with torch.inference_mode():
            batch["output"] = self.model(inputs).detach().numpy()
        return batch

ds = (
    ray.data.from_numpy(np.ones((32, 100)))
    .map_batches(
        TorchPredictor,
        compute=ray.data.ActorPoolStrategy(size=2),
    )
)

from typing import Dict
import numpy as np
import torch
import ray

class TorchPredictor:
    def __init__(self):
        self.model = torch.nn.Identity().cuda()
        self.model.eval()

    def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
        inputs = torch.as_tensor(batch["data"], dtype=torch.float32).cuda()
        with torch.inference_mode():
            batch["output"] = self.model(inputs).detach().cpu().numpy()
        return batch

ds = (
    ray.data.from_numpy(np.ones((32, 100)))
    .map_batches(
        TorchPredictor,
        # Two workers with one GPU each
        compute=ray.data.ActorPoolStrategy(size=2),
        # Batch size is required if you're using GPUs.
        batch_size=4,
        num_gpus=1
    )
)

Specifying CPUs, GPUs, and Memory#

You can optionally specify logical resources per transformation by using one of the following parameters: num_cpus, num_gpus, memory, resources.

  • num_cpus: The number of CPUs to use for the transformation.

  • num_gpus: The number of GPUs to use for the transformation. Ray automatically configures the proper CUDA_VISIBLE_DEVICES environment variable so that GPUs are isolated from other tasks/actors.

  • memory: The amount of memory to use for the transformation. This is useful for avoiding out-of-memory errors by telling Ray how much memory your function uses, and preventing Ray from scheduling too many tasks on a node.

  • resources: A dictionary of resources to use for the transformation. This is useful for specifying custom resources.

Note that these are logical resources and don’t impose limits on actual physical resource usage.

Also, both num_cpus and num_gpus support fractional values less than 1. For example, specifying num_cpus=0.5 on a cluster with 4 CPUs allows 8 concurrent tasks/actors to run. You can read more about resources in Ray here: Specifying Task or Actor Resource Requirements.

def uses_lots_of_memory(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
    ...

# Tell Ray that the function uses 1 GiB of memory
ds.map_batches(uses_lots_of_memory, memory=1 * 1024 * 1024)

Specifying Concurrency#

You can specify the concurrency of the transformation by using the compute parameter.

For functions, use compute=ray.data.TaskPoolStrategy(size=n) to cap the number of concurrent tasks. By default, Ray Data automatically determines the number of concurrent tasks. For classes, use compute=ray.data.ActorPoolStrategy(size=n) to use a fixed size actor pool of n workers. If compute isn’t specified, an autoscaling actor pool is used by default.

import ray

ds = ray.data.range(10).map_batches(lambda batch: {"id": batch["id"] * 2}, compute=ray.data.TaskPoolStrategy(size=2))
ds.take_all()

[{'id': 0}, {'id': 2}, {'id': 4}, {'id': 6}, {'id': 8}, {'id': 10}, {'id': 12}, {'id': 14}, {'id': 16}, {'id': 18}]

Ordering of rows#

When transforming data, the order of blocks isn’t preserved by default.

If the order of blocks needs to be preserved/deterministic, you can use sort() method, or set ray.data.ExecutionOptions.preserve_order to True. Note that setting this flag may negatively impact performance on larger cluster setups where stragglers are more likely.

import ray

ctx = ray.data.DataContext().get_current()

# By default, this is set to False.
ctx.execution_options.preserve_order = True

Group-by and transforming groups#

To transform groups, call groupby() to group rows based on provided key column values. Then, call map_groups() to execute a transformation on each group.

from typing import Dict
import numpy as np
import ray

items = [
    {"image": np.zeros((32, 32, 3)), "label": label}
    for _ in range(10) for label in range(100)
]

def normalize_images(group: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
    group["image"] = (group["image"] - group["image"].mean()) / group["image"].std()
    return group

ds = (
    ray.data.from_items(items)
    .groupby("label")
    .map_groups(normalize_images)
)

import pandas as pd
import ray

def normalize_features(group: pd.DataFrame) -> pd.DataFrame:
    target = group.drop("target")
    group = (group - group.min()) / group.std()
    group["target"] = target
    return group

ds = (
    ray.data.read_csv("s3://anonymous@air-example-data/iris.csv")
    .groupby("target")
    .map_groups(normalize_features)
)

Advanced: Distributed UDFs with Placement Groups#

While all transformations are automatically parallelized across your Ray cluster, often times these transformations can be distributed themselves. For example, if you’re using a large model, you may want to distribute the model across multiple nodes. You can do this by using placement groups and ray_remote_args_fn, which can dynamically create placement groups for each model replica.

import ray
from typing import Dict
import numpy as np
import torch

NUM_SHARDS = 2
@ray.remote
class ModelShard:
    def __init__(self):
        self.model = torch.nn.Linear(10, 10)

    def f(self, batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
        return batch

class DistributedModel:
    def __init__(self):
        self.shards = [ModelShard.remote() for _ in range(NUM_SHARDS)]

    def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
        return {"out": np.array(ray.get([shard.f.remote(batch) for shard in self.shards]))}

def ray_remote_args_fn():
    from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
    pg = ray.util.placement_group([{"CPU": 1}] * NUM_SHARDS)
    return {"scheduling_strategy": PlacementGroupSchedulingStrategy(placement_group=pg)}

ds = ray.data.range(10).map_batches(DistributedModel, ray_remote_args_fn=ray_remote_args_fn)
ds.take_all()

Advanced: Asynchronous Transforms#

Ray Data supports asynchronous functions by using the async keyword. This is useful for performing asynchronous operations such as fetching data from a database or making HTTP requests. Note that this only works when using a class-based transform function and currently requires uvloop==0.21.0.

import ray
from typing import Dict
import numpy as np

class AsyncTransform:
    async def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
        return batch

ds = ray.data.range(10).map_batches(AsyncTransform)
ds.take_all()

[{'id': 0},
{'id': 1},
{'id': 2},
{'id': 3},
{'id': 4},
{'id': 5},
{'id': 6},
{'id': 7},
{'id': 8},
{'id': 9}]

Expressions (Alpha)#

Ray Data expressions provide a way to specify column-based operations on datasets. Use col() to reference columns and lit() to create literal values. You can combine these with operators to create complex expressions for filtering, transformations, and computations.

Expressions have to be used with with_column(). The core advantage of expressions is that because they operate on specific columns, Ray Data’s optimizer can optimize the execution plan by reordering the operations.

See Expressions API for more details.

import ray
from ray.data.expressions import col

ds = ray.data.range(10).with_column("id_2", col("id") * 2)
ds.show()

To use a custom function with an expression, you can use udf().

from ray.data.expressions import col, udf
from ray.data.datatype import DataType
import pyarrow as pa
import pyarrow.compute as pc
import ray

# UDF that operates on a batch of values (PyArrow Array)
@udf(return_dtype=DataType.int32())
def add_one(x: pa.Array) -> pa.Array:
    return pc.add(x, 1)  # Vectorized operation on the entire Array

# UDF that combines multiple columns (each as a PyArrow Array)
@udf(return_dtype=DataType.string())
def format_name(first: pa.Array, last: pa.Array) -> pa.Array:
    return pc.binary_join_element_wise(first, last, " ")  # Vectorized string concatenation

# Use in dataset operations
ds = ray.data.from_items([
    {"value": 5, "first": "John", "last": "Doe"},
    {"value": 10, "first": "Jane", "last": "Smith"}
])
ds = ds.with_column("value_plus_one", add_one(col("value")))
ds = ds.with_column("full_name", format_name(col("first"), col("last")))
ds = ds.with_column("doubled_plus_one", add_one(col("value")) * 2)
ds.show()