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 :ref:`iterating over the Dataset <iterating-over-data>`, :ref:`saving the Dataset <saving-data>`, or :ref:`inspecting properties of the Dataset <inspecting-data>`.
This guide shows you how to:
To transform rows, call :meth:`~ray.data.Dataset.map` or :meth:`~ray.data.Dataset.flat_map`.
If your transformation returns exactly one row for each input row, call :meth:`~ray.data.Dataset.map`.
.. testcode::
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:https://anonymous@ray-example-data/image-datasets/simple", include_paths=True)
.map(parse_filename)
)
Tip
If your transformation is vectorized, call :meth:`~ray.data.Dataset.map_batches` for better performance. To learn more, see Transforming batches.
If your transformation returns multiple rows for each input row, call :meth:`~ray.data.Dataset.flat_map`.
.. testcode::
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()
)
.. testoutput::
[{'id': 0}, {'id': 0}, {'id': 1}, {'id': 1}, {'id': 2}, {'id': 2}]
If your transformation is vectorized like most NumPy or pandas operations, transforming batches is more performant than transforming rows.
Ray Data transforms batches with either tasks or actors. Actors perform setup exactly once. In contrast, tasks require setup every batch. So, if your transformation involves expensive setup like downloading model weights, use actors. Otherwise, use tasks.
To learn more about tasks and actors, read the :ref:`Ray Core Key Concepts <core-key-concepts>`.
To transform batches with tasks, call :meth:`~ray.data.Dataset.map_batches`. Ray Data uses tasks by default.
.. testcode::
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:https://anonymous@ray-example-data/image-datasets/simple")
.map_batches(increase_brightness)
)
To transform batches with actors, complete these steps:
- Implement a class. Perform setup in
__init__and transform data in__call__. - Create an :class:`~ray.data.ActorPoolStrategy` and configure the number of concurrent workers. Each worker transforms a partition of data.
- Call :meth:`~ray.data.Dataset.map_batches` and pass your
ActorPoolStrategytocompute.
.. tab-set::
.. tab-item:: CPU
.. testcode::
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))
)
.. testcode::
:hide:
ds.materialize()
.. tab-item:: GPU
.. testcode::
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
)
)
.. testcode::
:hide:
ds.materialize()
Ray Data represents batches as dicts of NumPy ndarrays or pandas DataFrames. By default, Ray Data represents batches as dicts of NumPy ndarrays.
To configure the batch type, specify batch_format in
:meth:`~ray.data.Dataset.map_batches`. You can return either format from your function.
.. tab-set::
.. tab-item:: NumPy
.. testcode::
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:https://anonymous@ray-example-data/image-datasets/simple")
.map_batches(increase_brightness, batch_format="numpy")
)
.. tab-item:: pandas
.. testcode::
import pandas as pd
import ray
def drop_nas(batch: pd.DataFrame) -> pd.DataFrame:
return batch.dropna()
ds = (
ray.data.read_csv("s3:https://anonymous@air-example-data/iris.csv")
.map_batches(drop_nas, batch_format="pandas")
)
Increasing batch_size improves the performance of vectorized transformations like
NumPy functions and model inference. However, if your batch size is too large, your
program might run out of memory. If you encounter an out-of-memory error, decrease your
batch_size.
Note
The default batch size depends on your resource type. If you're using CPUs, the default batch size is 4096. If you're using GPUs, you must specify an explicit batch size.
To transform groups, call :meth:`~ray.data.Dataset.groupby` to group rows. Then, call :meth:`~ray.data.grouped_data.GroupedData.map_groups` to transform the groups.
.. tab-set::
.. tab-item:: NumPy
.. testcode::
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)
)
.. tab-item:: pandas
.. testcode::
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:https://anonymous@air-example-data/iris.csv")
.groupby("target")
.map_groups(normalize_features)
)
To randomly shuffle all rows, call :meth:`~ray.data.Dataset.random_shuffle`.
.. testcode::
import ray
ds = (
ray.data.read_images("s3:https://anonymous@ray-example-data/image-datasets/simple")
.random_shuffle()
)
Tip
:meth:`~ray.data.Dataset.random_shuffle` is slow. For better performance, try :ref:`Iterating over batches with shuffling <iterating-over-batches-with-shuffling>`.
A :class:`~ray.data.dataset.Dataset` operates on a sequence of distributed data
:term:`blocks <block>`. If you want to achieve more fine-grained parallelization,
increase the number of blocks by setting a higher parallelism at read time.
To change the number of blocks for an existing Dataset, call :meth:`Dataset.repartition() <ray.data.Dataset.repartition>`.
.. testcode::
import ray
ds = ray.data.range(10000, parallelism=1000)
# Repartition the data into 100 blocks. Since shuffle=False, Ray Data will minimize
# data movement during this operation by merging adjacent blocks.
ds = ds.repartition(100, shuffle=False).materialize()
# Repartition the data into 200 blocks, and force a full data shuffle.
# This operation will be more expensive
ds = ds.repartition(200, shuffle=True).materialize()