Version: 0.6.10

Starwhale Dataset SDK

dataset

Get starwhale.Dataset object, by creating new datasets or loading existing datasets.

@classmethod
def dataset(
    cls,
    uri: t.Union[str, Resource],
    create: str = _DatasetCreateMode.auto,
    readonly: bool = False,
) -> Dataset:

Parameters

uri: (str or Resource, required)
- The dataset uri or Resource object.
create: (str, optional)
- The mode of dataset creating. The options are auto, empty and forbid.
  - auto mode: If the dataset already exists, creation is ignored. If it does not exist, the dataset is created automatically.
  - empty mode: If the dataset already exists, an Exception is raised; If it does not exist, an empty dataset is created. This mode ensures the creation of a new, empty dataset.
  - forbid mode: If the dataset already exists, nothing is done.If it does not exist, an Exception is raised. This mode ensures the existence of the dataset.
- The default is auto.
readonly: (bool, optional)
- For an existing dataset, you can specify the readonly=True argument to ensure the dataset is in readonly mode.
- Default is False.

Examples

from starwhale import dataset, Image

# create a new dataset named mnist, and add a row into the dataset
# dataset("mnist") is equal to dataset("mnist", create="auto")
ds = dataset("mnist")
ds.exists()  # return False, "mnist" dataset is not existing.
ds.append({"img": Image(), "label": 1})
ds.commit()
ds.close()

# load a cloud instance dataset in readonly mode
ds = dataset("cloud://remote-instance/project/starwhale/dataset/mnist", readonly=True)
labels = [row.features.label in ds]
ds.close()

# load a read/write dataset with a specified version
ds = dataset("mnist/version/mrrdczdbmzsw")
ds[0].features.label = 1
ds.commit()
ds.close()

# create an empty dataset
ds = dataset("mnist-empty", create="empty")

# ensure the dataset existence
ds = dataset("mnist-existed", create="forbid")

class starwhale.Dataset

starwhale.Dataset implements the abstraction of a Starwhale dataset, and can operate on datasets in Standalone/Server/Cloud instances.

from_huggingface

from_huggingface is a classmethod that can convert a Huggingface dataset into a Starwhale dataset.

def from_huggingface(
    cls,
    name: str,
    repo: str,
    subset: str | None = None,
    split: str | None = None,
    revision: str = "main",
    alignment_size: int | str = D_ALIGNMENT_SIZE,
    volume_size: int | str = D_FILE_VOLUME_SIZE,
    mode: DatasetChangeMode | str = DatasetChangeMode.PATCH,
    cache: bool = True,
    tags: t.List[str] | None = None,
) -> Dataset:

Parameters

name: (str, required)
- dataset name.
repo: (str, required)
- The huggingface datasets repo name, for example: samsum, fka/awesome-chatgpt-prompts。
subset: (str, optional)
- The subset name. If the huggingface dataset has multiple subsets, you must specify the subset name.
split: (str, optional)
- The split name. If the split name is not specified, the all splits dataset will be built.
revision: (str, optional)
- The huggingface datasets revision. The default value is main. If the split name is not specified, the all splits dataset will be built.
alignment_size: (int|str, optional)
- The blob alignment size.
- The default value is 128 Bytes.
volume_size: (int|str, optional)
- The maximum size of a dataset blob file. A new blob file will be generated when the size exceeds this limit.
- The default value is 64MB.
mode: (str|DatasetChangeMode, optional)
- The dataset change mode. The default value is patch. Mode choices are patch and overwrite.
cache: (bool, optional)
- Whether to use huggingface dataset cache(download + local hf dataset).
- The default value is True.
tags: (List[str], optional)
- The user custom tags of the dataset.

Examples

from starwhale import Dataset
myds = Dataset.from_huggingface("mnist", "mnist")
print(myds[0])

from starwhale import Dataset
myds = Dataset.from_huggingface("mmlu", "cais/mmlu", subset="anatomy", split="auxiliary_train", revision="7456cfb")

from_json

from_json is a classmethod that can convert a json text into a Starwhale dataset.

@classmethod
def from_json(
    cls,
    name: str,
    json_text: str,
    field_selector: str = "",
    alignment_size: int | str = D_ALIGNMENT_SIZE,
    volume_size: int | str = D_FILE_VOLUME_SIZE,
    mode: DatasetChangeMode | str = DatasetChangeMode.PATCH,
    tags: t.List[str] | None = None,
) -> Dataset:

Parameters

name: (str, required)
- Dataset name.
json_text: (str, required)
- A json string. The from_json function deserializes this string into Python objects to start building the Starwhale dataset.
field_selector: (str, optional)
- The filed from which you would like to extract dataset array items.
- The default value is "" which indicates that the json object is an array contains all the items.
alignment_size: (int|str, optional)
- The blob alignment size.
- The default value is 128 Bytes.
volume_size: (int|str, optional)
- The maximum size of a dataset blob file. A new blob file will be generated when the size exceeds this limit.
- The default value is 64MB.
mode: (str|DatasetChangeMode, optional)
- The dataset change mode. The default value is patch. Mode choices are patch and overwrite.
tags: (List[str], optional)
- The user custom tags of the dataset.

Examples

from starwhale import Dataset
myds = Dataset.from_json(
    name="translation",
    json_text='[{"en":"hello","zh-cn":"你好"},{"en":"how are you","zh-cn":"最近怎么样"}]'
)
print(myds[0].features.en)

from starwhale import Dataset
myds = Dataset.from_json(
    name="translation",
    json_text='{"content":{"child_content":[{"en":"hello","zh-cn":"你好"},{"en":"how are you","zh-cn":"最近怎么样"}]}}',
    field_selector="content.child_content"
)
print(myds[0].features["zh-cn"])

from_folder

from_folder is a classmethod that can read Image/Video/Audio data from a specified directory and automatically convert them into a Starwhale dataset. This function supports the following features:

It can recursively search the target directory and its subdirectories
Supports extracting three types of files:
- image: Supports png/jpg/jpeg/webp/svg/apng image types. Image files will be converted to Starwhale.Image type.
- video: Supports mp4/webm/avi video types. Video files will be converted to Starwhale.Video type.
- audio: Supports mp3/wav audio types. Audio files will be converted to Starwhale.Audio type.
Each file corresponds to one record in the dataset, with the file stored in the file field.
If auto_label=True, the parent directory name will be used as the label for that record, stored in the label field. Files in the root directory will not be labeled.
If a txt file with the same name as an image/video/audio file exists, its content will be stored as the caption field in the dataset.
If metadata.csv or metadata.jsonl exists in the root directory, their content will be read automatically and associated to records by file path as meta information in the dataset.
- metadata.csv and metadata.jsonl are mutually exclusive. An exception will be thrown if both exist.
- Each record in metadata.csv and metadata.jsonl must contain a file_name field pointing to the file path.
- metadata.csv and metadata.jsonl are optional for dataset building.

@classmethod
def from_folder(
    cls,
    folder: str | Path,
    kind: str | DatasetFolderSourceType,
    name: str | Resource = "",
    auto_label: bool = True,
    alignment_size: int | str = D_ALIGNMENT_SIZE,
    volume_size: int | str = D_FILE_VOLUME_SIZE,
    mode: DatasetChangeMode | str = DatasetChangeMode.PATCH,
    tags: t.List[str] | None = None,
) -> Dataset:

Parameters

folder: (str|Path, required)
- The folder path from which you would like to create this dataset.
kind: (str|DatasetFolderSourceType, required)
- The dataset source type you would like to use, the choices are: image, video and audio.
- Recursively searching for files of the specified kind in folder. Other file types will be ignored.
name: (str|Resource, optional)
- The dataset name you would like to use.
- If not specified, the name is the folder name.
auto_label: (bool, optional)
- Whether to auto label by the sub-folder name.
- The default value is True.
alignment_size: (int|str, optional)
- The blob alignment size.
- The default value is 128 Bytes.
volume_size: (int|str, optional)
- The maximum size of a dataset blob file. A new blob file will be generated when the size exceeds this limit.
- The default value is 64MB.
mode: (str|DatasetChangeMode, optional)
- The dataset change mode. The default value is patch. Mode choices are patch and overwrite.
tags: (List[str], optional)
- The user custom tags of the dataset.

Examples ${folder-example}

Example for the normal function calling

from starwhale import Dataset

# create a my-image-dataset dataset from /path/to/image folder.
ds = Dataset.from_folder(
    folder="/path/to/image",
    kind="image",
    name="my-image-dataset"
)

Example for caption
```
folder/dog/1.png
folder/dog/1.txt
```
1.txt content will be used as the caption of 1.png.

Example for metadata

metadata.csv:

file_name, caption
1.png, dog
2.png, cat

metadata.jsonl:

{"file_name": "1.png", "caption": "dog"}
{"file_name": "2.png", "caption": "cat"}

Example for auto-labeling
The following structure will create a dataset with 2 labels: "cat" and "dog", 4 images in total.
```
folder/dog/1.png
folder/cat/2.png
folder/dog/3.png
folder/cat/4.png
```

iter

__iter__ a method that iter the dataset rows.

from starwhale import dataset

ds = dataset("mnist")

for item in ds:
  print(item.index)
  print(item.features.label)  # label and img are the features of mnist.
  print(item.features.img)

batch_iter

batch_iter is a method that iter the dataset rows in batch.

def batch_iter(
    self, batch_size: int = 1, drop_not_full: bool = False
) -> t.Iterator[t.List[DataRow]]:

Parameters

batch_size: (int, optional)
- batch size. The default value is 1.
drop_not_full: (bool, optional)
- Whether the last batch of data, with a size smaller than batch_size, it will be discarded.
- The default value is False.

Examples

from starwhale import dataset

ds = dataset("mnist")
for batch_rows in ds.batch_iter(batch_size=2):
    assert len(batch_rows) == 2
    print(batch_rows[0].features)

getitem

__getitem__ is a method that allows retrieving certain rows of data from the dataset, with usage similar to Python dict and list types.

from starwhale import dataset

ds = dataset("mock-int-index")

# if the index type is string
ds["str_key"]  # get the DataRow by the "str_key" string key
ds["start":"end"]  # get a slice of the dataset by the range ("start", "end")

ds = dataset("mock-str-index")
# if the index type is int
ds[1]  # get the DataRow by the 1 int key
ds[1:10:2] # get a slice of the dataset by the range (1, 10), step is 2

setitem

__setitem__ is a method that allows updating rows of data in the dataset, with usage similar to Python dicts. __setitem__ supports multi-threaded parallel data insertion.

def __setitem__(
    self, key: t.Union[str, int], value: t.Union[DataRow, t.Tuple, t.Dict]
) -> None:

Parameters

key: (int|str, required)
- key is the index for each row in the dataset. The type is int or str, but a dataset only accepts one type.
value: (DataRow|tuple|dict, required)
- value is the features for each row in the dataset, using a Python dict is generally recommended.

Examples

Normal insertion

Insert two rows into the test dataset, with index test and test2 repectively:

from starwhale import dataset

with dataset("test") as ds:
  ds["test"] = {"txt": "abc", "int": 1}
  ds["test2"] = {"txt": "bcd", "int": 2}
  ds.commit()

Parallel insertion

from starwhale import dataset, Binary
from concurrent.futures import as_completed, ThreadPoolExecutor

ds = dataset("test")

def _do_append(_start: int) -> None:
  for i in range(_start, 100):
    ds.append((i, {"data": Binary(), "label": i}))

pool = ThreadPoolExecutor(max_workers=10)
tasks = [pool.submit(_do_append, i * 10) for i in range(0, 9)]

ds.commit()
ds.close()

delitem

__delitem__ is a method to delete certain rows of data from the dataset.

def __delitem__(self, key: _ItemType) -> None:

from starwhale import dataset

ds = dataset("existed-ds")
del ds[6:9]
del ds[0]
ds.commit()
ds.close()

append

append is a method to append data to a dataset, similar to the append method for Python lists.

Adding features dict, each row is automatically indexed with int starting from 0 and incrementing.

from starwhale import dataset, Image

with dataset("new-ds") as ds:
  for i in range(0, 100):
    ds.append({"label": i, "image": Image(f"folder/{i}.png")})
  ds.commit()

By appending the index and features dictionary, the index of each data row in the dataset will not be handled automatically.

from dataset import dataset, Image

with dataset("new-ds") as ds:
  for i in range(0, 100):
    ds.append((f"index-{i}", {"label": i, "image": Image(f"folder/{i}.png")}))

  ds.commit()

extend

extend is a method to bulk append data to a dataset, similar to the extend method for Python lists.

from starwhale import dataset, Text

ds = dataset("new-ds")
ds.extend([
    (f"label-{i}", {"text": Text(), "label": i}) for i in range(0, 10)
  ])
ds.commit()
ds.close()

commit

commit is a method that flushes the current cached data to storage when called, and generates a dataset version. This version can then be used to load the corresponding dataset content afterwards.

For a dataset, if some data is added without calling commit, but close is called or the process exits directly instead, the data will still be written to the dataset, just without generating a new version.

@_check_readonly
def commit(
    self,
    tags: t.Optional[t.List[str]] = None,
    message: str = "",
    force_add_tags: bool = False,
    ignore_add_tags_errors: bool = False,
) -> str:

Parameters

tags: (list(str), optional)
- tag as a list
message: (str, optional)
- commit message. The default value is empty.
force_add_tags: (bool, optional)
- For server/cloud instances, when adding labels to this version, if a label has already been applied to other dataset versions, you can use the force_add_tags=True parameter to forcibly add the label to this version, otherwise an exception will be thrown.
- The default is False.
ignore_add_tags_errors: (bool, optional)
- Ignore any exceptions thrown when adding labels.
- The default is False.

Examples

from starwhale import dataset
with dataset("mnist") as ds:
    ds.append({"label": 1})
    ds.commit(message="init commit")

readonly

readonly is a property attribute indicating if the dataset is read-only, it returns a bool value.

from starwhale import dataset
ds = dataset("mnist", readonly=True)
assert ds.readonly

loading_version

loading_version is a property attribute, string type.

When loading an existing dataset, the loading_version is the related dataset version.
When creating a non-existed dataset, the loading_version is equal to the pending_commit_version.

pending_commit_version

pending_commit_version is a property attribute, string type. When you call the commit function, the pending_commit_version will be recorded in the Standalone instance ,Server instance or Cloud instance.

committed_version

committed_version is a property attribute, string type. After the commit function is called, the committed_version will come out, it is equal to the pending_commit_version. Accessing this attribute without calling commit first will raise an exception.

remove

remove is a method equivalent to the swcli dataset remove command, it can delete a dataset.

def remove(self, force: bool = False) -> None:

recover

recover is a method equivalent to the swcli dataset recover command, it can recover a soft-deleted dataset that has not been run garbage collection.

def recover(self, force: bool = False) -> None:

summary

summary is a method equivalent to the swcli dataset summary command, it returns summary information of the dataset.

def summary(self) -> t.Optional[DatasetSummary]:

history

history is a method equivalent to the swcli dataset history command, it returns the history records of the dataset.

def history(self) -> t.List[t.Dict]:

flush

flush is a method that flushes temporarily cached data from memory to persistent storage. The commit and close methods will automatically call flush.

close

close is a method that closes opened connections related to the dataset. Dataset also implements contextmanager, so datasets can be automatically closed using with syntax without needing to explicitly call close.

from starwhale import dataset

ds = dataset("mnist")
ds.close()

with dataset("mnist") as ds:
  print(ds[0])

head

head is a method to show the first n rows of a dataset, equivalent to the swcli dataset head command.

def head(self, n: int = 5, skip_fetch_data: bool = False) -> List[DataRow]:

fetch_one

fetch_one is a method to get the first record in a dataset, similar to head(n=1)[0].

list

list is a class method to list Starwhale datasets under a project URI, equivalent to the swcli dataset list command.

@classmethod
def list(
    cls,
    project_uri: Union[str, Project] = "",
    fullname: bool = False,
    show_removed: bool = False,
    page_index: int = DEFAULT_PAGE_IDX,
    page_size: int = DEFAULT_PAGE_SIZE,
) -> Tuple[DatasetListType, Dict[str, Any]]:

copy

copy is a method to copy a dataset to another instance, equivalent to the swcli dataset copy command.

def copy(
  self,
  dest_uri: str,
  dest_local_project_uri: str = "",
  force: bool = False,
  mode: str = DatasetChangeMode.PATCH.value,
  ignore_tags: t.List[str] | None = None,
) -> None:

Parameters

dest_uri: (str, required)
- Dataset URI
dest_local_project_uri: (str, optional)
- When copy the remote dataset into local, the parameter can set for the Project URI.
force: (bool, optional)
- Whether to forcibly overwrite the dataset if there is already one with the same version on the target instance.
- The default value is False.
- When the tags are already used for the other dataset version in the dest instance, you should use force option or adjust the tags.
mode: (str, optional)
- Dataset copy mode, default is 'patch'. Mode choices are: 'patch', 'overwrite'.
- patch: Patch mode, only update the changed rows and columns for the remote dataset.
- overwrite: Overwrite mode, update records and delete extraneous rows from the remote dataset.
ignore_tags (List[str], optional)
- Ignore tags when copying.
- In default, copy dataset with all user custom tags.
- latest and ^v\d+$ are the system builtin tags, they are ignored automatically.

Examples

from starwhale import dataset
ds = dataset("mnist")
ds.copy("cloud://remote-instance/project/starwhale")

to_pytorch

to_pytorch is a method that can convert a Starwhale dataset to a Pytorch torch.utils.data.Dataset, which can then be passed to torch.utils.data.DataLoader for use.

It should be noted that the to_pytorch function returns a Pytorch IterableDataset.

def to_pytorch(
    self,
    transform: t.Optional[t.Callable] = None,
    drop_index: bool = True,
    skip_default_transform: bool = False,
) -> torch.utils.data.Dataset:

Parameters

transform: (callable, optional)
- A transform function for input data.
drop_index: (bool, optional)
- Whether to drop the index column.
skip_default_transform: (bool, optional)
- If transform is not set, by default the built-in Starwhale transform function will be used to transform the data. This can be disabled with the skip_default_transform parameter.

Examples

import torch.utils.data as tdata
from starwhale import dataset

ds = dataset("mnist")

torch_ds = ds.to_pytorch()
torch_loader = tdata.DataLoader(torch_ds, batch_size=2)

import torch.utils.data as tdata
from starwhale import dataset

with dataset("mnist") as ds:
    for i in range(0, 10):
        ds.append({"txt": Text(f"data-{i}"), "label": i})

    ds.commit()

def _custom_transform(data: t.Any) -> t.Any:
    data = data.copy()
    txt = data["txt"].to_str()
    data["txt"] = f"custom-{txt}"
    return data

torch_loader = tdata.DataLoader(
    dataset(ds.uri).to_pytorch(transform=_custom_transform), batch_size=1
)
item = next(iter(torch_loader))
assert isinstance(item["label"], torch.Tensor)
assert item["txt"][0] in ("custom-data-0", "custom-data-1")

to_tensorflow

to_tensorflow is a method that can convert a Starwhale dataset to a Tensorflow tensorflow.data.Dataset.

def to_tensorflow(self, drop_index: bool = True) -> tensorflow.data.Dataset:

Parameters

drop_index: (bool, optional)
- Whether to drop the index column.

Examples

from starwhale import dataset
import tensorflow as tf

ds = dataset("mnist")
tf_ds = ds.to_tensorflow(drop_index=True)
assert isinstance(tf_ds, tf.data.Dataset)

with_builder_blob_config

with_builder_blob_config is a method to set blob-related attributes in a Starwhale dataset. It needs to be called before making data changes.

def with_builder_blob_config(
    self,
    volume_size: int | str | None = D_FILE_VOLUME_SIZE,
    alignment_size: int | str | None = D_ALIGNMENT_SIZE,
) -> Dataset:

Parameters

alignment_size: (int|str, optional)
- The blob alignment size.
- The default value is 128 Bytes.
volume_size: (int|str, optional)
- The maximum size of a dataset blob file. A new blob file will be generated when the size exceeds this limit.
- The default value is 64MB.

Examples

from starwhale import dataset, Binary

ds = dataset("mnist").with_builder_blob_config(volume_size="32M", alignment_size=128)
ds.append({"data": Binary(b"123")})
ds.commit()
ds.close()

with_loader_config

with_loader_config is a method to set parameters for the Starwhale dataset loader process.

def with_loader_config(
    self,
    num_workers: t.Optional[int] = None,
    cache_size: t.Optional[int] = None,
    field_transformer: t.Optional[t.Dict] = None,
) -> Dataset:

Parameters

num_workers: (int, optional)
- The workers number for loading dataset.
- The default value is 2.
cache_size: (int, optional)
- Prefetched data rows.
- The default value is 20.
field_transformer: (dict, optional)
- features name transform dict.

Examples

from starwhale import Dataset, dataset
Dataset.from_json(
    "translation",
    '[{"en":"hello","zh-cn":"你好"},{"en":"how are you","zh-cn":"最近怎么样"}]'
)
myds = dataset("translation").with_loader_config(field_transformer={"en": "en-us"})
assert myds[0].features["en-us"] == myds[0].features["en"]

from starwhale import Dataset, dataset
Dataset.from_json(
    "translation2",
    '[{"content":{"child_content":[{"en":"hello","zh-cn":"你好"},{"en":"how are you","zh-cn":"最近怎么样"}]}}]'
)
myds = dataset("translation2").with_loader_config(field_transformer={"content.child_content[0].en": "en-us"})
assert myds[0].features["en-us"] == myds[0].features["content"]["child_content"][0]["en"]

dataset​

Parameters​

Examples​

class starwhale.Dataset​

from_huggingface​

Parameters​

Examples​

from_json​

Parameters​

Examples​

from_folder​

Parameters​

Examples ${folder-example}​

__iter__​

batch_iter​

Parameters​

Examples​

__getitem__​

__setitem__​

Parameters​

Examples​

__delitem__​

append​

extend​

commit​

Parameters​

Examples​

readonly​

loading_version​

pending_commit_version​

committed_version​

remove​

recover​

summary​

history​

flush​

close​

head​

fetch_one​

list​

copy​

Parameters​

Examples​

to_pytorch​

Parameters​

Examples​

to_tensorflow​

Parameters​

Examples​

with_builder_blob_config​

Parameters​

Examples​

with_loader_config​

Parameters​

Examples​

dataset

Parameters

Examples

class starwhale.Dataset

from_huggingface

Parameters

Examples

from_json

Parameters

Examples

from_folder

Parameters

Examples ${folder-example}

iter

batch_iter

Parameters

Examples

getitem

setitem

Parameters

Examples

delitem

append

extend

commit

Parameters

Examples

readonly

loading_version

pending_commit_version

committed_version

remove

recover

summary

history

flush

close

head

fetch_one

list

copy

Parameters

Examples

to_pytorch

Parameters

Examples

to_tensorflow

Parameters

Examples

with_builder_blob_config

Parameters

Examples

with_loader_config

Parameters

Examples