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v4.0.3 Release
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python/deeplake/__init__.py

Lines changed: 2 additions & 1 deletion
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@@ -6,7 +6,7 @@
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import deeplake
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from ._deeplake import *
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__version__ = "4.0.2"
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__version__ = "4.0.3"
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__all__ = [
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"__version__",
@@ -34,6 +34,7 @@
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"ColumnAlreadyExistsError",
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"ColumnDoesNotExistError",
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"InvalidColumnValueError",
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"PushError",
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"GcsStorageProviderFailed",
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"History",
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"InvalidType",

python/deeplake/__init__.pyi

Lines changed: 79 additions & 3 deletions
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@@ -32,6 +32,7 @@ __all__ = [
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"ColumnAlreadyExistsError",
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"ColumnDoesNotExistError",
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"InvalidColumnValueError",
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"PushError",
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"GcsStorageProviderFailed",
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"History",
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"InvalidType",
@@ -380,6 +381,9 @@ class TagExistsError(Exception):
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class CannotTagUncommittedDatasetError(Exception):
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pass
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class PushError(Exception):
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pass
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class Tags:
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"""
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Provides access to the tags within a dataset.
@@ -1155,7 +1159,7 @@ class Dataset(DatasetView):
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"""Restores dataset from a pickled state.
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Args:
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state (dict): The pickled state used to restore the dataset.
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arg0 (dict): The pickled state used to restore the dataset.
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"""
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def add_column(
@@ -1331,6 +1335,57 @@ class Dataset(DatasetView):
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Asynchronously reverts any in-progress changes to the dataset you have made. Does not revert any changes that have been committed.
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"""
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def push(self, url: str, creds: dict[str, str] | None = None, token: str | None = None) -> None:
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"""
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Pushes any new history from this dataset to the dataset at the given url
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Similar to [deeplake.Dataset.pull][] but the other direction.
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Parameters:
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url: The URL of the destination dataset
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creds: Optional credentials needed to connect to the dataset
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token: Optional deeplake token
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"""
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...
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def push_async(self, url: str, creds: dict[str, str] | None = None, token: str | None = None) -> FutureVoid:
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"""
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Asynchronously Pushes new any history from this dataset to the dataset at the given url
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Similar to [deeplake.Dataset.pull_async][] but the other direction.
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Parameters:
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url: The URL of the destination dataset
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creds: Optional credentials needed to connect to the dataset
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token: Optional deeplake token
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"""
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...
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def pull(self, url: str, creds: dict[str, str] | None = None, token: str | None = None) -> None:
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"""
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Pulls any new history from the dataset at the passed url into this dataset.
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Similar to [deeplake.Dataset.push][] but the other direction.
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Parameters:
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url: The URL of the destination dataset
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creds: Optional credentials needed to connect to the dataset
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token: Optional deeplake token
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"""
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...
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def pull_async(self, url: str, creds: dict[str, str] | None = None, token: str | None = None) -> FutureVoid:
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"""
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Asynchronously pulls any new history from the dataset at the passed url into this dataset.
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Similar to [deeplake.Dataset.push_async][] but the other direction.
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Parameters:
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url: The URL of the destination dataset
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creds: Optional credentials needed to connect to the dataset
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token: Optional deeplake token
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"""
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...
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@property
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def history(self) -> History:
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"""
@@ -1406,7 +1461,7 @@ class ReadOnlyDataset(DatasetView):
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@property
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def tags(self) -> TagsView:
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"""
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The collection of [deeplake.TagsView][] within the dataset
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The collection of [deeplake.TagView][]s within the dataset
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"""
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...
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@@ -1474,6 +1529,27 @@ class ReadOnlyDataset(DatasetView):
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"""
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...
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def push(self, url: str, creds: dict[str, str] | None = None, token: str | None = None) -> None:
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"""
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Pushes any history from this dataset to the dataset at the given url
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Parameters:
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url: The URL of the destination dataset
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creds: Optional credentials needed to connect to the dataset
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token: Optional deeplake token
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"""
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...
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def push_async(self, url: str, creds: dict[str, str] | None = None, token: str | None = None) -> FutureVoid:
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"""
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Asynchronously Pushes any history from this dataset to the dataset at the given url
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Parameters:
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url: The URL of the destination dataset
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creds: Optional credentials needed to connect to the dataset
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token: Optional deeplake token
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"""
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...
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def __getstate__(self) -> tuple:
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"""Returns a dict that can be pickled and used to restore this dataset.
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@@ -1803,7 +1879,7 @@ def create(url: str, creds: dict[str, str] | None = None, token: str | None = No
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- To use credentials managed in your Activeloop organization, use they key 'creds_key': 'managed_key_name'. This requires the org_id dataset argument to be set.
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- If nothing is given is, credentials are fetched from the environment variables. This is also the case when creds is not passed for cloud datasets
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token (str, optional): Activeloop token, used for fetching credentials to the dataset at path if it is a Deep Lake dataset. This is optional, tokens are normally autogenerated.
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schema (dict): The initial schema to use for the dataset. See `deeplake.schema` such as [deeplake.schemas.TextDocuments][] for common starting schemas.
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schema (dict): The initial schema to use for the dataset. See `deeplake.schema` such as [deeplake.schemas.TextEmbeddings][] for common starting schemas.
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Examples:
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>>> import deeplake

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