Gcp
zenml.integrations.gcp
special
Initialization of the GCP ZenML integration.
The GCP integration submodule provides a way to run ZenML pipelines in a cloud
environment. Specifically, it allows the use of cloud artifact stores
and provides an io module to handle file operations on Google Cloud Storage
(GCS).
Additionally, the GCP secrets manager integration submodule provides a way to access the GCP secrets manager from within your ZenML Pipeline runs.
The Vertex AI integration submodule provides a way to run ZenML pipelines in a Vertex AI environment.
GcpIntegration (Integration)
Definition of Google Cloud Platform integration for ZenML.
Source code in zenml/integrations/gcp/__init__.py
class GcpIntegration(Integration):
"""Definition of Google Cloud Platform integration for ZenML."""
NAME = GCP
REQUIREMENTS = [
"kfp==1.8.13",
"gcsfs",
"google-cloud-secret-manager",
"google-cloud-aiplatform>=1.11.0",
]
@classmethod
def flavors(cls) -> List[Type[Flavor]]:
"""Declare the stack component flavors for the GCP integration.
Returns:
List of stack component flavors for this integration.
"""
from zenml.integrations.gcp.flavors import (
GCPArtifactStoreFlavor,
GCPSecretsManagerFlavor,
VertexOrchestratorFlavor,
VertexStepOperatorFlavor,
)
return [
VertexOrchestratorFlavor,
VertexStepOperatorFlavor,
GCPSecretsManagerFlavor,
GCPArtifactStoreFlavor,
]
flavors()
classmethod
Declare the stack component flavors for the GCP integration.
Returns:
| Type | Description |
|---|---|
List[Type[zenml.stack.flavor.Flavor]] |
List of stack component flavors for this integration. |
Source code in zenml/integrations/gcp/__init__.py
@classmethod
def flavors(cls) -> List[Type[Flavor]]:
"""Declare the stack component flavors for the GCP integration.
Returns:
List of stack component flavors for this integration.
"""
from zenml.integrations.gcp.flavors import (
GCPArtifactStoreFlavor,
GCPSecretsManagerFlavor,
VertexOrchestratorFlavor,
VertexStepOperatorFlavor,
)
return [
VertexOrchestratorFlavor,
VertexStepOperatorFlavor,
GCPSecretsManagerFlavor,
GCPArtifactStoreFlavor,
]
artifact_stores
special
Initialization of the GCP Artifact Store.
gcp_artifact_store
Implementation of the GCP Artifact Store.
GCPArtifactStore (BaseArtifactStore, AuthenticationMixin)
Artifact Store for Google Cloud Storage based artifacts.
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
class GCPArtifactStore(BaseArtifactStore, AuthenticationMixin):
"""Artifact Store for Google Cloud Storage based artifacts."""
_filesystem: Optional[gcsfs.GCSFileSystem] = None
@property
def config(self) -> GCPArtifactStoreConfig:
"""Returns the `GCPArtifactStoreConfig` config.
Returns:
The configuration.
"""
return cast(GCPArtifactStoreConfig, self._config)
@property
def filesystem(self) -> gcsfs.GCSFileSystem:
"""The gcsfs filesystem to access this artifact store.
Returns:
The gcsfs filesystem to access this artifact store.
"""
if not self._filesystem:
secret = self.get_authentication_secret(
expected_schema_type=GCPSecretSchema
)
token = secret.get_credential_dict() if secret else None
self._filesystem = gcsfs.GCSFileSystem(token=token)
return self._filesystem
def open(self, path: PathType, mode: str = "r") -> Any:
"""Open a file at the given path.
Args:
path: Path of the file to open.
mode: Mode in which to open the file. Currently, only
'rb' and 'wb' to read and write binary files are supported.
Returns:
A file-like object that can be used to read or write to the file.
"""
return self.filesystem.open(path=path, mode=mode)
def copyfile(
self, src: PathType, dst: PathType, overwrite: bool = False
) -> None:
"""Copy a file.
Args:
src: The path to copy from.
dst: The path to copy to.
overwrite: If a file already exists at the destination, this
method will overwrite it if overwrite=`True` and
raise a FileExistsError otherwise.
Raises:
FileExistsError: If a file already exists at the destination
and overwrite is not set to `True`.
"""
if not overwrite and self.filesystem.exists(dst):
raise FileExistsError(
f"Unable to copy to destination '{convert_to_str(dst)}', "
f"file already exists. Set `overwrite=True` to copy anyway."
)
# TODO [ENG-151]: Check if it works with overwrite=True or if we need to
# manually remove it first
self.filesystem.copy(path1=src, path2=dst)
def exists(self, path: PathType) -> bool:
"""Check whether a path exists.
Args:
path: The path to check.
Returns:
True if the path exists, False otherwise.
"""
return self.filesystem.exists(path=path) # type: ignore[no-any-return]
def glob(self, pattern: PathType) -> List[PathType]:
"""Return all paths that match the given glob pattern.
The glob pattern may include:
- '*' to match any number of characters
- '?' to match a single character
- '[...]' to match one of the characters inside the brackets
- '**' as the full name of a path component to match to search
in subdirectories of any depth (e.g. '/some_dir/**/some_file)
Args:
pattern: The glob pattern to match, see details above.
Returns:
A list of paths that match the given glob pattern.
"""
return [
f"{GCP_PATH_PREFIX}{path}"
for path in self.filesystem.glob(path=pattern)
]
def isdir(self, path: PathType) -> bool:
"""Check whether a path is a directory.
Args:
path: The path to check.
Returns:
True if the path is a directory, False otherwise.
"""
return self.filesystem.isdir(path=path) # type: ignore[no-any-return]
def listdir(self, path: PathType) -> List[PathType]:
"""Return a list of files in a directory.
Args:
path: The path of the directory to list.
Returns:
A list of paths of files in the directory.
"""
path_without_prefix = convert_to_str(path)
if path_without_prefix.startswith(GCP_PATH_PREFIX):
path_without_prefix = path_without_prefix[len(GCP_PATH_PREFIX) :]
def _extract_basename(file_dict: Dict[str, Any]) -> str:
"""Extracts the basename from a file info dict returned by GCP.
Args:
file_dict: A file info dict returned by the GCP filesystem.
Returns:
The basename of the file.
"""
file_path = cast(str, file_dict["name"])
base_name = file_path[len(path_without_prefix) :]
return base_name.lstrip("/")
return [
_extract_basename(dict_)
for dict_ in self.filesystem.listdir(path=path)
# gcsfs.listdir also returns the root directory, so we filter
# it out here
if _extract_basename(dict_)
]
def makedirs(self, path: PathType) -> None:
"""Create a directory at the given path.
If needed also create missing parent directories.
Args:
path: The path of the directory to create.
"""
self.filesystem.makedirs(path=path, exist_ok=True)
def mkdir(self, path: PathType) -> None:
"""Create a directory at the given path.
Args:
path: The path of the directory to create.
"""
self.filesystem.makedir(path=path)
def remove(self, path: PathType) -> None:
"""Remove the file at the given path.
Args:
path: The path of the file to remove.
"""
self.filesystem.rm_file(path=path)
def rename(
self, src: PathType, dst: PathType, overwrite: bool = False
) -> None:
"""Rename source file to destination file.
Args:
src: The path of the file to rename.
dst: The path to rename the source file to.
overwrite: If a file already exists at the destination, this
method will overwrite it if overwrite=`True` and
raise a FileExistsError otherwise.
Raises:
FileExistsError: If a file already exists at the destination
and overwrite is not set to `True`.
"""
if not overwrite and self.filesystem.exists(dst):
raise FileExistsError(
f"Unable to rename file to '{convert_to_str(dst)}', "
f"file already exists. Set `overwrite=True` to rename anyway."
)
# TODO [ENG-152]: Check if it works with overwrite=True or if we need
# to manually remove it first
self.filesystem.rename(path1=src, path2=dst)
def rmtree(self, path: PathType) -> None:
"""Remove the given directory.
Args:
path: The path of the directory to remove.
"""
self.filesystem.delete(path=path, recursive=True)
def stat(self, path: PathType) -> Dict[str, Any]:
"""Return stat info for the given path.
Args:
path: the path to get stat info for.
Returns:
A dictionary with the stat info.
"""
return self.filesystem.stat(path=path) # type: ignore[no-any-return]
def walk(
self,
top: PathType,
topdown: bool = True,
onerror: Optional[Callable[..., None]] = None,
) -> Iterable[Tuple[PathType, List[PathType], List[PathType]]]:
"""Return an iterator that walks the contents of the given directory.
Args:
top: Path of directory to walk.
topdown: Unused argument to conform to interface.
onerror: Unused argument to conform to interface.
Yields:
An Iterable of Tuples, each of which contain the path of the current
directory path, a list of directories inside the current directory
and a list of files inside the current directory.
"""
# TODO [ENG-153]: Additional params
for (
directory,
subdirectories,
files,
) in self.filesystem.walk(path=top):
yield f"{GCP_PATH_PREFIX}{directory}", subdirectories, files
config: GCPArtifactStoreConfig
property
readonly
Returns the GCPArtifactStoreConfig config.
Returns:
| Type | Description |
|---|---|
GCPArtifactStoreConfig |
The configuration. |
filesystem: GCSFileSystem
property
readonly
The gcsfs filesystem to access this artifact store.
Returns:
| Type | Description |
|---|---|
GCSFileSystem |
The gcsfs filesystem to access this artifact store. |
copyfile(self, src, dst, overwrite=False)
Copy a file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
src |
Union[bytes, str] |
The path to copy from. |
required |
dst |
Union[bytes, str] |
The path to copy to. |
required |
overwrite |
bool |
If a file already exists at the destination, this
method will overwrite it if overwrite= |
False |
Exceptions:
| Type | Description |
|---|---|
FileExistsError |
If a file already exists at the destination
and overwrite is not set to |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def copyfile(
self, src: PathType, dst: PathType, overwrite: bool = False
) -> None:
"""Copy a file.
Args:
src: The path to copy from.
dst: The path to copy to.
overwrite: If a file already exists at the destination, this
method will overwrite it if overwrite=`True` and
raise a FileExistsError otherwise.
Raises:
FileExistsError: If a file already exists at the destination
and overwrite is not set to `True`.
"""
if not overwrite and self.filesystem.exists(dst):
raise FileExistsError(
f"Unable to copy to destination '{convert_to_str(dst)}', "
f"file already exists. Set `overwrite=True` to copy anyway."
)
# TODO [ENG-151]: Check if it works with overwrite=True or if we need to
# manually remove it first
self.filesystem.copy(path1=src, path2=dst)
exists(self, path)
Check whether a path exists.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
Union[bytes, str] |
The path to check. |
required |
Returns:
| Type | Description |
|---|---|
bool |
True if the path exists, False otherwise. |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def exists(self, path: PathType) -> bool:
"""Check whether a path exists.
Args:
path: The path to check.
Returns:
True if the path exists, False otherwise.
"""
return self.filesystem.exists(path=path) # type: ignore[no-any-return]
glob(self, pattern)
Return all paths that match the given glob pattern.
The glob pattern may include: - '' to match any number of characters - '?' to match a single character - '[...]' to match one of the characters inside the brackets - '' as the full name of a path component to match to search in subdirectories of any depth (e.g. '/some_dir/*/some_file)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
pattern |
Union[bytes, str] |
The glob pattern to match, see details above. |
required |
Returns:
| Type | Description |
|---|---|
List[Union[bytes, str]] |
A list of paths that match the given glob pattern. |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def glob(self, pattern: PathType) -> List[PathType]:
"""Return all paths that match the given glob pattern.
The glob pattern may include:
- '*' to match any number of characters
- '?' to match a single character
- '[...]' to match one of the characters inside the brackets
- '**' as the full name of a path component to match to search
in subdirectories of any depth (e.g. '/some_dir/**/some_file)
Args:
pattern: The glob pattern to match, see details above.
Returns:
A list of paths that match the given glob pattern.
"""
return [
f"{GCP_PATH_PREFIX}{path}"
for path in self.filesystem.glob(path=pattern)
]
isdir(self, path)
Check whether a path is a directory.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
Union[bytes, str] |
The path to check. |
required |
Returns:
| Type | Description |
|---|---|
bool |
True if the path is a directory, False otherwise. |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def isdir(self, path: PathType) -> bool:
"""Check whether a path is a directory.
Args:
path: The path to check.
Returns:
True if the path is a directory, False otherwise.
"""
return self.filesystem.isdir(path=path) # type: ignore[no-any-return]
listdir(self, path)
Return a list of files in a directory.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
Union[bytes, str] |
The path of the directory to list. |
required |
Returns:
| Type | Description |
|---|---|
List[Union[bytes, str]] |
A list of paths of files in the directory. |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def listdir(self, path: PathType) -> List[PathType]:
"""Return a list of files in a directory.
Args:
path: The path of the directory to list.
Returns:
A list of paths of files in the directory.
"""
path_without_prefix = convert_to_str(path)
if path_without_prefix.startswith(GCP_PATH_PREFIX):
path_without_prefix = path_without_prefix[len(GCP_PATH_PREFIX) :]
def _extract_basename(file_dict: Dict[str, Any]) -> str:
"""Extracts the basename from a file info dict returned by GCP.
Args:
file_dict: A file info dict returned by the GCP filesystem.
Returns:
The basename of the file.
"""
file_path = cast(str, file_dict["name"])
base_name = file_path[len(path_without_prefix) :]
return base_name.lstrip("/")
return [
_extract_basename(dict_)
for dict_ in self.filesystem.listdir(path=path)
# gcsfs.listdir also returns the root directory, so we filter
# it out here
if _extract_basename(dict_)
]
makedirs(self, path)
Create a directory at the given path.
If needed also create missing parent directories.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
Union[bytes, str] |
The path of the directory to create. |
required |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def makedirs(self, path: PathType) -> None:
"""Create a directory at the given path.
If needed also create missing parent directories.
Args:
path: The path of the directory to create.
"""
self.filesystem.makedirs(path=path, exist_ok=True)
mkdir(self, path)
Create a directory at the given path.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
Union[bytes, str] |
The path of the directory to create. |
required |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def mkdir(self, path: PathType) -> None:
"""Create a directory at the given path.
Args:
path: The path of the directory to create.
"""
self.filesystem.makedir(path=path)
open(self, path, mode='r')
Open a file at the given path.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
Union[bytes, str] |
Path of the file to open. |
required |
mode |
str |
Mode in which to open the file. Currently, only 'rb' and 'wb' to read and write binary files are supported. |
'r' |
Returns:
| Type | Description |
|---|---|
Any |
A file-like object that can be used to read or write to the file. |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def open(self, path: PathType, mode: str = "r") -> Any:
"""Open a file at the given path.
Args:
path: Path of the file to open.
mode: Mode in which to open the file. Currently, only
'rb' and 'wb' to read and write binary files are supported.
Returns:
A file-like object that can be used to read or write to the file.
"""
return self.filesystem.open(path=path, mode=mode)
remove(self, path)
Remove the file at the given path.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
Union[bytes, str] |
The path of the file to remove. |
required |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def remove(self, path: PathType) -> None:
"""Remove the file at the given path.
Args:
path: The path of the file to remove.
"""
self.filesystem.rm_file(path=path)
rename(self, src, dst, overwrite=False)
Rename source file to destination file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
src |
Union[bytes, str] |
The path of the file to rename. |
required |
dst |
Union[bytes, str] |
The path to rename the source file to. |
required |
overwrite |
bool |
If a file already exists at the destination, this
method will overwrite it if overwrite= |
False |
Exceptions:
| Type | Description |
|---|---|
FileExistsError |
If a file already exists at the destination
and overwrite is not set to |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def rename(
self, src: PathType, dst: PathType, overwrite: bool = False
) -> None:
"""Rename source file to destination file.
Args:
src: The path of the file to rename.
dst: The path to rename the source file to.
overwrite: If a file already exists at the destination, this
method will overwrite it if overwrite=`True` and
raise a FileExistsError otherwise.
Raises:
FileExistsError: If a file already exists at the destination
and overwrite is not set to `True`.
"""
if not overwrite and self.filesystem.exists(dst):
raise FileExistsError(
f"Unable to rename file to '{convert_to_str(dst)}', "
f"file already exists. Set `overwrite=True` to rename anyway."
)
# TODO [ENG-152]: Check if it works with overwrite=True or if we need
# to manually remove it first
self.filesystem.rename(path1=src, path2=dst)
rmtree(self, path)
Remove the given directory.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
Union[bytes, str] |
The path of the directory to remove. |
required |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def rmtree(self, path: PathType) -> None:
"""Remove the given directory.
Args:
path: The path of the directory to remove.
"""
self.filesystem.delete(path=path, recursive=True)
stat(self, path)
Return stat info for the given path.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
Union[bytes, str] |
the path to get stat info for. |
required |
Returns:
| Type | Description |
|---|---|
Dict[str, Any] |
A dictionary with the stat info. |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def stat(self, path: PathType) -> Dict[str, Any]:
"""Return stat info for the given path.
Args:
path: the path to get stat info for.
Returns:
A dictionary with the stat info.
"""
return self.filesystem.stat(path=path) # type: ignore[no-any-return]
walk(self, top, topdown=True, onerror=None)
Return an iterator that walks the contents of the given directory.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
top |
Union[bytes, str] |
Path of directory to walk. |
required |
topdown |
bool |
Unused argument to conform to interface. |
True |
onerror |
Optional[Callable[..., NoneType]] |
Unused argument to conform to interface. |
None |
Yields:
| Type | Description |
|---|---|
Iterable[Tuple[Union[bytes, str], List[Union[bytes, str]], List[Union[bytes, str]]]] |
An Iterable of Tuples, each of which contain the path of the current directory path, a list of directories inside the current directory and a list of files inside the current directory. |
Source code in zenml/integrations/gcp/artifact_stores/gcp_artifact_store.py
def walk(
self,
top: PathType,
topdown: bool = True,
onerror: Optional[Callable[..., None]] = None,
) -> Iterable[Tuple[PathType, List[PathType], List[PathType]]]:
"""Return an iterator that walks the contents of the given directory.
Args:
top: Path of directory to walk.
topdown: Unused argument to conform to interface.
onerror: Unused argument to conform to interface.
Yields:
An Iterable of Tuples, each of which contain the path of the current
directory path, a list of directories inside the current directory
and a list of files inside the current directory.
"""
# TODO [ENG-153]: Additional params
for (
directory,
subdirectories,
files,
) in self.filesystem.walk(path=top):
yield f"{GCP_PATH_PREFIX}{directory}", subdirectories, files
constants
Constants for the VertexAI integration.
flavors
special
GCP integration flavors.
gcp_artifact_store_flavor
GCP artifact store flavor.
GCPArtifactStoreConfig (BaseArtifactStoreConfig, AuthenticationConfigMixin)
pydantic-model
Configuration for GCP Artifact Store.
Source code in zenml/integrations/gcp/flavors/gcp_artifact_store_flavor.py
class GCPArtifactStoreConfig(
BaseArtifactStoreConfig, AuthenticationConfigMixin
):
"""Configuration for GCP Artifact Store."""
SUPPORTED_SCHEMES: ClassVar[Set[str]] = {GCP_PATH_PREFIX}
GCPArtifactStoreFlavor (BaseArtifactStoreFlavor)
Flavor of the GCP artifact store.
Source code in zenml/integrations/gcp/flavors/gcp_artifact_store_flavor.py
class GCPArtifactStoreFlavor(BaseArtifactStoreFlavor):
"""Flavor of the GCP artifact store."""
@property
def name(self) -> str:
"""Name of the flavor.
Returns:
The name of the flavor.
"""
return GCP_ARTIFACT_STORE_FLAVOR
@property
def config_class(self) -> Type[GCPArtifactStoreConfig]:
"""Returns GCPArtifactStoreConfig config class.
Returns:
The config class.
"""
return GCPArtifactStoreConfig
@property
def implementation_class(self) -> Type["GCPArtifactStore"]:
"""Implementation class for this flavor.
Returns:
The implementation class.
"""
from zenml.integrations.gcp.artifact_stores import GCPArtifactStore
return GCPArtifactStore
config_class: Type[zenml.integrations.gcp.flavors.gcp_artifact_store_flavor.GCPArtifactStoreConfig]
property
readonly
Returns GCPArtifactStoreConfig config class.
Returns:
| Type | Description |
|---|---|
Type[zenml.integrations.gcp.flavors.gcp_artifact_store_flavor.GCPArtifactStoreConfig] |
The config class. |
implementation_class: Type[GCPArtifactStore]
property
readonly
Implementation class for this flavor.
Returns:
| Type | Description |
|---|---|
Type[GCPArtifactStore] |
The implementation class. |
name: str
property
readonly
Name of the flavor.
Returns:
| Type | Description |
|---|---|
str |
The name of the flavor. |
gcp_secrets_manager_flavor
GCP secrets manager flavor.
GCPSecretsManagerConfig (BaseSecretsManagerConfig)
pydantic-model
Configuration for the GCP Secrets Manager.
Attributes:
| Name | Type | Description |
|---|---|---|
project_id |
str |
This is necessary to access the correct GCP project. The project_id of your GCP project space that contains the Secret Manager. |
Source code in zenml/integrations/gcp/flavors/gcp_secrets_manager_flavor.py
class GCPSecretsManagerConfig(BaseSecretsManagerConfig):
"""Configuration for the GCP Secrets Manager.
Attributes:
project_id: This is necessary to access the correct GCP project.
The project_id of your GCP project space that contains the Secret
Manager.
"""
SUPPORTS_SCOPING: ClassVar[bool] = True
project_id: str
@classmethod
def _validate_scope(
cls,
scope: SecretsManagerScope,
namespace: Optional[str],
) -> None:
"""Validate the scope and namespace value.
Args:
scope: Scope value.
namespace: Optional namespace value.
"""
if namespace:
validate_gcp_secret_name_or_namespace(namespace)
GCPSecretsManagerFlavor (BaseSecretsManagerFlavor)
Class for the GCPSecretsManagerFlavor.
Source code in zenml/integrations/gcp/flavors/gcp_secrets_manager_flavor.py
class GCPSecretsManagerFlavor(BaseSecretsManagerFlavor):
"""Class for the `GCPSecretsManagerFlavor`."""
@property
def name(self) -> str:
"""Name of the flavor.
Returns:
The name of the flavor.
"""
return GCP_SECRETS_MANAGER_FLAVOR
@property
def config_class(self) -> Type[GCPSecretsManagerConfig]:
"""Returns GCPSecretsManagerConfig config class.
Returns:
The config class.
"""
return GCPSecretsManagerConfig
@property
def implementation_class(self) -> Type["GCPSecretsManager"]:
"""Implementation class for this flavor.
Returns:
The implementation class.
"""
from zenml.integrations.gcp.secrets_manager import GCPSecretsManager
return GCPSecretsManager
config_class: Type[zenml.integrations.gcp.flavors.gcp_secrets_manager_flavor.GCPSecretsManagerConfig]
property
readonly
Returns GCPSecretsManagerConfig config class.
Returns:
| Type | Description |
|---|---|
Type[zenml.integrations.gcp.flavors.gcp_secrets_manager_flavor.GCPSecretsManagerConfig] |
The config class. |
implementation_class: Type[GCPSecretsManager]
property
readonly
Implementation class for this flavor.
Returns:
| Type | Description |
|---|---|
Type[GCPSecretsManager] |
The implementation class. |
name: str
property
readonly
Name of the flavor.
Returns:
| Type | Description |
|---|---|
str |
The name of the flavor. |
validate_gcp_secret_name_or_namespace(name)
Validate a secret name or namespace.
A Google secret ID is a string with a maximum length of 255 characters and can contain uppercase and lowercase letters, numerals, and the hyphen (-) and underscore (_) characters. For scoped secrets, we have to limit the size of the name and namespace even further to allow space for both in the Google secret ID.
Given that we also save secret names and namespaces as labels, we are also limited by the limitation that Google imposes on label values: max 63 characters and must only contain lowercase letters, numerals and the hyphen (-) and underscore (_) characters
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
name |
str |
the secret name or namespace |
required |
Exceptions:
| Type | Description |
|---|---|
ValueError |
if the secret name or namespace is invalid |
Source code in zenml/integrations/gcp/flavors/gcp_secrets_manager_flavor.py
def validate_gcp_secret_name_or_namespace(name: str) -> None:
"""Validate a secret name or namespace.
A Google secret ID is a string with a maximum length of 255 characters
and can contain uppercase and lowercase letters, numerals, and the
hyphen (-) and underscore (_) characters. For scoped secrets, we have to
limit the size of the name and namespace even further to allow space for
both in the Google secret ID.
Given that we also save secret names and namespaces as labels, we are
also limited by the limitation that Google imposes on label values: max
63 characters and must only contain lowercase letters, numerals
and the hyphen (-) and underscore (_) characters
Args:
name: the secret name or namespace
Raises:
ValueError: if the secret name or namespace is invalid
"""
if not re.fullmatch(r"[a-z0-9_\-]+", name):
raise ValueError(
f"Invalid secret name or namespace '{name}'. Must contain "
f"only lowercase alphanumeric characters and the hyphen (-) and "
f"underscore (_) characters."
)
if name and len(name) > 63:
raise ValueError(
f"Invalid secret name or namespace '{name}'. The length is "
f"limited to maximum 63 characters."
)
vertex_orchestrator_flavor
Vertex orchestrator flavor.
VertexOrchestratorConfig (BaseOrchestratorConfig, GoogleCredentialsConfigMixin, VertexOrchestratorSettings)
pydantic-model
Configuration for the Vertex orchestrator.
Attributes:
| Name | Type | Description |
|---|---|---|
project |
Optional[str] |
GCP project name. If |
location |
str |
Name of GCP region where the pipeline job will be executed. Vertex AI Pipelines is available in the following regions: https://cloud.google.com/vertex-ai/docs/general/locations#feature -availability |
pipeline_root |
Optional[str] |
a Cloud Storage URI that will be used by the Vertex AI
Pipelines. If not provided but the artifact store in the stack used
to execute the pipeline is a
|
encryption_spec_key_name |
Optional[str] |
The Cloud KMS resource identifier of the
customer managed encryption key used to protect the job. Has the form:
|
workload_service_account |
Optional[str] |
the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If not provided, the default service account will be used. |
network |
Optional[str] |
the full name of the Compute Engine Network to which the job
should be peered. For example, |
cpu_limit |
Optional[str] |
The maximum CPU limit for this operator. This string value can be a number (integer value for number of CPUs) as string, or a number followed by "m", which means 1/1000. You can specify at most 96 CPUs. (see. https://cloud.google.com/vertex-ai/docs/pipelines/machine-types) |
memory_limit |
Optional[str] |
The maximum memory limit for this operator. This string value can be a number, or a number followed by "K" (kilobyte), "M" (megabyte), or "G" (gigabyte). At most 624GB is supported. |
gpu_limit |
Optional[int] |
The GPU limit (positive number) for the operator. For more information about GPU resources, see: https://cloud.google.com/vertex-ai/docs/training/configure-compute#specifying_gpus |
Source code in zenml/integrations/gcp/flavors/vertex_orchestrator_flavor.py
class VertexOrchestratorConfig( # type: ignore[misc] # https://github.com/pydantic/pydantic/issues/4173
BaseOrchestratorConfig,
GoogleCredentialsConfigMixin,
VertexOrchestratorSettings,
):
"""Configuration for the Vertex orchestrator.
Attributes:
project: GCP project name. If `None`, the project will be inferred from
the environment.
location: Name of GCP region where the pipeline job will be executed.
Vertex AI Pipelines is available in the following regions:
https://cloud.google.com/vertex-ai/docs/general/locations#feature
-availability
pipeline_root: a Cloud Storage URI that will be used by the Vertex AI
Pipelines. If not provided but the artifact store in the stack used
to execute the pipeline is a
`zenml.integrations.gcp.artifact_stores.GCPArtifactStore`,
then a subdirectory of the artifact store will be used.
encryption_spec_key_name: The Cloud KMS resource identifier of the
customer managed encryption key used to protect the job. Has the form:
`projects/<PRJCT>/locations/<REGION>/keyRings/<KR>/cryptoKeys/<KEY>`
. The key needs to be in the same region as where the compute
resource is created.
workload_service_account: the service account for workload run-as
account. Users submitting jobs must have act-as permission on this
run-as account.
If not provided, the default service account will be used.
network: the full name of the Compute Engine Network to which the job
should be peered. For example, `projects/12345/global/networks/myVPC`
If not provided, the job will not be peered with any network.
cpu_limit: The maximum CPU limit for this operator. This string value
can be a number (integer value for number of CPUs) as string,
or a number followed by "m", which means 1/1000. You can specify
at most 96 CPUs.
(see. https://cloud.google.com/vertex-ai/docs/pipelines/machine-types)
memory_limit: The maximum memory limit for this operator. This string
value can be a number, or a number followed by "K" (kilobyte),
"M" (megabyte), or "G" (gigabyte). At most 624GB is supported.
gpu_limit: The GPU limit (positive number) for the operator.
For more information about GPU resources, see:
https://cloud.google.com/vertex-ai/docs/training/configure-compute#specifying_gpus
"""
project: Optional[str] = None
location: str
pipeline_root: Optional[str] = None
encryption_spec_key_name: Optional[str] = None
workload_service_account: Optional[str] = None
network: Optional[str] = None
cpu_limit: Optional[str] = None
memory_limit: Optional[str] = None
gpu_limit: Optional[int] = None
_resource_deprecation = deprecation_utils.deprecate_pydantic_attributes(
"cpu_limit", "memory_limit", "gpu_limit"
)
@property
def is_remote(self) -> bool:
"""Checks if this stack component is running remotely.
This designation is used to determine if the stack component can be
used with a local ZenML database or if it requires a remote ZenML
server.
Returns:
True if this config is for a remote component, False otherwise.
"""
return True
is_remote: bool
property
readonly
Checks if this stack component is running remotely.
This designation is used to determine if the stack component can be used with a local ZenML database or if it requires a remote ZenML server.
Returns:
| Type | Description |
|---|---|
bool |
True if this config is for a remote component, False otherwise. |
VertexOrchestratorFlavor (BaseOrchestratorFlavor)
Vertex Orchestrator flavor.
Source code in zenml/integrations/gcp/flavors/vertex_orchestrator_flavor.py
class VertexOrchestratorFlavor(BaseOrchestratorFlavor):
"""Vertex Orchestrator flavor."""
@property
def name(self) -> str:
"""Name of the orchestrator flavor.
Returns:
Name of the orchestrator flavor.
"""
return GCP_VERTEX_ORCHESTRATOR_FLAVOR
@property
def config_class(self) -> Type[VertexOrchestratorConfig]:
"""Returns VertexOrchestratorConfig config class.
Returns:
The config class.
"""
return VertexOrchestratorConfig
@property
def implementation_class(self) -> Type["VertexOrchestrator"]:
"""Implementation class for this flavor.
Returns:
Implementation class for this flavor.
"""
from zenml.integrations.gcp.orchestrators import VertexOrchestrator
return VertexOrchestrator
config_class: Type[zenml.integrations.gcp.flavors.vertex_orchestrator_flavor.VertexOrchestratorConfig]
property
readonly
Returns VertexOrchestratorConfig config class.
Returns:
| Type | Description |
|---|---|
Type[zenml.integrations.gcp.flavors.vertex_orchestrator_flavor.VertexOrchestratorConfig] |
The config class. |
implementation_class: Type[VertexOrchestrator]
property
readonly
Implementation class for this flavor.
Returns:
| Type | Description |
|---|---|
Type[VertexOrchestrator] |
Implementation class for this flavor. |
name: str
property
readonly
Name of the orchestrator flavor.
Returns:
| Type | Description |
|---|---|
str |
Name of the orchestrator flavor. |
VertexOrchestratorSettings (BaseSettings)
pydantic-model
Settings for the Vertex orchestrator.
Attributes:
| Name | Type | Description |
|---|---|---|
synchronous |
bool |
If |
labels |
Dict[str, str] |
Labels to assign to the pipeline job. |
node_selector_constraint |
Optional[Tuple[str, str]] |
Each constraint is a key-value pair label. For the container to be eligible to run on a node, the node must have each of the constraints appeared as labels. For example a GPU type can be providing by one of the following tuples: - ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_A100") - ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_K80") - ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_P4") - ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_P100") - ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_T4") - ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_V100") Hint: the selected region (location) must provide the requested accelerator (see https://cloud.google.com/compute/docs/gpus/gpu-regions-zones). |
pod_settings |
Optional[zenml.integrations.kubernetes.pod_settings.KubernetesPodSettings] |
Pod settings to apply. |
Source code in zenml/integrations/gcp/flavors/vertex_orchestrator_flavor.py
class VertexOrchestratorSettings(BaseSettings):
"""Settings for the Vertex orchestrator.
Attributes:
synchronous: If `True`, running a pipeline using this orchestrator will
block until all steps finished running on Vertex AI Pipelines
service.
labels: Labels to assign to the pipeline job.
node_selector_constraint: Each constraint is a key-value pair label.
For the container to be eligible to run on a node, the node must have
each of the constraints appeared as labels.
For example a GPU type can be providing by one of the following tuples:
- ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_A100")
- ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_K80")
- ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_P4")
- ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_P100")
- ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_T4")
- ("cloud.google.com/gke-accelerator", "NVIDIA_TESLA_V100")
Hint: the selected region (location) must provide the requested accelerator
(see https://cloud.google.com/compute/docs/gpus/gpu-regions-zones).
pod_settings: Pod settings to apply.
"""
labels: Dict[str, str] = {}
synchronous: bool = False
node_selector_constraint: Optional[Tuple[str, str]] = None
pod_settings: Optional[KubernetesPodSettings] = None
_node_selector_deprecation = (
deprecation_utils.deprecate_pydantic_attributes(
"node_selector_constraint"
)
)
vertex_step_operator_flavor
Vertex step operator flavor.
VertexStepOperatorConfig (BaseStepOperatorConfig, GoogleCredentialsConfigMixin, VertexStepOperatorSettings)
pydantic-model
Configuration for the Vertex step operator.
Attributes:
| Name | Type | Description |
|---|---|---|
region |
str |
Region name, e.g., |
project |
Optional[str] |
GCP project name. If left None, inferred from the environment. |
encryption_spec_key_name |
Optional[str] |
Encryption spec key name. |
Source code in zenml/integrations/gcp/flavors/vertex_step_operator_flavor.py
class VertexStepOperatorConfig( # type: ignore[misc] # https://github.com/pydantic/pydantic/issues/4173
BaseStepOperatorConfig,
GoogleCredentialsConfigMixin,
VertexStepOperatorSettings,
):
"""Configuration for the Vertex step operator.
Attributes:
region: Region name, e.g., `europe-west1`.
project: GCP project name. If left None, inferred from the
environment.
encryption_spec_key_name: Encryption spec key name.
"""
region: str
project: Optional[str] = None
# customer managed encryption key resource name
# will be applied to all Vertex AI resources if set
encryption_spec_key_name: Optional[str] = None
@property
def is_remote(self) -> bool:
"""Checks if this stack component is running remotely.
This designation is used to determine if the stack component can be
used with a local ZenML database or if it requires a remote ZenML
server.
Returns:
True if this config is for a remote component, False otherwise.
"""
return True
is_remote: bool
property
readonly
Checks if this stack component is running remotely.
This designation is used to determine if the stack component can be used with a local ZenML database or if it requires a remote ZenML server.
Returns:
| Type | Description |
|---|---|
bool |
True if this config is for a remote component, False otherwise. |
VertexStepOperatorFlavor (BaseStepOperatorFlavor)
Vertex Step Operator flavor.
Source code in zenml/integrations/gcp/flavors/vertex_step_operator_flavor.py
class VertexStepOperatorFlavor(BaseStepOperatorFlavor):
"""Vertex Step Operator flavor."""
@property
def name(self) -> str:
"""Name of the flavor.
Returns:
Name of the flavor.
"""
return GCP_VERTEX_STEP_OPERATOR_FLAVOR
@property
def config_class(self) -> Type[VertexStepOperatorConfig]:
"""Returns `VertexStepOperatorConfig` config class.
Returns:
The config class.
"""
return VertexStepOperatorConfig
@property
def implementation_class(self) -> Type["VertexStepOperator"]:
"""Implementation class for this flavor.
Returns:
The implementation class.
"""
from zenml.integrations.gcp.step_operators import VertexStepOperator
return VertexStepOperator
config_class: Type[zenml.integrations.gcp.flavors.vertex_step_operator_flavor.VertexStepOperatorConfig]
property
readonly
Returns VertexStepOperatorConfig config class.
Returns:
| Type | Description |
|---|---|
Type[zenml.integrations.gcp.flavors.vertex_step_operator_flavor.VertexStepOperatorConfig] |
The config class. |
implementation_class: Type[VertexStepOperator]
property
readonly
Implementation class for this flavor.
Returns:
| Type | Description |
|---|---|
Type[VertexStepOperator] |
The implementation class. |
name: str
property
readonly
Name of the flavor.
Returns:
| Type | Description |
|---|---|
str |
Name of the flavor. |
VertexStepOperatorSettings (BaseSettings)
pydantic-model
Settings for the Vertex step operator.
Attributes:
| Name | Type | Description |
|---|---|---|
accelerator_type |
Optional[str] |
Defines which accelerator (GPU, TPU) is used for the job. Check out out this table to see which accelerator type and count are compatible with your chosen machine type: https://cloud.google.com/vertex-ai/docs/training/configure-compute#gpu-compatibility-table. |
accelerator_count |
int |
Defines number of accelerators to be used for the job. Check out out this table to see which accelerator type and count are compatible with your chosen machine type: https://cloud.google.com/vertex-ai/docs/training/configure-compute#gpu-compatibility-table. |
machine_type |
str |
Machine type specified here https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types. |
Source code in zenml/integrations/gcp/flavors/vertex_step_operator_flavor.py
class VertexStepOperatorSettings(BaseSettings):
"""Settings for the Vertex step operator.
Attributes:
accelerator_type: Defines which accelerator (GPU, TPU) is used for the
job. Check out out this table to see which accelerator
type and count are compatible with your chosen machine type:
https://cloud.google.com/vertex-ai/docs/training/configure-compute#gpu-compatibility-table.
accelerator_count: Defines number of accelerators to be used for the
job. Check out out this table to see which accelerator
type and count are compatible with your chosen machine type:
https://cloud.google.com/vertex-ai/docs/training/configure-compute#gpu-compatibility-table.
machine_type: Machine type specified here
https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types.
"""
accelerator_type: Optional[str] = None
accelerator_count: int = 0
machine_type: str = "n1-standard-4"
google_credentials_mixin
Implementation of the Google credentials mixin.
GoogleCredentialsConfigMixin (StackComponentConfig)
pydantic-model
Config mixin for Google Cloud Platform credentials.
Attributes:
| Name | Type | Description |
|---|---|---|
service_account_path |
Optional[str] |
path to the service account credentials file to be used for authentication. If not provided, the default credentials will be used. |
Source code in zenml/integrations/gcp/google_credentials_mixin.py
class GoogleCredentialsConfigMixin(StackComponentConfig):
"""Config mixin for Google Cloud Platform credentials.
Attributes:
service_account_path: path to the service account credentials file to be
used for authentication. If not provided, the default credentials
will be used.
"""
service_account_path: Optional[str] = None
GoogleCredentialsMixin (StackComponent)
StackComponent mixin to get Google Cloud Platform credentials.
Source code in zenml/integrations/gcp/google_credentials_mixin.py
class GoogleCredentialsMixin(StackComponent):
"""StackComponent mixin to get Google Cloud Platform credentials."""
@property
def config(self) -> GoogleCredentialsConfigMixin:
"""Returns the `GoogleCredentialsConfigMixin` config.
Returns:
The configuration.
"""
return cast(GoogleCredentialsConfigMixin, self._config)
def _get_authentication(self) -> Tuple["Credentials", str]:
"""Get GCP credentials and the project ID associated with the credentials.
If `service_account_path` is provided, then the credentials will be
loaded from the file at that path. Otherwise, the default credentials
will be used.
Returns:
A tuple containing the credentials and the project ID associated to
the credentials.
"""
if self.config.service_account_path:
credentials, project_id = load_credentials_from_file(
self.config.service_account_path
)
else:
credentials, project_id = default()
return credentials, project_id
config: GoogleCredentialsConfigMixin
property
readonly
Returns the GoogleCredentialsConfigMixin config.
Returns:
| Type | Description |
|---|---|
GoogleCredentialsConfigMixin |
The configuration. |
orchestrators
special
Initialization for the VertexAI orchestrator.
vertex_orchestrator
Implementation of the VertexAI orchestrator.
VertexOrchestrator (BaseOrchestrator, GoogleCredentialsMixin)
Orchestrator responsible for running pipelines on Vertex AI.
Source code in zenml/integrations/gcp/orchestrators/vertex_orchestrator.py
class VertexOrchestrator(BaseOrchestrator, GoogleCredentialsMixin):
"""Orchestrator responsible for running pipelines on Vertex AI."""
_pipeline_root: str
@property
def config(self) -> VertexOrchestratorConfig:
"""Returns the `VertexOrchestratorConfig` config.
Returns:
The configuration.
"""
return cast(VertexOrchestratorConfig, self._config)
@property
def settings_class(self) -> Optional[Type["BaseSettings"]]:
"""Settings class for the Vertex orchestrator.
Returns:
The settings class.
"""
return VertexOrchestratorSettings
@property
def validator(self) -> Optional[StackValidator]:
"""Validates that the stack contains a container registry.
Also validates that the artifact store is not local.
Returns:
A StackValidator instance.
"""
def _validate_stack_requirements(stack: "Stack") -> Tuple[bool, str]:
"""Validates that all the stack components are not local.
Args:
stack: The stack to validate.
Returns:
A tuple of (is_valid, error_message).
"""
# Validate that the container registry is not local.
container_registry = stack.container_registry
if container_registry and container_registry.config.is_local:
return False, (
f"The Vertex orchestrator does not support local "
f"container registries. You should replace the component '"
f"{container_registry.name}' "
f"{container_registry.type.value} to a remote one."
)
# Validate that the rest of the components are not local.
for stack_comp in stack.components.values():
# For Forward compatibility a list of components is returned,
# but only the first item is relevant for now
# TODO: [server] make sure the ComponentModel actually has
# a local_path property or implement similar check
local_path = stack_comp.local_path
if not local_path:
continue
return False, (
f"The '{stack_comp.name}' {stack_comp.type.value} is a "
f"local stack component. The Vertex AI Pipelines "
f"orchestrator requires that all the components in the "
f"stack used to execute the pipeline have to be not local, "
f"because there is no way for Vertex to connect to your "
f"local machine. You should use a flavor of "
f"{stack_comp.type.value} other than '"
f"{stack_comp.flavor}'."
)
# If the `pipeline_root` has not been defined in the orchestrator
# configuration, and the artifact store is not a GCP artifact store,
# then raise an error.
if (
not self.config.pipeline_root
and stack.artifact_store.flavor != GCP_ARTIFACT_STORE_FLAVOR
):
return False, (
f"The attribute `pipeline_root` has not been set and it "
f"cannot be generated using the path of the artifact store "
f"because it is not a "
f"`zenml.integrations.gcp.artifact_store.GCPArtifactStore`."
f" To solve this issue, set the `pipeline_root` attribute "
f"manually executing the following command: "
f"`zenml orchestrator update {stack.orchestrator.name} "
f'--pipeline_root="<Cloud Storage URI>"`.'
)
return True, ""
return StackValidator(
required_components={StackComponentType.CONTAINER_REGISTRY},
custom_validation_function=_validate_stack_requirements,
)
@property
def root_directory(self) -> str:
"""Returns path to the root directory for files for this orchestrator.
Returns:
The path to the root directory for all files concerning this
orchestrator.
"""
return os.path.join(
get_global_config_directory(), "vertex", str(self.id)
)
@property
def pipeline_directory(self) -> str:
"""Returns path to directory where kubeflow pipelines files are stored.
Returns:
Path to the pipeline directory.
"""
return os.path.join(self.root_directory, "pipelines")
def prepare_pipeline_deployment(
self,
deployment: "PipelineDeployment",
stack: "Stack",
) -> None:
"""Build a Docker image and push it to the container registry.
Args:
deployment: The pipeline deployment configuration.
stack: The stack on which the pipeline will be deployed.
"""
docker_image_builder = PipelineDockerImageBuilder()
repo_digest = docker_image_builder.build_and_push_docker_image(
deployment=deployment, stack=stack
)
deployment.add_extra(ORCHESTRATOR_DOCKER_IMAGE_KEY, repo_digest)
def _configure_container_resources(
self,
container_op: dsl.ContainerOp,
resource_settings: "ResourceSettings",
node_selector_constraint: Optional[Tuple[str, str]] = None,
) -> None:
"""Adds resource requirements to the container.
Args:
container_op: The kubeflow container operation to configure.
resource_settings: The resource settings to use for this
container.
node_selector_constraint: Node selector constraint to apply to
the container.
"""
# Set optional CPU, RAM and GPU constraints for the pipeline
cpu_limit = resource_settings.cpu_count or self.config.cpu_limit
if cpu_limit is not None:
container_op = container_op.set_cpu_limit(str(cpu_limit))
memory_limit = (
resource_settings.memory[:-1]
if resource_settings.memory
else self.config.memory_limit
)
if memory_limit is not None:
container_op = container_op.set_memory_limit(memory_limit)
gpu_limit = (
resource_settings.gpu_count
if resource_settings.gpu_count is not None
else self.config.gpu_limit
)
if gpu_limit is not None and gpu_limit > 0:
container_op = container_op.set_gpu_limit(gpu_limit)
if node_selector_constraint:
constraint_label, value = node_selector_constraint
if not (
constraint_label
== GKE_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL
and gpu_limit == 0
):
container_op.add_node_selector_constraint(
constraint_label, value
)
def prepare_or_run_pipeline(
self,
deployment: "PipelineDeployment",
stack: "Stack",
) -> Any:
"""Creates a KFP JSON pipeline.
# noqa: DAR402
This is an intermediary representation of the pipeline which is then
deployed to Vertex AI Pipelines service.
How it works:
-------------
Before this method is called the `prepare_pipeline_deployment()` method
builds a Docker image that contains the code for the pipeline, all steps
the context around these files.
Based on this Docker image a callable is created which builds
container_ops for each step (`_construct_kfp_pipeline`). The function
`kfp.components.load_component_from_text` is used to create the
`ContainerOp`, because using the `dsl.ContainerOp` class directly is
deprecated when using the Kubeflow SDK v2. The step entrypoint command
with the entrypoint arguments is the command that will be executed by
the container created using the previously created Docker image.
This callable is then compiled into a JSON file that is used as the
intermediary representation of the Kubeflow pipeline.
This file then is submitted to the Vertex AI Pipelines service for
execution.
Args:
deployment: The pipeline deployment to prepare or run.
stack: The stack the pipeline will run on.
Raises:
ValueError: If the attribute `pipeline_root` is not set and it
can be not generated using the path of the artifact store in the
stack because it is not a
`zenml.integrations.gcp.artifact_store.GCPArtifactStore`.
"""
orchestrator_run_name = get_orchestrator_run_name(
pipeline_name=deployment.pipeline.name
)
# If the `pipeline_root` has not been defined in the orchestrator
# configuration,
# try to create it from the artifact store if it is a
# `GCPArtifactStore`.
if not self.config.pipeline_root:
artifact_store = stack.artifact_store
self._pipeline_root = f"{artifact_store.path.rstrip('/')}/vertex_pipeline_root/{deployment.pipeline.name}/{orchestrator_run_name}"
logger.info(
"The attribute `pipeline_root` has not been set in the "
"orchestrator configuration. One has been generated "
"automatically based on the path of the `GCPArtifactStore` "
"artifact store in the stack used to execute the pipeline. "
"The generated `pipeline_root` is `%s`.",
self._pipeline_root,
)
else:
self._pipeline_root = self.config.pipeline_root
if deployment.schedule:
logger.warning(
"Pipeline scheduling configuration was provided, but Vertex "
"AI Pipelines does not support scheduling yet. Creating "
"a one-time run instead."
)
image_name = deployment.pipeline.extra[ORCHESTRATOR_DOCKER_IMAGE_KEY]
def _construct_kfp_pipeline() -> None:
"""Create a `ContainerOp` for each step.
This should contain the name of the Docker image and configures the
entrypoint of the Docker image to run the step.
Additionally, this gives each `ContainerOp` information about its
direct downstream steps.
If this callable is passed to the `compile()` method of
`KFPV2Compiler` all `dsl.ContainerOp` instances will be
automatically added to a singular `dsl.Pipeline` instance.
"""
command = StepEntrypointConfiguration.get_entrypoint_command()
step_name_to_container_op: Dict[str, dsl.ContainerOp] = {}
for step_name, step in deployment.steps.items():
arguments = (
StepEntrypointConfiguration.get_entrypoint_arguments(
step_name=step_name,
)
)
# Create the `ContainerOp` for the step. Using the
# `dsl.ContainerOp`
# class directly is deprecated when using the Kubeflow SDK v2.
container_op = kfp.components.load_component_from_text(
f"""
name: {step.config.name}
implementation:
container:
image: {image_name}
command: {command + arguments}"""
)()
container_op.set_env_variable(
name=ENV_ZENML_VERTEX_RUN_ID,
value=dslv2.PIPELINE_JOB_NAME_PLACEHOLDER,
)
# Set upstream tasks as a dependency of the current step
for upstream_step_name in step.spec.upstream_steps:
upstream_container_op = step_name_to_container_op[
upstream_step_name
]
container_op.after(upstream_container_op)
settings = cast(
VertexOrchestratorSettings,
self.get_settings(step),
)
if settings.pod_settings:
apply_pod_settings(
container_op=container_op,
settings=settings.pod_settings,
)
self._configure_container_resources(
container_op=container_op,
resource_settings=step.config.resource_settings,
node_selector_constraint=settings.node_selector_constraint,
)
container_op.set_caching_options(enable_caching=False)
step_name_to_container_op[step.config.name] = container_op
# Save the generated pipeline to a file.
fileio.makedirs(self.pipeline_directory)
pipeline_file_path = os.path.join(
self.pipeline_directory,
f"{orchestrator_run_name}.json",
)
# Compile the pipeline using the Kubeflow SDK V2 compiler that allows
# to generate a JSON representation of the pipeline that can be later
# upload to Vertex AI Pipelines service.
KFPV2Compiler().compile(
pipeline_func=_construct_kfp_pipeline,
package_path=pipeline_file_path,
pipeline_name=_clean_pipeline_name(deployment.pipeline.name),
)
logger.info(
"Writing Vertex workflow definition to `%s`.", pipeline_file_path
)
settings = cast(
VertexOrchestratorSettings, self.get_settings(deployment)
)
# Using the Google Cloud AIPlatform client, upload and execute the
# pipeline
# on the Vertex AI Pipelines service.
self._upload_and_run_pipeline(
pipeline_name=deployment.pipeline.name,
pipeline_file_path=pipeline_file_path,
run_name=orchestrator_run_name,
settings=settings,
)
def _upload_and_run_pipeline(
self,
pipeline_name: str,
pipeline_file_path: str,
run_name: str,
settings: VertexOrchestratorSettings,
) -> None:
"""Uploads and run the pipeline on the Vertex AI Pipelines service.
Args:
pipeline_name: Name of the pipeline.
pipeline_file_path: Path of the JSON file containing the compiled
Kubeflow pipeline (compiled with Kubeflow SDK v2).
run_name: Orchestrator run name.
settings: Pipeline level settings for this orchestrator.
"""
# We have to replace the hyphens in the run name with underscores
# and lower case the string, because the Vertex AI Pipelines service
# requires this format.
job_id = _clean_pipeline_name(run_name)
# Get the credentials that would be used to create the Vertex AI
# Pipelines
# job.
credentials, project_id = self._get_authentication()
if self.config.project and self.config.project != project_id:
logger.warning(
"Authenticated with project `%s`, but this orchestrator is "
"configured to use the project `%s`.",
project_id,
self.config.project,
)
# If the project was set in the configuration, use it. Otherwise, use
# the project that was used to authenticate.
project_id = self.config.project if self.config.project else project_id
# Instantiate the Vertex AI Pipelines job
run = aiplatform.PipelineJob(
display_name=pipeline_name,
template_path=pipeline_file_path,
job_id=job_id,
pipeline_root=self._pipeline_root,
parameter_values=None,
enable_caching=False,
encryption_spec_key_name=self.config.encryption_spec_key_name,
labels=settings.labels,
credentials=credentials,
project=self.config.project,
location=self.config.location,
)
logger.info(
"Submitting pipeline job with job_id `%s` to Vertex AI Pipelines "
"service.",
job_id,
)
# Submit the job to Vertex AI Pipelines service.
try:
if self.config.workload_service_account:
logger.info(
"The Vertex AI Pipelines job workload will be executed "
"using `%s` "
"service account.",
self.config.workload_service_account,
)
if self.config.network:
logger.info(
"The Vertex AI Pipelines job will be peered with `%s` "
"network.",
self.config.network,
)
run.submit(
service_account=self.config.workload_service_account,
network=self.config.network,
)
logger.info(
"View the Vertex AI Pipelines job at %s", run._dashboard_uri()
)
if settings.synchronous:
logger.info(
"Waiting for the Vertex AI Pipelines job to finish..."
)
run.wait()
except google_exceptions.ClientError as e:
logger.warning(
"Failed to create the Vertex AI Pipelines job: %s", e
)
except RuntimeError as e:
logger.error(
"The Vertex AI Pipelines job execution has failed: %s", e
)
def get_orchestrator_run_id(self) -> str:
"""Returns the active orchestrator run id.
Raises:
RuntimeError: If the environment variable specifying the run id
is not set.
Returns:
The orchestrator run id.
"""
try:
return os.environ[ENV_ZENML_VERTEX_RUN_ID]
except KeyError:
raise RuntimeError(
"Unable to read run id from environment variable "
f"{ENV_ZENML_VERTEX_RUN_ID}."
)
config: VertexOrchestratorConfig
property
readonly
Returns the VertexOrchestratorConfig config.
Returns:
| Type | Description |
|---|---|
VertexOrchestratorConfig |
The configuration. |
pipeline_directory: str
property
readonly
Returns path to directory where kubeflow pipelines files are stored.
Returns:
| Type | Description |
|---|---|
str |
Path to the pipeline directory. |
root_directory: str
property
readonly
Returns path to the root directory for files for this orchestrator.
Returns:
| Type | Description |
|---|---|
str |
The path to the root directory for all files concerning this orchestrator. |
settings_class: Optional[Type[BaseSettings]]
property
readonly
Settings class for the Vertex orchestrator.
Returns:
| Type | Description |
|---|---|
Optional[Type[BaseSettings]] |
The settings class. |
validator: Optional[zenml.stack.stack_validator.StackValidator]
property
readonly
Validates that the stack contains a container registry.
Also validates that the artifact store is not local.
Returns:
| Type | Description |
|---|---|
Optional[zenml.stack.stack_validator.StackValidator] |
A StackValidator instance. |
get_orchestrator_run_id(self)
Returns the active orchestrator run id.
Exceptions:
| Type | Description |
|---|---|
RuntimeError |
If the environment variable specifying the run id is not set. |
Returns:
| Type | Description |
|---|---|
str |
The orchestrator run id. |
Source code in zenml/integrations/gcp/orchestrators/vertex_orchestrator.py
def get_orchestrator_run_id(self) -> str:
"""Returns the active orchestrator run id.
Raises:
RuntimeError: If the environment variable specifying the run id
is not set.
Returns:
The orchestrator run id.
"""
try:
return os.environ[ENV_ZENML_VERTEX_RUN_ID]
except KeyError:
raise RuntimeError(
"Unable to read run id from environment variable "
f"{ENV_ZENML_VERTEX_RUN_ID}."
)
prepare_or_run_pipeline(self, deployment, stack)
Creates a KFP JSON pipeline.
noqa: DAR402
This is an intermediary representation of the pipeline which is then deployed to Vertex AI Pipelines service.
How it works:
Before this method is called the prepare_pipeline_deployment() method
builds a Docker image that contains the code for the pipeline, all steps
the context around these files.
Based on this Docker image a callable is created which builds
container_ops for each step (_construct_kfp_pipeline). The function
kfp.components.load_component_from_text is used to create the
ContainerOp, because using the dsl.ContainerOp class directly is
deprecated when using the Kubeflow SDK v2. The step entrypoint command
with the entrypoint arguments is the command that will be executed by
the container created using the previously created Docker image.
This callable is then compiled into a JSON file that is used as the intermediary representation of the Kubeflow pipeline.
This file then is submitted to the Vertex AI Pipelines service for execution.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
deployment |
PipelineDeployment |
The pipeline deployment to prepare or run. |
required |
stack |
Stack |
The stack the pipeline will run on. |
required |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the attribute |
Source code in zenml/integrations/gcp/orchestrators/vertex_orchestrator.py
def prepare_or_run_pipeline(
self,
deployment: "PipelineDeployment",
stack: "Stack",
) -> Any:
"""Creates a KFP JSON pipeline.
# noqa: DAR402
This is an intermediary representation of the pipeline which is then
deployed to Vertex AI Pipelines service.
How it works:
-------------
Before this method is called the `prepare_pipeline_deployment()` method
builds a Docker image that contains the code for the pipeline, all steps
the context around these files.
Based on this Docker image a callable is created which builds
container_ops for each step (`_construct_kfp_pipeline`). The function
`kfp.components.load_component_from_text` is used to create the
`ContainerOp`, because using the `dsl.ContainerOp` class directly is
deprecated when using the Kubeflow SDK v2. The step entrypoint command
with the entrypoint arguments is the command that will be executed by
the container created using the previously created Docker image.
This callable is then compiled into a JSON file that is used as the
intermediary representation of the Kubeflow pipeline.
This file then is submitted to the Vertex AI Pipelines service for
execution.
Args:
deployment: The pipeline deployment to prepare or run.
stack: The stack the pipeline will run on.
Raises:
ValueError: If the attribute `pipeline_root` is not set and it
can be not generated using the path of the artifact store in the
stack because it is not a
`zenml.integrations.gcp.artifact_store.GCPArtifactStore`.
"""
orchestrator_run_name = get_orchestrator_run_name(
pipeline_name=deployment.pipeline.name
)
# If the `pipeline_root` has not been defined in the orchestrator
# configuration,
# try to create it from the artifact store if it is a
# `GCPArtifactStore`.
if not self.config.pipeline_root:
artifact_store = stack.artifact_store
self._pipeline_root = f"{artifact_store.path.rstrip('/')}/vertex_pipeline_root/{deployment.pipeline.name}/{orchestrator_run_name}"
logger.info(
"The attribute `pipeline_root` has not been set in the "
"orchestrator configuration. One has been generated "
"automatically based on the path of the `GCPArtifactStore` "
"artifact store in the stack used to execute the pipeline. "
"The generated `pipeline_root` is `%s`.",
self._pipeline_root,
)
else:
self._pipeline_root = self.config.pipeline_root
if deployment.schedule:
logger.warning(
"Pipeline scheduling configuration was provided, but Vertex "
"AI Pipelines does not support scheduling yet. Creating "
"a one-time run instead."
)
image_name = deployment.pipeline.extra[ORCHESTRATOR_DOCKER_IMAGE_KEY]
def _construct_kfp_pipeline() -> None:
"""Create a `ContainerOp` for each step.
This should contain the name of the Docker image and configures the
entrypoint of the Docker image to run the step.
Additionally, this gives each `ContainerOp` information about its
direct downstream steps.
If this callable is passed to the `compile()` method of
`KFPV2Compiler` all `dsl.ContainerOp` instances will be
automatically added to a singular `dsl.Pipeline` instance.
"""
command = StepEntrypointConfiguration.get_entrypoint_command()
step_name_to_container_op: Dict[str, dsl.ContainerOp] = {}
for step_name, step in deployment.steps.items():
arguments = (
StepEntrypointConfiguration.get_entrypoint_arguments(
step_name=step_name,
)
)
# Create the `ContainerOp` for the step. Using the
# `dsl.ContainerOp`
# class directly is deprecated when using the Kubeflow SDK v2.
container_op = kfp.components.load_component_from_text(
f"""
name: {step.config.name}
implementation:
container:
image: {image_name}
command: {command + arguments}"""
)()
container_op.set_env_variable(
name=ENV_ZENML_VERTEX_RUN_ID,
value=dslv2.PIPELINE_JOB_NAME_PLACEHOLDER,
)
# Set upstream tasks as a dependency of the current step
for upstream_step_name in step.spec.upstream_steps:
upstream_container_op = step_name_to_container_op[
upstream_step_name
]
container_op.after(upstream_container_op)
settings = cast(
VertexOrchestratorSettings,
self.get_settings(step),
)
if settings.pod_settings:
apply_pod_settings(
container_op=container_op,
settings=settings.pod_settings,
)
self._configure_container_resources(
container_op=container_op,
resource_settings=step.config.resource_settings,
node_selector_constraint=settings.node_selector_constraint,
)
container_op.set_caching_options(enable_caching=False)
step_name_to_container_op[step.config.name] = container_op
# Save the generated pipeline to a file.
fileio.makedirs(self.pipeline_directory)
pipeline_file_path = os.path.join(
self.pipeline_directory,
f"{orchestrator_run_name}.json",
)
# Compile the pipeline using the Kubeflow SDK V2 compiler that allows
# to generate a JSON representation of the pipeline that can be later
# upload to Vertex AI Pipelines service.
KFPV2Compiler().compile(
pipeline_func=_construct_kfp_pipeline,
package_path=pipeline_file_path,
pipeline_name=_clean_pipeline_name(deployment.pipeline.name),
)
logger.info(
"Writing Vertex workflow definition to `%s`.", pipeline_file_path
)
settings = cast(
VertexOrchestratorSettings, self.get_settings(deployment)
)
# Using the Google Cloud AIPlatform client, upload and execute the
# pipeline
# on the Vertex AI Pipelines service.
self._upload_and_run_pipeline(
pipeline_name=deployment.pipeline.name,
pipeline_file_path=pipeline_file_path,
run_name=orchestrator_run_name,
settings=settings,
)
prepare_pipeline_deployment(self, deployment, stack)
Build a Docker image and push it to the container registry.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
deployment |
PipelineDeployment |
The pipeline deployment configuration. |
required |
stack |
Stack |
The stack on which the pipeline will be deployed. |
required |
Source code in zenml/integrations/gcp/orchestrators/vertex_orchestrator.py
def prepare_pipeline_deployment(
self,
deployment: "PipelineDeployment",
stack: "Stack",
) -> None:
"""Build a Docker image and push it to the container registry.
Args:
deployment: The pipeline deployment configuration.
stack: The stack on which the pipeline will be deployed.
"""
docker_image_builder = PipelineDockerImageBuilder()
repo_digest = docker_image_builder.build_and_push_docker_image(
deployment=deployment, stack=stack
)
deployment.add_extra(ORCHESTRATOR_DOCKER_IMAGE_KEY, repo_digest)
secrets_manager
special
ZenML integration for GCP Secrets Manager.
The GCP Secrets Manager allows your pipeline to directly access the GCP secrets manager and use the secrets within during runtime.
gcp_secrets_manager
Implementation of the GCP Secrets Manager.
GCPSecretsManager (BaseSecretsManager)
Class to interact with the GCP secrets manager.
Source code in zenml/integrations/gcp/secrets_manager/gcp_secrets_manager.py
class GCPSecretsManager(BaseSecretsManager):
"""Class to interact with the GCP secrets manager."""
CLIENT: ClassVar[Any] = None
@property
def config(self) -> GCPSecretsManagerConfig:
"""Returns the `GCPSecretsManagerConfig` config.
Returns:
The configuration.
"""
return cast(GCPSecretsManagerConfig, self._config)
@classmethod
def _ensure_client_connected(cls) -> None:
if cls.CLIENT is None:
cls.CLIENT = secretmanager.SecretManagerServiceClient()
@property
def parent_name(self) -> str:
"""Construct the GCP parent path to the secret manager.
Returns:
The parent path to the secret manager
"""
return f"projects/{self.config.project_id}"
def _convert_secret_content(
self, secret: BaseSecretSchema
) -> Dict[str, str]:
"""Convert the secret content into a Google compatible representation.
This method implements two currently supported modes of adapting between
the naming schemas used for ZenML secrets and Google secrets:
* for a scoped Secrets Manager, a Google secret is created for each
ZenML secret with a name that reflects the ZenML secret name and scope
and a value that contains all its key-value pairs in JSON format.
* for an unscoped (i.e. legacy) Secrets Manager, this method creates
multiple Google secret entries for a single ZenML secret by adding the
secret name to the key name of each secret key-value pair. This allows
using the same key across multiple secrets. This is only kept for
backwards compatibility and will be removed some time in the future.
Args:
secret: The ZenML secret
Returns:
A dictionary with the Google secret name as key and the secret
contents as value.
"""
if self.config.scope == SecretsManagerScope.NONE:
# legacy per-key secret mapping
return {f"{secret.name}_{k}": v for k, v in secret.content.items()}
return {
self._get_scoped_secret_name(
secret.name, separator=ZENML_GCP_SECRET_SCOPE_PATH_SEPARATOR
): json.dumps(secret_to_dict(secret)),
}
def _get_secret_labels(
self, secret: BaseSecretSchema
) -> List[Tuple[str, str]]:
"""Return a list of Google secret label values for a given secret.
Args:
secret: the secret object
Returns:
A list of Google secret label values
"""
if self.config.scope == SecretsManagerScope.NONE:
# legacy per-key secret labels
return [
(ZENML_GROUP_KEY, secret.name),
(ZENML_SCHEMA_NAME, secret.TYPE),
]
metadata = self._get_secret_metadata(secret)
return list(metadata.items())
def _get_secret_scope_filters(
self,
secret_name: Optional[str] = None,
) -> str:
"""Return a Google filter expression for the entire scope or just a scoped secret.
These filters can be used when querying the Google Secrets Manager
for all secrets or for a single secret available in the configured
scope (see https://cloud.google.com/secret-manager/docs/filtering).
Args:
secret_name: Optional secret name to include in the scope metadata.
Returns:
Google filter expression uniquely identifying all secrets
or a named secret within the configured scope.
"""
if self.config.scope == SecretsManagerScope.NONE:
# legacy per-key secret label filters
if secret_name:
return f"labels.{ZENML_GROUP_KEY}={secret_name}"
else:
return f"labels.{ZENML_GROUP_KEY}:*"
metadata = self._get_secret_scope_metadata(secret_name)
filters = [f"labels.{l}={v}" for (l, v) in metadata.items()]
if secret_name:
filters.append(f"name:{secret_name}")
return " AND ".join(filters)
def _list_secrets(self, secret_name: Optional[str] = None) -> List[str]:
"""List all secrets matching a name.
This method lists all the secrets in the current scope without loading
their contents. An optional secret name can be supplied to filter out
all but a single secret identified by name.
Args:
secret_name: Optional secret name to filter for.
Returns:
A list of secret names in the current scope and the optional
secret name.
"""
self._ensure_client_connected()
set_of_secrets = set()
# List all secrets.
for secret in self.CLIENT.list_secrets(
request={
"parent": self.parent_name,
"filter": self._get_secret_scope_filters(secret_name),
}
):
if self.config.scope == SecretsManagerScope.NONE:
name = secret.labels[ZENML_GROUP_KEY]
else:
name = secret.labels[ZENML_SECRET_NAME_LABEL]
# filter by secret name, if one was given
if name and (not secret_name or name == secret_name):
set_of_secrets.add(name)
return list(set_of_secrets)
def register_secret(self, secret: BaseSecretSchema) -> None:
"""Registers a new secret.
Args:
secret: the secret to register
Raises:
SecretExistsError: if the secret already exists
"""
validate_gcp_secret_name_or_namespace(secret.name)
self._ensure_client_connected()
if self._list_secrets(secret.name):
raise SecretExistsError(
f"A Secret with the name {secret.name} already exists"
)
adjusted_content = self._convert_secret_content(secret)
for k, v in adjusted_content.items():
# Create the secret, this only creates an empty secret with the
# supplied name.
gcp_secret = self.CLIENT.create_secret(
request={
"parent": self.parent_name,
"secret_id": k,
"secret": {
"replication": {"automatic": {}},
"labels": self._get_secret_labels(secret),
},
}
)
logger.debug("Created empty secret: %s", gcp_secret.name)
self.CLIENT.add_secret_version(
request={
"parent": gcp_secret.name,
"payload": {"data": str(v).encode()},
}
)
logger.debug("Added value to secret.")
def get_secret(self, secret_name: str) -> BaseSecretSchema:
"""Get a secret by its name.
Args:
secret_name: the name of the secret to get
Returns:
The secret.
Raises:
KeyError: if the secret does not exist
"""
validate_gcp_secret_name_or_namespace(secret_name)
self._ensure_client_connected()
zenml_secret: Optional[BaseSecretSchema] = None
if self.config.scope == SecretsManagerScope.NONE:
# Legacy secrets are mapped to multiple Google secrets, one for
# each secret key
secret_contents = {}
zenml_schema_name = ""
# List all secrets.
for google_secret in self.CLIENT.list_secrets(
request={
"parent": self.parent_name,
"filter": self._get_secret_scope_filters(secret_name),
}
):
secret_version_name = google_secret.name + "/versions/latest"
response = self.CLIENT.access_secret_version(
request={"name": secret_version_name}
)
secret_value = response.payload.data.decode("UTF-8")
secret_key = remove_group_name_from_key(
google_secret.name.split("/")[-1], secret_name
)
secret_contents[secret_key] = secret_value
zenml_schema_name = google_secret.labels[ZENML_SCHEMA_NAME]
if not secret_contents:
raise KeyError(
f"Can't find the specified secret '{secret_name}'"
)
secret_contents["name"] = secret_name
secret_schema = SecretSchemaClassRegistry.get_class(
secret_schema=zenml_schema_name
)
zenml_secret = secret_schema(**secret_contents)
else:
# Scoped secrets are mapped 1-to-1 with Google secrets
google_secret_name = self.CLIENT.secret_path(
self.config.project_id,
self._get_scoped_secret_name(
secret_name,
separator=ZENML_GCP_SECRET_SCOPE_PATH_SEPARATOR,
),
)
try:
# fetch the latest secret version
google_secret = self.CLIENT.get_secret(name=google_secret_name)
except google_exceptions.NotFound:
raise KeyError(
f"Can't find the specified secret '{secret_name}'"
)
# make sure the secret has the correct scope labels to filter out
# unscoped secrets with similar names
scope_labels = self._get_secret_scope_metadata(secret_name)
# all scope labels need to be included in the google secret labels,
# otherwise the secret does not belong to the current scope
if not scope_labels.items() <= google_secret.labels.items():
raise KeyError(
f"Can't find the specified secret '{secret_name}'"
)
try:
# fetch the latest secret version
response = self.CLIENT.access_secret_version(
name=f"{google_secret_name}/versions/latest"
)
except google_exceptions.NotFound:
raise KeyError(
f"Can't find the specified secret '{secret_name}'"
)
secret_value = response.payload.data.decode("UTF-8")
zenml_secret = secret_from_dict(
json.loads(secret_value), secret_name=secret_name
)
return zenml_secret
def get_all_secret_keys(self) -> List[str]:
"""Get all secret keys.
Returns:
A list of all secret keys
"""
return self._list_secrets()
def update_secret(self, secret: BaseSecretSchema) -> None:
"""Update an existing secret by creating new versions of the existing secrets.
Args:
secret: the secret to update
Raises:
KeyError: if the secret does not exist
"""
validate_gcp_secret_name_or_namespace(secret.name)
self._ensure_client_connected()
if not self._list_secrets(secret.name):
raise KeyError(f"Can't find the specified secret '{secret.name}'")
adjusted_content = self._convert_secret_content(secret)
for k, v in adjusted_content.items():
# Create the secret, this only creates an empty secret with the
# supplied name.
google_secret_name = self.CLIENT.secret_path(
self.config.project_id, k
)
payload = {"data": str(v).encode()}
self.CLIENT.add_secret_version(
request={"parent": google_secret_name, "payload": payload}
)
def delete_secret(self, secret_name: str) -> None:
"""Delete an existing secret by name.
Args:
secret_name: the name of the secret to delete
Raises:
KeyError: if the secret no longer exists
"""
validate_gcp_secret_name_or_namespace(secret_name)
self._ensure_client_connected()
if not self._list_secrets(secret_name):
raise KeyError(f"Can't find the specified secret '{secret_name}'")
# Go through all gcp secrets and delete the ones with the secret_name
# as label.
for secret in self.CLIENT.list_secrets(
request={
"parent": self.parent_name,
"filter": self._get_secret_scope_filters(secret_name),
}
):
self.CLIENT.delete_secret(request={"name": secret.name})
def delete_all_secrets(self) -> None:
"""Delete all existing secrets."""
self._ensure_client_connected()
# List all secrets.
for secret in self.CLIENT.list_secrets(
request={
"parent": self.parent_name,
"filter": self._get_secret_scope_filters(),
}
):
logger.info(f"Deleting Google secret {secret.name}")
self.CLIENT.delete_secret(request={"name": secret.name})
config: GCPSecretsManagerConfig
property
readonly
Returns the GCPSecretsManagerConfig config.
Returns:
| Type | Description |
|---|---|
GCPSecretsManagerConfig |
The configuration. |
parent_name: str
property
readonly
Construct the GCP parent path to the secret manager.
Returns:
| Type | Description |
|---|---|
str |
The parent path to the secret manager |
delete_all_secrets(self)
Delete all existing secrets.
Source code in zenml/integrations/gcp/secrets_manager/gcp_secrets_manager.py
def delete_all_secrets(self) -> None:
"""Delete all existing secrets."""
self._ensure_client_connected()
# List all secrets.
for secret in self.CLIENT.list_secrets(
request={
"parent": self.parent_name,
"filter": self._get_secret_scope_filters(),
}
):
logger.info(f"Deleting Google secret {secret.name}")
self.CLIENT.delete_secret(request={"name": secret.name})
delete_secret(self, secret_name)
Delete an existing secret by name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
secret_name |
str |
the name of the secret to delete |
required |
Exceptions:
| Type | Description |
|---|---|
KeyError |
if the secret no longer exists |
Source code in zenml/integrations/gcp/secrets_manager/gcp_secrets_manager.py
def delete_secret(self, secret_name: str) -> None:
"""Delete an existing secret by name.
Args:
secret_name: the name of the secret to delete
Raises:
KeyError: if the secret no longer exists
"""
validate_gcp_secret_name_or_namespace(secret_name)
self._ensure_client_connected()
if not self._list_secrets(secret_name):
raise KeyError(f"Can't find the specified secret '{secret_name}'")
# Go through all gcp secrets and delete the ones with the secret_name
# as label.
for secret in self.CLIENT.list_secrets(
request={
"parent": self.parent_name,
"filter": self._get_secret_scope_filters(secret_name),
}
):
self.CLIENT.delete_secret(request={"name": secret.name})
get_all_secret_keys(self)
Get all secret keys.
Returns:
| Type | Description |
|---|---|
List[str] |
A list of all secret keys |
Source code in zenml/integrations/gcp/secrets_manager/gcp_secrets_manager.py
def get_all_secret_keys(self) -> List[str]:
"""Get all secret keys.
Returns:
A list of all secret keys
"""
return self._list_secrets()
get_secret(self, secret_name)
Get a secret by its name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
secret_name |
str |
the name of the secret to get |
required |
Returns:
| Type | Description |
|---|---|
BaseSecretSchema |
The secret. |
Exceptions:
| Type | Description |
|---|---|
KeyError |
if the secret does not exist |
Source code in zenml/integrations/gcp/secrets_manager/gcp_secrets_manager.py
def get_secret(self, secret_name: str) -> BaseSecretSchema:
"""Get a secret by its name.
Args:
secret_name: the name of the secret to get
Returns:
The secret.
Raises:
KeyError: if the secret does not exist
"""
validate_gcp_secret_name_or_namespace(secret_name)
self._ensure_client_connected()
zenml_secret: Optional[BaseSecretSchema] = None
if self.config.scope == SecretsManagerScope.NONE:
# Legacy secrets are mapped to multiple Google secrets, one for
# each secret key
secret_contents = {}
zenml_schema_name = ""
# List all secrets.
for google_secret in self.CLIENT.list_secrets(
request={
"parent": self.parent_name,
"filter": self._get_secret_scope_filters(secret_name),
}
):
secret_version_name = google_secret.name + "/versions/latest"
response = self.CLIENT.access_secret_version(
request={"name": secret_version_name}
)
secret_value = response.payload.data.decode("UTF-8")
secret_key = remove_group_name_from_key(
google_secret.name.split("/")[-1], secret_name
)
secret_contents[secret_key] = secret_value
zenml_schema_name = google_secret.labels[ZENML_SCHEMA_NAME]
if not secret_contents:
raise KeyError(
f"Can't find the specified secret '{secret_name}'"
)
secret_contents["name"] = secret_name
secret_schema = SecretSchemaClassRegistry.get_class(
secret_schema=zenml_schema_name
)
zenml_secret = secret_schema(**secret_contents)
else:
# Scoped secrets are mapped 1-to-1 with Google secrets
google_secret_name = self.CLIENT.secret_path(
self.config.project_id,
self._get_scoped_secret_name(
secret_name,
separator=ZENML_GCP_SECRET_SCOPE_PATH_SEPARATOR,
),
)
try:
# fetch the latest secret version
google_secret = self.CLIENT.get_secret(name=google_secret_name)
except google_exceptions.NotFound:
raise KeyError(
f"Can't find the specified secret '{secret_name}'"
)
# make sure the secret has the correct scope labels to filter out
# unscoped secrets with similar names
scope_labels = self._get_secret_scope_metadata(secret_name)
# all scope labels need to be included in the google secret labels,
# otherwise the secret does not belong to the current scope
if not scope_labels.items() <= google_secret.labels.items():
raise KeyError(
f"Can't find the specified secret '{secret_name}'"
)
try:
# fetch the latest secret version
response = self.CLIENT.access_secret_version(
name=f"{google_secret_name}/versions/latest"
)
except google_exceptions.NotFound:
raise KeyError(
f"Can't find the specified secret '{secret_name}'"
)
secret_value = response.payload.data.decode("UTF-8")
zenml_secret = secret_from_dict(
json.loads(secret_value), secret_name=secret_name
)
return zenml_secret
register_secret(self, secret)
Registers a new secret.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
secret |
BaseSecretSchema |
the secret to register |
required |
Exceptions:
| Type | Description |
|---|---|
SecretExistsError |
if the secret already exists |
Source code in zenml/integrations/gcp/secrets_manager/gcp_secrets_manager.py
def register_secret(self, secret: BaseSecretSchema) -> None:
"""Registers a new secret.
Args:
secret: the secret to register
Raises:
SecretExistsError: if the secret already exists
"""
validate_gcp_secret_name_or_namespace(secret.name)
self._ensure_client_connected()
if self._list_secrets(secret.name):
raise SecretExistsError(
f"A Secret with the name {secret.name} already exists"
)
adjusted_content = self._convert_secret_content(secret)
for k, v in adjusted_content.items():
# Create the secret, this only creates an empty secret with the
# supplied name.
gcp_secret = self.CLIENT.create_secret(
request={
"parent": self.parent_name,
"secret_id": k,
"secret": {
"replication": {"automatic": {}},
"labels": self._get_secret_labels(secret),
},
}
)
logger.debug("Created empty secret: %s", gcp_secret.name)
self.CLIENT.add_secret_version(
request={
"parent": gcp_secret.name,
"payload": {"data": str(v).encode()},
}
)
logger.debug("Added value to secret.")
update_secret(self, secret)
Update an existing secret by creating new versions of the existing secrets.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
secret |
BaseSecretSchema |
the secret to update |
required |
Exceptions:
| Type | Description |
|---|---|
KeyError |
if the secret does not exist |
Source code in zenml/integrations/gcp/secrets_manager/gcp_secrets_manager.py
def update_secret(self, secret: BaseSecretSchema) -> None:
"""Update an existing secret by creating new versions of the existing secrets.
Args:
secret: the secret to update
Raises:
KeyError: if the secret does not exist
"""
validate_gcp_secret_name_or_namespace(secret.name)
self._ensure_client_connected()
if not self._list_secrets(secret.name):
raise KeyError(f"Can't find the specified secret '{secret.name}'")
adjusted_content = self._convert_secret_content(secret)
for k, v in adjusted_content.items():
# Create the secret, this only creates an empty secret with the
# supplied name.
google_secret_name = self.CLIENT.secret_path(
self.config.project_id, k
)
payload = {"data": str(v).encode()}
self.CLIENT.add_secret_version(
request={"parent": google_secret_name, "payload": payload}
)
remove_group_name_from_key(combined_key_name, group_name)
Removes the secret group name from the secret key.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
combined_key_name |
str |
Full name as it is within the gcp secrets manager |
required |
group_name |
str |
Group name (the ZenML Secret name) |
required |
Returns:
| Type | Description |
|---|---|
str |
The cleaned key |
Exceptions:
| Type | Description |
|---|---|
RuntimeError |
If the group name is not found in the key |
Source code in zenml/integrations/gcp/secrets_manager/gcp_secrets_manager.py
def remove_group_name_from_key(combined_key_name: str, group_name: str) -> str:
"""Removes the secret group name from the secret key.
Args:
combined_key_name: Full name as it is within the gcp secrets manager
group_name: Group name (the ZenML Secret name)
Returns:
The cleaned key
Raises:
RuntimeError: If the group name is not found in the key
"""
if combined_key_name.startswith(group_name + "_"):
return combined_key_name[len(group_name + "_") :]
else:
raise RuntimeError(
f"Key-name `{combined_key_name}` does not have the "
f"prefix `{group_name}`. Key could not be "
f"extracted."
)
step_operators
special
Initialization for the VertexAI Step Operator.
vertex_step_operator
Implementation of a VertexAI step operator.
Code heavily inspired by TFX Implementation: https://github.com/tensorflow/tfx/blob/master/tfx/extensions/ google_cloud_ai_platform/training_clients.py
VertexStepOperator (BaseStepOperator, GoogleCredentialsMixin)
Step operator to run a step on Vertex AI.
This class defines code that can set up a Vertex AI environment and run the ZenML entrypoint command in it.
Source code in zenml/integrations/gcp/step_operators/vertex_step_operator.py
class VertexStepOperator(BaseStepOperator, GoogleCredentialsMixin):
"""Step operator to run a step on Vertex AI.
This class defines code that can set up a Vertex AI environment and run the
ZenML entrypoint command in it.
"""
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Initializes the step operator and validates the accelerator type.
Args:
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
"""
super().__init__(*args, **kwargs)
@property
def config(self) -> VertexStepOperatorConfig:
"""Returns the `VertexStepOperatorConfig` config.
Returns:
The configuration.
"""
return cast(VertexStepOperatorConfig, self._config)
@property
def settings_class(self) -> Optional[Type["BaseSettings"]]:
"""Settings class for the Vertex step operator.
Returns:
The settings class.
"""
return VertexStepOperatorSettings
@property
def validator(self) -> Optional[StackValidator]:
"""Validates the stack.
Returns:
A validator that checks that the stack contains a remote container
registry and a remote artifact store.
"""
def _validate_remote_components(stack: "Stack") -> Tuple[bool, str]:
if stack.artifact_store.config.is_local:
return False, (
"The Vertex step operator runs code remotely and "
"needs to write files into the artifact store, but the "
f"artifact store `{stack.artifact_store.name}` of the "
"active stack is local. Please ensure that your stack "
"contains a remote artifact store when using the Vertex "
"step operator."
)
container_registry = stack.container_registry
assert container_registry is not None
if container_registry.config.is_local:
return False, (
"The Vertex step operator runs code remotely and "
"needs to push/pull Docker images, but the "
f"container registry `{container_registry.name}` of the "
"active stack is local. Please ensure that your stack "
"contains a remote container registry when using the "
"Vertex step operator."
)
return True, ""
return StackValidator(
required_components={StackComponentType.CONTAINER_REGISTRY},
custom_validation_function=_validate_remote_components,
)
def prepare_pipeline_deployment(
self,
deployment: "PipelineDeployment",
stack: "Stack",
) -> None:
"""Build a Docker image and push it to the container registry.
Args:
deployment: The pipeline deployment configuration.
stack: The stack on which the pipeline will be deployed.
"""
steps_to_run = [
step
for step in deployment.steps.values()
if step.config.step_operator == self.name
]
if not steps_to_run:
return
docker_image_builder = PipelineDockerImageBuilder()
image_digest = docker_image_builder.build_and_push_docker_image(
deployment=deployment,
stack=stack,
)
for step in steps_to_run:
step.config.extra[VERTEX_DOCKER_IMAGE_DIGEST_KEY] = image_digest
def launch(
self,
info: "StepRunInfo",
entrypoint_command: List[str],
) -> None:
"""Launches a step on VertexAI.
Args:
info: Information about the step run.
entrypoint_command: Command that executes the step.
Raises:
RuntimeError: If the run fails.
ConnectionError: If the run fails due to a connection error.
"""
resource_settings = info.config.resource_settings
if resource_settings.cpu_count or resource_settings.memory:
logger.warning(
"Specifying cpus or memory is not supported for "
"the Vertex step operator. If you want to run this step "
"operator on specific resources, you can do so by configuring "
"a different machine_type type like this: "
"`zenml step-operator update %s "
"--machine_type=<MACHINE_TYPE>`",
self.name,
)
settings = cast(VertexStepOperatorSettings, self.get_settings(info))
validate_accelerator_type(settings.accelerator_type)
job_labels = {"source": f"zenml-{__version__.replace('.', '_')}"}
# Step 1: Authenticate with Google
credentials, project_id = self._get_authentication()
if self.config.project:
if self.config.project != project_id:
logger.warning(
"Authenticated with project `%s`, but this orchestrator is "
"configured to use the project `%s`.",
project_id,
self.config.project,
)
else:
self.config.project = project_id
image_name = info.config.extra[VERTEX_DOCKER_IMAGE_DIGEST_KEY]
# Step 3: Launch the job
# The AI Platform services require regional API endpoints.
client_options = {
"api_endpoint": self.config.region + VERTEX_ENDPOINT_SUFFIX
}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.JobServiceClient(
credentials=credentials, client_options=client_options
)
accelerator_count = (
resource_settings.gpu_count or settings.accelerator_count
)
custom_job = {
"display_name": info.run_name,
"job_spec": {
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": settings.machine_type,
"accelerator_type": settings.accelerator_type,
"accelerator_count": accelerator_count
if settings.accelerator_type
else 0,
},
"replica_count": 1,
"container_spec": {
"image_uri": image_name,
"command": entrypoint_command,
"args": [],
},
}
]
},
"labels": job_labels,
"encryption_spec": {
"kmsKeyName": self.config.encryption_spec_key_name
}
if self.config.encryption_spec_key_name
else {},
}
logger.debug("Vertex AI Job=%s", custom_job)
parent = (
f"projects/{self.config.project}/locations/{self.config.region}"
)
logger.info(
"Submitting custom job='%s', path='%s' to Vertex AI Training.",
custom_job["display_name"],
parent,
)
response = client.create_custom_job(
parent=parent, custom_job=custom_job
)
logger.debug("Vertex AI response:", response)
# Step 4: Monitor the job
# Monitors the long-running operation by polling the job state
# periodically, and retries the polling when a transient connectivity
# issue is encountered.
#
# Long-running operation monitoring:
# The possible states of "get job" response can be found at
# https://cloud.google.com/ai-platform/training/docs/reference/rest/v1/projects.jobs#State
# where SUCCEEDED/FAILED/CANCELED are considered to be final states.
# The following logic will keep polling the state of the job until
# the job enters a final state.
#
# During the polling, if a connection error was encountered, the GET
# request will be retried by recreating the Python API client to
# refresh the lifecycle of the connection being used. See
# https://github.com/googleapis/google-api-python-client/issues/218
# for a detailed description of the problem. If the error persists for
# _CONNECTION_ERROR_RETRY_LIMIT consecutive attempts, the function
# will raise ConnectionError.
retry_count = 0
job_id = response.name
while response.state not in VERTEX_JOB_STATES_COMPLETED:
time.sleep(POLLING_INTERVAL_IN_SECONDS)
try:
response = client.get_custom_job(name=job_id)
retry_count = 0
# Handle transient connection error.
except ConnectionError as err:
if retry_count < CONNECTION_ERROR_RETRY_LIMIT:
retry_count += 1
logger.warning(
"ConnectionError (%s) encountered when polling job: "
"%s. Trying to recreate the API client.",
err,
job_id,
)
# Recreate the Python API client.
client = aiplatform.gapic.JobServiceClient(
client_options=client_options
)
else:
logger.error(
"Request failed after %s retries.",
CONNECTION_ERROR_RETRY_LIMIT,
)
raise
if response.state in VERTEX_JOB_STATES_FAILED:
err_msg = (
"Job '{}' did not succeed. Detailed response {}.".format(
job_id, response
)
)
logger.error(err_msg)
raise RuntimeError(err_msg)
# Cloud training complete
logger.info("Job '%s' successful.", job_id)
config: VertexStepOperatorConfig
property
readonly
Returns the VertexStepOperatorConfig config.
Returns:
| Type | Description |
|---|---|
VertexStepOperatorConfig |
The configuration. |
settings_class: Optional[Type[BaseSettings]]
property
readonly
Settings class for the Vertex step operator.
Returns:
| Type | Description |
|---|---|
Optional[Type[BaseSettings]] |
The settings class. |
validator: Optional[zenml.stack.stack_validator.StackValidator]
property
readonly
Validates the stack.
Returns:
| Type | Description |
|---|---|
Optional[zenml.stack.stack_validator.StackValidator] |
A validator that checks that the stack contains a remote container registry and a remote artifact store. |
__init__(self, *args, **kwargs)
special
Initializes the step operator and validates the accelerator type.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
*args |
Any |
Variable length argument list. |
() |
**kwargs |
Any |
Arbitrary keyword arguments. |
{} |
Source code in zenml/integrations/gcp/step_operators/vertex_step_operator.py
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Initializes the step operator and validates the accelerator type.
Args:
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
"""
super().__init__(*args, **kwargs)
launch(self, info, entrypoint_command)
Launches a step on VertexAI.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
info |
StepRunInfo |
Information about the step run. |
required |
entrypoint_command |
List[str] |
Command that executes the step. |
required |
Exceptions:
| Type | Description |
|---|---|
RuntimeError |
If the run fails. |
ConnectionError |
If the run fails due to a connection error. |
Source code in zenml/integrations/gcp/step_operators/vertex_step_operator.py
def launch(
self,
info: "StepRunInfo",
entrypoint_command: List[str],
) -> None:
"""Launches a step on VertexAI.
Args:
info: Information about the step run.
entrypoint_command: Command that executes the step.
Raises:
RuntimeError: If the run fails.
ConnectionError: If the run fails due to a connection error.
"""
resource_settings = info.config.resource_settings
if resource_settings.cpu_count or resource_settings.memory:
logger.warning(
"Specifying cpus or memory is not supported for "
"the Vertex step operator. If you want to run this step "
"operator on specific resources, you can do so by configuring "
"a different machine_type type like this: "
"`zenml step-operator update %s "
"--machine_type=<MACHINE_TYPE>`",
self.name,
)
settings = cast(VertexStepOperatorSettings, self.get_settings(info))
validate_accelerator_type(settings.accelerator_type)
job_labels = {"source": f"zenml-{__version__.replace('.', '_')}"}
# Step 1: Authenticate with Google
credentials, project_id = self._get_authentication()
if self.config.project:
if self.config.project != project_id:
logger.warning(
"Authenticated with project `%s`, but this orchestrator is "
"configured to use the project `%s`.",
project_id,
self.config.project,
)
else:
self.config.project = project_id
image_name = info.config.extra[VERTEX_DOCKER_IMAGE_DIGEST_KEY]
# Step 3: Launch the job
# The AI Platform services require regional API endpoints.
client_options = {
"api_endpoint": self.config.region + VERTEX_ENDPOINT_SUFFIX
}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.JobServiceClient(
credentials=credentials, client_options=client_options
)
accelerator_count = (
resource_settings.gpu_count or settings.accelerator_count
)
custom_job = {
"display_name": info.run_name,
"job_spec": {
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": settings.machine_type,
"accelerator_type": settings.accelerator_type,
"accelerator_count": accelerator_count
if settings.accelerator_type
else 0,
},
"replica_count": 1,
"container_spec": {
"image_uri": image_name,
"command": entrypoint_command,
"args": [],
},
}
]
},
"labels": job_labels,
"encryption_spec": {
"kmsKeyName": self.config.encryption_spec_key_name
}
if self.config.encryption_spec_key_name
else {},
}
logger.debug("Vertex AI Job=%s", custom_job)
parent = (
f"projects/{self.config.project}/locations/{self.config.region}"
)
logger.info(
"Submitting custom job='%s', path='%s' to Vertex AI Training.",
custom_job["display_name"],
parent,
)
response = client.create_custom_job(
parent=parent, custom_job=custom_job
)
logger.debug("Vertex AI response:", response)
# Step 4: Monitor the job
# Monitors the long-running operation by polling the job state
# periodically, and retries the polling when a transient connectivity
# issue is encountered.
#
# Long-running operation monitoring:
# The possible states of "get job" response can be found at
# https://cloud.google.com/ai-platform/training/docs/reference/rest/v1/projects.jobs#State
# where SUCCEEDED/FAILED/CANCELED are considered to be final states.
# The following logic will keep polling the state of the job until
# the job enters a final state.
#
# During the polling, if a connection error was encountered, the GET
# request will be retried by recreating the Python API client to
# refresh the lifecycle of the connection being used. See
# https://github.com/googleapis/google-api-python-client/issues/218
# for a detailed description of the problem. If the error persists for
# _CONNECTION_ERROR_RETRY_LIMIT consecutive attempts, the function
# will raise ConnectionError.
retry_count = 0
job_id = response.name
while response.state not in VERTEX_JOB_STATES_COMPLETED:
time.sleep(POLLING_INTERVAL_IN_SECONDS)
try:
response = client.get_custom_job(name=job_id)
retry_count = 0
# Handle transient connection error.
except ConnectionError as err:
if retry_count < CONNECTION_ERROR_RETRY_LIMIT:
retry_count += 1
logger.warning(
"ConnectionError (%s) encountered when polling job: "
"%s. Trying to recreate the API client.",
err,
job_id,
)
# Recreate the Python API client.
client = aiplatform.gapic.JobServiceClient(
client_options=client_options
)
else:
logger.error(
"Request failed after %s retries.",
CONNECTION_ERROR_RETRY_LIMIT,
)
raise
if response.state in VERTEX_JOB_STATES_FAILED:
err_msg = (
"Job '{}' did not succeed. Detailed response {}.".format(
job_id, response
)
)
logger.error(err_msg)
raise RuntimeError(err_msg)
# Cloud training complete
logger.info("Job '%s' successful.", job_id)
prepare_pipeline_deployment(self, deployment, stack)
Build a Docker image and push it to the container registry.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
deployment |
PipelineDeployment |
The pipeline deployment configuration. |
required |
stack |
Stack |
The stack on which the pipeline will be deployed. |
required |
Source code in zenml/integrations/gcp/step_operators/vertex_step_operator.py
def prepare_pipeline_deployment(
self,
deployment: "PipelineDeployment",
stack: "Stack",
) -> None:
"""Build a Docker image and push it to the container registry.
Args:
deployment: The pipeline deployment configuration.
stack: The stack on which the pipeline will be deployed.
"""
steps_to_run = [
step
for step in deployment.steps.values()
if step.config.step_operator == self.name
]
if not steps_to_run:
return
docker_image_builder = PipelineDockerImageBuilder()
image_digest = docker_image_builder.build_and_push_docker_image(
deployment=deployment,
stack=stack,
)
for step in steps_to_run:
step.config.extra[VERTEX_DOCKER_IMAGE_DIGEST_KEY] = image_digest
validate_accelerator_type(accelerator_type=None)
Validates that the accelerator type is valid.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
accelerator_type |
Optional[str] |
The accelerator type to validate. |
None |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If the accelerator type is not valid. |
Source code in zenml/integrations/gcp/step_operators/vertex_step_operator.py
def validate_accelerator_type(accelerator_type: Optional[str] = None) -> None:
"""Validates that the accelerator type is valid.
Args:
accelerator_type: The accelerator type to validate.
Raises:
ValueError: If the accelerator type is not valid.
"""
accepted_vals = list(aiplatform.gapic.AcceleratorType.__members__.keys())
if accelerator_type and accelerator_type.upper() not in accepted_vals:
raise ValueError(
f"Accelerator must be one of the following: {accepted_vals}"
)