Stack Deployments

`zenml.stack_deployments` `special`

ZenML Stack Deployments.

`aws_stack_deployment`

Functionality to deploy a ZenML stack to AWS.

`AWSZenMLCloudStackDeployment (ZenMLCloudStackDeployment)`

AWS ZenML Cloud Stack Deployment.

Source code in zenml/stack_deployments/aws_stack_deployment.py

class AWSZenMLCloudStackDeployment(ZenMLCloudStackDeployment):
    """AWS ZenML Cloud Stack Deployment."""

    provider: ClassVar[StackDeploymentProvider] = StackDeploymentProvider.AWS
    deployment: ClassVar[str] = AWS_DEPLOYMENT_TYPE

    @classmethod
    def description(cls) -> str:
        """Return a description of the ZenML Cloud Stack Deployment.

        This will be displayed when the user is prompted to deploy
        the ZenML stack.

        Returns:
            A MarkDown description of the ZenML Cloud Stack Deployment.
        """
        return """
Provision and register a basic AWS ZenML stack authenticated and connected to
all the necessary cloud infrastructure resources required to run pipelines in
AWS.
"""

    @classmethod
    def instructions(cls) -> str:
        """Return instructions on how to deploy the ZenML stack to the specified cloud provider.

        This will be displayed before the user is prompted to deploy the ZenML
        stack.

        Returns:
            MarkDown instructions on how to deploy the ZenML stack to the
            specified cloud provider.
        """
        return """
You will be redirected to the AWS console in your browser where you'll be asked
to log into your AWS account and create a CloudFormation ZenML stack. The stack
parameters will be pre-filled with the necessary information to connect ZenML to
your AWS account, so you should only need to review and confirm the stack.

**NOTE**: The CloudFormation stack will create the following new resources in
your AWS account. Please ensure you have the necessary permissions and are aware
of any potential costs:

- An S3 bucket registered as a [ZenML artifact store](https://docs.zenml.io/stack-components/artifact-stores/s3).
- An ECR repository registered as a [ZenML container registry](https://docs.zenml.io/stack-components/container-registries/aws).
- Sagemaker registered as a [ZenML orchestrator](https://docs.zenml.io/stack-components/orchestrators/sagemaker).
- An IAM user and IAM role with the minimum necessary permissions to access the
above resources.
- An AWS access key used to give access to ZenML to connect to the above
resources through a [ZenML service connector](https://docs.zenml.io/how-to/auth-management/aws-service-connector).

The CloudFormation stack will automatically create an AWS secret key and
will share it with ZenML to give it permission to access the resources created
by the stack. You can revoke these permissions at any time by deleting the
CloudFormation stack.

**Estimated costs**

A small training job would cost around: $0.60

These are rough estimates and actual costs may vary based on your usage and specific AWS pricing. 
Some services may be eligible for the AWS Free Tier. Use [the AWS Pricing Calculator](https://calculator.aws)
for a detailed estimate based on your usage.

💡 **After the CloudFormation stack is deployed, you can return to the CLI to
view details about the associated ZenML stack automatically registered with
ZenML.**
"""

    @classmethod
    def post_deploy_instructions(cls) -> str:
        """Return instructions on what to do after the deployment is complete.

        This will be displayed after the deployment is complete.

        Returns:
            MarkDown instructions on what to do after the deployment is
            complete.
        """
        return """
The ZenML stack has been successfully deployed and registered. You can delete
the CloudFormation at any time to revoke ZenML's access to your AWS account and
to clean up the resources created by the stack by using the AWS CloudFormation
console.
"""

    @classmethod
    def integrations(cls) -> List[str]:
        """Return the ZenML integrations required for the stack.

        Returns:
            The list of ZenML integrations that need to be installed for the
            stack to be usable.
        """
        return [
            "aws",
            "s3",
        ]

    @classmethod
    def permissions(cls) -> Dict[str, List[str]]:
        """Return the permissions granted to ZenML to access the cloud resources.

        Returns:
            The permissions granted to ZenML to access the cloud resources, as
            a dictionary grouping permissions by resource.
        """
        return {
            "S3 Bucket": [
                "s3:ListBucket",
                "s3:GetObject",
                "s3:PutObject",
                "s3:DeleteObject",
            ],
            "ECR Repository": [
                "ecr:DescribeRepositories",
                "ecr:ListRepositories",
                "ecr:DescribeRegistry",
                "ecr:BatchGetImage",
                "ecr:DescribeImages",
                "ecr:BatchCheckLayerAvailability",
                "ecr:GetDownloadUrlForLayer",
                "ecr:InitiateLayerUpload",
                "ecr:UploadLayerPart",
                "ecr:CompleteLayerUpload",
                "ecr:PutImage",
                "ecr:GetAuthorizationToken",
            ],
            "SageMaker (Client)": [
                "sagemaker:CreatePipeline",
                "sagemaker:StartPipelineExecution",
                "sagemaker:DescribePipeline",
                "sagemaker:DescribePipelineExecution",
            ],
            "SageMaker (Jobs)": [
                "AmazonSageMakerFullAccess",
            ],
        }

    @classmethod
    def locations(cls) -> Dict[str, str]:
        """Return the locations where the ZenML stack can be deployed.

        Returns:
            The regions where the ZenML stack can be deployed as a map of region
            names to region descriptions.
        """
        # Return a list of all possible AWS regions

        # Based on the AWS regions listed at
        # https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-regions-availability-zones.html
        return {
            "US East (Ohio)": "us-east-2",
            "US East (N. Virginia)": "us-east-1",
            "US West (N. California)": "us-west-1",
            "US West (Oregon)": "us-west-2",
            "Africa (Cape Town)": "af-south-1",
            "Asia Pacific (Hong Kong)": "ap-east-1",
            "Asia Pacific (Hyderabad)": "ap-south-2",
            "Asia Pacific (Jakarta)": "ap-southeast-3",
            "Asia Pacific (Melbourne)": "ap-southeast-4",
            "Asia Pacific (Mumbai)": "ap-south-1",
            "Asia Pacific (Osaka)": "ap-northeast-3",
            "Asia Pacific (Seoul)": "ap-northeast-2",
            "Asia Pacific (Singapore)": "ap-southeast-1",
            "Asia Pacific (Sydney)": "ap-southeast-2",
            "Asia Pacific (Tokyo)": "ap-northeast-1",
            "Canada (Central)": "ca-central-1",
            "Canada West (Calgary)": "ca-west-1",
            "Europe (Frankfurt)": "eu-central-1",
            "Europe (Ireland)": "eu-west-1",
            "Europe (London)": "eu-west-2",
            "Europe (Milan)": "eu-south-1",
            "Europe (Paris)": "eu-west-3",
            "Europe (Spain)": "eu-south-2",
            "Europe (Stockholm)": "eu-north-1",
            "Europe (Zurich)": "eu-central-2",
            "Israel (Tel Aviv)": "il-central-1",
            "Middle East (Bahrain)": "me-south-1",
            "Middle East (UAE)": "me-central-1",
            "South America (São Paulo)": "sa-east-1",
        }

    def get_deployment_config(
        self,
    ) -> StackDeploymentConfig:
        """Return the configuration to deploy the ZenML stack to the specified cloud provider.

        The configuration should include:

        * a cloud provider console URL where the user will be redirected to
        deploy the ZenML stack. The URL should include as many pre-filled
        URL query parameters as possible.
        * a textual description of the URL
        * some deployment providers may require additional configuration
        parameters to be passed to the cloud provider in addition to the
        deployment URL query parameters. Where that is the case, this method
        should also return a string that the user can copy and paste into the
        cloud provider console to deploy the ZenML stack (e.g. a set of
        environment variables, or YAML configuration snippet etc.).

        Returns:
            The configuration to deploy the ZenML stack to the specified cloud
            provider.
        """
        params = dict(
            stackName=self.stack_name,
            templateURL="https://zenml-cf-templates.s3.eu-central-1.amazonaws.com/aws-ecr-s3-sagemaker.yaml",
            param_ResourceName=f"zenml-{random_str(6).lower()}",
            param_ZenMLServerURL=self.zenml_server_url,
            param_ZenMLServerAPIToken=self.zenml_server_api_token,
        )
        # Encode the parameters as URL query parameters
        query_params = "&".join([f"{k}={v}" for k, v in params.items()])

        region = ""
        if self.location:
            region = f"region={self.location}"

        url = (
            f"https://console.aws.amazon.com/cloudformation/home?"
            f"{region}#/stacks/create/review?{query_params}"
        )

        return StackDeploymentConfig(
            deployment_url=url,
            deployment_url_text="AWS CloudFormation Console",
            configuration=None,
        )

`description()` `classmethod`

Return a description of the ZenML Cloud Stack Deployment.

This will be displayed when the user is prompted to deploy the ZenML stack.

Returns:

Type	Description
`str`	A MarkDown description of the ZenML Cloud Stack Deployment.

Source code in zenml/stack_deployments/aws_stack_deployment.py

    @classmethod
    def description(cls) -> str:
        """Return a description of the ZenML Cloud Stack Deployment.

        This will be displayed when the user is prompted to deploy
        the ZenML stack.

        Returns:
            A MarkDown description of the ZenML Cloud Stack Deployment.
        """
        return """
Provision and register a basic AWS ZenML stack authenticated and connected to
all the necessary cloud infrastructure resources required to run pipelines in
AWS.
"""

`get_deployment_config(self)`

Return the configuration to deploy the ZenML stack to the specified cloud provider.

The configuration should include:

a cloud provider console URL where the user will be redirected to deploy the ZenML stack. The URL should include as many pre-filled URL query parameters as possible.
a textual description of the URL
some deployment providers may require additional configuration parameters to be passed to the cloud provider in addition to the deployment URL query parameters. Where that is the case, this method should also return a string that the user can copy and paste into the cloud provider console to deploy the ZenML stack (e.g. a set of environment variables, or YAML configuration snippet etc.).

Returns:

Type	Description
`StackDeploymentConfig`	The configuration to deploy the ZenML stack to the specified cloud provider.

Source code in zenml/stack_deployments/aws_stack_deployment.py

def get_deployment_config(
    self,
) -> StackDeploymentConfig:
    """Return the configuration to deploy the ZenML stack to the specified cloud provider.

    The configuration should include:

    * a cloud provider console URL where the user will be redirected to
    deploy the ZenML stack. The URL should include as many pre-filled
    URL query parameters as possible.
    * a textual description of the URL
    * some deployment providers may require additional configuration
    parameters to be passed to the cloud provider in addition to the
    deployment URL query parameters. Where that is the case, this method
    should also return a string that the user can copy and paste into the
    cloud provider console to deploy the ZenML stack (e.g. a set of
    environment variables, or YAML configuration snippet etc.).

    Returns:
        The configuration to deploy the ZenML stack to the specified cloud
        provider.
    """
    params = dict(
        stackName=self.stack_name,
        templateURL="https://zenml-cf-templates.s3.eu-central-1.amazonaws.com/aws-ecr-s3-sagemaker.yaml",
        param_ResourceName=f"zenml-{random_str(6).lower()}",
        param_ZenMLServerURL=self.zenml_server_url,
        param_ZenMLServerAPIToken=self.zenml_server_api_token,
    )
    # Encode the parameters as URL query parameters
    query_params = "&".join([f"{k}={v}" for k, v in params.items()])

    region = ""
    if self.location:
        region = f"region={self.location}"

    url = (
        f"https://console.aws.amazon.com/cloudformation/home?"
        f"{region}#/stacks/create/review?{query_params}"
    )

    return StackDeploymentConfig(
        deployment_url=url,
        deployment_url_text="AWS CloudFormation Console",
        configuration=None,
    )

`instructions()` `classmethod`

Return instructions on how to deploy the ZenML stack to the specified cloud provider.

This will be displayed before the user is prompted to deploy the ZenML stack.

Returns:

Type	Description
`str`	MarkDown instructions on how to deploy the ZenML stack to the specified cloud provider.

Source code in zenml/stack_deployments/aws_stack_deployment.py

    @classmethod
    def instructions(cls) -> str:
        """Return instructions on how to deploy the ZenML stack to the specified cloud provider.

        This will be displayed before the user is prompted to deploy the ZenML
        stack.

        Returns:
            MarkDown instructions on how to deploy the ZenML stack to the
            specified cloud provider.
        """
        return """
You will be redirected to the AWS console in your browser where you'll be asked
to log into your AWS account and create a CloudFormation ZenML stack. The stack
parameters will be pre-filled with the necessary information to connect ZenML to
your AWS account, so you should only need to review and confirm the stack.

**NOTE**: The CloudFormation stack will create the following new resources in
your AWS account. Please ensure you have the necessary permissions and are aware
of any potential costs:

- An S3 bucket registered as a [ZenML artifact store](https://docs.zenml.io/stack-components/artifact-stores/s3).
- An ECR repository registered as a [ZenML container registry](https://docs.zenml.io/stack-components/container-registries/aws).
- Sagemaker registered as a [ZenML orchestrator](https://docs.zenml.io/stack-components/orchestrators/sagemaker).
- An IAM user and IAM role with the minimum necessary permissions to access the
above resources.
- An AWS access key used to give access to ZenML to connect to the above
resources through a [ZenML service connector](https://docs.zenml.io/how-to/auth-management/aws-service-connector).

The CloudFormation stack will automatically create an AWS secret key and
will share it with ZenML to give it permission to access the resources created
by the stack. You can revoke these permissions at any time by deleting the
CloudFormation stack.

**Estimated costs**

A small training job would cost around: $0.60

These are rough estimates and actual costs may vary based on your usage and specific AWS pricing. 
Some services may be eligible for the AWS Free Tier. Use [the AWS Pricing Calculator](https://calculator.aws)
for a detailed estimate based on your usage.

💡 **After the CloudFormation stack is deployed, you can return to the CLI to
view details about the associated ZenML stack automatically registered with
ZenML.**
"""

`integrations()` `classmethod`

Return the ZenML integrations required for the stack.

Returns:

Type	Description
`List[str]`	The list of ZenML integrations that need to be installed for the stack to be usable.

Source code in zenml/stack_deployments/aws_stack_deployment.py

@classmethod
def integrations(cls) -> List[str]:
    """Return the ZenML integrations required for the stack.

    Returns:
        The list of ZenML integrations that need to be installed for the
        stack to be usable.
    """
    return [
        "aws",
        "s3",
    ]

`locations()` `classmethod`

Return the locations where the ZenML stack can be deployed.

Returns:

Type	Description
`Dict[str, str]`	The regions where the ZenML stack can be deployed as a map of region names to region descriptions.

Source code in zenml/stack_deployments/aws_stack_deployment.py

@classmethod
def locations(cls) -> Dict[str, str]:
    """Return the locations where the ZenML stack can be deployed.

    Returns:
        The regions where the ZenML stack can be deployed as a map of region
        names to region descriptions.
    """
    # Return a list of all possible AWS regions

    # Based on the AWS regions listed at
    # https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-regions-availability-zones.html
    return {
        "US East (Ohio)": "us-east-2",
        "US East (N. Virginia)": "us-east-1",
        "US West (N. California)": "us-west-1",
        "US West (Oregon)": "us-west-2",
        "Africa (Cape Town)": "af-south-1",
        "Asia Pacific (Hong Kong)": "ap-east-1",
        "Asia Pacific (Hyderabad)": "ap-south-2",
        "Asia Pacific (Jakarta)": "ap-southeast-3",
        "Asia Pacific (Melbourne)": "ap-southeast-4",
        "Asia Pacific (Mumbai)": "ap-south-1",
        "Asia Pacific (Osaka)": "ap-northeast-3",
        "Asia Pacific (Seoul)": "ap-northeast-2",
        "Asia Pacific (Singapore)": "ap-southeast-1",
        "Asia Pacific (Sydney)": "ap-southeast-2",
        "Asia Pacific (Tokyo)": "ap-northeast-1",
        "Canada (Central)": "ca-central-1",
        "Canada West (Calgary)": "ca-west-1",
        "Europe (Frankfurt)": "eu-central-1",
        "Europe (Ireland)": "eu-west-1",
        "Europe (London)": "eu-west-2",
        "Europe (Milan)": "eu-south-1",
        "Europe (Paris)": "eu-west-3",
        "Europe (Spain)": "eu-south-2",
        "Europe (Stockholm)": "eu-north-1",
        "Europe (Zurich)": "eu-central-2",
        "Israel (Tel Aviv)": "il-central-1",
        "Middle East (Bahrain)": "me-south-1",
        "Middle East (UAE)": "me-central-1",
        "South America (São Paulo)": "sa-east-1",
    }

`permissions()` `classmethod`

Return the permissions granted to ZenML to access the cloud resources.

Returns:

Type	Description
`Dict[str, List[str]]`	The permissions granted to ZenML to access the cloud resources, as a dictionary grouping permissions by resource.

Source code in zenml/stack_deployments/aws_stack_deployment.py

@classmethod
def permissions(cls) -> Dict[str, List[str]]:
    """Return the permissions granted to ZenML to access the cloud resources.

    Returns:
        The permissions granted to ZenML to access the cloud resources, as
        a dictionary grouping permissions by resource.
    """
    return {
        "S3 Bucket": [
            "s3:ListBucket",
            "s3:GetObject",
            "s3:PutObject",
            "s3:DeleteObject",
        ],
        "ECR Repository": [
            "ecr:DescribeRepositories",
            "ecr:ListRepositories",
            "ecr:DescribeRegistry",
            "ecr:BatchGetImage",
            "ecr:DescribeImages",
            "ecr:BatchCheckLayerAvailability",
            "ecr:GetDownloadUrlForLayer",
            "ecr:InitiateLayerUpload",
            "ecr:UploadLayerPart",
            "ecr:CompleteLayerUpload",
            "ecr:PutImage",
            "ecr:GetAuthorizationToken",
        ],
        "SageMaker (Client)": [
            "sagemaker:CreatePipeline",
            "sagemaker:StartPipelineExecution",
            "sagemaker:DescribePipeline",
            "sagemaker:DescribePipelineExecution",
        ],
        "SageMaker (Jobs)": [
            "AmazonSageMakerFullAccess",
        ],
    }

`post_deploy_instructions()` `classmethod`

Return instructions on what to do after the deployment is complete.

This will be displayed after the deployment is complete.

Returns:

Type	Description
`str`	MarkDown instructions on what to do after the deployment is complete.

Source code in zenml/stack_deployments/aws_stack_deployment.py

    @classmethod
    def post_deploy_instructions(cls) -> str:
        """Return instructions on what to do after the deployment is complete.

        This will be displayed after the deployment is complete.

        Returns:
            MarkDown instructions on what to do after the deployment is
            complete.
        """
        return """
The ZenML stack has been successfully deployed and registered. You can delete
the CloudFormation at any time to revoke ZenML's access to your AWS account and
to clean up the resources created by the stack by using the AWS CloudFormation
console.
"""

`gcp_stack_deployment`

Functionality to deploy a ZenML stack to GCP.

`GCPZenMLCloudStackDeployment (ZenMLCloudStackDeployment)`

GCP ZenML Cloud Stack Deployment.

Source code in zenml/stack_deployments/gcp_stack_deployment.py

class GCPZenMLCloudStackDeployment(ZenMLCloudStackDeployment):
    """GCP ZenML Cloud Stack Deployment."""

    provider: ClassVar[StackDeploymentProvider] = StackDeploymentProvider.GCP
    deployment: ClassVar[str] = GCP_DEPLOYMENT_TYPE

    @classmethod
    def description(cls) -> str:
        """Return a description of the ZenML Cloud Stack Deployment.

        This will be displayed when the user is prompted to deploy
        the ZenML stack.

        Returns:
            A MarkDown description of the ZenML Cloud Stack Deployment.
        """
        return """
Provision and register a basic GCP ZenML stack authenticated and connected to
all the necessary cloud infrastructure resources required to run pipelines in
GCP.
"""

    @classmethod
    def instructions(cls) -> str:
        """Return instructions on how to deploy the ZenML stack to the specified cloud provider.

        This will be displayed before the user is prompted to deploy the ZenML
        stack.

        Returns:
            MarkDown instructions on how to deploy the ZenML stack to the
            specified cloud provider.
        """
        return """
You will be redirected to a GCP Cloud Shell console in your browser where you'll
be asked to log into your GCP project and then create a Deployment Manager
deployment to provision the necessary cloud resources for ZenML.

**NOTE**: The Deployment Manager deployment will create the following new
resources in your GCP project. Please ensure you have the necessary permissions
and are aware of any potential costs:

- A GCS bucket registered as a [ZenML artifact store](https://docs.zenml.io/stack-components/artifact-stores/gcp).
- A Google Artifact Registry registered as a [ZenML container registry](https://docs.zenml.io/stack-components/container-registries/gcp).
- Vertex AI registered as a [ZenML orchestrator](https://docs.zenml.io/stack-components/orchestrators/vertex).
- GCP Cloud Build registered as a [ZenML image builder](https://docs.zenml.io/stack-components/image-builders/gcp).
- A GCP Service Account with the minimum necessary permissions to access the
above resources.
- An GCP Service Account access key used to give access to ZenML to connect to
the above resources through a [ZenML service connector](https://docs.zenml.io/how-to/auth-management/gcp-service-connector).

The Deployment Manager deployment will automatically create a GCP Service
Account secret key and will share it with ZenML to give it permission to access
the resources created by the stack. You can revoke these permissions at any time
by deleting the Deployment Manager deployment in the GCP Cloud Console.

**Estimated costs**

A small training job would cost around: $0.60

These are rough estimates and actual costs may vary based on your usage and specific GCP pricing. 
Some services may be eligible for the GCP Free Tier. Use [the GCP Pricing Calculator](https://cloud.google.com/products/calculator)
for a detailed estimate based on your usage.

⚠️  **The Cloud Shell session will warn you that the ZenML GitHub repository is
untrusted. We recommend that you review [the contents of the repository](https://github.com/zenml-io/zenml/tree/main/infra/gcp)
and then check the `Trust repo` checkbox to proceed with the deployment,
otherwise the Cloud Shell session will not be authenticated to access your
GCP projects. You will also get a chance to review the scripts that will be
executed in the Cloud Shell session before proceeding.**

💡 **After the Deployment Manager deployment is complete, you can close the Cloud
Shell session and return to the CLI to view details about the associated ZenML
stack automatically registered with ZenML.**
"""

    @classmethod
    def post_deploy_instructions(cls) -> str:
        """Return instructions on what to do after the deployment is complete.

        This will be displayed after the deployment is complete.

        Returns:
            MarkDown instructions on what to do after the deployment is
            complete.
        """
        return """
The ZenML stack has been successfully deployed and registered. You can delete
the Deployment Manager deployment at any time to revoke ZenML's access to your
GCP project and to clean up the resources created by the stack by using
[the GCP Cloud Console](https://console.cloud.google.com/dm/deployments).
"""

    @classmethod
    def integrations(cls) -> List[str]:
        """Return the ZenML integrations required for the stack.

        Returns:
            The list of ZenML integrations that need to be installed for the
            stack to be usable.
        """
        return [
            "gcp",
        ]

    @classmethod
    def permissions(cls) -> Dict[str, List[str]]:
        """Return the permissions granted to ZenML to access the cloud resources.

        Returns:
            The permissions granted to ZenML to access the cloud resources, as
            a dictionary grouping permissions by resource.
        """
        return {
            "GCS Bucket": [
                "roles/storage.objectUser",
            ],
            "GCP Artifact Registry": [
                "roles/artifactregistry.createOnPushWriter",
            ],
            "Vertex AI (Client)": [
                "roles/aiplatform.user",
            ],
            "Vertex AI (Jobs)": [
                "roles/aiplatform.serviceAgent",
            ],
            "Cloud Build (Client)": [
                "roles/cloudbuild.builds.editor",
            ],
        }

    @classmethod
    def locations(cls) -> Dict[str, str]:
        """Return the locations where the ZenML stack can be deployed.

        Returns:
            The regions where the ZenML stack can be deployed as a map of region
            names to region descriptions.
        """
        # Return a list of all possible GCP regions

        # Based on the AWS regions listed at
        # https://cloud.google.com/about/locations
        return {
            "Africa (Johannesburg)": "africa-south1",
            "Asia Pacific (Taiwan)": "asia-east1",
            "Asia Pacific (Hong Kong)": "asia-east2",
            "Asia Pacific (Tokyo)": "asia-northeast1",
            "Asia Pacific (Osaka)": "asia-northeast2",
            "Asia Pacific (Seoul)": "asia-northeast3",
            "Asia Pacific (Mumbai)": "asia-south1",
            "Asia Pacific (Delhi)": "asia-south2",
            "Asia Pacific (Singapore)": "asia-southeast1",
            "Asia Pacific (Jakarta)": "asia-southeast2",
            "Australia (Sydney)": "australia-southeast1",
            "Australia (Melbourne)": "australia-southeast2",
            "Europe (Belgium)": "europe-west1",
            "Europe (London)": "europe-west2",
            "Europe (Frankfurt)": "europe-west3",
            "Europe (Netherlands)": "europe-west4",
            "Europe (Zurich)": "europe-west6",
            "Europe (Milan)": "europe-west8",
            "Europe (Paris)": "europe-west9",
            "Europe (Berlin)": "europe-west10",
            "Europe (Turin)": "europe-west12",
            "Europe (Warsaw)": "europe-central2",
            "Europe (Finland)": "europe-north1",
            "Europe (Madrid)": "europe-southwest1",
            "Middle East (Doha)": "me-central1",
            "Middle East (Dubai)": "me-central2",
            "Middle East (Tel Aviv)": "me-west1",
            "North America (Montreal)": "northamerica-northeast1",
            "North America (Toronto)": "northamerica-northeast2",
            "South America (Sao Paulo)": "southamerica-east1",
            "South America (Santiago)": "southamerica-west1",
            "US Central (Iowa)": "us-central1",
            "US East (South Carolina)": "us-east1",
            "US East (Northern Virginia)": "us-east4",
            "US East (Columbus)": "us-east5",
            "US South (Dallas)": "us-south1",
            "US West (Oregon)": "us-west1",
            "US West (Los Angeles)": "us-west2",
            "US West (Salt Lake City)": "us-west3",
            "US West (Las Vegas)": "us-west4",
        }

    def get_deployment_config(
        self,
    ) -> StackDeploymentConfig:
        """Return the configuration to deploy the ZenML stack to the specified cloud provider.

        The configuration should include:

        * a cloud provider console URL where the user will be redirected to
        deploy the ZenML stack. The URL should include as many pre-filled
        URL query parameters as possible.
        * a textual description of the URL
        * some deployment providers may require additional configuration
        parameters to be passed to the cloud provider in addition to the
        deployment URL query parameters. Where that is the case, this method
        should also return a string that the user can copy and paste into the
        cloud provider console to deploy the ZenML stack (e.g. a set of
        environment variables, or YAML configuration snippet etc.).

        Returns:
            The configuration to deploy the ZenML stack to the specified cloud
            provider.
        """
        params = dict(
            cloudshell_git_repo="https://github.com/zenml-io/zenml",
            cloudshell_workspace="infra/gcp",
            cloudshell_open_in_editor="gcp-gar-gcs-vertex.jinja,gcp-gar-gcs-vertex-deploy.sh",
            cloudshell_tutorial="gcp-gar-gcs-vertex.md",
            ephemeral="true",
        )
        # Encode the parameters as URL query parameters
        query_params = "&".join([f"{k}={v}" for k, v in params.items()])
        url = (
            f"https://shell.cloud.google.com/cloudshell/editor?{query_params}"
        )

        config = f"""
### BEGIN CONFIGURATION ###
ZENML_STACK_NAME={self.stack_name}
ZENML_STACK_REGION={self.location or "europe-west3"}
ZENML_SERVER_URL={self.zenml_server_url}
ZENML_SERVER_API_TOKEN={self.zenml_server_api_token}
### END CONFIGURATION ###"""

        return StackDeploymentConfig(
            deployment_url=url,
            deployment_url_text="GCP Cloud Shell Console",
            configuration=config,
        )

`description()` `classmethod`

Return a description of the ZenML Cloud Stack Deployment.

This will be displayed when the user is prompted to deploy the ZenML stack.

Returns:

Type	Description
`str`	A MarkDown description of the ZenML Cloud Stack Deployment.

Source code in zenml/stack_deployments/gcp_stack_deployment.py

    @classmethod
    def description(cls) -> str:
        """Return a description of the ZenML Cloud Stack Deployment.

        This will be displayed when the user is prompted to deploy
        the ZenML stack.

        Returns:
            A MarkDown description of the ZenML Cloud Stack Deployment.
        """
        return """
Provision and register a basic GCP ZenML stack authenticated and connected to
all the necessary cloud infrastructure resources required to run pipelines in
GCP.
"""

`get_deployment_config(self)`

Return the configuration to deploy the ZenML stack to the specified cloud provider.

The configuration should include:

a cloud provider console URL where the user will be redirected to deploy the ZenML stack. The URL should include as many pre-filled URL query parameters as possible.
a textual description of the URL
some deployment providers may require additional configuration parameters to be passed to the cloud provider in addition to the deployment URL query parameters. Where that is the case, this method should also return a string that the user can copy and paste into the cloud provider console to deploy the ZenML stack (e.g. a set of environment variables, or YAML configuration snippet etc.).

Returns:

Type	Description
`StackDeploymentConfig`	The configuration to deploy the ZenML stack to the specified cloud provider.

Source code in zenml/stack_deployments/gcp_stack_deployment.py

    def get_deployment_config(
        self,
    ) -> StackDeploymentConfig:
        """Return the configuration to deploy the ZenML stack to the specified cloud provider.

        The configuration should include:

        * a cloud provider console URL where the user will be redirected to
        deploy the ZenML stack. The URL should include as many pre-filled
        URL query parameters as possible.
        * a textual description of the URL
        * some deployment providers may require additional configuration
        parameters to be passed to the cloud provider in addition to the
        deployment URL query parameters. Where that is the case, this method
        should also return a string that the user can copy and paste into the
        cloud provider console to deploy the ZenML stack (e.g. a set of
        environment variables, or YAML configuration snippet etc.).

        Returns:
            The configuration to deploy the ZenML stack to the specified cloud
            provider.
        """
        params = dict(
            cloudshell_git_repo="https://github.com/zenml-io/zenml",
            cloudshell_workspace="infra/gcp",
            cloudshell_open_in_editor="gcp-gar-gcs-vertex.jinja,gcp-gar-gcs-vertex-deploy.sh",
            cloudshell_tutorial="gcp-gar-gcs-vertex.md",
            ephemeral="true",
        )
        # Encode the parameters as URL query parameters
        query_params = "&".join([f"{k}={v}" for k, v in params.items()])
        url = (
            f"https://shell.cloud.google.com/cloudshell/editor?{query_params}"
        )

        config = f"""
### BEGIN CONFIGURATION ###
ZENML_STACK_NAME={self.stack_name}
ZENML_STACK_REGION={self.location or "europe-west3"}
ZENML_SERVER_URL={self.zenml_server_url}
ZENML_SERVER_API_TOKEN={self.zenml_server_api_token}
### END CONFIGURATION ###"""

        return StackDeploymentConfig(
            deployment_url=url,
            deployment_url_text="GCP Cloud Shell Console",
            configuration=config,
        )

`instructions()` `classmethod`

Return instructions on how to deploy the ZenML stack to the specified cloud provider.

This will be displayed before the user is prompted to deploy the ZenML stack.

Returns:

Type	Description
`str`	MarkDown instructions on how to deploy the ZenML stack to the specified cloud provider.

Source code in zenml/stack_deployments/gcp_stack_deployment.py

    @classmethod
    def instructions(cls) -> str:
        """Return instructions on how to deploy the ZenML stack to the specified cloud provider.

        This will be displayed before the user is prompted to deploy the ZenML
        stack.

        Returns:
            MarkDown instructions on how to deploy the ZenML stack to the
            specified cloud provider.
        """
        return """
You will be redirected to a GCP Cloud Shell console in your browser where you'll
be asked to log into your GCP project and then create a Deployment Manager
deployment to provision the necessary cloud resources for ZenML.

**NOTE**: The Deployment Manager deployment will create the following new
resources in your GCP project. Please ensure you have the necessary permissions
and are aware of any potential costs:

- A GCS bucket registered as a [ZenML artifact store](https://docs.zenml.io/stack-components/artifact-stores/gcp).
- A Google Artifact Registry registered as a [ZenML container registry](https://docs.zenml.io/stack-components/container-registries/gcp).
- Vertex AI registered as a [ZenML orchestrator](https://docs.zenml.io/stack-components/orchestrators/vertex).
- GCP Cloud Build registered as a [ZenML image builder](https://docs.zenml.io/stack-components/image-builders/gcp).
- A GCP Service Account with the minimum necessary permissions to access the
above resources.
- An GCP Service Account access key used to give access to ZenML to connect to
the above resources through a [ZenML service connector](https://docs.zenml.io/how-to/auth-management/gcp-service-connector).

The Deployment Manager deployment will automatically create a GCP Service
Account secret key and will share it with ZenML to give it permission to access
the resources created by the stack. You can revoke these permissions at any time
by deleting the Deployment Manager deployment in the GCP Cloud Console.

**Estimated costs**

A small training job would cost around: $0.60

These are rough estimates and actual costs may vary based on your usage and specific GCP pricing. 
Some services may be eligible for the GCP Free Tier. Use [the GCP Pricing Calculator](https://cloud.google.com/products/calculator)
for a detailed estimate based on your usage.

⚠️  **The Cloud Shell session will warn you that the ZenML GitHub repository is
untrusted. We recommend that you review [the contents of the repository](https://github.com/zenml-io/zenml/tree/main/infra/gcp)
and then check the `Trust repo` checkbox to proceed with the deployment,
otherwise the Cloud Shell session will not be authenticated to access your
GCP projects. You will also get a chance to review the scripts that will be
executed in the Cloud Shell session before proceeding.**

💡 **After the Deployment Manager deployment is complete, you can close the Cloud
Shell session and return to the CLI to view details about the associated ZenML
stack automatically registered with ZenML.**
"""

`integrations()` `classmethod`

Return the ZenML integrations required for the stack.

Returns:

Type	Description
`List[str]`	The list of ZenML integrations that need to be installed for the stack to be usable.

Source code in zenml/stack_deployments/gcp_stack_deployment.py

@classmethod
def integrations(cls) -> List[str]:
    """Return the ZenML integrations required for the stack.

    Returns:
        The list of ZenML integrations that need to be installed for the
        stack to be usable.
    """
    return [
        "gcp",
    ]

`locations()` `classmethod`

Return the locations where the ZenML stack can be deployed.

Returns:

Type	Description
`Dict[str, str]`	The regions where the ZenML stack can be deployed as a map of region names to region descriptions.

Source code in zenml/stack_deployments/gcp_stack_deployment.py

@classmethod
def locations(cls) -> Dict[str, str]:
    """Return the locations where the ZenML stack can be deployed.

    Returns:
        The regions where the ZenML stack can be deployed as a map of region
        names to region descriptions.
    """
    # Return a list of all possible GCP regions

    # Based on the AWS regions listed at
    # https://cloud.google.com/about/locations
    return {
        "Africa (Johannesburg)": "africa-south1",
        "Asia Pacific (Taiwan)": "asia-east1",
        "Asia Pacific (Hong Kong)": "asia-east2",
        "Asia Pacific (Tokyo)": "asia-northeast1",
        "Asia Pacific (Osaka)": "asia-northeast2",
        "Asia Pacific (Seoul)": "asia-northeast3",
        "Asia Pacific (Mumbai)": "asia-south1",
        "Asia Pacific (Delhi)": "asia-south2",
        "Asia Pacific (Singapore)": "asia-southeast1",
        "Asia Pacific (Jakarta)": "asia-southeast2",
        "Australia (Sydney)": "australia-southeast1",
        "Australia (Melbourne)": "australia-southeast2",
        "Europe (Belgium)": "europe-west1",
        "Europe (London)": "europe-west2",
        "Europe (Frankfurt)": "europe-west3",
        "Europe (Netherlands)": "europe-west4",
        "Europe (Zurich)": "europe-west6",
        "Europe (Milan)": "europe-west8",
        "Europe (Paris)": "europe-west9",
        "Europe (Berlin)": "europe-west10",
        "Europe (Turin)": "europe-west12",
        "Europe (Warsaw)": "europe-central2",
        "Europe (Finland)": "europe-north1",
        "Europe (Madrid)": "europe-southwest1",
        "Middle East (Doha)": "me-central1",
        "Middle East (Dubai)": "me-central2",
        "Middle East (Tel Aviv)": "me-west1",
        "North America (Montreal)": "northamerica-northeast1",
        "North America (Toronto)": "northamerica-northeast2",
        "South America (Sao Paulo)": "southamerica-east1",
        "South America (Santiago)": "southamerica-west1",
        "US Central (Iowa)": "us-central1",
        "US East (South Carolina)": "us-east1",
        "US East (Northern Virginia)": "us-east4",
        "US East (Columbus)": "us-east5",
        "US South (Dallas)": "us-south1",
        "US West (Oregon)": "us-west1",
        "US West (Los Angeles)": "us-west2",
        "US West (Salt Lake City)": "us-west3",
        "US West (Las Vegas)": "us-west4",
    }

`permissions()` `classmethod`

Return the permissions granted to ZenML to access the cloud resources.

Returns:

Type	Description
`Dict[str, List[str]]`	The permissions granted to ZenML to access the cloud resources, as a dictionary grouping permissions by resource.

Source code in zenml/stack_deployments/gcp_stack_deployment.py

@classmethod
def permissions(cls) -> Dict[str, List[str]]:
    """Return the permissions granted to ZenML to access the cloud resources.

    Returns:
        The permissions granted to ZenML to access the cloud resources, as
        a dictionary grouping permissions by resource.
    """
    return {
        "GCS Bucket": [
            "roles/storage.objectUser",
        ],
        "GCP Artifact Registry": [
            "roles/artifactregistry.createOnPushWriter",
        ],
        "Vertex AI (Client)": [
            "roles/aiplatform.user",
        ],
        "Vertex AI (Jobs)": [
            "roles/aiplatform.serviceAgent",
        ],
        "Cloud Build (Client)": [
            "roles/cloudbuild.builds.editor",
        ],
    }

`post_deploy_instructions()` `classmethod`

Return instructions on what to do after the deployment is complete.

This will be displayed after the deployment is complete.

Returns:

Type	Description
`str`	MarkDown instructions on what to do after the deployment is complete.

Source code in zenml/stack_deployments/gcp_stack_deployment.py

    @classmethod
    def post_deploy_instructions(cls) -> str:
        """Return instructions on what to do after the deployment is complete.

        This will be displayed after the deployment is complete.

        Returns:
            MarkDown instructions on what to do after the deployment is
            complete.
        """
        return """
The ZenML stack has been successfully deployed and registered. You can delete
the Deployment Manager deployment at any time to revoke ZenML's access to your
GCP project and to clean up the resources created by the stack by using
[the GCP Cloud Console](https://console.cloud.google.com/dm/deployments).
"""

`stack_deployment`

Functionality to deploy a ZenML stack to a cloud provider.

`ZenMLCloudStackDeployment (BaseModel)`

ZenML Cloud Stack CLI Deployment base class.

Source code in zenml/stack_deployments/stack_deployment.py

class ZenMLCloudStackDeployment(BaseModel):
    """ZenML Cloud Stack CLI Deployment base class."""

    provider: ClassVar[StackDeploymentProvider]
    deployment: ClassVar[str]
    stack_name: str
    zenml_server_url: str
    zenml_server_api_token: str
    location: Optional[str] = None

    @classmethod
    @abstractmethod
    def description(cls) -> str:
        """Return a description of the ZenML Cloud Stack Deployment.

        This will be displayed when the user is prompted to deploy
        the ZenML stack.

        Returns:
            A MarkDown description of the ZenML Cloud Stack Deployment.
        """

    @classmethod
    @abstractmethod
    def instructions(cls) -> str:
        """Return instructions on how to deploy the ZenML stack to the specified cloud provider.

        This will be displayed before the user is prompted to deploy the ZenML
        stack.

        Returns:
            MarkDown instructions on how to deploy the ZenML stack to the
            specified cloud provider.
        """

    @classmethod
    @abstractmethod
    def post_deploy_instructions(cls) -> str:
        """Return instructions on what to do after the deployment is complete.

        This will be displayed after the deployment is complete.

        Returns:
            MarkDown instructions on what to do after the deployment is
            complete.
        """

    @classmethod
    @abstractmethod
    def integrations(cls) -> List[str]:
        """Return the ZenML integrations required for the stack.

        Returns:
            The list of ZenML integrations that need to be installed for the
            stack to be usable.
        """

    @classmethod
    @abstractmethod
    def permissions(cls) -> Dict[str, List[str]]:
        """Return the permissions granted to ZenML to access the cloud resources.

        Returns:
            The permissions granted to ZenML to access the cloud resources, as
            a dictionary grouping permissions by resource.
        """

    @classmethod
    @abstractmethod
    def locations(cls) -> Dict[str, str]:
        """Return the locations where the ZenML stack can be deployed.

        Returns:
            The regions where the ZenML stack can be deployed as a map of region
            names to region descriptions.
        """

    @classmethod
    def get_deployment_info(cls) -> StackDeploymentInfo:
        """Return information about the ZenML Cloud Stack Deployment.

        Returns:
            Information about the ZenML Cloud Stack Deployment.
        """
        return StackDeploymentInfo(
            provider=cls.provider,
            description=cls.description(),
            instructions=cls.instructions(),
            post_deploy_instructions=cls.post_deploy_instructions(),
            integrations=cls.integrations(),
            permissions=cls.permissions(),
            locations=cls.locations(),
        )

    @abstractmethod
    def get_deployment_config(
        self,
    ) -> StackDeploymentConfig:
        """Return the configuration to deploy the ZenML stack to the specified cloud provider.

        The configuration should include:

        * a cloud provider console URL where the user will be redirected to
        deploy the ZenML stack. The URL should include as many pre-filled
        URL query parameters as possible.
        * a textual description of the URL
        * some deployment providers may require additional configuration
        parameters to be passed to the cloud provider in addition to the
        deployment URL query parameters. Where that is the case, this method
        should also return a string that the user can copy and paste into the
        cloud provider console to deploy the ZenML stack (e.g. a set of
        environment variables, or YAML configuration snippet etc.).

        Returns:
            The configuration to deploy the ZenML stack to the specified cloud
            provider.
        """

    def get_stack(
        self, date_start: Optional[datetime.datetime] = None
    ) -> Optional[DeployedStack]:
        """Return the ZenML stack that was deployed and registered.

        This method is called to retrieve a ZenML stack matching the deployment
        provider.

        Args:
            date_start: The date when the deployment started.

        Returns:
            The ZenML stack that was deployed and registered or None if a
            matching stack was not found.
        """
        client = Client()

        # It's difficult to find a stack that matches the CloudFormation
        # deployment 100% because the user can change the stack name before they
        # deploy the stack in GCP.
        #
        # We try to find a full GCP stack that matches the deployment provider
        # that was registered after this deployment was created.

        # Get all stacks created after the start date
        stacks = client.list_stacks(
            created=f"gt:{str(date_start.replace(microsecond=0))}"
            if date_start
            else None,
            sort_by="desc:created",
            size=50,
        )

        if not stacks.items:
            return None

        # Find a stack that best matches the deployment provider
        for stack in stacks.items:
            if not stack.labels:
                continue

            if stack.labels.get("zenml:provider") != self.provider.value:
                continue

            if stack.labels.get("zenml:deployment") != self.deployment:
                continue

            artifact_store = stack.components[
                StackComponentType.ARTIFACT_STORE
            ][0]

            if not artifact_store.connector:
                continue

            return DeployedStack(
                stack=stack,
                service_connector=artifact_store.connector,
            )

        return None

`description()` `classmethod`

Return a description of the ZenML Cloud Stack Deployment.

This will be displayed when the user is prompted to deploy the ZenML stack.

Returns:

Type	Description
`str`	A MarkDown description of the ZenML Cloud Stack Deployment.

Source code in zenml/stack_deployments/stack_deployment.py

@classmethod
@abstractmethod
def description(cls) -> str:
    """Return a description of the ZenML Cloud Stack Deployment.

    This will be displayed when the user is prompted to deploy
    the ZenML stack.

    Returns:
        A MarkDown description of the ZenML Cloud Stack Deployment.
    """

`get_deployment_config(self)`

Return the configuration to deploy the ZenML stack to the specified cloud provider.

The configuration should include:

a cloud provider console URL where the user will be redirected to deploy the ZenML stack. The URL should include as many pre-filled URL query parameters as possible.
a textual description of the URL
some deployment providers may require additional configuration parameters to be passed to the cloud provider in addition to the deployment URL query parameters. Where that is the case, this method should also return a string that the user can copy and paste into the cloud provider console to deploy the ZenML stack (e.g. a set of environment variables, or YAML configuration snippet etc.).

Returns:

Type	Description
`StackDeploymentConfig`	The configuration to deploy the ZenML stack to the specified cloud provider.

Source code in zenml/stack_deployments/stack_deployment.py

@abstractmethod
def get_deployment_config(
    self,
) -> StackDeploymentConfig:
    """Return the configuration to deploy the ZenML stack to the specified cloud provider.

    The configuration should include:

    * a cloud provider console URL where the user will be redirected to
    deploy the ZenML stack. The URL should include as many pre-filled
    URL query parameters as possible.
    * a textual description of the URL
    * some deployment providers may require additional configuration
    parameters to be passed to the cloud provider in addition to the
    deployment URL query parameters. Where that is the case, this method
    should also return a string that the user can copy and paste into the
    cloud provider console to deploy the ZenML stack (e.g. a set of
    environment variables, or YAML configuration snippet etc.).

    Returns:
        The configuration to deploy the ZenML stack to the specified cloud
        provider.
    """

`get_deployment_info()` `classmethod`

Return information about the ZenML Cloud Stack Deployment.

Returns:

Type	Description
`StackDeploymentInfo`	Information about the ZenML Cloud Stack Deployment.

Source code in zenml/stack_deployments/stack_deployment.py

@classmethod
def get_deployment_info(cls) -> StackDeploymentInfo:
    """Return information about the ZenML Cloud Stack Deployment.

    Returns:
        Information about the ZenML Cloud Stack Deployment.
    """
    return StackDeploymentInfo(
        provider=cls.provider,
        description=cls.description(),
        instructions=cls.instructions(),
        post_deploy_instructions=cls.post_deploy_instructions(),
        integrations=cls.integrations(),
        permissions=cls.permissions(),
        locations=cls.locations(),
    )

`get_stack(self, date_start=None)`

Return the ZenML stack that was deployed and registered.

This method is called to retrieve a ZenML stack matching the deployment provider.

Parameters:

Name	Type	Description	Default
`date_start`	`Optional[datetime.datetime]`	The date when the deployment started.	`None`

Returns:

Type	Description
`Optional[zenml.models.v2.misc.stack_deployment.DeployedStack]`	The ZenML stack that was deployed and registered or None if a matching stack was not found.

Source code in zenml/stack_deployments/stack_deployment.py

def get_stack(
    self, date_start: Optional[datetime.datetime] = None
) -> Optional[DeployedStack]:
    """Return the ZenML stack that was deployed and registered.

    This method is called to retrieve a ZenML stack matching the deployment
    provider.

    Args:
        date_start: The date when the deployment started.

    Returns:
        The ZenML stack that was deployed and registered or None if a
        matching stack was not found.
    """
    client = Client()

    # It's difficult to find a stack that matches the CloudFormation
    # deployment 100% because the user can change the stack name before they
    # deploy the stack in GCP.
    #
    # We try to find a full GCP stack that matches the deployment provider
    # that was registered after this deployment was created.

    # Get all stacks created after the start date
    stacks = client.list_stacks(
        created=f"gt:{str(date_start.replace(microsecond=0))}"
        if date_start
        else None,
        sort_by="desc:created",
        size=50,
    )

    if not stacks.items:
        return None

    # Find a stack that best matches the deployment provider
    for stack in stacks.items:
        if not stack.labels:
            continue

        if stack.labels.get("zenml:provider") != self.provider.value:
            continue

        if stack.labels.get("zenml:deployment") != self.deployment:
            continue

        artifact_store = stack.components[
            StackComponentType.ARTIFACT_STORE
        ][0]

        if not artifact_store.connector:
            continue

        return DeployedStack(
            stack=stack,
            service_connector=artifact_store.connector,
        )

    return None

`instructions()` `classmethod`

Return instructions on how to deploy the ZenML stack to the specified cloud provider.

This will be displayed before the user is prompted to deploy the ZenML stack.

Returns:

Type	Description
`str`	MarkDown instructions on how to deploy the ZenML stack to the specified cloud provider.

Source code in zenml/stack_deployments/stack_deployment.py

@classmethod
@abstractmethod
def instructions(cls) -> str:
    """Return instructions on how to deploy the ZenML stack to the specified cloud provider.

    This will be displayed before the user is prompted to deploy the ZenML
    stack.

    Returns:
        MarkDown instructions on how to deploy the ZenML stack to the
        specified cloud provider.
    """

`integrations()` `classmethod`

Return the ZenML integrations required for the stack.

Returns:

Type	Description
`List[str]`	The list of ZenML integrations that need to be installed for the stack to be usable.

Source code in zenml/stack_deployments/stack_deployment.py

@classmethod
@abstractmethod
def integrations(cls) -> List[str]:
    """Return the ZenML integrations required for the stack.

    Returns:
        The list of ZenML integrations that need to be installed for the
        stack to be usable.
    """

`locations()` `classmethod`

Return the locations where the ZenML stack can be deployed.

Returns:

Type	Description
`Dict[str, str]`	The regions where the ZenML stack can be deployed as a map of region names to region descriptions.

Source code in zenml/stack_deployments/stack_deployment.py

@classmethod
@abstractmethod
def locations(cls) -> Dict[str, str]:
    """Return the locations where the ZenML stack can be deployed.

    Returns:
        The regions where the ZenML stack can be deployed as a map of region
        names to region descriptions.
    """

`permissions()` `classmethod`

Return the permissions granted to ZenML to access the cloud resources.

Returns:

Type	Description
`Dict[str, List[str]]`	The permissions granted to ZenML to access the cloud resources, as a dictionary grouping permissions by resource.

Source code in zenml/stack_deployments/stack_deployment.py

@classmethod
@abstractmethod
def permissions(cls) -> Dict[str, List[str]]:
    """Return the permissions granted to ZenML to access the cloud resources.

    Returns:
        The permissions granted to ZenML to access the cloud resources, as
        a dictionary grouping permissions by resource.
    """

`post_deploy_instructions()` `classmethod`

Return instructions on what to do after the deployment is complete.

This will be displayed after the deployment is complete.

Returns:

Type	Description
`str`	MarkDown instructions on what to do after the deployment is complete.

Source code in zenml/stack_deployments/stack_deployment.py

@classmethod
@abstractmethod
def post_deploy_instructions(cls) -> str:
    """Return instructions on what to do after the deployment is complete.

    This will be displayed after the deployment is complete.

    Returns:
        MarkDown instructions on what to do after the deployment is
        complete.
    """

`utils`

Functionality to deploy a ZenML stack to a cloud provider.

`get_stack_deployment_class(provider)`

Get the ZenML Cloud Stack Deployment class for the specified provider.

Parameters:

Name	Type	Description	Default
`provider`	`StackDeploymentProvider`	The stack deployment provider.	required

Returns:

Type	Description
`Type[zenml.stack_deployments.stack_deployment.ZenMLCloudStackDeployment]`	The ZenML Cloud Stack Deployment class for the specified provider.

Source code in zenml/stack_deployments/utils.py

def get_stack_deployment_class(
    provider: StackDeploymentProvider,
) -> Type[ZenMLCloudStackDeployment]:
    """Get the ZenML Cloud Stack Deployment class for the specified provider.

    Args:
        provider: The stack deployment provider.

    Returns:
        The ZenML Cloud Stack Deployment class for the specified provider.
    """
    return STACK_DEPLOYMENT_PROVIDERS[provider]

Stack Deployments

zenml.stack_deployments special

aws_stack_deployment

AWSZenMLCloudStackDeployment (ZenMLCloudStackDeployment)

description() classmethod

get_deployment_config(self)

instructions() classmethod

integrations() classmethod

locations() classmethod

permissions() classmethod

post_deploy_instructions() classmethod

gcp_stack_deployment

GCPZenMLCloudStackDeployment (ZenMLCloudStackDeployment)

description() classmethod

get_deployment_config(self)

instructions() classmethod

integrations() classmethod

locations() classmethod

permissions() classmethod

post_deploy_instructions() classmethod

stack_deployment

ZenMLCloudStackDeployment (BaseModel)

description() classmethod

get_deployment_config(self)

get_deployment_info() classmethod

get_stack(self, date_start=None)

instructions() classmethod

integrations() classmethod

locations() classmethod

permissions() classmethod

post_deploy_instructions() classmethod

utils

get_stack_deployment_class(provider)

`zenml.stack_deployments` `special`

`aws_stack_deployment`

`AWSZenMLCloudStackDeployment (ZenMLCloudStackDeployment)`

`description()` `classmethod`

`get_deployment_config(self)`

`instructions()` `classmethod`

`integrations()` `classmethod`

`locations()` `classmethod`

`permissions()` `classmethod`

`post_deploy_instructions()` `classmethod`

`gcp_stack_deployment`

`GCPZenMLCloudStackDeployment (ZenMLCloudStackDeployment)`

`description()` `classmethod`

`get_deployment_config(self)`

`instructions()` `classmethod`

`integrations()` `classmethod`

`locations()` `classmethod`

`permissions()` `classmethod`

`post_deploy_instructions()` `classmethod`

`stack_deployment`

`ZenMLCloudStackDeployment (BaseModel)`

`description()` `classmethod`

`get_deployment_config(self)`

`get_deployment_info()` `classmethod`

`get_stack(self, date_start=None)`

`instructions()` `classmethod`

`integrations()` `classmethod`

`locations()` `classmethod`

`permissions()` `classmethod`

`post_deploy_instructions()` `classmethod`

`utils`

`get_stack_deployment_class(provider)`