The complete guide to managing Service Connectors and connecting ZenML to external resources.
This documentation section contains everything that you need to use Service Connectors to connect ZenML to external resources. A lot of information is covered, so it might be useful to use the following guide to navigate it:
if you're only getting started with Service Connectors, we suggest starting by familiarizing yourself with the terminology.
check out the section on Service Connector Types to understand the different Service Connector implementations that are available and when to use them.
jumping straight to the sections on Registering Service Connectors can get you set up quickly if you are only looking for a quick way to evaluate Service Connectors and their features.
if all you need to do is connect a ZenML Stack Component to an external resource or service like a Kubernetes cluster, a Docker container registry, or an object storage bucket, and you already have some Service Connectors available, the section on connecting Stack Components to resources is all you need.
In addition to this guide, there is an entire section dedicated to best security practices concerning the various authentication methods implemented by Service Connectors, such as which types of credentials to use in development or production and how to keep your security information safe. That section is particularly targeted at engineers with some knowledge of infrastructure, but it should be accessible to larger audiences.
Terminology
As with any high-level abstraction, some terminology is needed to express the concepts and operations involved. In spite of the fact that Service Connectors cover such a large area of application as authentication and authorization for a variety of resources from a range of different vendors, we managed to keep this abstraction clean and simple. In the following expandable sections, you'll learn more about Service Connector Types, Resource Types, Resource Names, and Service Connectors.
Service Connector Types
This term is used to represent and identify a particular Service Connector implementation and answer questions about its capabilities such as "what types of resources does this Service Connector give me access to", "what authentication methods does it support" and "what credentials and other information do I need to configure for it". This is analogous to the role Flavors play for Stack Components in that the Service Connector Type acts as the template from which one or more Service Connectors are created.
For example, the built-in AWS Service Connector Type shipped with ZenML supports a rich variety of authentication methods and provides access to AWS resources such as S3 buckets, EKS clusters and ECR registries.
The zenml service-connector list-types and zenml service-connector describe-type CLI commands can be used to explore the Service Connector Types available with your ZenML deployment. Extensive documentation is included covering supported authentication methods and Resource Types. The following are just some examples:
╔══════════════════════════════════════════════════════════════════════════════╗
║ 🔶 AWS Service Connector (connector type: aws) ║
╚══════════════════════════════════════════════════════════════════════════════╝
Authentication methods:
• 🔒 implicit
• 🔒 secret-key
• 🔒 sts-token
• 🔒 iam-role
• 🔒 session-token
• 🔒 federation-token
Resource types:
• 🔶 aws-generic
• 📦 s3-bucket
• 🌀 kubernetes-cluster
• 🐳 docker-registry
Supports auto-configuration: True
Available locally: True
Available remotely: False
The ZenML AWS Service Connector facilitates the authentication and access to
managed AWS services and resources. These encompass a range of resources,
including S3 buckets, ECR repositories, and EKS clusters. The connector provides
support for various authentication methods, including explicit long-lived AWS
secret keys, IAM roles, short-lived STS tokens and implicit authentication.
To ensure heightened security measures, this connector also enables the
generation of temporary STS security tokens that are scoped down to the minimum
permissions necessary for accessing the intended resource. Furthermore, it
includes automatic configuration and detection of credentials locally configured
through the AWS CLI.
This connector serves as a general means of accessing any AWS service by issuing
pre-authenticated boto3 sessions to clients. Additionally, the connector can
handle specialized authentication for S3, Docker and Kubernetes Python clients.
It also allows for the configuration of local Docker and Kubernetes CLIs.
The AWS Service Connector is part of the AWS ZenML integration. You can either
install the entire integration or use a pypi extra to install it independently
of the integration:
• pip install "zenml[connectors-aws]" installs only prerequisites for the AWS
Service Connector Type
• zenml integration install aws installs the entire AWS ZenML integration
It is not required to install and set up the AWS CLI on your local machine to
use the AWS Service Connector to link Stack Components to AWS resources and
services. However, it is recommended to do so if you are looking for a quick
setup that includes using the auto-configuration Service Connector features.
────────────────────────────────────────────────────────────────────────────────
╔══════════════════════════════════════════════════════════════════════════════╗
║ 🌀 AWS EKS Kubernetes cluster (resource type: kubernetes-cluster) ║
╚══════════════════════════════════════════════════════════════════════════════╝
Authentication methods: implicit, secret-key, sts-token, iam-role,
session-token, federation-token
Supports resource instances: True
Authentication methods:
• 🔒 implicit
• 🔒 secret-key
• 🔒 sts-token
• 🔒 iam-role
• 🔒 session-token
• 🔒 federation-token
Allows users to access an EKS cluster as a standard Kubernetes cluster resource.
When used by Stack Components, they are provided a pre-authenticated
python-kubernetes client instance.
The configured credentials must have at least the following AWS IAM permissions
associated with the ARNs of EKS clusters that the connector will be allowed to
access (e.g. arn:aws:eks:{region}:{account}:cluster/* represents all the EKS
clusters available in the target AWS region).
• eks:ListClusters
• eks:DescribeCluster
In addition to the above permissions, if the credentials are not associated with
the same IAM user or role that created the EKS cluster, the IAM principal must
be manually added to the EKS cluster's aws-auth ConfigMap, otherwise the
Kubernetes client will not be allowed to access the cluster's resources. This
makes it more challenging to use the AWS Implicit and AWS Federation Token
authentication methods for this resource. For more information, see this
documentation.
If set, the resource name must identify an EKS cluster using one of the
following formats:
• EKS cluster name (canonical resource name): {cluster-name}
• EKS cluster ARN: arn:aws:eks:{region}:{account}:cluster/{cluster-name}
EKS cluster names are region scoped. The connector can only be used to access
EKS clusters in the AWS region that it is configured to use.
────────────────────────────────────────────────────────────────────────────────
╔══════════════════════════════════════════════════════════════════════════════╗
║ 🔒 AWS Secret Key (auth method: secret-key) ║
╚══════════════════════════════════════════════════════════════════════════════╝
Supports issuing temporary credentials: False
Long-lived AWS credentials consisting of an AWS access key ID and secret access
key associated with an AWS IAM user or AWS account root user (not recommended).
This method is preferred during development and testing due to its simplicity
and ease of use. It is not recommended as a direct authentication method for
production use cases because the clients have direct access to long-lived
credentials and are granted the full set of permissions of the IAM user or AWS
account root user associated with the credentials. For production, it is
recommended to use the AWS IAM Role, AWS Session Token or AWS Federation Token
authentication method instead.
An AWS region is required and the connector may only be used to access AWS
resources in the specified region.
If you already have the local AWS CLI set up with these credentials, they will
be automatically picked up when auto-configuration is used.
Attributes:
• aws_access_key_id {string, secret, required}: AWS Access Key ID
• aws_secret_access_key {string, secret, required}: AWS Secret Access Key
• region {string, required}: AWS Region
• endpoint_url {string, optional}: AWS Endpoint URL
────────────────────────────────────────────────────────────────────────────────
Resource Types
Resource Types are a way of organizing resources into logical, well-known classes based on the standard and/or protocol used to access them, or simply based on their vendor. This creates a unified language that can be used to declare the types of resources that are provided by Service Connectors on one hand and the types of resources that are required by Stack Components on the other hand.
For example, we use the generic kubernetes-cluster resource type to refer to any and all Kubernetes clusters, since they are all generally accessible using the same standard libraries, clients and API regardless of whether they are Amazon EKS, Google GKE, Azure AKS or another flavor of managed or self-hosted deployment. Similarly, there is a generic docker-registry resource type that covers any and all container registries that implement the Docker/OCI interface, be it DockerHub, Amazon ECR, Google GCR, Azure ACR, K3D or something similar. Stack Components that need to connect to a Kubernetes cluster (e.g. the Kubernetes Orchestrator or the Seldon Model Deployer) can use the kubernetes-cluster resource type identifier to describe their resource requirements and remain agnostic of their vendor.
The term Resource Type is used in ZenML everywhere resources accessible through Service Connectors are involved. For example, to list all Service Connector Types that can be used to broker access to Kubernetes Clusters, you can pass the --resource-type flag to the CLI command:
From the above, you can see that there are not one but four Service Connector Types that can connect ZenML to Kubernetes clusters. The first one is a generic implementation that can be used with any standard Kubernetes cluster, including those that run on-premise. The other three deal exclusively with Kubernetes services managed by the AWS, GCP and Azure cloud providers.
Conversely, to list all currently registered Service Connector instances that provide access to Kubernetes clusters, one might run:
If a Resource Type is used to identify a class of resources, we also need some way to uniquely identify each resource instance belonging to that class that a Service Connector can provide access to. For example, an AWS Service Connector can be configured to provide access to multiple S3 buckets identifiable by their bucket names or their s3://bucket-name formatted URIs. Similarly, an AWS Service Connector can be configured to provide access to multiple EKS Kubernetes clusters in the same AWS region, each uniquely identifiable by their EKS cluster name. This is what we call Resource Names.
Resource Names make it generally easy to identify a particular resource instance accessible through a Service Connector, especially when used together with the Service Connector name and the Resource Type. The following ZenML CLI command output shows a few examples featuring Resource Names for S3 buckets, EKS clusters, ECR registries and general Kubernetes clusters. As you can see, the way we name resources varies from implementation to implementation and resource type to resource type:
Every Service Connector Type defines its own rules for how Resource Names are formatted. These rules are documented in the section belonging each resource type. For example:
╔══════════════════════════════════════════════════════════════════════════════╗
║ 🐳 AWS ECR container registry (resource type: docker-registry) ║
╚══════════════════════════════════════════════════════════════════════════════╝
Authentication methods: implicit, secret-key, sts-token, iam-role,
session-token, federation-token
Supports resource instances: False
Authentication methods:
• 🔒 implicit
• 🔒 secret-key
• 🔒 sts-token
• 🔒 iam-role
• 🔒 session-token
• 🔒 federation-token
Allows users to access one or more ECR repositories as a standard Docker
registry resource. When used by Stack Components, they are provided a
pre-authenticated python-docker client instance.
The configured credentials must have at least the following AWS IAM permissions
associated with the ARNs of one or more ECR repositories that the connector will
be allowed to access (e.g. arn:aws:ecr:{region}:{account}:repository/*
represents all the ECR repositories available in the target AWS region).
• ecr:DescribeRegistry
• ecr:DescribeRepositories
• ecr:ListRepositories
• ecr:BatchGetImage
• ecr:DescribeImages
• ecr:BatchCheckLayerAvailability
• ecr:GetDownloadUrlForLayer
• ecr:InitiateLayerUpload
• ecr:UploadLayerPart
• ecr:CompleteLayerUpload
• ecr:PutImage
• ecr:GetAuthorizationToken
This resource type is not scoped to a single ECR repository. Instead, a
connector configured with this resource type will grant access to all the ECR
repositories that the credentials are allowed to access under the configured AWS
region (i.e. all repositories under the Docker registry URL
https://{account-id}.dkr.ecr.{region}.amazonaws.com).
The resource name associated with this resource type uniquely identifies an ECR
registry using one of the following formats (the repository name is ignored,
only the registry URL/ARN is used):
• ECR repository URI (canonical resource name):
[https://]{account}.dkr.ecr.{region}.amazonaws.com[/{repository-name}]
• ECR repository ARN:
arn:aws:ecr:{region}:{account-id}:repository[/{repository-name}]
ECR repository names are region scoped. The connector can only be used to access
ECR repositories in the AWS region that it is configured to use.
────────────────────────────────────────────────────────────────────────────────
Service Connectors
The Service Connector is how you configure ZenML to authenticate and connect to one or more external resources. It stores the required configuration and security credentials and can optionally be scoped with a Resource Type and a Resource Name.
Depending on the Service Connector Type implementation, a Service Connector instance can be configured in one of the following modes with regards to the types and number of resources that it has access to:
a multi-type Service Connector instance that can be configured once and used to gain access to multiple types of resources. This is only possible with Service Connector Types that support multiple Resource Types to begin with, such as those that target multi-service cloud providers like AWS, GCP and Azure. In contrast, a single-type Service Connector can only be used with a single Resource Type. To configure a multi-type Service Connector, you can simply skip scoping its Resource Type during registration.
a multi-instance Service Connector instance can be configured once and used to gain access to multiple resources of the same type, each identifiable by a Resource Name. Not all types of connectors and not all types of resources support multiple instances. Some Service Connectors Types like the generic Kubernetes and Docker connector types only allow single-instance configurations: a Service Connector instance can only be used to access a single Kubernetes cluster and a single Docker registry. To configure a multi-instance Service Connector, you can simply skip scoping its Resource Name during registration.
The following is an example of configuring a multi-type AWS Service Connector instance capable of accessing multiple AWS resources of different types:
⠼ Registering service connector 'aws-s3-zenfiles'...
Successfully registered service connector `aws-s3-zenfiles` with access to the following resources:
┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────┼────────────────┨
┃ 📦 s3-bucket │ s3://zenfiles ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
Explore Service Connector Types
Service Connector Types are not only templates used to instantiate Service Connectors, they also form a body of knowledge that documents best security practices and guides users through the complicated world of authentication and authorization.
ZenML ships with a handful of Service Connector Types that enable you right out-of-the-box to connect ZenML to cloud resources and services available from cloud providers such as AWS and GCP, as well as on-premise infrastructure. In addition to built-in Service Connector Types, ZenML can be easily extended with custom Service Connector implementations.
To discover the Connector Types available with your ZenML deployment, you can use the zenml service-connector list-types CLI command:
Exploring the documentation embedded into Service Connector Types
A lot more is hidden behind a Service Connector Type than a name and a simple list of resource types. Before using a Service Connector Type to configure a Service Connector, you probably need to understand what it is, what it can offer and what are the supported authentication methods and their requirements. All this can be accessed directly through the CLI. Some examples are included here.
Showing information about the gcp Service Connector Type:
zenmlservice-connectordescribe-typegcp
Example Command Output
╔══════════════════════════════════════════════════════════════════════════════╗
║ 🔵 GCP Service Connector (connector type: gcp) ║
╚══════════════════════════════════════════════════════════════════════════════╝
Authentication methods:
• 🔒 implicit
• 🔒 user-account
• 🔒 service-account
• 🔒 oauth2-token
• 🔒 impersonation
Resource types:
• 🔵 gcp-generic
• 📦 gcs-bucket
• 🌀 kubernetes-cluster
• 🐳 docker-registry
Supports auto-configuration: True
Available locally: True
Available remotely: True
The ZenML GCP Service Connector facilitates the authentication and access to
managed GCP services and resources. These encompass a range of resources,
including GCS buckets, GCR container repositories and GKE clusters. The
connector provides support for various authentication methods, including GCP
user accounts, service accounts, short-lived OAuth 2.0 tokens and implicit
authentication.
To ensure heightened security measures, this connector always issues short-lived
OAuth 2.0 tokens to clients instead of long-lived credentials. Furthermore, it
includes automatic configuration and detection of credentials locally
configured through the GCP CLI.
This connector serves as a general means of accessing any GCP service by issuing
OAuth 2.0 credential objects to clients. Additionally, the connector can handle
specialized authentication for GCS, Docker and Kubernetes Python clients. It
also allows for the configuration of local Docker and Kubernetes CLIs.
The GCP Service Connector is part of the GCP ZenML integration. You can either
install the entire integration or use a pypi extra to install it independently
of the integration:
• pip install "zenml[connectors-gcp]" installs only prerequisites for the GCP
Service Connector Type
• zenml integration install gcp installs the entire GCP ZenML integration
It is not required to install and set up the GCP CLI on your local machine to
use the GCP Service Connector to link Stack Components to GCP resources and
services. However, it is recommended to do so if you are looking for a quick
setup that includes using the auto-configuration Service Connector features.
──────────────────────────────────────────────────────────────────────────────────
Fetching details about the GCP kubernetes-cluster resource type (i.e. the GKE cluster):
╔══════════════════════════════════════════════════════════════════════════════╗
║ 🌀 GCP GKE Kubernetes cluster (resource type: kubernetes-cluster) ║
╚══════════════════════════════════════════════════════════════════════════════╝
Authentication methods: implicit, user-account, service-account, oauth2-token,
impersonation
Supports resource instances: True
Authentication methods:
• 🔒 implicit
• 🔒 user-account
• 🔒 service-account
• 🔒 oauth2-token
• 🔒 impersonation
Allows Stack Components to access a GKE registry as a standard Kubernetes
cluster resource. When used by Stack Components, they are provided a
pre-authenticated Python Kubernetes client instance.
The configured credentials must have at least the following GCP permissions
associated with the GKE clusters that it can access:
• container.clusters.list
• container.clusters.get
In addition to the above permissions, the credentials should include permissions
to connect to and use the GKE cluster (i.e. some or all permissions in the
Kubernetes Engine Developer role).
If set, the resource name must identify an GKE cluster using one of the
following formats:
• GKE cluster name: {cluster-name}
GKE cluster names are project scoped. The connector can only be used to access
GKE clusters in the GCP project that it is configured to use.
────────────────────────────────────────────────────────────────────────────────
Displaying information about the service-account GCP authentication method:
╔══════════════════════════════════════════════════════════════════════════════╗
║ 🔒 GCP Service Account (auth method: service-account) ║
╚══════════════════════════════════════════════════════════════════════════════╝
Supports issuing temporary credentials: False
Use a GCP service account and its credentials to authenticate to GCP services.
This method requires a GCP service account and a service account key JSON
created for it.
The GCP connector generates temporary OAuth 2.0 tokens from the user account
credentials and distributes them to clients. The tokens have a limited lifetime
of 1 hour.
A GCP project is required and the connector may only be used to access GCP
resources in the specified project.
If you already have the GOOGLE_APPLICATION_CREDENTIALS environment variable
configured to point to a service account key JSON file, it will be automatically
picked up when auto-configuration is used.
Attributes:
• service_account_json {string, secret, required}: GCP Service Account Key JSON
• project_id {string, required}: GCP Project ID where the target resource is
located.
────────────────────────────────────────────────────────────────────────────────
Basic Service Connector Types
Service Connector Types like the Kubernetes Service Connector and Docker Service Connector can only handle one resource at a time: a Kubernetes cluster and a Docker container registry respectively. These basic Service Connector Types are the easiest to instantiate and manage, as each Service Connector instance is tied exactly to one resource (i.e. they are single-instance connectors).
The following output shows two Service Connector instances configured from basic Service Connector Types:
a Docker Service Connector that grants authenticated access to the DockerHub registry and allows pushing/pulling images that are stored in private repositories belonging to a DockerHub account
a Kubernetes Service Connector that authenticates access to a Kubernetes cluster running on-premise and allows managing containerized workloads running there.
Cloud service providers like AWS, GCP and Azure implement one or more authentication schemes that are unified across a wide range or resources and services, all managed under the same umbrella. This allows users to access many different resources with a single set of authentication credentials. Some authentication methods are straightforward to set up, but are only meant to be used for development and testing. Other authentication schemes are powered by extensive roles and permissions management systems and are targeted at production environments where security and operations at scale are big concerns. The corresponding cloud provider Service Connector Types are designed accordingly:
they support multiple types of resources (e.g. Kubernetes clusters, Docker registries, a form of object storage)
they usually include some form of "generic" Resource Type that can be used by clients to access types of resources that are not yet part of the supported set. When this generic Resource Type is used, clients and Stack Components that access the connector are provided some form of generic session, credentials or client that can be used to access any of the cloud provider resources. For example, in the AWS case, clients accessing the aws-generic Resource Type are issued a pre-authenticated boto3 Session object that can be used to access any AWS service.
they support multiple authentication methods. Some of these allow clients direct access to long-lived, broad-access credentials and are only recommended for local development use. Others support distributing temporary API tokens automatically generated from long-lived credentials, which are safer for production use-cases, but may be more difficult to set up. A few authentication methods even support down-scoping the permissions of temporary API tokens so that they only allow access to the target resource and restrict access to everything else. This is covered at length in the section on best practices for authentication methods.
there is flexibility regarding the range of resources that a single cloud provider Service Connector instance configured with a single set of credentials can be scoped to access:
a multi-type Service Connector instance can access any type of resources from the range of supported Resource Types
a multi-instance Service Connector instance can access multiple resources of the same type
a single-instance Service Connector instance is scoped to access a single resource
The following output shows three different Service Connectors configured from the same GCP Service Connector Type using three different scopes but with the same credentials:
a multi-type GCP Service Connector that allows access to every possible resource accessible with the configured credentials
a multi-instance GCS Service Connector that allows access to multiple GCS buckets
a single-instance GCS Service Connector that only permits access to one GCS bucket
You only need to be aware of local and remote availability for Service Connector Types if you are explicitly looking to use a Service Connector Type without installing its package prerequisites or if you are implementing or using a custom Service Connector Type implementation with your ZenML deployment. In all other cases, you may safely ignore this section.
The LOCAL and REMOTE flags in the zenml service-connector list-types output indicate if the Service Connector implementation is available locally (i.e. where the ZenML client and pipelines are running) and remotely (i.e. where the ZenML server is running).
All built-in Service Connector Types are by default available on the ZenML server, but some built-in Service Connector Types require additional Python packages to be installed to be available in your local environment. See the section documenting each Service Connector Type to find what these prerequisites are and how to install them.
The local/remote availability determines the possible actions and operations that can be performed with a Service Connector. The following are possible with a Service Connector Type that is available either locally or remotely:
Service Connector registration, update, and discovery (i.e. the zenml service-connector register, zenml service-connector update, zenml service-connector list and zenml service-connector describe CLI commands).
Service Connector verification: checking whether its configuration and credentials are valid and can be actively used to access the remote resources (i.e. the zenml service-connector verify CLI commands).
Listing the resources that can be accessed through a Service Connector (i.e. the zenml service-connector verify and zenml service-connector list-resources CLI commands)
Connecting a Stack Component to a remote resource via a Service Connector
The following operations are only possible with Service Connector Types that are locally available (with some notable exceptions covered in the information box that follows):
Service Connector auto-configuration and discovery of credentials stored by a local client, CLI, or SDK (e.g. aws or kubectl).
Using the configuration and credentials managed by a Service Connector to configure a local client, CLI, or SDK (e.g. docker or kubectl).
Running pipelines with a Stack Component that is connected to a remote resource through a Service Connector
One interesting and useful byproduct of the way cloud provider Service Connectors are designed is the fact that you don't need to have the cloud provider Service Connector Type available client-side to be able to access some of its resources. Take the following situation for example:
the GCP Service Connector Type can provide access to GKE Kubernetes clusters and GCR Docker container registries.
however, you don't need the GCP Service Connector Type or any GCP libraries to be installed on the ZenML clients to connect to and use those Kubernetes clusters or Docker registries in your ML pipelines.
the Kubernetes Service Connector Type is enough to access any Kubernetes cluster, regardless of its provenance (AWS, GCP, etc.)
the Docker Service Connector Type is enough to access any Docker container registry, regardless of its provenance (AWS, GCP, etc.)
If you are looking for a quick, assisted tour, we recommend using the interactive CLI mode to configure Service Connectors, especially if this is your first time doing it:
zenml service-connector register -i
Interactive Service Connector registration example
zenmlservice-connectorregister-i
Example Command Output
Please enter a name for the service connector: gcp-interactive
Please enter a description for the service connector []: Interactive GCP connector example
╔══════════════════════════════════════════════════════════════════════════════╗
║ Available service connector types ║
╚══════════════════════════════════════════════════════════════════════════════╝
🌀 Kubernetes Service Connector (connector type: kubernetes)
Authentication methods:
• 🔒 password
• 🔒 token
Resource types:
• 🌀 kubernetes-cluster
Supports auto-configuration: True
Available locally: True
Available remotely: True
This ZenML Kubernetes service connector facilitates authenticating and
connecting to a Kubernetes cluster.
The connector can be used to access to any generic Kubernetes cluster by
providing pre-authenticated Kubernetes python clients to Stack Components that
are linked to it and also allows configuring the local Kubernetes CLI (i.e.
kubectl).
The Kubernetes Service Connector is part of the Kubernetes ZenML integration.
You can either install the entire integration or use a pypi extra to install it
independently of the integration:
• pip install "zenml[connectors-kubernetes]" installs only prerequisites for the
Kubernetes Service Connector Type
• zenml integration install kubernetes installs the entire Kubernetes ZenML
integration
A local Kubernetes CLI (i.e. kubectl ) and setting up local kubectl
configuration contexts is not required to access Kubernetes clusters in your
Stack Components through the Kubernetes Service Connector.
🐳 Docker Service Connector (connector type: docker)
Authentication methods:
• 🔒 password
Resource types:
• 🐳 docker-registry
Supports auto-configuration: False
Available locally: True
Available remotely: True
The ZenML Docker Service Connector allows authenticating with a Docker or OCI
container registry and managing Docker clients for the registry.
This connector provides pre-authenticated python-docker Python clients to Stack
Components that are linked to it.
No Python packages are required for this Service Connector. All prerequisites
are included in the base ZenML Python package. Docker needs to be installed on
environments where container images are built and pushed to the target container
registry.
[...]
────────────────────────────────────────────────────────────────────────────────
Please select a service connector type (kubernetes, docker, azure, aws, gcp): gcp
╔══════════════════════════════════════════════════════════════════════════════╗
║ Available resource types ║
╚══════════════════════════════════════════════════════════════════════════════╝
🔵 Generic GCP resource (resource type: gcp-generic)
Authentication methods: implicit, user-account, service-account, oauth2-token,
impersonation
Supports resource instances: False
Authentication methods:
• 🔒 implicit
• 🔒 user-account
• 🔒 service-account
• 🔒 oauth2-token
• 🔒 impersonation
This resource type allows Stack Components to use the GCP Service Connector to
connect to any GCP service or resource. When used by Stack Components, they are
provided a Python google-auth credentials object populated with a GCP OAuth 2.0
token. This credentials object can then be used to create GCP Python clients for
any particular GCP service.
This generic GCP resource type is meant to be used with Stack Components that
are not represented by other, more specific resource type, like GCS buckets,
Kubernetes clusters or Docker registries. For example, it can be used with the
Google Cloud Builder Image Builder stack component, or the Vertex AI
Orchestrator and Step Operator. It should be accompanied by a matching set of
GCP permissions that allow access to the set of remote resources required by the
client and Stack Component.
The resource name represents the GCP project that the connector is authorized to
access.
📦 GCP GCS bucket (resource type: gcs-bucket)
Authentication methods: implicit, user-account, service-account, oauth2-token,
impersonation
Supports resource instances: True
Authentication methods:
• 🔒 implicit
• 🔒 user-account
• 🔒 service-account
• 🔒 oauth2-token
• 🔒 impersonation
Allows Stack Components to connect to GCS buckets. When used by Stack
Components, they are provided a pre-configured GCS Python client instance.
The configured credentials must have at least the following GCP permissions
associated with the GCS buckets that it can access:
• storage.buckets.list
• storage.buckets.get
• storage.objects.create
• storage.objects.delete
• storage.objects.get
• storage.objects.list
• storage.objects.update
For example, the GCP Storage Admin role includes all of the required
permissions, but it also includes additional permissions that are not required
by the connector.
If set, the resource name must identify a GCS bucket using one of the following
formats:
• GCS bucket URI: gs://{bucket-name}
• GCS bucket name: {bucket-name}
[...]
────────────────────────────────────────────────────────────────────────────────
Please select a resource type or leave it empty to create a connector that can be used to access any of the supported resource types (gcp-generic, gcs-bucket, kubernetes-cluster, docker-registry). []: gcs-bucket
Would you like to attempt auto-configuration to extract the authentication configuration from your local environment ? [y/N]: y
Service connector auto-configured successfully with the following configuration:
Service connector 'gcp-interactive' of type 'gcp' is 'private'.
'gcp-interactive' gcp Service
Connector Details
┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┓
┃ PROPERTY │ VALUE ┃
┠──────────────────┼─────────────────┨
┃ NAME │ gcp-interactive ┃
┠──────────────────┼─────────────────┨
┃ TYPE │ 🔵 gcp ┃
┠──────────────────┼─────────────────┨
┃ AUTH METHOD │ user-account ┃
┠──────────────────┼─────────────────┨
┃ RESOURCE TYPES │ 📦 gcs-bucket ┃
┠──────────────────┼─────────────────┨
┃ RESOURCE NAME │ <multiple> ┃
┠──────────────────┼─────────────────┨
┃ SESSION DURATION │ N/A ┃
┠──────────────────┼─────────────────┨
┃ EXPIRES IN │ N/A ┃
┠──────────────────┼─────────────────┨
┃ SHARED │ ➖ ┃
┗━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┛
Configuration
┏━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━┓
┃ PROPERTY │ VALUE ┃
┠───────────────────┼────────────┨
┃ project_id │ zenml-core ┃
┠───────────────────┼────────────┨
┃ user_account_json │ [HIDDEN] ┃
┗━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━┛
No labels are set for this service connector.
The service connector configuration has access to the following resources:
┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────┼─────────────────────────────────────────────────┨
┃ 📦 gcs-bucket │ gs://annotation-gcp-store ┃
┃ │ gs://zenml-bucket-sl ┃
┃ │ gs://zenml-core.appspot.com ┃
┃ │ gs://zenml-core_cloudbuild ┃
┃ │ gs://zenml-datasets ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Would you like to continue with the auto-discovered configuration or switch to manual ? (auto, manual) [auto]:
The following GCP GCS bucket instances are reachable through this connector:
- gs://annotation-gcp-store
- gs://zenml-bucket-sl
- gs://zenml-core.appspot.com
- gs://zenml-core_cloudbuild
- gs://zenml-datasets
Please select one or leave it empty to create a connector that can be used to access any of them []: gs://zenml-datasets
Successfully registered service connector `gcp-interactive` with access to the following resources:
┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────┼─────────────────────┨
┃ 📦 gcs-bucket │ gs://zenml-datasets ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━┛
Regardless of how you came here, you should already have some idea of the following:
the type of resources that you want to connect ZenML to. This may be a Kubernetes cluster, a Docker container registry or an object storage service like AWS S3 or GCS.
the Service Connector implementation (i.e. Service Connector Type) that you want to use to connect to those resources. This could be one of the cloud provider Service Connector Types like AWS and GCP that provide access to a broader range of services, or one of the basic Service Connector Types like Kubernetes or Docker that only target a specific resource.
the credentials and authentication method that you want to use
Other questions that should be answered in this section:
are you just looking to connect a ZenML Stack Component to a single resource? or would you rather configure a wide-access ZenML Service Connector that gives ZenML and all its users access to a broader range of resource types and resource instances with a single set of credentials issued by your cloud provider?
have you already provisioned all the authentication prerequisites (e.g. service accounts, roles, permissions) and prepared the credentials you will need to configure the Service Connector? If you already have one of the cloud provider CLIs configured with credentials on your local host, you can easily use the Service Connector auto-configuration capabilities to get faster where you need to go.
For help answering these questions, you can also use the interactive CLI mode to register Service Connectors and/or consult the documentation dedicated to each individual Service Connector Type.
Auto-configuration
Many Service Connector Types support using auto-configuration to discover and extract configuration information and credentials directly from your local environment. This assumes that you have already installed and set up the local CLI or SDK associated with the type of resource or cloud provider that you're willing to use. The Service Connector auto-configuration feature relies on these CLIs being configured with valid credentials to work properly. Some examples are listed here, but you should consult the documentation section for the Service Connector Type of choice to find out if and how auto-configuration is supported:
Successfully registered service connector `kubernetes-auto` with access to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────────────┼────────────────┨
┃ 🌀 kubernetes-cluster │ 35.185.95.223 ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
Scopes: multi-type, multi-instance, and single-instance
These terms are briefly explained in the Terminology section: you can register a Service Connector that grants access to multiple types of resources, to multiple instances of the same Resource Type, or to a single resource.
Service Connectors created from basic Service Connector Types like Kubernetes and Docker are single-resource by default, while Service Connectors used to connect to managed cloud resources like AWS and GCP can take all three forms.
Example of registering Service Connectors with different scopes
The following example shows registering three different Service Connectors configured from the same AWS Service Connector Type using three different scopes but with the same credentials:
a multi-type AWS Service Connector that allows access to every possible resource accessible with the configured credentials
a multi-instance AWS Service Connector that allows access to multiple S3 buckets
a single-instance AWS Service Connector that only permits access to one S3 bucket
⠼ Registering service connector 'aws-s3-zenfiles'...
Successfully registered service connector `aws-s3-zenfiles` with access to the following resources:
┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────┼────────────────┨
┃ 📦 s3-bucket │ s3://zenfiles ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
The following might help understand the difference between scopes:
the difference between a multi-instance and a multi-type Service Connector is that the Resource Type scope is locked to a particular value during configuration for the multi-instance Service Connector
similarly, the difference between a multi-instance and a multi-type Service Connector is that the Resource Name (Resource ID) scope is locked to a particular value during configuration for the single-instance Service Connector
Service Connector Verification
When registering Service Connectors, the authentication configuration and credentials are automatically verified to ensure that they can indeed be used to gain access to the target resources:
for multi-type Service Connectors, this verification means checking that the configured credentials can be used to authenticate successfully to the remote service, as well as listing all resources that the credentials have permission to access for each Resource Type supported by the Service Connector Type.
for multi-instance Service Connectors, this verification step means listing all resources that the credentials have permission to access in addition to validating that the credentials can be used to authenticate to the target service or platform.
for single-instance Service Connectors, the verification step simply checks that the configured credentials have permission to access the target resource.
The verification can also be performed later on an already registered Service Connector. Furthermore, for multi-type and multi-instance Service Connectors, the verification operation can be scoped to a Resource Type and a Resource Name.
Example of on-demand Service Connector verification
The following shows how a multi-type, a multi-instance and a single-instance Service Connector can be verified with multiple scopes after registration.
First, listing the Service Connectors will clarify which scopes they are configured with:
Verifying the multi-type Service Connector displays all resources that can be accessed through the Service Connector. This is like asking "are these credentials valid? can they be used to authenticate to AWS ? and if so, what resources can they access?":
zenml service-connector verify aws-multi-type
Example Command Output
Service connector 'aws-multi-type' is correctly configured with valid credentials and has access to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────────────┼──────────────────────────────────────────────┨
┃ 🔶 aws-generic │ us-east-1 ┃
┠───────────────────────┼──────────────────────────────────────────────┨
┃ 📦 s3-bucket │ s3://aws-ia-mwaa-715803424590 ┃
┃ │ s3://zenfiles ┃
┃ │ s3://zenml-demos ┃
┃ │ s3://zenml-generative-chat ┃
┠───────────────────────┼──────────────────────────────────────────────┨
┃ 🌀 kubernetes-cluster │ zenhacks-cluster ┃
┠───────────────────────┼──────────────────────────────────────────────┨
┃ 🐳 docker-registry │ 715803424590.dkr.ecr.us-east-1.amazonaws.com ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
You can scope the verification down to a particular Resource Type or all the way down to a Resource Name. This is the equivalent of asking "are these credentials valid and which S3 buckets are they authorized to access ?" and "can these credentials be used to access this particular Kubernetes cluster in AWS ?":
Service connector 'aws-multi-type' is correctly configured with valid credentials and has access to the following resources:
┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────┼───────────────────────────────────────┨
┃ 📦 s3-bucket │ s3://aws-ia-mwaa-715803424590 ┃
┃ │ s3://zenfiles ┃
┃ │ s3://zenml-demos ┃
┃ │ s3://zenml-generative-chat ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Service connector 'aws-multi-type' is correctly configured with valid credentials and has access to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────────────┼──────────────────┨
┃ 🌀 kubernetes-cluster │ zenhacks-cluster ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┛
Verifying the multi-instance Service Connector displays all the resources that it can access. We can also scope the verification to a single resource:
Service connector 'aws-s3-multi-instance' is correctly configured with valid credentials and has access to the following resources:
┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────┼───────────────────────────────────────┨
┃ 📦 s3-bucket │ s3://aws-ia-mwaa-715803424590 ┃
┃ │ s3://zenfiles ┃
┃ │ s3://zenml-demos ┃
┃ │ s3://zenml-generative-chat ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Service connector 'aws-s3-multi-instance' is correctly configured with valid credentials and has access to the following resources:
┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────┼──────────────────┨
┃ 📦 s3-bucket │ s3://zenml-demos ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┛
Finally, verifying the single-instance Service Connector is straight-forward and requires no further explanation:
zenml service-connector verify aws-s3-zenfiles
Example Command Output
Service connector 'aws-s3-zenfiles' is correctly configured with valid credentials and has access to the following resources:
┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────┼────────────────┨
┃ 📦 s3-bucket │ s3://zenfiles ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
Configure local clients
Yet another neat feature built into some Service Container Types that is the opposite of Service Connector auto-configuration is the ability to configure local CLI and SDK utilities installed on your host, like the Docker or Kubernetes CLI (kubectl) with credentials issued by a compatible Service Connector.
You may need to exercise this feature to get direct CLI access to a remote service in order to manually manage some configurations or resources, to debug some workloads or to simply verify that the Service Connector credentials are actually working.
When configuring local CLI utilities with credentials extracted from Service Connectors, keep in mind that most Service Connectors, particularly those used with cloud platforms, usually exercise the security best practice of issuing temporary credentials such as API tokens. The implication is that your local CLI may only be allowed access to the remote service for a short time before those credentials expire, then you need to fetch another set of credentials from the Service Connector.
Examples of local CLI configuration
The following examples show how the local Kubernetes kubectl CLI can be configured with credentials issued by a Service Connector and then used to access a Kubernetes cluster directly:
The following 'kubernetes-cluster' resources can be accessed by service connectors configured in your workspace:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE NAMES ┃
┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────────────┼─────────────────────────────────────────────────────────────────────────────────────┨
┃ 9d953320-3560-4a78-817c-926a3898064d │ gcp-user-multi │ 🔵 gcp │ 🌀 kubernetes-cluster │ zenml-test-cluster ┃
┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────────────┼─────────────────────────────────────────────────────────────────────────────────────┨
┃ 4a550c82-aa64-4a48-9c7f-d5e127d77a44 │ aws-multi-type │ 🔶 aws │ 🌀 kubernetes-cluster │ zenhacks-cluster ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
$ zenml service-connector login gcp-user-multi --resource-type kubernetes-cluster --resource-id zenml-test-cluster
⠇ Attempting to configure local client using service connector 'gcp-user-multi'...
Updated local kubeconfig with the cluster details. The current kubectl context was set to 'gke_zenml-core_zenml-test-cluster'.
The 'gcp-user-multi' Kubernetes Service Connector connector was used to successfully configure the local Kubernetes cluster client/SDK.
# Verify that the local kubectl client is now configured to access the remote Kubernetes cluster
$ kubectl cluster-info
Kubernetes control plane is running at https://35.185.95.223
GLBCDefaultBackend is running at https://35.185.95.223/api/v1/namespaces/kube-system/services/default-http-backend:http/proxy
KubeDNS is running at https://35.185.95.223/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy
Metrics-server is running at https://35.185.95.223/api/v1/namespaces/kube-system/services/https:metrics-server:/proxy
$ zenml service-connector login aws-multi-type --resource-type kubernetes-cluster --resource-id zenhacks-cluster
⠏ Attempting to configure local client using service connector 'aws-multi-type'...
Updated local kubeconfig with the cluster details. The current kubectl context was set to 'arn:aws:eks:us-east-1:715803424590:cluster/zenhacks-cluster'.
The 'aws-multi-type' Kubernetes Service Connector connector was used to successfully configure the local Kubernetes cluster client/SDK.
# Verify that the local kubectl client is now configured to access the remote Kubernetes cluster
$ kubectl cluster-info
Kubernetes control plane is running at https://A5F8F4142FB12DDCDE9F21F6E9B07A18.gr7.us-east-1.eks.amazonaws.com
CoreDNS is running at https://A5F8F4142FB12DDCDE9F21F6E9B07A18.gr7.us-east-1.eks.amazonaws.com/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy
The same is possible with the local Docker client:
Service connector 'aws-session-token' is correctly configured with valid credentials and has access to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE NAMES ┃
┠──────────────────────────────────────┼───────────────────┼────────────────┼────────────────────┼──────────────────────────────────────────────┨
┃ 3ae3e595-5cbc-446e-be64-e54e854e0e3f │ aws-session-token │ 🔶 aws │ 🐳 docker-registry │ 715803424590.dkr.ecr.us-east-1.amazonaws.com ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
$zenml service-connector login aws-session-token --resource-type docker-registry
⠏ Attempting to configure local client using service connector 'aws-session-token'...
WARNING! Your password will be stored unencrypted in /home/stefan/.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store
The 'aws-session-token' Docker Service Connector connector was used to successfully configure the local Docker/OCI container registry client/SDK.
# Verify that the local Docker client is now configured to access the remote Docker container registry
$ docker pull 715803424590.dkr.ecr.us-east-1.amazonaws.com/zenml-server
Using default tag: latest
latest: Pulling from zenml-server
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Discover available resources
One of the questions that you may have as a ZenML user looking to register and connect a Stack Component to an external resource is "what resources do I even have access to ?". Sure, you can browse through all the registered Service connectors and manually verify each one to find a particular resource that you are looking for, but this is counterproductive.
A better way is to ask ZenML directly questions such as:
what are the Kubernetes clusters that I can get access to through Service Connectors?
can I access this particular S3 bucket through one of the Service Connectors? Which one?
The zenml service-connector list-resources CLI command can be used exactly for this purpose.
Resource discovery examples
It is possible to show globally all the various resources that can be accessed through all available Service Connectors, and all Service Connectors that are in an error state. This operation is expensive and may take some time to complete, depending on the number of Service Connectors involved. The output also includes any errors that may have occurred during the discovery process:
More interesting is to scope the search to a particular Resource Type. This yields fewer, more accurate results, especially if you have many multi-type Service Connectors configured:
The 's3-bucket' resource with name 'zenfiles' can be accessed by service connectors configured in your workspace:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE NAMES ┃
┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────┼────────────────┨
┃ 373a73c2-8295-45d4-a768-45f5a0f744ea │ aws-multi-type │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────┼────────────────┨
┃ fa9325ab-ce01-4404-aec3-61a3af395d48 │ aws-s3-multi-instance │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────┼────────────────┨
┃ 19edc05b-92db-49de-bc84-aa9b3fb8261a │ aws-s3-zenfiles │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
Connect Stack Components to resources
Service Connectors and the resources and services that they can authenticate to and grant access to are only useful because they are a means of providing Stack Components a better and easier way of accessing external resources.
If you are looking for a quick, assisted tour, we recommend using the interactive CLI mode to connect a Stack Component to a compatible Service Connector, especially if this is your first time doing it, e.g.:
To connect a Stack Component to an external resource or service, you first need to register one or more Service Connectors, or have someone else in your team with more infrastructure knowledge do it for you. If you already have that covered, you might want to ask ZenML "which resources/services am I even authorized to access with the available Service Connectors?". The resource discovery feature is designed exactly for this purpose. This last check is already included in the interactive ZenML CLI command used to connect a Stack Component to a remote resource.
Not all Stack Components support being connected to an external resource or service via a Service Connector. Whether a Stack Component can use a Service Connector to connect to a remote resource or service or not is shown in the Stack Component flavor details:
$ zenml artifact-store flavor describe s3
Configuration class: S3ArtifactStoreConfig
Configuration for the S3 Artifact Store.
[...]
This flavor supports connecting to external resources with a Service
Connector. It requires a 's3-bucket' resource. You can get a list of
all available connectors and the compatible resources that they can
access by running:
'zenml service-connector list-resources --resource-type s3-bucket'
If no compatible Service Connectors are yet registered, you can can
register a new one by running:
'zenml service-connector register -i'
For Stack Components that do support Service Connectors, their flavor indicates the Resource Type and, optionally, Service Connector Type compatible with the Stack Component. This can be used to figure out which resources are available and which Service Connectors can grant access to them. In some cases it is even possible to figure out the exact Resource Name based on the attributes already configured in the Stack Component, which is how ZenML can decide automatically which Resource Name to use in the interactive mode:
$ zenml artifact-store register s3-zenfiles --flavor s3 --path=s3://zenfiles
Running with active workspace: 'default' (global)
Running with active stack: 'default' (global)
Successfully registered artifact_store `s3-zenfiles`.
$ zenml service-connector list-resources --resource-type s3-bucket --resource-id zenfiles
The 's3-bucket' resource with name 'zenfiles' can be accessed by service connectors configured in your workspace:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE NAMES ┃
┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────┼────────────────┨
┃ 4a550c82-aa64-4a48-9c7f-d5e127d77a44 │ aws-multi-type │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────┼────────────────┨
┃ 66c0922d-db84-4e2c-9044-c13ce1611613 │ aws-multi-instance │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────┼────────────────┨
┃ 65c82e59-cba0-4a01-b8f6-d75e8a1d0f55 │ aws-single-instance │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
$ zenml artifact-store connect s3-zenfiles --connector aws-multi-type
Running with active workspace: 'default' (global)
Running with active stack: 'default' (global)
Successfully connected artifact store `s3-zenfiles` to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE NAMES ┃
┠──────────────────────────────────────┼────────────────┼────────────────┼───────────────┼────────────────┨
┃ 4a550c82-aa64-4a48-9c7f-d5e127d77a44 │ aws-multi-type │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
The following is an example of connecting the same Stack Component to the remote resource using the interactive CLI mode:
zenml artifact-store connect s3-zenfiles -i
Example Command Output
The following connectors have compatible resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE NAMES ┃
┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────┼────────────────┨
┃ 373a73c2-8295-45d4-a768-45f5a0f744ea │ aws-multi-type │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────┼────────────────┨
┃ fa9325ab-ce01-4404-aec3-61a3af395d48 │ aws-s3-multi-instance │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────┼────────────────┨
┃ 19edc05b-92db-49de-bc84-aa9b3fb8261a │ aws-s3-zenfiles │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
Please enter the name or ID of the connector you want to use: aws-s3-zenfiles
Successfully connected artifact store `s3-zenfiles` to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE NAMES ┃
┠──────────────────────────────────────┼─────────────────┼────────────────┼───────────────┼────────────────┨
┃ 19edc05b-92db-49de-bc84-aa9b3fb8261a │ aws-s3-zenfiles │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
End-to-end examples
To get an idea of what a complete end-to-end journey looks like, from registering Service Connector all the way to configuring Stacks and Stack Components and running pipelines that access remote resources through Service Connectors, take a look at the following full-fledged examples:
