A production-grade MLOps platform involves interactions between a diverse combination of third-party libraries and external services sourced from various different vendors. One of the most daunting hurdles in building and operating an MLOps platform composed of multiple components is configuring and maintaining uninterrupted and secured access to the infrastructure resources and services that it consumes.
In layman's terms, your pipeline code needs to "connect" to a handful of different services to run successfully and do what it's designed to do. For example, it might need to connect to a private AWS S3 bucket to read and store artifacts, a Kubernetes cluster to execute steps with Kubeflow or Tekton, and a private GCR container registry to build and store container images. ZenML makes this possible by allowing you to configure authentication information and credentials embedded directly into your Stack Components, but this doesn't scale well when you have more than a few Stack Components and has many other disadvantages related to usability and security.
Gaining access to infrastructure resources and services requires knowledge about the different authentication and authorization mechanisms and involves configuring and maintaining valid credentials. It gets even more complicated when these different services need to access each other. For instance, the Kubernetes container running your pipeline step needs access to the S3 bucket to store artifacts or needs to access a cloud service like AWS SageMaker, VertexAI, or AzureML to run a CPU/GPU intensive task like training a model.
The challenge comes from setting up and implementing proper authentication and authorization with the best security practices in mind, while at the same time keeping this complexity away from the day-to-day routines of coding and running pipelines.
The hard-to-swallow truth is there is no single standard that unifies all authentication and authorization-related matters or a single, well-defined set of security best practices that you can follow. However, with ZenML you get the next best thing, an abstraction that keeps the complexity of authentication and authorization away from your code and makes it easier to tackle them: the ZenML Service Connectors.
A representative use-case
The range of features covered by Service Connectors is extensive and going through the entire Service Connector Guide can be overwhelming. If all you want is to get a quick overview of how Service Connectors work and what they can do for you, this section is for you.
This is a representative example of how you would use a Service Connector to connect ZenML to a cloud service. This example uses the AWS Service Connector to connect ZenML to an AWS S3 bucket and then link an S3 Artifact Store Stack Component to it.
Some details about the current alternatives to using Service Connectors and their drawbacks are provided below. Feel free to skip them if you are already familiar with them or just want to get to the good part.
Alternatives to Service Connectors
There are quicker alternatives to using a Service Connector to link an S3 Artifact Store to a private AWS S3 bucket. Let's lay them out first and then explain why using a Service Connector is the better option:
the authentication information can be embedded directly into the Stack Component, although this is not recommended for security reasons:
All these options work, but they have many drawbacks:
first of all, not all Stack Components support referencing secrets in their configuration attributes, so this is not a universal solution.
some Stack Components, like those linked to Kubernetes clusters, rely on credentials being set up on the machine where the pipeline is running, which makes pipelines less portable and more difficult to set up. In other cases, you also need to install and set up cloud-specific SDKs and CLIs to be able to use the Stack Component.
people configuring and using Stack Components linked to cloud resources need to be given access to cloud credentials, or even provision the credentials themselves, which requires access to the cloud provider platform and knowledge about how to do it.
in many cases, you can only configure long-lived credentials directly in Stack Components. This is a security risk because they can inadvertently grant access to key resources and services to a malicious party if they are compromised. Implementing a process that rotates credentials regularly is a complex task that requires a lot of effort and maintenance.
Stack Components don't implement any kind of verification regarding the validity and permission of configured credentials. If the credentials are invalid or if they lack the proper permissions to access the remote resource or service, you will only find this out later, when running a pipeline will fail at runtime.
ultimately, given that different Stack Component flavors rely on the same type of resource or cloud provider, it is not good design to duplicate the logic that handles authentication and authorization in each Stack Component implementation.
These drawbacks are addressed by Service Connectors.
Without Service Connectors, credentials are stored directly in the Stack Component configuration or ZenML Secret and are directly used in the runtime environment. The Stack Component implementation is directly responsible for validating credentials, authenticating and connecting to the infrastructure service. This is illustrated in the following diagram:
When Service Connectors are involved in the authentication and authorization process, they can act as brokers. The credentials validation and authentication process takes place on the ZenML server. In most cases, the main credentials never have to leave the ZenML server as the Service Connector automatically converts them into short-lived credentials with a reduced set of privileges and issues these credentials to clients. Furthermore, multiple Stack Components of different flavors can use the same Service Connector to access different types or resources with the same credentials:
In working with Service Connectors, the first step is usually finding out what types of resources you can connect ZenML to. Maybe you have already planned out the infrastructure options for your MLOps platform and are looking to find out whether ZenML can accommodate them. Or perhaps you want to use a particular Stack Component flavor in your Stack and are wondering whether you can use a Service Connector to connect it to external resources.
Listing the available Service Connector Types will give you a good idea of what you can do with Service Connectors:
Service Connector Types are also displayed in the dashboard during the configuration of a new Service Connector:
The cloud provider of choice for our example is AWS and we're looking to hook up an S3 bucket to an S3 Artifact Store Stack Component. We'll use the AWS Service Connector Type.
Interactive structured docs with 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 on-site directly through the CLI or in the dashboard. Some examples are included here.
Showing information about the AWS Service Connector Type:
zenmlservice-connectordescribe-typeaws
Example Command Output
╔══════════════════════════════════════════════════════════════════════════════╗
║ 🔶 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: True
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.
────────────────────────────────────────────────────────────────────────────────
Dashboard equivalent:
Fetching details about the S3 bucket resource type:
╔══════════════════════════════════════════════════════════════════════════════╗
║ 📦 AWS S3 bucket (resource type: s3-bucket) ║
╚══════════════════════════════════════════════════════════════════════════════╝
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 connect to S3 buckets. When used by Stack Components, they are
provided a pre-configured boto3 S3 client instance.
The configured credentials must have at least the following AWS IAM permissions
associated with the ARNs of S3 buckets that the connector will be allowed to
access (e.g. arn:aws:s3:::* and arn:aws:s3:::*/* represent all the available S3
buckets).
• s3:ListBucket
• s3:GetObject
• s3:PutObject
• s3:DeleteObject
• s3:ListAllMyBuckets
If set, the resource name must identify an S3 bucket using one of the following
formats:
• S3 bucket URI (canonical resource name): s3://{bucket-name}
• S3 bucket ARN: arn:aws:s3:::{bucket-name}
• S3 bucket name: {bucket-name}
────────────────────────────────────────────────────────────────────────────────
Dashboard equivalent:
Displaying information about the AWS Session Token authentication method:
╔══════════════════════════════════════════════════════════════════════════════╗
║ 🔒 AWS Session Token (auth method: session-token) ║
╚══════════════════════════════════════════════════════════════════════════════╝
Supports issuing temporary credentials: True
Generates temporary session STS tokens for IAM users. The connector needs to be
configured with an AWS secret key associated with an IAM user or AWS account
root user (not recommended). The connector will generate temporary STS tokens
upon request by calling the GetSessionToken STS API.
These STS tokens have an expiration period longer that those issued through the
AWS IAM Role authentication method and are more suitable for long-running
processes that cannot automatically re-generate credentials upon expiration.
An AWS region is required and the connector may only be used to access AWS
resources in the specified region.
The default expiration period for generated STS tokens is 12 hours with a
minimum of 15 minutes and a maximum of 36 hours. Temporary credentials obtained
by using the AWS account root user credentials (not recommended) have a maximum
duration of 1 hour.
As a precaution, when long-lived credentials (i.e. AWS Secret Keys) are detected
on your environment by the Service Connector during auto-configuration, this
authentication method is automatically chosen instead of the AWS Secret Key
authentication method alternative.
Generated STS tokens inherit the full set of permissions of the IAM user or AWS
account root user that is calling the GetSessionToken API. Depending on your
security needs, this may not be suitable for production use, as it can lead to
accidental privilege escalation. Instead, it is recommended to use the AWS
Federation Token or AWS IAM Role authentication methods to restrict the
permissions of the generated STS tokens.
For more information on session tokens and the GetSessionToken AWS API, see: the
official AWS documentation on the subject.
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
────────────────────────────────────────────────────────────────────────────────
Dashboard equivalent:
Not all Stack Components support being linked to a Service Connector. This is indicated in the flavor description of each Stack Component. Our example uses the S3 Artifact Store, which does support it:
$zenmlartifact-storeflavordescribes3Configurationclass:S3ArtifactStoreConfig[...]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
accessbyrunning:'zenml service-connector list-resources --resource-type s3-bucket'IfnocompatibleServiceConnectorsareyetregistered,youcanregisteranewonebyrunning:'zenml service-connector register -i'
The second step is registering a Service Connector that effectively enables ZenML to authenticate to and access one or more remote resources. This step is best handled by someone with some infrastructure knowledge, but there are sane defaults and auto-detection mechanisms built into most Service Connectors that can make this a walk in the park even for the uninitiated. For our simple example, we're registering an AWS Service Connector with AWS credentials automatically lifted up from your local host, giving ZenML access to the same resources that you can access from your local machine through the AWS CLI.
This step assumes the AWS CLI is already installed and set up with credentials on your machine (e.g. by running aws configure).
⠼ Registering service connector 'aws-s3'...
Successfully registered service connector `aws-s3` with access to the following resources:
┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────┼───────────────────────────────────────┨
┃ 📦 s3-bucket │ s3://aws-ia-mwaa-715803424590 ┃
┃ │ s3://zenbytes-bucket ┃
┃ │ s3://zenfiles ┃
┃ │ s3://zenml-demos ┃
┃ │ s3://zenml-generative-chat ┃
┃ │ s3://zenml-public-datasets ┃
┃ │ s3://zenml-public-swagger-spec ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
The CLI validates and shows all S3 buckets that can be accessed with the auto-discovered credentials.
The ZenML CLI provides an interactive way of registering Service Connectors. Just use the -i command line argument and follow the interactive guide:
zenml service-connector register -i
What happens during auto-configuration
A quick glance into the Service Connector configuration that was automatically detected gives a better idea of what happened:
zenmlservice-connectordescribeaws-s3
Example Command Output
Service connector 'aws-s3' of type 'aws' with id '96a92154-4ec7-4722-bc18-21eeeadb8a4f' is owned by user 'default' and is 'private'.
'aws-s3' aws Service Connector Details
┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ PROPERTY │ VALUE ┃
┠──────────────────┼──────────────────────────────────────┨
┃ ID │ 96a92154-4ec7-4722-bc18-21eeeadb8a4f ┃
┠──────────────────┼──────────────────────────────────────┨
┃ NAME │ aws-s3 ┃
┠──────────────────┼──────────────────────────────────────┨
┃ TYPE │ 🔶 aws ┃
┠──────────────────┼──────────────────────────────────────┨
┃ AUTH METHOD │ session-token ┃
┠──────────────────┼──────────────────────────────────────┨
┃ RESOURCE TYPES │ 📦 s3-bucket ┃
┠──────────────────┼──────────────────────────────────────┨
┃ RESOURCE NAME │ <multiple> ┃
┠──────────────────┼──────────────────────────────────────┨
┃ SECRET ID │ a8c6d0ff-456a-4b25-8557-f0d7e3c12c5f ┃
┠──────────────────┼──────────────────────────────────────┨
┃ SESSION DURATION │ 43200s ┃
┠──────────────────┼──────────────────────────────────────┨
┃ EXPIRES IN │ N/A ┃
┠──────────────────┼──────────────────────────────────────┨
┃ OWNER │ default ┃
┠──────────────────┼──────────────────────────────────────┨
┃ SHARED │ ➖ ┃
┠──────────────────┼──────────────────────────────────────┨
┃ CREATED_AT │ 2023-06-15 18:45:17.822337 ┃
┠──────────────────┼──────────────────────────────────────┨
┃ UPDATED_AT │ 2023-06-15 18:45:17.822341 ┃
┗━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Configuration
┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━┓
┃ PROPERTY │ VALUE ┃
┠───────────────────────┼───────────┨
┃ region │ us-east-1 ┃
┠───────────────────────┼───────────┨
┃ aws_access_key_id │ [HIDDEN] ┃
┠───────────────────────┼───────────┨
┃ aws_secret_access_key │ [HIDDEN] ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━┛
The AWS Service Connector discovered and lifted the AWS Secret Key that was configured on the local machine and securely stored it in the Secrets Store.
Moreover, the following security best practice is automatically enforced by the AWS connector: the AWS Secret Key will be kept hidden on the ZenML Server and the clients will never use it directly to gain access to any AWS resources. Instead, the AWS Service Connector will generate short-lived security tokens and distribute those to clients. It will also take care of issuing new tokens when those expire. This is identifiable from the session-token authentication method and the session duration configuration attributes.
One way to confirm this is to ask ZenML to show us the exact configuration that a Service Connector client would see, but this requires us to pick an S3 bucket for which temporary credentials can be generated:
Service connector 'aws-s3 (s3-bucket | s3://zenfiles client)' of type 'aws' with id '96a92154-4ec7-4722-bc18-21eeeadb8a4f' is owned by user 'default' and is 'private'.
'aws-s3 (s3-bucket | s3://zenfiles client)' aws Service
Connector Details
┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ PROPERTY │ VALUE ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ ID │ 96a92154-4ec7-4722-bc18-21eeeadb8a4f ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ NAME │ aws-s3 (s3-bucket | s3://zenfiles client) ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ TYPE │ 🔶 aws ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ AUTH METHOD │ sts-token ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ RESOURCE TYPES │ 📦 s3-bucket ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ RESOURCE NAME │ s3://zenfiles ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ SECRET ID │ ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ SESSION DURATION │ N/A ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ EXPIRES IN │ 11h59m56s ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ OWNER │ default ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ SHARED │ ➖ ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ CREATED_AT │ 2023-06-15 18:56:33.880081 ┃
┠──────────────────┼───────────────────────────────────────────┨
┃ UPDATED_AT │ 2023-06-15 18:56:33.880082 ┃
┗━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Configuration
┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━┓
┃ PROPERTY │ VALUE ┃
┠───────────────────────┼───────────┨
┃ region │ us-east-1 ┃
┠───────────────────────┼───────────┨
┃ aws_access_key_id │ [HIDDEN] ┃
┠───────────────────────┼───────────┨
┃ aws_secret_access_key │ [HIDDEN] ┃
┠───────────────────────┼───────────┨
┃ aws_session_token │ [HIDDEN] ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━┛
As can be seen, this configuration is of a temporary STS AWS token that will expire in 12 hours. The AWS Secret Key is not visible on the client side.
The next step in this journey is configuring and connecting one (or more) Stack Components to a remote resource via the Service Connector registered in the previous step. This is as easy as saying "I want this S3 Artifact Store to use the s3://my-bucket S3 bucket" and doesn't require any knowledge whatsoever about the authentication mechanisms or even the provenance of those resources. The following example creates an S3 Artifact store and connects it to an S3 bucket with the earlier connector:
$ zenml artifact-store register s3-zenfiles --flavor s3 --path=s3://zenfiles
Successfully registered artifact_store `s3-zenfiles`.
$ zenml artifact-store connect s3-zenfiles --connector aws-s3
Successfully connected artifact store `s3-zenfiles` to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE NAMES ┃
┠──────────────────────────────────────┼────────────────┼────────────────┼───────────────┼────────────────┨
┃ 96a92154-4ec7-4722-bc18-21eeeadb8a4f │ aws-s3 │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
The ZenML CLI provides an even easier and more interactive way of connecting a stack component to an external resource. Just pass the -i command line argument and follow the interactive guide:
from zenml import step, pipeline@stepdefsimple_step_one() ->str:"""Simple step one."""return"Hello World!"@stepdefsimple_step_two(msg:str) ->None:"""Simple step two."""print(msg)@pipelinedefsimple_pipeline() ->None:"""Define single step pipeline.""" message =simple_step_one()simple_step_two(msg=message)if__name__=="__main__":simple_pipeline()
Save this as run.py and run it with the following command:
pythonrun.py
Example Command Output
Running pipeline simple_pipeline on stack s3-zenfiles (caching enabled)
Step simple_step_one has started.
Step simple_step_one has finished in 1.065s.
Step simple_step_two has started.
Hello World!
Step simple_step_two has finished in 5.681s.
Pipeline run simple_pipeline-2023_06_15-19_29_42_159831 has finished in 12.522s.
Dashboard URL: http://127.0.0.1:8237/default/pipelines/8267b0bc-9cbd-42ac-9b56-4d18275bdbb4/runs
This example is just a simple demonstration of how to use Service Connectors to connect ZenML Stack Components to your infrastructure. The range of features and possibilities is much larger. ZenML ships with built-in Service Connectors able to connect and authenticate to AWS, GCP, and Azure and offers many different authentication methods and security best practices. Follow the resources below for more information.
