page_content stringlengths 74 2.86k | parent_section stringclasses 7
values | url stringlengths 21 129 | token_count int64 17 755 |
|---|---|---|---|
│ s3://zenml-generative-chat ┃┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
zenml service-connector verify aws-s3-multi-instance --resource-id s3://zenml-demos
Example Command Output
Service connector 'aws-s3-multi-instance' is correctly configured with valid credentials and has acces... | how-to | https://docs.zenml.io/how-to/auth-management/service-connectors-guide | 439 |
:
zenml integration install great_expectations -yDepending on how you configure the Great Expectations Data Validator, it can reduce or even completely eliminate the complexity associated with setting up the store backends for Great Expectations. If you're only looking for a quick and easy way of adding Great Expectat... | stack-components | https://docs.zenml.io/v/docs/stack-components/data-validators/great-expectations | 347 |
ht want to use torch.save() and torch.load() here.(Optional) How to Visualize the Artifact
Optionally, you can override the save_visualizations() method to automatically save visualizations for all artifacts saved by your materializer. These visualizations are then shown next to your artifacts in the dashboard:
Curre... | how-to | https://docs.zenml.io/how-to/handle-data-artifacts/handle-custom-data-types | 358 |
Name your pipeline runs
In the output logs of a pipeline run you will see the name of the run:
Pipeline run training_pipeline-2023_05_24-12_41_04_576473 has finished in 3.742s.
This name is automatically generated based on the current date and time. To change the name for a run, pass run_name as a parameter to the w... | how-to | https://docs.zenml.io/v/docs/how-to/build-pipelines/name-your-pipeline-and-runs | 279 |
used for things like ServerSideEncryption and ACL.To include these advanced parameters in your Artifact Store configuration, pass them using JSON format during registration, e.g.:
zenml artifact-store register minio_store -f s3 \
--path='s3://minio_bucket' \
--authentication_secret=s3_secret \
--client_kwargs='{"en... | stack-components | https://docs.zenml.io/v/docs/stack-components/artifact-stores/s3 | 180 |
ep
def print_data(data: np.ndarray):
print(data)@pipeline
def printing_pipeline():
# One can also pass data directly into the ExternalArtifact
# to create a new artifact on the fly
data = ExternalArtifact(value=np.array([0]))
print_data(data=data)
if __name__ == "__main__":
printing_pipeline()
Optionally, you... | user-guide | https://docs.zenml.io/v/docs/user-guide/starter-guide/manage-artifacts | 433 |
re you point to the flavor class via dot notation:zenml data-validator flavor register <path.to.MyDataValidatorFlavor>
For example, if your flavor class MyDataValidatorFlavor is defined in flavors/my_flavor.py, you'd register it by doing:
zenml data-validator flavor register flavors.my_flavor.MyDataValidatorFlavor
Z... | stack-components | https://docs.zenml.io/stack-components/data-validators/custom | 393 |
Running with active stack: 'default' (repository)Successfully connected artifact store `s3-zenfiles` to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOU... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/aws-service-connector | 674 |
Deploying ZenML
Deploying ZenML is the first step to production.
When you first get started with ZenML, it is based on the following architecture on your machine:
The SQLite database that you can see in this diagram is used to store all the metadata we produced in the previous guide (pipelines, models, artifacts, et... | user-guide | https://docs.zenml.io/user-guide/production-guide/deploying-zenml | 357 |
t_repository: str
user: Optional[str]
resources:cpu_count: Optional[PositiveFloat]
gpu_count: Optional[NonNegativeInt]
memory: Optional[ConstrainedStrValue]
step_operator: Optional[str]
success_hook_source:
attribute: Optional[str]
module: str
type: SourceType
train_model:
enable_artifact_metadata: Optional[... | how-to | https://docs.zenml.io/how-to/use-configuration-files/autogenerate-a-template-yaml-file | 424 |
Evaluation and metrics
Track how your RAG pipeline improves using evaluation and metrics.
In this section, we'll explore how to evaluate the performance of your RAG pipeline using metrics and visualizations. Evaluating your RAG pipeline is crucial to understanding how well it performs and identifying areas for improv... | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/evaluation | 384 |
can interact with the pods using kubectl commands.For more advanced configuration options and additional details, refer to the full Kubernetes Orchestrator documentation.
PreviousKubeflow
NextMLflow
Last updated 19 days ago | how-to | https://docs.zenml.io/v/docs/how-to/popular-integrations/kubernetes | 44 |
lines orchestrator, and an ECR container registry.Registering the stack components and creating a ZenML stack.
By following these steps, you can leverage the power of AWS services, such as S3 for artifact storage, SageMaker Pipelines for orchestration, and ECR for container management, all within the ZenML framework. ... | how-to | https://docs.zenml.io/v/docs/how-to/popular-integrations/aws-guide | 320 |
hat will hold the data that you want to visualize.Build a custom materializer for this custom class with the visualization logic implemented in the save_visualizations() method.
Return your custom class from any of your ZenML steps.
Example: Facets Data Skew Visualization
As an example, have a look at the models, ma... | how-to | https://docs.zenml.io/how-to/visualize-artifacts/creating-custom-visualizations | 418 |
│ us-east-1 ┃┠───────────────────────┼──────────────────────────────────────────────┨
┃ 📦 s3-bucket │ s3://aws-ia-mwaa-715803424590 ┃
┃ │ s3://zenfiles ┃
┃ │ s3://zenml-demos ... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/service-connectors-guide | 514 |
Google Cloud VertexAI
Executing individual steps in Vertex AI.
Vertex AI offers specialized compute instances to run your training jobs and has a comprehensive UI to track and manage your models and logs. ZenML's Vertex AI step operator allows you to submit individual steps to be run on Vertex AI compute instances.
... | stack-components | https://docs.zenml.io/stack-components/step-operators/vertex | 408 |
The step creates and returns a FacetsComparison.When the step finishes, ZenML will search for a materializer class that can handle this type, finds the FacetsMaterializer, and calls the save_visualizations() method which creates the visualization and saves it into your artifact store as an HTML file.
When you open y... | how-to | https://docs.zenml.io/how-to/visualize-artifacts/creating-custom-visualizations | 109 |
registry │ demozenmlcontainerregistry.azurecr.io ┃┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
```
Combine all Stack Components together into a Stack and set it as active (also throw in a local Image Builder for comple... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/azure-service-connector | 539 |
# only. Leave blank if using account/key or MSI.--rclone_config_az_use_msi="" \ # use a managed service identity to
# authenticate (only works in Azure).
--rclone_config_az_client_id="" \ # client ID of the service principal
# to use for authentication.
--rclone_config_az_client_secret="" \ # client secret of the... | stack-components | https://docs.zenml.io/v/docs/stack-components/model-deployers/seldon | 401 |
e found, including all files inside build context.Step 1/10 : FROM zenmldocker/zenml:0.40.0-py3.8
Step 2/10 : WORKDIR /app
Step 3/10 : COPY .zenml_user_requirements .
Step 4/10 : RUN pip install --default-timeout=60 --no-cache-dir -r .zenml_user_requirements
Step 5/10 : COPY .zenml_integration_requirements .
Step... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/azure-service-connector | 464 |
or describe azure-implicit
Example Command OutputService connector 'azure-implicit' of type 'azure' with id 'ad645002-0cd4-4d4f-ae20-499ce888a00a' is owned by user 'default' and is 'private'.
'azure-implicit' azure Service Connector Details
┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/azure-service-connector | 419 |
e to the query.
def get_completion_from_messages(messages, model=OPENAI_MODEL, temperature=0.4, max_tokens=1000
):
"""Generates a completion response from the given messages using the specified model."""
model = MODEL_NAME_MAP.get(model, model)
completion_response = litellm.completion(
model=model,
messages=mess... | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/rag-with-zenml/basic-rag-inference-pipeline | 342 |
Develop a Custom Annotator
Learning how to develop a custom annotator.
Before diving into the specifics of this component type, it is beneficial to familiarize yourself with our general guide to writing custom component flavors in ZenML. This guide provides an essential understanding of ZenML's component flavor conce... | stack-components | https://docs.zenml.io/v/docs/stack-components/annotators/custom | 190 |
ake a look here for a guide on how to set that up.A remote artifact store as part of your stack. This is needed so that both your orchestration environment and SageMaker can read and write step artifacts. Check out the documentation page of the artifact store you want to use for more information on how to set that up a... | stack-components | https://docs.zenml.io/v/docs/stack-components/step-operators/sagemaker | 400 |
lly registered orchestrator `<ORCHESTRATOR_NAME>`.$ zenml service-connector list-resources --resource-type kubernetes-cluster -e
The following 'kubernetes-cluster' resources can be accessed by service connectors configured in your workspace:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━... | stack-components | https://docs.zenml.io/v/docs/stack-components/orchestrators/kubernetes | 508 |
API key, run:
...
Set up your secrets in GithubFor our Github Actions we will need to set up some secrets for our repository. Specifically, you should use github secrets to store the ZENML_API_KEY that you created above.
The other values that are loaded from secrets into the environment here can also be set explici... | user-guide | https://docs.zenml.io/v/docs/user-guide/production-guide/ci-cd | 421 |
mated evaluation using synthetic generated queriesFor a broader evaluation we can examine a larger number of queries to check the retrieval component's performance. We do this by using an LLM to generate synthetic data. In our case we take the text of each document chunk and pass it to an LLM, telling it to generate a ... | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/evaluation/retrieval | 508 |
━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
GCP OAuth 2.0 tokenUses temporary OAuth 2.0 tokens explicitly configured by the user.
This method has the major limitation that the user must regularly generate new tokens and update the connector configuration as OAuth 2.0 tokens expire. On the other hand, this method is ideal in cases ... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/gcp-service-connector | 460 |
Implement a custom stack component
How to write a custom stack component flavor
When building a sophisticated MLOps Platform, you will often need to come up with custom-tailored solutions for your infrastructure or tooling. ZenML is built around the values of composability and reusability which is why the stack compo... | how-to | https://docs.zenml.io/v/docs/how-to/stack-deployment/implement-a-custom-stack-component | 362 |
should pick the one that best fits your use case.If you already have one or more GCP Service Connectors configured in your ZenML deployment, you can check which of them can be used to access the GCS bucket you want to use for your GCS Artifact Store by running e.g.:
zenml service-connector list-resources --resource-t... | stack-components | https://docs.zenml.io/stack-components/artifact-stores/gcp | 497 |
Embeddings generation
Generate embeddings to improve retrieval performance.
In this section, we'll explore how to generate embeddings for your data to improve retrieval performance in your RAG pipeline. Embeddings are a crucial part of the retrieval mechanism in RAG, as they represent the data in a high-dimensional s... | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/rag-with-zenml/embeddings-generation | 359 |
Configure the server environment
How to configure the server environment
The ZenML server environment is configured using environment variables. You will need to set these before deploying your server instance. Please refer to the full list of environment variables available to you here.
PreviousHandling dependencie... | how-to | https://docs.zenml.io/v/docs/how-to/configure-python-environments/configure-the-server-environment | 65 |
─────────────────────────────────────────────────┨┃ │ │ │ 🌀 kubernetes-cluster │ zenhacks-cluster ┃
┠──────────────────────────────────────┼─────────────────... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/service-connectors-guide | 301 |
SageMaker Pipelines orchestrator stack component:You'll need the IAM role ARN that we noted down earlier to register the orchestrator. This is the 'execution role' ARN you need to pass to the orchestrator.
zenml orchestrator register sagemaker-orchestrator --flavor=sagemaker --region=<YOUR_REGION> --execution_role=<R... | how-to | https://docs.zenml.io/how-to/popular-integrations/aws-guide | 464 |
s to provide GCP credentials to the step operator:use the gcloud CLI to authenticate locally with GCP. This only works in combination with the local orchestrator.Copygcloud auth login
zenml step-operator register <STEP_OPERATOR_NAME> \
--flavor=vertex \
--project=<GCP_PROJECT> \
--region=<REGION> \
# --m... | stack-components | https://docs.zenml.io/stack-components/step-operators/vertex | 453 |
ssions, the credentials should include permissionsto 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 s... | how-to | https://docs.zenml.io/how-to/auth-management/service-connectors-guide | 436 |
tored in the
secrets store.
"""
@abstractmethoddef delete_secret_values(self, secret_id: UUID) -> None:
"""Deletes secret values for an existing secret.
Args:
secret_id: The ID of the secret.
Raises:
KeyError: if no secret values for the given ID are stored in the
secrets store.
"""
This is a slimmed-down ve... | getting-started | https://docs.zenml.io/v/docs/getting-started/deploying-zenml/manage-the-deployed-services/custom-secret-stores | 306 |
s:
🔒 password
Resource types:
🐳 docker-registrySupports 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-authenti... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/service-connectors-guide | 465 |
━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛
```register and connect a Vertex AI Orchestrator Stack Component to the target GCP projectNOTE: If we do not specify a workload service account, the Vertex AI Pipelines Orchestrator uses the Compute Engine default service account in the target project to run pipelines. You... | how-to | https://docs.zenml.io/how-to/auth-management/gcp-service-connector | 573 |
🌎Environment Variables
How to control ZenML behavior with environmental variables.
There are a few pre-defined environmental variables that can be used to control the behavior of ZenML. See the list below with default values and options:
Logging verbosity
export ZENML_LOGGING_VERBOSITY=INFO
Choose from INFO, WARN... | reference | https://docs.zenml.io/reference/environment-variables | 404 |
he need to rerun unchanged parts of your pipeline.With ZenML, you can easily trace an artifact back to its origins and understand the exact sequence of executions that led to its creation, such as a trained model. This feature enables you to gain insights into the entire lineage of your artifacts, providing a clear und... | how-to | https://docs.zenml.io/v/docs/how-to/handle-data-artifacts/artifact-versioning | 303 |
y on your machine or running remotely as a server.All metadata is now stored, tracked, and managed by ZenML itself. The Metadata Store stack component type and all its implementations have been deprecated and removed. It is no longer possible to register them or include them in ZenML stacks. This is a key architectural... | reference | https://docs.zenml.io/reference/migration-guide/migration-zero-twenty | 440 |
thon file_that_runs_a_zenml_pipeline.py
Tekton UITekton comes with its own UI that you can use to find further details about your pipeline runs, such as the logs of your steps.
To find the Tekton UI endpoint, we can use the following command:
kubectl get ingress -n tekton-pipelines -o jsonpath='{.items[0].spec.rule... | stack-components | https://docs.zenml.io/stack-components/orchestrators/tekton | 462 |
iple> │ ➖ │ default │ 40m58s │ ┃┃ │ │ │ │ 📦 blob-container │ │ │ │ │ ┃
┃ │ │ │ │ 🌀 kubernetes-cluster ... | how-to | https://docs.zenml.io/how-to/auth-management/azure-service-connector | 412 |
GitHub Container Registry
Storing container images in GitHub.
The GitHub container registry is a container registry flavor that comes built-in with ZenML and uses the GitHub Container Registry to store container images.
When to use it
You should use the GitHub container registry if:
one or more components of your ... | stack-components | https://docs.zenml.io/stack-components/container-registries/github | 371 |
Local Docker Orchestrator
Orchestrating your pipelines to run in Docker.
The local Docker orchestrator is an orchestrator flavor that comes built-in with ZenML and runs your pipelines locally using Docker.
When to use it
You should use the local Docker orchestrator if:
you want the steps of your pipeline to run lo... | stack-components | https://docs.zenml.io/stack-components/orchestrators/local-docker | 426 |
] = None,
model_source_uri: Optional[str] = None,tags: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[RegistryModelVersion]:
"""Lists all model versions for a registered model."""
@abstractmethod
def get_model_version(self, name: str, version: str) -> RegistryModelVersion:
"""Gets a model version for ... | stack-components | https://docs.zenml.io/v/docs/stack-components/model-registries/custom | 407 |
onSageMakerFullAccess managed policy permissions).If using a remote orchestrator: the remote environment in which the orchestrator runs needs to be able to implicitly authenticate to AWS and assume the IAM role specified when registering the SageMaker step operator. This is only possible if the orchestrator is also run... | stack-components | https://docs.zenml.io/v/docs/stack-components/step-operators/sagemaker | 403 |
> \
--default_slack_channel_id=<SLACK_CHANNEL_ID>Here is where you can find the required parameters:
<SLACK_CHANNEL_ID>: Open your desired Slack channel in a browser, and copy out the last part of the URL starting with C.....
<SLACK_TOKEN>: This is the Slack token of your bot. You can find it in the Slack app settin... | stack-components | https://docs.zenml.io/v/docs/stack-components/alerters/slack | 470 |
Cache previous executions
Iterating quickly with ZenML through caching.
Developing machine learning pipelines is iterative in nature. ZenML speeds up development in this work with step caching.
In the logs of your previous runs, you might have noticed at this point that rerunning the pipeline a second time will use ... | user-guide | https://docs.zenml.io/v/docs/user-guide/starter-guide/cache-previous-executions | 378 |
lly registered orchestrator `<ORCHESTRATOR_NAME>`.$ zenml service-connector list-resources --resource-type kubernetes-cluster -e
The following 'kubernetes-cluster' resources can be accessed by service connectors configured in your workspace:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━... | stack-components | https://docs.zenml.io/stack-components/orchestrators/kubernetes | 508 |
│ 🔶 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 ... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management | 471 |
e.g. feature drift, label drift, new labels etc.).Model Performance Checks for tabular or computer vision data: evaluate a model and detect problems with its performance (e.g. confusion matrix, boosting overfit, model error analysis)
You should consider one of the other Data Validator flavors if you need a different s... | stack-components | https://docs.zenml.io/stack-components/data-validators/deepchecks | 375 |
lowing configuration values in custom-values.yaml:the database configuration, if you mean to use an external database:the database URL, formatted as mysql://<username>:<password>@<hostname>:<port>/<database>CA and/or client TLS certificates, if you’re using SSL to secure the connection to the database
the Ingress conf... | getting-started | https://docs.zenml.io/v/docs/getting-started/deploying-zenml/deploy-with-helm | 421 |
How to configure a pipeline with a YAML
Specify a configuration file
All configuration that can be specified in a YAML file can also be specified in code itself. However, it is best practice to use a YAML file to separate config from code.
You can use the with_options(config_path=<PATH_TO_CONFIG>) pattern to apply y... | how-to | https://docs.zenml.io/v/docs/how-to/use-configuration-files/how-to-use-config | 244 |
in our active stack. This can be done in two ways:If you have a Service Connector configured to access the remote Kubernetes cluster, you no longer need to set the kubernetes_context attribute to a local kubectl context. In fact, you don't need the local Kubernetes CLI at all. You can connect the stack component to the... | stack-components | https://docs.zenml.io/stack-components/orchestrators/kubernetes | 125 |
the GCP Secrets Manager as a secrets store backendThe GCP Secrets Store uses the ZenML GCP Service Connector under the hood to authenticate with the GCP Secrets Manager API. This means that you can use any of the authentication methods supported by the GCP Service Connector to authenticate with the GCP Secrets Manager ... | getting-started | https://docs.zenml.io/getting-started/deploying-zenml/deploy-with-helm | 426 |
AzureML
Executing individual steps in AzureML.
AzureML offers specialized compute instances to run your training jobs and has a comprehensive UI to track and manage your models and logs. ZenML's AzureML step operator allows you to submit individual steps to be run on AzureML compute instances.
When to use it
You sh... | stack-components | https://docs.zenml.io/v/docs/stack-components/step-operators/azureml | 404 |
───────┼────────────────────┼────────────────────┨┃ │ cloud_kubeflow_stack │ b94df4d2-5b65-4201-945a-61436c9c5384 │ │ default │ cloud_artifact_store │ cloud_orchestrator │ eks_seldon │ cloud_registry │ aws_secret_manager ┃
┠────────┼──────────────────────┼──────────────────────────────────────... | reference | https://docs.zenml.io/reference/migration-guide/migration-zero-twenty | 507 |
hestrator_url"].value
Run pipelines on a scheduleThe Vertex Pipelines orchestrator supports running pipelines on a schedule using its native scheduling capability.
How to schedule a pipeline
from zenml.config.schedule import Schedule
# Run a pipeline every 5th minute
pipeline_instance.run(
schedule=Schedule(
cro... | stack-components | https://docs.zenml.io/stack-components/orchestrators/vertex | 416 |
Run pipelines asynchronously
The best way to trigger a pipeline run so that it runs in the background
By default your pipelines will run synchronously. This means your terminal will follow along the logs as the pipeline is being built/runs.
This behavior can be changed in multiple ways. Either the orchestrator can b... | how-to | https://docs.zenml.io/v/docs/how-to/build-pipelines/run-pipelines-asynchronously | 167 |
Deploy a stack using mlstacks
Deploying an entire stack with mlstacks.
mlstacks is a Python package that allows you to quickly spin up MLOps infrastructure using Terraform. It is designed to be used with ZenML, but can be used with any MLOps tool or platform. You can deploy a modular MLOps stack for AWS, GCP or K3D u... | how-to | https://docs.zenml.io/v/docs/how-to/stack-deployment/deploy-a-stack-using-mlstacks | 405 |
p a custom alerter as described on the Feast page,and where can I find the 'How to use it?' guide?". Expected URL ending: feature-stores.
Got: ['https://docs.zenml.io/stacks-and-components/component-guide/alerters/custom',
'https://docs.zenml.io/v/docs/stacks-and-components/component-guide/alerters/custom',
'https:/... | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/reranking/evaluating-reranking-performance | 446 |
to the container registry.
Authentication MethodsIntegrating and using an Azure Container Registry in your pipelines is not possible without employing some form of authentication. If you're looking for a quick way to get started locally, you can use the Local Authentication method. However, the recommended way to auth... | stack-components | https://docs.zenml.io/v/docs/stack-components/container-registries/azure | 330 |
our active stack:
from zenml.client import Clientexperiment_tracker = Client().active_stack.experiment_tracker
@step(experiment_tracker=experiment_tracker.name)
def tf_trainer(...):
...
MLflow UI
MLflow comes with its own UI that you can use to find further details about your tracked experiments.
You can find th... | stack-components | https://docs.zenml.io/v/docs/stack-components/experiment-trackers/mlflow | 391 |
ssions, the credentials should include permissionsto 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 s... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/service-connectors-guide | 436 |
ml service-connector describe eks-zenhacks-clusterExample Command Output
Service connector 'eks-zenhacks-cluster' of type 'aws' with id 'be53166a-b39c-4e39-8e31-84658e50eec4' is owned by user 'default' and is 'private'.
'eks-zenhacks-cluster' aws Service Connector Details
┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━... | how-to | https://docs.zenml.io/how-to/auth-management/best-security-practices | 567 |
DockerHub
Storing container images in DockerHub.
The DockerHub container registry is a container registry flavor that comes built-in with ZenML and uses DockerHub to store container images.
When to use it
You should use the DockerHub container registry if:
one or more components of your stack need to pull or push ... | stack-components | https://docs.zenml.io/stack-components/container-registries/dockerhub | 399 |
to access the Artifact Store to load served modelsTo enable these use cases, it is recommended to use an Azure Service Connector to link your Azure Artifact Store to the remote Azure Blob storage container.
To set up the Azure Artifact Store to authenticate to Azure and access an Azure Blob storage container, it is re... | stack-components | https://docs.zenml.io/stack-components/artifact-stores/azure | 454 |
User Management
In ZenML Pro, there is a slightly different entity hierarchy as compared to the open-source ZenML framework. This document walks you through the key differences and new concepts that are pro-only.
Organizations, Tenants, and Roles
ZenML Pro arranges various aspects of your work experience around the ... | getting-started | https://docs.zenml.io/v/docs/getting-started/zenml-pro/user-management | 310 |
306:3306 -e MYSQL_ROOT_PASSWORD=password mysql:8.0The ZenML client on the host machine can then be configured to connect directly to the database with a slightly different zenml connect command:
zenml connect --url mysql://127.0.0.1/zenml --username root --password password
Note The localhost hostname will not work w... | getting-started | https://docs.zenml.io/v/docs/getting-started/deploying-zenml/deploy-with-docker | 421 |
a_new_local_stack -o default -a my_artifact_storestack : This is the CLI group that enables interactions with the stacks
register: Here we want to register a new stack. Explore other operations withzenml stack --help.
a_new_local_stack : This is the unique name that the stack will have.
--orchestrator or -o are use... | user-guide | https://docs.zenml.io/v/docs/user-guide/production-guide/understand-stacks | 497 |
K_NAME> -i <IMAGE_BUILDER_NAME> ... --set
CaveatsAs described in this Google Cloud Build documentation page, Google Cloud Build uses containers to execute the build steps which are automatically attached to a network called cloudbuild that provides some Application Default Credentials (ADC), that allow the container t... | stack-components | https://docs.zenml.io/v/docs/stack-components/image-builders/gcp | 273 |
Automatically retry steps
Automatically configure your steps to retry if they fail.
ZenML provides a built-in retry mechanism that allows you to configure automatic retries for your steps in case of failures. This can be useful when dealing with intermittent issues or transient errors. A common pattern when trying to... | how-to | https://docs.zenml.io/v/docs/how-to/build-pipelines/retry-steps | 346 |
vel of permissions required to function correctly.Using long-lived credentials on their own still isn't ideal, because if leaked, they pose a security risk, even when they have limited permissions attached. The good news is that ZenML Service Connectors include additional mechanisms that, when used in combination with ... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/best-security-practices | 350 |
in the stack.
zenml integration install mlflow -yOnce the MLflow integration is installed, you can register an MLflow model registry component in your stack:
zenml model-registry register mlflow_model_registry --flavor=mlflow
# Register and set a stack with the new model registry as the active stack
zenml stack reg... | stack-components | https://docs.zenml.io/stack-components/model-registries/mlflow | 426 |
ral options are presented.
hyperparameter tuning?Our dedicated documentation guide on implementing this is the place to learn more.
reset things when something goes wrong?
To reset your ZenML client, you can run zenml clean which will wipe your local metadata database and reset your client. Note that this is a destr... | reference | https://docs.zenml.io/v/docs/reference/how-do-i | 327 |
t into the step
print(0.01)
trainer(gamma=gamma)Important note, in the above case, the value of the step would be the one defined in the steps key (i.e. 0.001). So the YAML config always takes precedence over pipeline parameters that are passed down to steps in code. Read this section for more details.
Normally, par... | how-to | https://docs.zenml.io/how-to/use-configuration-files/what-can-be-configured | 411 |
🪄Core concepts
Discovering the core concepts behind ZenML.
ZenML is an extensible, open-source MLOps framework for creating portable, production-ready MLOps pipelines. It's built for data scientists, ML Engineers, and MLOps Developers to collaborate as they develop to production. In order to achieve this goal, ZenML... | getting-started | https://docs.zenml.io/v/docs/getting-started/core-concepts | 410 |
Fetching pipelines
Inspecting a finished pipeline run and its outputs.
Once a pipeline run has been completed, we can access the corresponding information in code, which enables the following:
Loading artifacts like models or datasets saved by previous runs
Accessing metadata or configurations of previous runs
Pro... | how-to | https://docs.zenml.io/how-to/build-pipelines/fetching-pipelines | 398 |
─────────────────────────────────────────────────┨┃ AUTH METHOD │ user-account ┃
┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
┃ RESOURCE TYPES │ 🔵 gcp-generic, 📦 gcs-bucket, 🌀 kubernetes-cluster, �... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/gcp-service-connector | 383 |
AWS Sagemaker Orchestrator
Orchestrating your pipelines to run on Amazon Sagemaker.
Sagemaker Pipelines is a serverless ML workflow tool running on AWS. It is an easy way to quickly run your code in a production-ready, repeatable cloud orchestrator that requires minimal setup without provisioning and paying for stand... | stack-components | https://docs.zenml.io/stack-components/orchestrators/sagemaker | 411 |
our pipeline runs, such as the logs of your steps.To access the Sagemaker Pipelines UI, you will have to launch Sagemaker Studio via the AWS Sagemaker UI. Make sure that you are launching it from within your desired AWS region.
Once the Studio UI has launched, click on the 'Pipeline' button on the left side. From ther... | stack-components | https://docs.zenml.io/v/docs/stack-components/orchestrators/sagemaker | 343 |
total_tests) * 100
return round(failure_rate, 2)This function takes a sample size and a flag indicating whether to use reranking and evaluates the retrieval performance based on the generated questions and relevant documents. It queries similar documents for each question and checks whether the expected URL ending is... | user-guide | https://docs.zenml.io/user-guide/llmops-guide/reranking/evaluating-reranking-performance | 433 |
'default' ...
Creating default user 'default' ...Creating default stack for user 'default' in workspace default...
Active workspace not set. Setting it to the default.
The active stack is not set. Setting the active stack to the default workspace stack.
Using the default store for the global config.
Unable to find... | reference | https://docs.zenml.io/v/docs/reference/global-settings | 484 |
━━━━━┛
Configuration
┏━━━━━━━━━━━━┯━━━━━━━━━━━━┓┃ PROPERTY │ VALUE ┃
┠────────────┼────────────┨
┃ project_id │ zenml-core ┃
┠────────────┼────────────┨
┃ token │ [HIDDEN] ┃
┗━━━━━━━━━━━━┷━━━━━━━━━━━━┛
Note the temporary nature of the Service Connector. It will expire and become unusable in 1 hour... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/gcp-service-connector | 609 |
┃┃ │ │ │ │ HTTP response headers: HTTPHeaderDict({'Audit-Id': '72558f83-e050-4fe3-93e5-9f7e66988a4c', 'Cache-Control': ┃
┃ │ ... | how-to | https://docs.zenml.io/how-to/auth-management/service-connectors-guide | 356 |
ry_similar_docs(
question: str,
url_ending: str,use_reranking: bool = False,
returned_sample_size: int = 5,
) -> Tuple[str, str, List[str]]:
"""Query similar documents for a given question and URL ending."""
embedded_question = get_embeddings(question)
db_conn = get_db_conn()
num_docs = 20 if use_reranking else... | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/reranking/implementing-reranking | 397 |
🌲Control logging
Configuring ZenML's default logging behavior
ZenML produces various kinds of logs:
The ZenML Server produces server logs (like any FastAPI server).
The Client or Runner environment produces logs, for example after running a pipeline. These are steps that are typically before, after, and during the... | how-to | https://docs.zenml.io/v/docs/how-to/control-logging | 141 |
into play when the component is ultimately in use.The design behind this interaction lets us separate the configuration of the flavor from its implementation. This way we can register flavors and components even when the major dependencies behind their implementation are not installed in our local setting (assuming the... | stack-components | https://docs.zenml.io/stack-components/artifact-stores/custom | 272 |
-registry │ iam-role │ │ ┃┃ │ │ │ session-token │ │ ┃
┃ │ │ │ federation-token │ │ ┃
┠──────────────────────────────┼───────────────┼──... | how-to | https://docs.zenml.io/how-to/auth-management | 466 |
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 one of the other, more specific resource types like GCS buckets, Kubernetes clusters, or Docker registries. For example, it can be used with the Google Cloud Image Build... | how-to | https://docs.zenml.io/how-to/auth-management/gcp-service-connector | 363 |
ource-id zenfiles --client
Example Command OutputService connector 'aws-federation-token (s3-bucket | s3://zenfiles client)' of type 'aws' with id '868b17d4-b950-4d89-a6c4-12e520e66610' is owned by user 'default' and is 'private'.
'aws-federation-token (s3-bucket | s3://zenfiles client)' aws Service
Connector Detail... | how-to | https://docs.zenml.io/how-to/auth-management/aws-service-connector | 484 |
⛓️Build a pipeline
Building pipelines is as simple as adding the `@step` and `@pipeline` decorators to your code.
@step # Just add this decorator
def load_data() -> dict:
training_data = [[1, 2], [3, 4], [5, 6]]
labels = [0, 1, 0]
return {'features': training_data, 'labels': labels}
@step
def train_model(data:... | how-to | https://docs.zenml.io/v/docs/how-to/build-pipelines | 378 |
the creation of the custom flavor through the CLI.The CustomModelRegistryConfig class is imported when someone tries to register/update a stack component with this custom flavor. Most of all, during the registration process of the stack component, the config will be used to validate the values given by the user. As Con... | stack-components | https://docs.zenml.io/v/docs/stack-components/model-registries/custom | 195 |
entials JSON to clients instead (not recommended).A GCP project is required and the connector may only be used to access GCP resources in the specified roject. This project must be the same as the one for which the external account was configured.
If you already have the GOOGLE_APPLICATION_CREDENTIALS environment vari... | how-to | https://docs.zenml.io/how-to/auth-management/gcp-service-connector | 427 |
pipeline again:
python run.py --training-pipelineNow you should notice the machine that gets provisioned on your cloud provider would have a different configuration as compared to last time. As easy as that!
Bear in mind that not every orchestrator supports ResourceSettings directly. To learn more, you can read about... | user-guide | https://docs.zenml.io/user-guide/production-guide/configure-pipeline | 93 |
MLflow Model Registry
Managing MLFlow logged models and artifacts
MLflow is a popular tool that helps you track experiments, manage models and even deploy them to different environments. ZenML already provides a MLflow Experiment Tracker that you can use to track your experiments, and an MLflow Model Deployer that yo... | stack-components | https://docs.zenml.io/v/docs/stack-components/model-registries/mlflow | 369 |
ome additional important configuration parameters:namespace is the namespace under which the driver and executor pods will run.
service_account is the service account that will be used by various Spark components (to create and watch the pods).
Additionally, the _backend_configuration method is adjusted to handle the... | stack-components | https://docs.zenml.io/v/docs/stack-components/step-operators/spark-kubernetes | 409 |
to install and configure the AWS CLI on your hostyou don't need to care about enabling your other stack components (orchestrators, step operators, and model deployers) to have access to the artifact store through IAM roles and policies
you can combine the S3 artifact store with other stack components that are not run... | stack-components | https://docs.zenml.io/v/docs/stack-components/artifact-stores/s3 | 432 |
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