page_content stringlengths 74 2.86k | parent_section stringclasses 7
values | url stringlengths 21 129 | token_count int64 17 755 |
|---|---|---|---|
rray": [[1,2,3,4]] } }'
Using a Service ConnectorTo set up the Seldon Core Model Deployer to authenticate to a remote Kubernetes cluster, it is recommended to leverage the many features provided by the Service Connectors such as auto-configuration, local client login, best security practices regarding long-lived crede... | stack-components | https://docs.zenml.io/v/docs/stack-components/model-deployers/seldon | 460 |
──────────┼──────────────────────────────────────┨┃ UPDATED_AT │ 2023-06-16 10:15:26.393772 ┃
┗━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Configuration
┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━┓
┃ PROPERTY │ VALUE ┃
┠───────────────────────┼───────────┨
┃ region ... | how-to | https://docs.zenml.io/how-to/auth-management/best-security-practices | 573 |
cribe aws-federation-token
Example Command OutputService connector 'aws-federation-token' of type 'aws' with id '868b17d4-b950-4d89-a6c4-12e520e66610' is owned by user 'default' and is 'private'.
'aws-federation-token' aws Service Connector Details
┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━... | how-to | https://docs.zenml.io/how-to/auth-management/aws-service-connector | 455 |
Associate a pipeline with a Model
The most common use-case for a Model is to associate it with a pipeline.
from zenml import pipeline
from zenml.model.model import Model
@pipeline(
model=Model(
name="ClassificationModel", # Give your models unique names
tags=["MVP", "Tabular"] # Use tags for future filtering
... | how-to | https://docs.zenml.io/v/docs/how-to/use-the-model-control-plane/associate-a-pipeline-with-a-model | 266 |
Disable colorful logging
How to disable colorful logging in ZenML.
By default, ZenML uses colorful logging to make it easier to read logs. However, if you wish to disable this feature, you can do so by setting the following environment variable:
ZENML_LOGGING_COLORS_DISABLED=true
Note that setting this on the clien... | how-to | https://docs.zenml.io/how-to/control-logging/disable-colorful-logging | 214 |
ter an S3 Artifact Store and add it to your stack:zenml integration install s3 -y
The only configuration parameter mandatory for registering an S3 Artifact Store is the root path URI, which needs to point to an S3 bucket and take the form s3://bucket-name. Please read the documentation relevant to the S3 service that ... | stack-components | https://docs.zenml.io/v/docs/stack-components/artifact-stores/s3 | 398 |
OUP> --project=<PROJECT> \
--token=<GITLAB_TOKEN>where <NAME> is the name of the code repository you are registering, <GROUP> is the group of the project, <PROJECT> is the name of the project, <GITLAB_TOKEN> is your GitLab Personal Access Token, and <GITLAB_URL> is the URL of the GitLab instance which defaults to http... | how-to | https://docs.zenml.io/v/docs/how-to/setting-up-a-project-repository/connect-your-git-repository | 433 |
asked to access:
from zenml.client import Clientclient = Client()
# Get a Service Connector client for a particular S3 bucket
connector_client = client.get_service_connector_client(
name_id_or_prefix="aws-federation-multi",
resource_type="s3-bucket",
resource_id="s3://zenfiles"
# Get the S3 boto3 python client ... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/best-security-practices | 404 |
Storing embeddings in a vector database
Store embeddings in a vector database for efficient retrieval.
The process of generating the embeddings doesn't take too long, especially if the machine on which the step is running has a GPU, but it's still not something we want to do every time we need to retrieve a document.... | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/rag-with-zenml/storing-embeddings-in-a-vector-database | 398 |
Controlling Model versions
Each model can have many versions. Model versions are a way for you to track different iterations of your training process, complete with some extra dashboard and API functionality to support the full ML lifecycle.
E.g. Based on your business rules during training, you can associate model v... | how-to | https://docs.zenml.io/how-to/use-the-model-control-plane/model-versions | 375 |
tion Token" and "IAM Role" authentication methods.It's not easy to showcase this without using some ZenML Python Client code, but here is an example that proves that the AWS client token issued to an S3 client can only access the S3 bucket resource it was issued for, even if the originating AWS Service Connector is abl... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/best-security-practices | 508 |
enml.models import PipelineDeploymentResponseModelfrom zenml.orchestrators import ContainerizedOrchestrator
from zenml.stack import Stack
class MyOrchestrator(ContainerizedOrchestrator):
def get_orchestrator_run_id(self) -> str:
# Return an ID that is different each time a pipeline is run, but the
# same for all s... | stack-components | https://docs.zenml.io/v/docs/stack-components/orchestrators/custom | 429 |
│ oauth2-token │ │ ┃┃ │ │ │ impersonation │ │ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┷━━━━━━━┷━━━━━━━━┛
Prerequisites
The GCP Service Connector is part of the GCP ZenML integration.... | how-to | https://docs.zenml.io/how-to/auth-management/gcp-service-connector | 375 |
el, save_artifact
from zenml.client import Client@step
def f_() -> None:
# produce new artifact
new_artifact = save_artifact(data="Hello, World!", name="manual_artifact")
# and link it inside a step
link_artifact_to_model(
artifact_version_id=new_artifact.id,
model=Model(name="MyModel", version="0.0.42"),
# us... | how-to | https://docs.zenml.io/how-to/use-the-model-control-plane/linking-model-binaries-data-to-models | 166 |
nML, namely an orchestrator and an artifact store.Keep in mind, that each one of these components is built on top of base abstractions and is completely extensible.
Orchestrator
An Orchestrator is a workhorse that coordinates all the steps to run in a pipeline. Since pipelines can be set up with complex combinations ... | getting-started | https://docs.zenml.io/getting-started/core-concepts | 352 |
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Configuration┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ PROPERTY │ VALUE ┃
┠───────────────────────┼─────────────────────────────────────────... | how-to | https://docs.zenml.io/how-to/auth-management/best-security-practices | 429 |
─────────────────────────────────────────────────┨┃ ID │ 9a810521-ef41-4e45-bb48-8569c5943dc6 ┃
┠──────────────────┼─────────────────────────────────────────────────────────────────────┨
┃ NAME │ aws-implicit (s3-bucket | s3://sagemaker-studio-d8a14tvjsmb clien... | how-to | https://docs.zenml.io/how-to/auth-management/aws-service-connector | 397 |
heck out this 3-minute video for more information.You can keep changing the Config and Settings of your flavor after registration. ZenML will pick up these "live" changes when running pipelines.
Note that changing the config in a breaking way requires an update of the component (not a flavor). E.g., adding a mandatory... | how-to | https://docs.zenml.io/how-to/stack-deployment/implement-a-custom-stack-component | 348 |
.
Authentication Methods
Implicit authenticationImplicit authentication to AWS services using environment variables, local configuration files or IAM roles.
This method may constitute a security risk, because it can give users access to the same cloud resources and services that the ZenML Server itself is configured... | how-to | https://docs.zenml.io/how-to/auth-management/aws-service-connector | 356 |
HyperAI Orchestrator
Orchestrating your pipelines to run on HyperAI.ai instances.
HyperAI is a cutting-edge cloud compute platform designed to make AI accessible for everyone. The HyperAI orchestrator is an orchestrator flavor that allows you to easily deploy your pipelines on HyperAI instances.
This component is on... | stack-components | https://docs.zenml.io/v/docs/stack-components/orchestrators/hyperai | 318 |
:
zenml model-registry flavor list
How to use itModel registries are an optional component in the ZenML stack that is tied to the experiment tracker. This means that a model registry can only be used if you are also using an experiment tracker. If you're not using an experiment tracker, you can still store your model... | stack-components | https://docs.zenml.io/stack-components/model-registries | 225 |
bute: Optional[str]
module: str
type: SourceTypeWhen you want to configure your pipeline with a certain stack in mind, you can do so as well:
`...write_run_configuration_template(stack=<Insert_stack_here>)
PreviousFind out which configuration was used for a run
NextCustomize Docker builds
Last updated 19 days ago | how-to | https://docs.zenml.io/v/docs/how-to/use-configuration-files/autogenerate-a-template-yaml-file | 71 |
nment or the Stack Component.
End-to-end examplesThis is an example of an end-to-end workflow involving Service Connectors that use a single multi-type AWS Service Connector to give access to multiple resources for multiple Stack Components. A complete ZenML Stack is registered and composed of the following Stack Comp... | how-to | https://docs.zenml.io/how-to/auth-management/aws-service-connector | 495 |
ipelines/8267b0bc-9cbd-42ac-9b56-4d18275bdbb4/runsThis 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 t... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management | 270 |
e by passing the page argument to the list method.You can further restrict your search by passing additional arguments that will be used to filter the results. E.g., most resources have a user_id associated with them that can be set to only list resources created by that specific user. The available filter argument opt... | reference | https://docs.zenml.io/v/docs/reference/python-client | 416 |
Amazon Elastic Container Registry (ECR)
Storing container images in Amazon ECR.
The AWS container registry is a container registry flavor provided with the ZenML aws integration and uses Amazon ECR to store container images.
When to use it
You should use the AWS container registry if:
one or more components of you... | stack-components | https://docs.zenml.io/v/docs/stack-components/container-registries/aws | 404 |
┃┠──────────────────┼─────────────────────────────────────────────────────────────────────┨
┃ RESOURCE TYPES │ 🌀 kubernetes-cluster ┃
┠──────────────────┼──────────────────────────────────────────────────────────────────... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/best-security-practices | 429 |
─────────────────────────────────────────────────┨┃ MODEL_URI │ s3://zenprojects/seldon_model_deployer_step/output/884/seldon ┃
┠────────────────────────┼────────────────────────────────────────────────────────────────────────────────────────┨
┃ PIPELINE_NAME │ seldon_de... | stack-components | https://docs.zenml.io/v/docs/stack-components/model-deployers | 431 |
━━━━━━━━━┷━━━━━━━━┛
Local and remote availabilityYou 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 implemen... | how-to | https://docs.zenml.io/how-to/auth-management/service-connectors-guide | 352 |
f"{input_one} {input_two}"
print(combined_str)@pipeline
def my_pipeline():
output_step_one = step_1()
step_2(input_one="hello", input_two=output_step_one)
if __name__ == "__main__":
my_pipeline()Saving that to a run.py file and running it gives us:
Example Command Output
```text
$ python run.py
Reu... | how-to | https://docs.zenml.io/how-to/auth-management/gcp-service-connector | 567 |
setting the VAULT_NAMESPACE environment variable.ZENML_SECRETS_STORE_MAX_VERSIONS: The maximum number of secret versions to keep for each Vault secret. If not set, the default value of 1 will be used (only the latest version will be kept).
These configuration options are only relevant if you're using a custom secrets... | getting-started | https://docs.zenml.io/getting-started/deploying-zenml/deploy-with-docker | 376 |
Specify pip dependencies and apt packages
The configuration for specifying pip and apt dependencies only works in the remote pipeline case, and is disregarded for local pipelines (i.e. pipelines that run locally without having to build a Docker image).
When a pipeline is run with a remote orchestrator a Dockerfile is... | how-to | https://docs.zenml.io/how-to/customize-docker-builds/specify-pip-dependencies-and-apt-packages | 399 |
Seldon
Deploying models to Kubernetes with Seldon Core.
Seldon Core is a production grade source-available model serving platform. It packs a wide range of features built around deploying models to REST/GRPC microservices that include monitoring and logging, model explainers, outlier detectors and various continuous ... | stack-components | https://docs.zenml.io/v/docs/stack-components/model-deployers/seldon | 355 |
thod instead, we would not need to make this copy.It is worth noting that copying the artifact to a local path may not always be necessary and can potentially be a performance bottleneck.
import os
from typing import Any, ClassVar, Dict, Optional, Tuple, Type, Union
import pandas as pd
from zenml.artifact_stores.ba... | how-to | https://docs.zenml.io/v/docs/how-to/handle-data-artifacts/handle-custom-data-types | 484 |
Understanding stacks
Learning how to switch the infrastructure backend of your code.
Now that we have ZenML deployed, we can take the next steps in making sure that our machine learning workflows are production-ready. As you were running your first pipelines, you might have already noticed the term stack in the logs ... | user-guide | https://docs.zenml.io/user-guide/production-guide/understand-stacks | 505 |
ttings={
"orchestrator.vm_gcp": skypilot_settingsCode Example:
from zenml.integrations.skypilot_azure.flavors.skypilot_orchestrator_azure_vm_flavor import SkypilotAzureOrchestratorSettings
skypilot_settings = SkypilotAzureOrchestratorSettings(
cpus="2",
memory="16",
accelerators="V100:2",
accelerator_args={"tpu_... | stack-components | https://docs.zenml.io/v/docs/stack-components/orchestrators/skypilot-vm | 436 |
View logs on the dashboard
By default, ZenML uses a logging handler to capture the logs that occur during the execution of a step. Users are free to use the default python logging module or print statements, and ZenML's logging handler will catch these logs and store them.
import logging
from zenml import step
@ste... | how-to | https://docs.zenml.io/how-to/control-logging/view-logs-on-the-dasbhoard | 319 |
ry connect aws-us-east-1 --connector aws-us-east-1Successfully connected container registry `aws-us-east-1` to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID ... | stack-components | https://docs.zenml.io/v/docs/stack-components/container-registries/aws | 532 |
Manage artifacts
Understand and adjust how ZenML versions your data.
Data sits at the heart of every machine learning workflow. Managing and versioning this data correctly is essential for reproducibility and traceability within your ML pipelines. ZenML takes a proactive approach to data versioning, ensuring that eve... | user-guide | https://docs.zenml.io/user-guide/starter-guide/manage-artifacts | 372 |
Deploy with custom images
Deploying ZenML with custom Docker images.
In most cases, deploying ZenML with the default zenmlhub/zenml-server Docker image should work just fine. However, there are some scenarios when you might need to deploy ZenML with a custom Docker image:
You have implemented a custom artifact store... | getting-started | https://docs.zenml.io/getting-started/deploying-zenml/deploy-with-custom-image | 447 |
kdownString .csv / .html / .md (depending on type)ZenML provides a built-in CloudpickleMaterializer that can handle any object by saving it with cloudpickle. However, this is not production-ready because the resulting artifacts cannot be loaded when running with a different Python version. In such cases, you should con... | how-to | https://docs.zenml.io/v/docs/how-to/handle-data-artifacts/handle-custom-data-types | 155 |
┃┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────┨
┃ 19edc05b-92db-49de-bc84-aa9b3fb8261a │ aws-s3-zenfiles ... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/service-connectors-guide | 376 |
Understanding reranking
Understand how reranking works.
What is reranking?
Reranking is the process of refining the initial ranking of documents retrieved by a retrieval system. In the context of Retrieval-Augmented Generation (RAG), reranking plays a crucial role in improving the relevance and quality of the retrie... | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/reranking/understanding-reranking | 372 |
s used for each run.
Creating a GitHub RepositoryWhile ZenML supports many different flavors of git repositories, this guide will focus on GitHub. To create a repository on GitHub:
Sign in to GitHub.
Click the "+" icon and select "New repository."
Name your repository, set its visibility, and add a README or .gitig... | user-guide | https://docs.zenml.io/user-guide/production-guide/connect-code-repository | 424 |
nt import Client
@pipeline
def do_predictions():# model name and version are directly passed into client method
model = Client().get_model_version("iris_classifier", ModelStages.PRODUCTION)
inference_data = load_data()
predict(
# Here, we load in the `trained_model` from a trainer step
model=model.get_model_arti... | how-to | https://docs.zenml.io/how-to/use-the-model-control-plane/load-artifacts-from-model | 121 |
of your models at different stages of development.if you have pipelines that regularly ingest new data, you should use data validation to run regular data integrity checks to signal problems before they are propagated downstream.
in continuous training pipelines, you should use data validation techniques to compare ne... | stack-components | https://docs.zenml.io/stack-components/data-validators | 357 |
this, run the following command:
zenml downgradeNote that downgrading the ZenML version may cause unexpected behavior, such as model schema validation failures or even data loss. In such cases, you may need to purge the local database and re-initialize the global configuration to bring it back to its default factory ... | reference | https://docs.zenml.io/v/docs/reference/global-settings | 90 |
(
EvidentlyColumnMapping,
evidently_report_step,text_data_report = evidently_report_step.with_options(
parameters=dict(
column_mapping=EvidentlyColumnMapping(
target="Rating",
numerical_features=["Age", "Positive_Feedback_Count"],
categorical_features=[
"Division_Name",
"Department_Name",
"Class_Name",
],
t... | stack-components | https://docs.zenml.io/stack-components/data-validators/evidently | 428 |
ator(dataset=df_train)
data_validation_pipeline()As can be seen from the step definition , the step takes in a dataset and it returns a Deepchecks SuiteResult object that contains the test results:
@step
def deepchecks_data_integrity_check_step(
dataset: pd.DataFrame,
check_list: Optional[Sequence[DeepchecksDataIn... | stack-components | https://docs.zenml.io/stack-components/data-validators/deepchecks | 360 |
ser. For more information, see this documentation.For more information on user federation tokens, session policies, and the GetFederationToken AWS API, see the official AWS documentation on the subject.
For more information about the difference between this method and the AWS IAM Role authentication method, consult th... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/aws-service-connector | 465 |
for federated users by impersonating another user.The connector needs to be configured with an AWS secret key associated with an IAM user or AWS account root user (not recommended). The IAM user must have permission to call the GetFederationToken STS API (i.e. allow the sts:GetFederationToken action on the * IAM resour... | how-to | https://docs.zenml.io/how-to/auth-management/aws-service-connector | 364 |
Deleting a Model
Learn how to delete models.
Deleting a model or a specific model version means removing all links between the Model entity and artifacts + pipeline runs, and will also delete all metadata associated with that Model.
Deleting all versions of a model
zenml model delete <MODEL_NAME>
from zenml.client... | how-to | https://docs.zenml.io/how-to/use-the-model-control-plane/delete-a-model | 130 |
lementation_class(self) -> Type[BaseStepOperator]:"""Returns the implementation class for this flavor."""
This is a slimmed-down version of the base implementation which aims to highlight the abstraction layer. In order to see the full implementation and get the complete docstrings, please check the SDK docs .
Build ... | stack-components | https://docs.zenml.io/stack-components/step-operators/custom | 352 |
nment or the Stack Component.
End-to-end examplesThis is an example of an end-to-end workflow involving Service Connectors that use a single multi-type AWS Service Connector to give access to multiple resources for multiple Stack Components. A complete ZenML Stack is registered and composed of the following Stack Comp... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/aws-service-connector | 495 |
9/10 : COPY . .
Step 10/10 : RUN chmod -R a+rw .Amazon ECR requires you to create a repository before you can push an image to it. ZenML is trying to push the image 715803424590.dkr.ecr.us-east-1.amazonaws.com/zenml:simple_pipeline-orchestrator but could only detect the following repositories: []. We will try to push... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/aws-service-connector | 323 |
r │ zenhacks-cluster ┃┠───────────────────────┼──────────────────────────────────────────────┨
┃ 🐳 docker-registry │ 715803424590.dkr.ecr.us-east-1.amazonaws.com ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
The following is an example of configuring a mul... | how-to | https://docs.zenml.io/how-to/auth-management/service-connectors-guide | 585 |
to the active stack
zenml stack update -s <NAME>Once you added the step operator to your active stack, you can use it to execute individual steps of your pipeline by specifying it in the @step decorator as follows:
from zenml import step
@step(step_operator= <NAME>)
def trainer(...) -> ...:
"""Train a model."""
... | stack-components | https://docs.zenml.io/v/docs/stack-components/step-operators/vertex | 435 |
━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━┛$ zenml orchestrator connect <ORCHESTRATOR_NAME> --connector aws-iam-multi-us
Running with active workspace: 'default' (repository)
Running with active stack: 'default' (repository)
Successfully connected orchestrator `<ORCHESTRATOR_NAME>` to the following resources:
┏━... | stack-components | https://docs.zenml.io/stack-components/orchestrators/kubernetes | 581 |
zation or use to authenticate automated workloads.In fact, cloud platforms don't even allow using user account passwords directly as a credential when authenticating to the cloud platform APIs. There is always a process in place that allows exchanging the account/password credential for another form of long-lived crede... | how-to | https://docs.zenml.io/how-to/auth-management/best-security-practices | 347 |
his flavor."""
return BaseContainerRegistryConfig@property
def implementation_class(self) -> Type[BaseContainerRegistry]:
"""Implementation class."""
return BaseContainerRegistry
This is a slimmed-down version of the base implementation which aims to highlight the abstraction layer. In order to see the full implem... | stack-components | https://docs.zenml.io/stack-components/container-registries/custom | 387 |
s within a unique directory in the artifact store:Materializers are designed to be extensible and customizable, allowing you to define your own serialization and deserialization logic for specific data types or storage systems. By default, ZenML provides built-in materializers for common data types and uses cloudpickle... | how-to | https://docs.zenml.io/how-to/handle-data-artifacts/artifact-versioning | 318 |
Troubleshoot the deployed server
Troubleshooting tips for your ZenML deployment
In this document, we will go over some common issues that you might face when deploying ZenML and how to solve them.
Viewing logs
Analyzing logs is a great way to debug issues. Depending on whether you have a Kubernetes (using Helm or z... | getting-started | https://docs.zenml.io/v/docs/getting-started/deploying-zenml/manage-the-deployed-services/troubleshoot-your-deployed-server | 432 |
""
train_dataloader, test_dataloader = importer()model = trainer(train_dataloader)
accuracy = evaluator(test_dataloader=test_dataloader, model=model)
bento = bento_builder(model=model)
@pipeline
def local_deploy_pipeline(
bento_loader,
deployer,
):
"""Link all the steps and artifacts together"""
bento = bento... | stack-components | https://docs.zenml.io/v/docs/stack-components/model-deployers/bentoml | 436 |
ser. For more information, see this documentation.For more information on user federation tokens, session policies, and the GetFederationToken AWS API, see the official AWS documentation on the subject.
For more information about the difference between this method and the AWS IAM Role authentication method, consult th... | how-to | https://docs.zenml.io/how-to/auth-management/aws-service-connector | 465 |
ckets).
s3:ListBucket
s3:GetObject
s3:PutObjects3: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}
──────... | how-to | https://docs.zenml.io/how-to/auth-management | 474 |
Improve retrieval by finetuning embeddings
Finetune embeddings to improve retrieval performance.
🚧 This guide is a work in progress. Please check back soon for updates.
Coming soon!
PreviousEvaluating reranking performance
NextFinetuning LLMs with ZenML
Last updated 2 months ago | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/finetuning-embeddings | 64 |
with same pipeline name, step name and model nameexisting_services = model_deployer.find_model_server(
pipeline_name=pipeline_name,
pipeline_step_name=pipeline_step_name,
model_name=model_name,
if not existing_services:
raise RuntimeError(
f"No MLflow prediction service deployed by step "
f"'{pipeline_step_name... | stack-components | https://docs.zenml.io/v/docs/stack-components/model-deployers/mlflow | 336 |
ds until the pipeline shows up in the Airflow UI).The ability to provision resources using the zenml stack up command is deprecated and will be removed in a future release. While it is still available for the Airflow orchestrator, we recommend following the steps to set up a local Airflow server manually.
Install the ... | stack-components | https://docs.zenml.io/stack-components/orchestrators/airflow | 428 |
│ s3://zenml-demos ┃┃ │ s3://zenml-generative-chat ┃
┃ │ s3://zenml-public-datasets ┃
┠───────────────────────┼──────────────────────────────────────────────┨
┃ 🌀 kubernetes-cluster │ zenhacks-cluster ... | how-to | https://docs.zenml.io/v/docs/how-to/auth-management/aws-service-connector | 514 |
ow the recommendations from the Project templates.In steps/alerts/notify_on.py, you will find a step to notify the user about success and a function used to notify the user about step failure using the Alerter from the active stack.
We use @step for success notification to only notify the user about a fully successful... | how-to | https://docs.zenml.io/how-to/build-pipelines/use-failure-success-hooks | 422 |
ggingFaceModelDeployer.get_active_model_deployer()# fetch existing services with same pipeline name, step name and model name
existing_services = model_deployer.find_model_server(
pipeline_name=pipeline_name,
pipeline_step_name=pipeline_step_name,
model_name=model_name,
running=running,
if not existing_services:
... | stack-components | https://docs.zenml.io/v/docs/stack-components/model-deployers/huggingface | 282 |
b.com/your-username/your-template.git your-projectReplace https://github.com/your-username/your-template.git with the URL of your template repository, and your-project with the name of the new project you want to create.
Use your template with ZenML. Once your template is ready, you can use it with the zenml init comm... | how-to | https://docs.zenml.io/v/docs/how-to/setting-up-a-project-repository/using-project-templates | 343 |
igure the local Generic Azure resource client/SDK.Stack Components use
The Azure Artifact Store Stack Component can be connected to a remote Azure blob storage container through an Azure Service Connector.
The Azure Service Connector can also be used with any Orchestrator or Model Deployer stack component flavor that... | how-to | https://docs.zenml.io/how-to/auth-management/azure-service-connector | 348 |
to your stack:
zenml integration install wandb -yThe Weights & Biases Experiment Tracker needs to be configured with the credentials required to connect to the Weights & Biases platform using one of the available authentication methods.
Authentication Methods
You need to configure the following credentials for authe... | stack-components | https://docs.zenml.io/v/docs/stack-components/experiment-trackers/wandb | 420 |
0.58.2Bleeding Edge0.58.2Legacy Docs
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Getting Started⭐Introduction🧙Installation🪄Core concepts🤔Deploying ZenMLDeploy with ZenML CLIDeploy with DockerDep... | stacks-and-components | https://docs.zenml.io/stacks-and-components/component-guide/annotators/label-studio | 319 |
ne:
while gc.collect():
torch.cuda.empty_cache()You can then call this function at the beginning of your GPU-enabled steps:
from zenml import step
@step
def training_step(...):
cleanup_memory()
# train a model
Note that resetting the memory cache will potentially affect others using the same GPU, so use this ju... | how-to | https://docs.zenml.io/how-to/training-with-gpus | 281 |
e following:
Question: What are Plasma Phoenixes?Answer: Plasma Phoenixes are majestic creatures made of pure energy that soar above the chromatic canyons of Zenml World. They leave fiery trails behind them, painting the sky with dazzling displays of colors.
Question: What kinds of creatures live on the prismatic sho... | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/rag-with-zenml/rag-85-loc | 392 |
Develop a custom data validator
How to develop a custom data validator
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/data-validators/custom | 361 |
Use pipeline/step parameters
Steps and pipelines can be parameterized just like any other python function that you are familiar with.
Parameters for your steps
When calling a step in a pipeline, the inputs provided to the step function can either be an artifact or a parameter. An artifact represents the output of an... | how-to | https://docs.zenml.io/v/docs/how-to/build-pipelines/use-pipeline-step-parameters | 451 |
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/v/docs/how-to/auth-management/gcp-service-connector | 427 |
ant feedback of actual contact with the raw data.)Samples generated for inference: Your model will be making predictions on real-world data being passed in. If you store and label this data, you’ll gain a valuable set of data that you can use to compare your labels with what the model was predicting, another possible w... | stack-components | https://docs.zenml.io/v/docs/stack-components/annotators | 348 |
eral means of accessing any AWS service by issuingpre-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... | how-to | https://docs.zenml.io/how-to/auth-management/service-connectors-guide | 450 |
llowing credentials for authentication to Neptune:api_token: API key token of your Neptune account. You can create a free Neptune account here. If left blank, Neptune will attempt to retrieve the token from your environment variables.
project: The name of the project where you're sending the new run, in the form "work... | stack-components | https://docs.zenml.io/stack-components/experiment-trackers/neptune | 356 |
ps import step
@step
def my_step() -> None:
...@pipeline
def my_pipeline(my_step):
my_step()
# Old: Create an instance of the pipeline and then call `pipeline_instance.configure(...)`
pipeline_instance = my_pipeline(my_step=my_step())
pipeline_instance.configure(enable_cache=False)
from zenml import pipeline, ... | reference | https://docs.zenml.io/reference/migration-guide/migration-zero-forty | 453 |
_settings})
def my_pipeline() -> None:
my_step()# Or configure the pipelines options
my_pipeline = my_pipeline.with_options(
settings={"docker": docker_settings}
Configuring them on a step gives you more fine-grained control and enables you to build separate specialized Docker images for different steps of your pi... | how-to | https://docs.zenml.io/how-to/customize-docker-builds/docker-settings-on-a-pipeline | 416 |
Reranking for better retrieval
Add reranking to your RAG inference for better retrieval performance.
Rerankers are a crucial component of retrieval systems that use LLMs. They help improve the quality of the retrieved documents by reordering them based on additional features or scores. In this section, we'll explore ... | user-guide | https://docs.zenml.io/v/docs/user-guide/llmops-guide/reranking | 224 |
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 feat... | how-to | https://docs.zenml.io/how-to/auth-management/service-connectors-guide | 477 |
Promote a Model
Stages and Promotion
Model stages are a way to model the progress that different versions takes through various stages in its lifecycle. A ZenML Model version can be promoted to a different stage through the Dashboard, the ZenML CLI or code.
This is a way to signify the progression of your model vers... | how-to | https://docs.zenml.io/how-to/use-the-model-control-plane/promote-a-model | 423 |
or.local_docker": LocalDockerOrchestratorSettings(run_args={"cpu_count": 3}
@pipeline(settings=settings)
def simple_pipeline():
return_one()
Enabling CUDA for GPU-backed hardware
Note that if you wish to use this orchestrator to run steps on a GPU, you will need to follow the instructions on this page to ensure th... | stack-components | https://docs.zenml.io/stack-components/orchestrators/local-docker | 118 |
End of preview. Expand in Data Studio
Dataset Card for rag_qa_embedding_questions_0_60_0
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/zenml/rag_qa_embedding_questions_0_60_0/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config "https://huggingface.co/datasets/zenml/rag_qa_embedding_questions_0_60_0/raw/main/pipeline.yaml"
Dataset structure
The examples have the following structure per configuration:
Configuration: default
{
"anchor": "\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u251b\n\nExplore Service Connector TypesService 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.\n\nZenML 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.\n\nTo discover the Connector Types available with your ZenML deployment, you can use the zenml service-connector list-types CLI command:\n\nzenml service-connector list-types\n\nExample Command Output\n\n\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u252f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u252f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u252f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u252f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u252f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n\n\u2503 NAME \u2502 TYPE \u2502 RESOURCE TYPES \u2502 AUTH METHODS \u2502 LOCAL \u2502 REMOTE \u2503\n\n\u2520\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2528\n\n\u2503 Kubernetes Service Connector \u2502 \ud83c\udf00 kubernetes \u2502 \ud83c\udf00 kubernetes-cluster \u2502 password \u2502 \u2705 \u2502 \u2705 \u2503\n\n\u2503 \u2502 \u2502 \u2502 token \u2502 \u2502 \u2503\n\n\u2520\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2528\n\n\u2503 Docker Service Connector \u2502 \ud83d\udc33 docker \u2502 \ud83d\udc33 docker-registry \u2502 password \u2502 \u2705 \u2502 \u2705 \u2503\n\n\u2520\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2528\n\n\u2503 Azure Service Connector \u2502 \ud83c\udde6 azure \u2502 \ud83c\udde6 azure-generic \u2502 implicit \u2502 \u2705 \u2502 \u2705 \u2503\n\n\u2503 \u2502 \u2502 \ud83d\udce6 blob-container \u2502 service-principal \u2502 \u2502 \u2503",
"distilabel_metadata": {
"raw_output_generate_sentence_pair_0": "## Positive\n\nWhat command can be used to list available Service Connector Types in ZenML?\n\n## Negative\n\nCan you provide a list of popular Azure services?"
},
"model_name": "gpt-4o",
"negative": "Can you provide a list of popular Azure services?",
"parent_section": "how-to",
"positive": "What command can be used to list available Service Connector Types in ZenML?",
"token_count": 499,
"url": "https://docs.zenml.io/v/docs/how-to/auth-management/service-connectors-guide"
}
This subset can be loaded as:
from datasets import load_dataset
ds = load_dataset("zenml/rag_qa_embedding_questions_0_60_0", "default")
Or simply as it follows, since there's only one configuration and is named default:
from datasets import load_dataset
ds = load_dataset("zenml/rag_qa_embedding_questions_0_60_0")
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