README.md

---
base_model: Snowflake/snowflake-arctic-embed-m
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1490
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How does ZenML facilitate connecting your deployment to various
    cloud providers and infrastructure services?
  sentences:
  - '🔌Connect services (AWS, GCP, Azure, K8s etc)


    Connect your ZenML deployment to a cloud provider and other infrastructure services
    and resources.


    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.'
  - '                                                 ┃┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨


    ┃ ID               │ e316bcb3-6659-467b-81e5-5ec25bfd36b0                                    ┃


    ┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨


    ┃ NAME             │ aws-sts-token                                                           ┃


    ┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨


    ┃ TYPE             │ 🔶 aws                                                                  ┃


    ┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨


    ┃ AUTH METHOD      │ sts-token                                                               ┃


    ┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨


    ┃ RESOURCE TYPES   │ 🔶 aws-generic, 📦 s3-bucket, 🌀 kubernetes-cluster, 🐳 docker-registry
    ┃


    ┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨


    ┃ RESOURCE NAME    │ <multiple>                                                              ┃


    ┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨


    ┃ SECRET ID        │ 971318c9-8db9-4297-967d-80cda070a121                                    ┃


    ┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨


    ┃ SESSION DURATION │ N/A                                                                     ┃


    ┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨


    ┃ EXPIRES IN       │ 11h58m17s                                                               ┃


    ┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨


    ┃ OWNER            │ default                                                                 ┃'
  - 'io      ┃


    ┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛If you already have one or more Docker
    Service Connectors configured in your ZenML deployment, you can check which of
    them can be used to access the container registry you want to use for your Default
    Container Registry by running e.g.:


    zenml service-connector list-resources --connector-type docker --resource-id <REGISTRY_URI>


    Example Command Output


    $ zenml service-connector list-resources --connector-type docker --resource-id
    docker.io


    The  resource with name ''docker.io'' can be accessed by ''docker'' service connectors
    configured in your workspace:


    ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓


    ┃             CONNECTOR ID             │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE
    TYPE      │ RESOURCE NAMES ┃


    ┠──────────────────────────────────────┼────────────────┼────────────────┼────────────────────┼────────────────┨


    ┃ cf55339f-dbc8-4ee6-862e-c25aff411292 │ dockerhub      │ 🐳 docker      │ 🐳 docker-registry
    │ docker.io      ┃


    ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛


    After having set up or decided on a Docker Service Connector to use to connect
    to the target container registry, you can register the Docker Container Registry
    as follows:


    # Register the container registry and reference the target registry URI


    zenml container-registry register <CONTAINER_REGISTRY_NAME> -f default \


    --uri=<REGISTRY_URL>


    # Connect the container registry to the target registry via a Docker Service Connector


    zenml container-registry connect <CONTAINER_REGISTRY_NAME> -i


    A non-interactive version that connects the Default Container Registry to a target
    registry through a Docker Service Connector:


    zenml container-registry connect <CONTAINER_REGISTRY_NAME> --connector <CONNECTOR_ID>


    Example Command Output


    $ zenml container-registry connect dockerhub --connector dockerhub'
- source_sentence: How can I configure the orchestrator settings for each cloud provider
    in ZenML?
  sentences:
  - 'kip scoping its Resource Type during registration.a multi-instance Service Connector
    instance can be configured once and used to gain access to multiple resources
    of the same type, each identifiable by a Resource Name. Not all types of connectors
    and not all types of resources support multiple instances. Some Service Connectors
    Types like the generic Kubernetes and Docker connector types only allow single-instance
    configurations: a Service Connector instance can only be used to access a single
    Kubernetes cluster and a single Docker registry. To configure a multi-instance
    Service Connector, you can simply skip scoping its Resource Name during registration.


    The following is an example of configuring a multi-type AWS Service Connector
    instance capable of accessing multiple AWS resources of different types:


    zenml service-connector register aws-multi-type --type aws --auto-configure


    Example Command Output


    ⠋ Registering service connector ''aws-multi-type''...


    Successfully registered service connector `aws-multi-type` with access to the
    following resources:


    ┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓


    ┃     RESOURCE TYPE     │ RESOURCE NAMES                               ┃


    ┠───────────────────────┼──────────────────────────────────────────────┨


    ┃    🔶 aws-generic     │ us-east-1                                    ┃


    ┠───────────────────────┼──────────────────────────────────────────────┨


    ┃     📦 s3-bucket      │ s3://aws-ia-mwaa-715803424590                ┃


    ┃                       │ s3://zenfiles                                ┃


    ┃                       │ s3://zenml-demos                             ┃


    ┃                       │ s3://zenml-generative-chat                   ┃


    ┃                       │ s3://zenml-public-datasets                   ┃


    ┃                       │ s3://zenml-public-swagger-spec               ┃


    ┠───────────────────────┼──────────────────────────────────────────────┨


    ┃ 🌀 kubernetes-cluster │ zenhacks-cluster                             ┃'
  - 'ister <STACK_NAME> -a <AZURE_STORE_NAME> ... --setWhen you register the Azure
    Artifact Store, you can create a ZenML Secret to store a variety of Azure credentials
    and then reference it in the Artifact Store configuration:


    to use an Azure storage account key , set account_name to your account name and
    one of account_key or sas_token to the Azure key or SAS token value as attributes
    in the ZenML secret


    to use an Azure storage account key connection string , configure the connection_string
    attribute in the ZenML secret to your Azure Storage Key connection string


    to use Azure Service Principal credentials , create an Azure Service Principal
    and then set account_name to your account name and client_id, client_secret and
    tenant_id to the client ID, secret and tenant ID of your service principal in
    the ZenML secret


    This method has some advantages over the implicit authentication method:


    you don''t need to install and configure the Azure CLI on your host


    you 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
    Azure Managed Identities


    you can combine the Azure artifact store with other stack components that are
    not running in Azure


    Configuring Azure credentials in a ZenML secret and then referencing them in the
    Artifact Store configuration could look like this:


    # Store the Azure storage account key in a ZenML secret


    zenml secret create az_secret \


    --account_name=''<YOUR_AZURE_ACCOUNT_NAME>'' \


    --account_key=''<YOUR_AZURE_ACCOUNT_KEY>''


    # or if you want to use a connection string


    zenml secret create az_secret \


    --connection_string=''<YOUR_AZURE_CONNECTION_STRING>''


    # or if you want to use Azure ServicePrincipal credentials


    zenml secret create az_secret \


    --account_name=''<YOUR_AZURE_ACCOUNT_NAME>'' \


    --tenant_id=''<YOUR_AZURE_TENANT_ID>'' \


    --client_id=''<YOUR_AZURE_CLIENT_ID>'' \


    --client_secret=''<YOUR_AZURE_CLIENT_SECRET>'''
  - '. If not set, the cluster will not be autostopped.down: Tear down the cluster
    after all jobs finish (successfully or abnormally). If idle_minutes_to_autostop
    is also set, the cluster will be torn down after the specified idle time. Note
    that if errors occur during provisioning/data syncing/setting up, the cluster
    will not be torn down for debugging purposes.


    stream_logs: If True, show the logs in the terminal as they are generated while
    the cluster is running.


    docker_run_args: Additional arguments to pass to the docker run command. For example,
    [''--gpus=all''] to use all GPUs available on the VM.


    The following code snippets show how to configure the orchestrator settings for
    each cloud provider:


    Code Example:


    from zenml.integrations.skypilot_aws.flavors.skypilot_orchestrator_aws_vm_flavor
    import SkypilotAWSOrchestratorSettings


    skypilot_settings = SkypilotAWSOrchestratorSettings(


    cpus="2",


    memory="16",


    accelerators="V100:2",


    accelerator_args={"tpu_vm": True, "runtime_version": "tpu-vm-base"},


    use_spot=True,


    spot_recovery="recovery_strategy",


    region="us-west-1",


    zone="us-west1-a",


    image_id="ami-1234567890abcdef0",


    disk_size=100,


    disk_tier="high",


    cluster_name="my_cluster",


    retry_until_up=True,


    idle_minutes_to_autostop=60,


    down=True,


    stream_logs=True


    docker_run_args=["--gpus=all"]


    @pipeline(


    settings={


    "orchestrator.vm_aws": skypilot_settings


    Code Example:


    from zenml.integrations.skypilot_gcp.flavors.skypilot_orchestrator_gcp_vm_flavor
    import SkypilotGCPOrchestratorSettings


    skypilot_settings = SkypilotGCPOrchestratorSettings(


    cpus="2",


    memory="16",


    accelerators="V100:2",


    accelerator_args={"tpu_vm": True, "runtime_version": "tpu-vm-base"},


    use_spot=True,


    spot_recovery="recovery_strategy",


    region="us-west1",


    zone="us-west1-a",


    image_id="ubuntu-pro-2004-focal-v20231101",


    disk_size=100,


    disk_tier="high",


    cluster_name="my_cluster",


    retry_until_up=True,


    idle_minutes_to_autostop=60,


    down=True,


    stream_logs=True


    @pipeline(


    settings={


    "orchestrator.vm_gcp": skypilot_settings'
- source_sentence: What command do you use to create the resources after setting up
    the roleRef for a Kubernetes cluster?
  sentences:
  - 'pace: spark-namespace


    roleRef:


    kind: ClusterRolename: edit


    apiGroup: rbac.authorization.k8s.io


    ---


    And then execute the following command to create the resources:


    aws eks --region=$REGION update-kubeconfig --name=$EKS_CLUSTER_NAME


    kubectl create -f rbac.yaml


    Lastly, note down the namespace and the name of the service account since you
    will need them when registering the stack component in the next step.


    How to use it


    To use the KubernetesSparkStepOperator, you need:


    the ZenML spark integration. If you haven''t installed it already, runCopyzenml
    integration install spark


    Docker installed and running.


    A remote artifact store as part of your stack.


    A remote container registry as part of your stack.


    A Kubernetes cluster deployed.


    We can then register the step operator and use it in our active stack:


    zenml step-operator register spark_step_operator \


    --flavor=spark-kubernetes \


    --master=k8s://$EKS_API_SERVER_ENDPOINT \


    --namespace=<SPARK_KUBERNETES_NAMESPACE> \


    --service_account=<SPARK_KUBERNETES_SERVICE_ACCOUNT>


    # Register the stack


    zenml stack register spark_stack \


    o default \


    s spark_step_operator \


    a spark_artifact_store \


    c spark_container_registry \


    i local_builder \


    --set


    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=<STEP_OPERATOR_NAME>)


    def step_on_spark(...) -> ...:


    """Some step that should run with Spark on Kubernetes."""


    ...


    After successfully running any step with a KubernetesSparkStepOperator, you should
    be able to see that a Spark driver pod was created in your cluster for each pipeline
    step when running kubectl get pods -n $KUBERNETES_NAMESPACE.


    Instead of hardcoding a step operator name, you can also use the Client to dynamically
    use the step operator of your active stack:


    from zenml.client import Client


    step_operator = Client().active_stack.step_operator


    @step(step_operator=step_operator.name)'
  - 'et_historical_features(entity_dict, features)


    ...Note that ZenML''s use of Pydantic to serialize and deserialize inputs stored
    in the ZenML metadata means that we are limited to basic data types. Pydantic
    cannot handle Pandas DataFrames, for example, or datetime values, so in the above
    code you can see that we have to convert them at various points.


    For more information and a full list of configurable attributes of the Feast feature
    store, check out the SDK Docs .


    PreviousFeature Stores


    NextDevelop a Custom Feature Store


    Last updated 8 days ago'
  - 'to get a quick global overview of our performance.# passing the results from
    all our previous evaluation steps


    @step(enable_cache=False)


    def visualize_evaluation_results(


    small_retrieval_eval_failure_rate: float,


    small_retrieval_eval_failure_rate_reranking: float,


    full_retrieval_eval_failure_rate: float,


    full_retrieval_eval_failure_rate_reranking: float,


    failure_rate_bad_answers: float,


    failure_rate_bad_immediate_responses: float,


    failure_rate_good_responses: float,


    average_toxicity_score: float,


    average_faithfulness_score: float,


    average_helpfulness_score: float,


    average_relevance_score: float,


    ) -> Optional[Image.Image]:


    """Visualizes the evaluation results."""


    step_context = get_step_context()


    pipeline_run_name = step_context.pipeline_run.name


    normalized_scores = [


    score / 20


    for score in [


    small_retrieval_eval_failure_rate,


    small_retrieval_eval_failure_rate_reranking,


    full_retrieval_eval_failure_rate,


    full_retrieval_eval_failure_rate_reranking,


    failure_rate_bad_answers,


    scores = normalized_scores + [


    failure_rate_bad_immediate_responses,


    failure_rate_good_responses,


    average_toxicity_score,


    average_faithfulness_score,


    average_helpfulness_score,


    average_relevance_score,


    labels = [


    "Small Retrieval Eval Failure Rate",


    "Small Retrieval Eval Failure Rate Reranking",


    "Full Retrieval Eval Failure Rate",


    "Full Retrieval Eval Failure Rate Reranking",


    "Failure Rate Bad Answers",


    "Failure Rate Bad Immediate Responses",


    "Failure Rate Good Responses",


    "Average Toxicity Score",


    "Average Faithfulness Score",


    "Average Helpfulness Score",


    "Average Relevance Score",


    # Create a new figure and axis


    fig, ax = plt.subplots(figsize=(10, 6))


    # Plot the horizontal bar chart


    y_pos = np.arange(len(labels))


    ax.barh(y_pos, scores, align="center")


    ax.set_yticks(y_pos)


    ax.set_yticklabels(labels)


    ax.invert_yaxis()  # Labels read top-to-bottom


    ax.set_xlabel("Score")


    ax.set_xlim(0, 5)


    ax.set_title(f"Evaluation Metrics for {pipeline_run_name}")


    # Adjust the layout


    plt.tight_layout()'
- source_sentence: What is the command to register and connect a Vertex AI Orchestrator
    Stack Component to the target GCP project using ZenML?
  sentences:
  - '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:


    raise RuntimeError(


    f"No Hugging Face inference endpoint deployed by step "


    f"''{pipeline_step_name}'' in pipeline ''{pipeline_name}'' with name "


    f"''{model_name}'' is currently running."


    return existing_services[0]


    # Use the service for inference


    @step


    def predictor(


    service: HuggingFaceDeploymentService,


    data: str


    ) -> Annotated[str, "predictions"]:


    """Run a inference request against a prediction service"""


    prediction = service.predict(data)


    return prediction


    @pipeline


    def huggingface_deployment_inference_pipeline(


    pipeline_name: str, pipeline_step_name: str = "huggingface_model_deployer_step",


    ):


    inference_data = ...


    model_deployment_service = prediction_service_loader(


    pipeline_name=pipeline_name,


    pipeline_step_name=pipeline_step_name,


    predictions = predictor(model_deployment_service, inference_data)


    For more information and a full list of configurable attributes of the Hugging
    Face Model Deployer, check out the SDK Docs.


    PreviousBentoML


    NextDevelop a Custom Model Deployer


    Last updated 15 days ago'
  - 'Set up CI/CD


    Managing the lifecycle of a ZenML pipeline with Continuous Integration and Delivery


    Until now, we have been executing ZenML pipelines locally. While this is a good
    mode of operating pipelines, in production it is often desirable to mediate runs
    through a central workflow engine baked into your CI.


    This allows data scientists to experiment with data processing and model training
    locally and then have code changes automatically tested and validated through
    the standard pull request/merge request peer review process. Changes that pass
    the CI and code-review are then deployed automatically to production. Here is
    how this could look like:


    Breaking it down


    To illustrate this, let''s walk through how this process might be set up on a
    GitHub Repository.


    A data scientist wants to make improvements to the ML pipeline. They clone the
    repository, create a new branch, and experiment with new models or data processing
    steps on their local machine.


    Once the data scientist thinks they have improved the pipeline, they create a
    pull request for their branch on GitHub. This automatically triggers a GitHub
    Action that will run the same pipeline in the staging environment (e.g. a pipeline
    running on a cloud stack in GCP), potentially with different test data. As long
    as the pipeline does not run successfully in the staging environment, the PR cannot
    be merged. The pipeline also generates a set of metrics and test results that
    are automatically published to the PR, where they can be peer-reviewed to decide
    if the changes should be merged.


    Once the PR has been reviewed and passes all checks, the branch is merged into
    main. This automatically triggers another GitHub Action that now runs a pipeline
    in the production environment, which trains the same model on production data,
    runs some checks to compare its performance with the model currently served in
    production and then, if all checks pass, automatically deploys the new model.'
  - '━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛


    ```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 must grant this account the Vertex AI Service
    Agent role, otherwise the pipelines will fail. More information on other configurations
    possible for the Vertex AI Orchestrator can be found here.Copyzenml orchestrator
    register vertex-ai-zenml-core --flavor=vertex --location=europe-west1 --synchronous=true


    Example Command Output


    ```text


    Running with active workspace: ''default'' (repository)


    Running with active stack: ''default'' (repository)


    Successfully registered orchestrator `vertex-ai-zenml-core`.


    ```


    ```sh


    zenml orchestrator connect vertex-ai-zenml-core --connector vertex-ai-zenml-core


    ```


    Example Command Output


    ```text


    Running with active workspace: ''default'' (repository)


    Running with active stack: ''default'' (repository)


    Successfully connected orchestrator `vertex-ai-zenml-core` to the following resources:


    ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓


    ┃             CONNECTOR ID             │ CONNECTOR NAME       │ CONNECTOR TYPE
    │ RESOURCE TYPE  │ RESOURCE NAMES ┃


    ┠──────────────────────────────────────┼──────────────────────┼────────────────┼────────────────┼────────────────┨


    ┃ f97671b9-8c73-412b-bf5e-4b7c48596f5f │ vertex-ai-zenml-core │ 🔵 gcp         │
    🔵 gcp-generic │ zenml-core     ┃


    ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛


    ```


    Register and connect a GCP Container Registry Stack Component to a GCR container
    registry:Copyzenml container-registry register gcr-zenml-core --flavor gcp --uri=gcr.io/zenml-core


    Example Command Output


    ```text


    Running with active workspace: ''default'' (repository)'
- source_sentence: How can I develop a custom step operator in ZenML?
  sentences:
  - 'Develop a Custom Step Operator


    Learning how to develop a custom step operator.


    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 concepts.


    Base Abstraction


    The BaseStepOperator is the abstract base class that needs to be subclassed in
    order to run specific steps of your pipeline in a separate environment. As step
    operators can come in many shapes and forms, the base class exposes a deliberately
    basic and generic interface:


    from abc import ABC, abstractmethod


    from typing import List, Type


    from zenml.enums import StackComponentType


    from zenml.stack import StackComponent, StackComponentConfig, Flavor


    from zenml.config.step_run_info import StepRunInfo


    class BaseStepOperatorConfig(StackComponentConfig):


    """Base config for step operators."""


    class BaseStepOperator(StackComponent, ABC):


    """Base class for all ZenML step operators."""


    @abstractmethod


    def launch(


    self,


    info: StepRunInfo,


    entrypoint_command: List[str],


    ) -> None:


    """Abstract method to execute a step.


    Subclasses must implement this method and launch a **synchronous**


    job that executes the `entrypoint_command`.


    Args:


    info: Information about the step run.


    entrypoint_command: Command that executes the step.


    """


    class BaseStepOperatorFlavor(Flavor):


    """Base class for all ZenML step operator flavors."""


    @property


    @abstractmethod


    def name(self) -> str:


    """Returns the name of the flavor."""


    @property


    def type(self) -> StackComponentType:


    """Returns the flavor type."""


    return StackComponentType.STEP_OPERATOR


    @property


    def config_class(self) -> Type[BaseStepOperatorConfig]:


    """Returns the config class for this flavor."""


    return BaseStepOperatorConfig


    @property


    @abstractmethod


    def implementation_class(self) -> Type[BaseStepOperator]:'
  - '-grade deployments.


    Installing the mlstacks extraTo install mlstacks, either run pip install mlstacks
    or pip install "zenml[mlstacks]" to install it along with ZenML.


    MLStacks uses Terraform on the backend to manage infrastructure. You will need
    to have Terraform installed. Please visit the Terraform docs for installation
    instructions.


    MLStacks also uses Helm to deploy Kubernetes resources. You will need to have
    Helm installed. Please visit the Helm docs for installation instructions.


    Deploying a stack component


    The ZenML CLI allows you to deploy individual stack components using the deploy
    subcommand which is implemented for all supported stack components. You can find
    the list of supported stack components here.


    Deploying a stack


    For deploying a full stack, use the zenml stack deploy command. See the stack
    deployment page for more details of which cloud providers and stack components
    are supported.


    How does mlstacks work?


    MLStacks is built around the concept of a stack specification. A stack specification
    is a YAML file that describes the stack and includes references to component specification
    files. A component specification is a YAML file that describes a component. (Currently
    all deployments of components (in various combinations) must be defined within
    the context of a stack.)


    ZenML handles the creation of stack specifications for you when you run one of
    the deploy subcommands using the CLI. A valid specification is generated and used
    by mlstacks to deploy your stack using Terraform. The Terraform definitions and
    state are stored in your global configuration directory along with any state files
    generated while deploying your stack.


    Your configuration directory could be in a number of different places depending
    on your operating system, but read more about it in the Click docs to see which
    location applies to your situation.


    Deploy stack components individuallyIndividually deploying different stack components.'
  - '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 credentials and fine-grained access
    control and reusing the same credentials across multiple stack components.


    Depending on where your target Kubernetes cluster is running, you can use one
    of the following Service Connectors:


    the AWS Service Connector, if you are using an AWS EKS cluster.


    the GCP Service Connector, if you are using a GKE cluster.


    the Azure Service Connector, if you are using an AKS cluster.


    the generic Kubernetes Service Connector for any other Kubernetes cluster.


    If you don''t already have a Service Connector configured in your ZenML deployment,
    you can register one using the interactive CLI command. You have the option to
    configure a Service Connector that can be used to access more than one Kubernetes
    cluster or even more than one type of cloud resource:


    zenml service-connector register -i


    A non-interactive CLI example that leverages the AWS CLI configuration on your
    local machine to auto-configure an AWS Service Connector targeting a single EKS
    cluster is:


    zenml service-connector register <CONNECTOR_NAME> --type aws --resource-type kubernetes-cluster
    --resource-name <EKS_CLUSTER_NAME> --auto-configure


    Example Command Output


    $ zenml service-connector register eks-zenhacks --type aws --resource-type kubernetes-cluster
    --resource-id zenhacks-cluster --auto-configure


    ⠼ Registering service connector ''eks-zenhacks''...


    Successfully registered service connector `eks-zenhacks` with access to the following
    resources:


    ┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┓


    ┃     RESOURCE TYPE     │ RESOURCE NAMES   ┃


    ┠───────────────────────┼──────────────────┨


    ┃ 🌀 kubernetes-cluster │ zenhacks-cluster ┃


    ┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┛'
model-index:
- name: zenml/finetuned-snowflake-arctic-embed-m
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 384
      type: dim_384
    metrics:
    - type: cosine_accuracy@1
      value: 0.29518072289156627
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6385542168674698
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7228915662650602
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7891566265060241
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.29518072289156627
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.21285140562248994
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14457831325301201
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0789156626506024
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.29518072289156627
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6385542168674698
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7228915662650602
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7891566265060241
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5552191347520903
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.47847819850831885
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.48706201897841145
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.3253012048192771
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6144578313253012
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6987951807228916
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7891566265060241
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3253012048192771
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2048192771084337
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1397590361445783
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0789156626506024
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3253012048192771
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6144578313253012
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6987951807228916
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7891566265060241
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5597682297824715
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4859987569324918
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4930658557873217
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.2710843373493976
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5662650602409639
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6385542168674698
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7891566265060241
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2710843373493976
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18875502008032125
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.12771084337349395
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0789156626506024
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2710843373493976
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5662650602409639
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6385542168674698
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7891566265060241
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5242689178594545
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4403614457831327
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4468744710389297
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.25301204819277107
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4759036144578313
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5783132530120482
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6626506024096386
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.25301204819277107
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15863453815261042
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11566265060240961
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06626506024096386
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.25301204819277107
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4759036144578313
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5783132530120482
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6626506024096386
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.45397796379806826
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.38746175176898084
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.39859357699776915
      name: Cosine Map@100
---

# zenml/finetuned-snowflake-arctic-embed-m

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision 71bc94c8f9ea1e54fba11167004205a65e5da2cc -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("zenml/finetuned-snowflake-arctic-embed-m")
# Run inference
sentences = [
    'How can I develop a custom step operator in ZenML?',
    'Develop a Custom Step Operator\n\nLearning how to develop a custom step operator.\n\nBefore 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 concepts.\n\nBase Abstraction\n\nThe BaseStepOperator is the abstract base class that needs to be subclassed in order to run specific steps of your pipeline in a separate environment. As step operators can come in many shapes and forms, the base class exposes a deliberately basic and generic interface:\n\nfrom abc import ABC, abstractmethod\n\nfrom typing import List, Type\n\nfrom zenml.enums import StackComponentType\n\nfrom zenml.stack import StackComponent, StackComponentConfig, Flavor\n\nfrom zenml.config.step_run_info import StepRunInfo\n\nclass BaseStepOperatorConfig(StackComponentConfig):\n\n"""Base config for step operators."""\n\nclass BaseStepOperator(StackComponent, ABC):\n\n"""Base class for all ZenML step operators."""\n\n@abstractmethod\n\ndef launch(\n\nself,\n\ninfo: StepRunInfo,\n\nentrypoint_command: List[str],\n\n) -> None:\n\n"""Abstract method to execute a step.\n\nSubclasses must implement this method and launch a **synchronous**\n\njob that executes the `entrypoint_command`.\n\nArgs:\n\ninfo: Information about the step run.\n\nentrypoint_command: Command that executes the step.\n\n"""\n\nclass BaseStepOperatorFlavor(Flavor):\n\n"""Base class for all ZenML step operator flavors."""\n\n@property\n\n@abstractmethod\n\ndef name(self) -> str:\n\n"""Returns the name of the flavor."""\n\n@property\n\ndef type(self) -> StackComponentType:\n\n"""Returns the flavor type."""\n\nreturn StackComponentType.STEP_OPERATOR\n\n@property\n\ndef config_class(self) -> Type[BaseStepOperatorConfig]:\n\n"""Returns the config class for this flavor."""\n\nreturn BaseStepOperatorConfig\n\n@property\n\n@abstractmethod\n\ndef implementation_class(self) -> Type[BaseStepOperator]:',
    'rray": [[1,2,3,4]] } }\'\n\nUsing 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 credentials and fine-grained access control and reusing the same credentials across multiple stack components.\n\nDepending on where your target Kubernetes cluster is running, you can use one of the following Service Connectors:\n\nthe AWS Service Connector, if you are using an AWS EKS cluster.\n\nthe GCP Service Connector, if you are using a GKE cluster.\n\nthe Azure Service Connector, if you are using an AKS cluster.\n\nthe generic Kubernetes Service Connector for any other Kubernetes cluster.\n\nIf you don\'t already have a Service Connector configured in your ZenML deployment, you can register one using the interactive CLI command. You have the option to configure a Service Connector that can be used to access more than one Kubernetes cluster or even more than one type of cloud resource:\n\nzenml service-connector register -i\n\nA non-interactive CLI example that leverages the AWS CLI configuration on your local machine to auto-configure an AWS Service Connector targeting a single EKS cluster is:\n\nzenml service-connector register <CONNECTOR_NAME> --type aws --resource-type kubernetes-cluster --resource-name <EKS_CLUSTER_NAME> --auto-configure\n\nExample Command Output\n\n$ zenml service-connector register eks-zenhacks --type aws --resource-type kubernetes-cluster --resource-id zenhacks-cluster --auto-configure\n\n⠼ Registering service connector \'eks-zenhacks\'...\n\nSuccessfully registered service connector `eks-zenhacks` with access to the following resources:\n\n┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┓\n\n┃     RESOURCE TYPE     │ RESOURCE NAMES   ┃\n\n┠───────────────────────┼──────────────────┨\n\n┃ 🌀 kubernetes-cluster │ zenhacks-cluster ┃\n\n┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┛',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.2952     |
| cosine_accuracy@3   | 0.6386     |
| cosine_accuracy@5   | 0.7229     |
| cosine_accuracy@10  | 0.7892     |
| cosine_precision@1  | 0.2952     |
| cosine_precision@3  | 0.2129     |
| cosine_precision@5  | 0.1446     |
| cosine_precision@10 | 0.0789     |
| cosine_recall@1     | 0.2952     |
| cosine_recall@3     | 0.6386     |
| cosine_recall@5     | 0.7229     |
| cosine_recall@10    | 0.7892     |
| cosine_ndcg@10      | 0.5552     |
| cosine_mrr@10       | 0.4785     |
| **cosine_map@100**  | **0.4871** |

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.3253     |
| cosine_accuracy@3   | 0.6145     |
| cosine_accuracy@5   | 0.6988     |
| cosine_accuracy@10  | 0.7892     |
| cosine_precision@1  | 0.3253     |
| cosine_precision@3  | 0.2048     |
| cosine_precision@5  | 0.1398     |
| cosine_precision@10 | 0.0789     |
| cosine_recall@1     | 0.3253     |
| cosine_recall@3     | 0.6145     |
| cosine_recall@5     | 0.6988     |
| cosine_recall@10    | 0.7892     |
| cosine_ndcg@10      | 0.5598     |
| cosine_mrr@10       | 0.486      |
| **cosine_map@100**  | **0.4931** |

#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.2711     |
| cosine_accuracy@3   | 0.5663     |
| cosine_accuracy@5   | 0.6386     |
| cosine_accuracy@10  | 0.7892     |
| cosine_precision@1  | 0.2711     |
| cosine_precision@3  | 0.1888     |
| cosine_precision@5  | 0.1277     |
| cosine_precision@10 | 0.0789     |
| cosine_recall@1     | 0.2711     |
| cosine_recall@3     | 0.5663     |
| cosine_recall@5     | 0.6386     |
| cosine_recall@10    | 0.7892     |
| cosine_ndcg@10      | 0.5243     |
| cosine_mrr@10       | 0.4404     |
| **cosine_map@100**  | **0.4469** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.253      |
| cosine_accuracy@3   | 0.4759     |
| cosine_accuracy@5   | 0.5783     |
| cosine_accuracy@10  | 0.6627     |
| cosine_precision@1  | 0.253      |
| cosine_precision@3  | 0.1586     |
| cosine_precision@5  | 0.1157     |
| cosine_precision@10 | 0.0663     |
| cosine_recall@1     | 0.253      |
| cosine_recall@3     | 0.4759     |
| cosine_recall@5     | 0.5783     |
| cosine_recall@10    | 0.6627     |
| cosine_ndcg@10      | 0.454      |
| cosine_mrr@10       | 0.3875     |
| **cosine_map@100**  | **0.3986** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 1,490 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                          | anchor                                                                               |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               |
  | details | <ul><li>min: 9 tokens</li><li>mean: 21.08 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 374.42 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
  |:------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>How can I configure SSH keys for authentication in the HyperAI orchestrator using the ZenML framework?</code>                       | <code>authentication.<br><br>ED25519 key based authentication.SSH private keys configured in the connector will be distributed to all clients that use them to run pipelines with the HyperAI orchestrator. SSH keys are long-lived credentials that give unrestricted access to HyperAI instances.<br><br>When configuring the Service Connector, it is required to provide at least one hostname via hostnames and the username with which to login. Optionally, it is possible to provide an ssh_passphrase if applicable. This way, it is possible to use the HyperAI service connector in multiple ways:<br><br>Create one service connector per HyperAI instance with different SSH keys.<br><br>Configure a reused SSH key just once for multiple HyperAI instances, then select the individual instance when creating the HyperAI orchestrator component.<br><br>Auto-configuration<br><br>This Service Connector does not support auto-discovery and extraction of authentication credentials from HyperAI instances. If this feature is useful to you or your organization, please let us know by messaging us in Slack or creating an issue on GitHub.<br><br>Stack Components use<br><br>The HyperAI Service Connector can be used by the HyperAI Orchestrator to deploy pipeline runs to HyperAI instances.<br><br>PreviousAzure Service Connector<br><br>NextManage stacks<br><br>Last updated 19 days ago</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
  | <code>What additional settings are required to enable CUDA for GPU-backed hardware when using the LocalDockerOrchestratorSettings?</code> | <code>or.local_docker": LocalDockerOrchestratorSettings(run_args={"cpu_count": 3}<br><br>@pipeline(settings=settings)<br><br>def simple_pipeline():<br><br>return_one()<br><br>Enabling CUDA for GPU-backed hardware<br><br>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 that it works. It requires adding some extra settings customization and is essential to enable CUDA for the GPU to give its full acceleration.<br><br>PreviousLocal Orchestrator<br><br>NextKubeflow Orchestrator<br><br>Last updated 15 days ago</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
  | <code>What is the SECRET ID for the gcs-bucket resource type?</code>                                                                      | <code>──────────┼──────────────────────────────────────┨┃ RESOURCE TYPES   │ 📦 gcs-bucket                        ┃<br><br>┠──────────────────┼──────────────────────────────────────┨<br><br>┃ RESOURCE NAME    │ <multiple>                           ┃<br><br>┠──────────────────┼──────────────────────────────────────┨<br><br>┃ SECRET ID        │ 0d0a42bb-40a4-4f43-af9e-6342eeca3f28 ┃<br><br>┠──────────────────┼──────────────────────────────────────┨<br><br>┃ SESSION DURATION │ N/A                                  ┃<br><br>┠──────────────────┼──────────────────────────────────────┨<br><br>┃ EXPIRES IN       │ N/A                                  ┃<br><br>┠──────────────────┼──────────────────────────────────────┨<br><br>┃ OWNER            │ default                              ┃<br><br>┠──────────────────┼──────────────────────────────────────┨<br><br>┃ WORKSPACE        │ default                              ┃<br><br>┠──────────────────┼──────────────────────────────────────┨<br><br>┃ SHARED           │ ➖                                   ┃<br><br>┠──────────────────┼──────────────────────────────────────┨<br><br>┃ CREATED_AT       │ 2023-05-19 08:15:48.056937           ┃<br><br>┠──────────────────┼──────────────────────────────────────┨<br><br>┃ UPDATED_AT       │ 2023-05-19 08:15:48.056940           ┃<br><br>┗━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛<br><br>Configuration<br><br>┏━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━┓<br><br>┃ PROPERTY             │ VALUE      ┃<br><br>┠──────────────────────┼────────────┨<br><br>┃ project_id           │ zenml-core ┃<br><br>┠──────────────────────┼────────────┨<br><br>┃ service_account_json │ [HIDDEN]   ┃<br><br>┗━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━┛<br><br>GCP Service Account impersonation<br><br>Generates temporary STS credentials by impersonating another GCP service account.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: True
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step  | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 |
|:----------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.6667     | 1     | 0.3889                 | 0.4114                 | 0.4339                 | 0.2694                |
| 1.9583     | 3     | 0.4463                 | 0.4920                 | 0.4852                 | 0.3876                |
| **2.5833** | **4** | **0.4469**             | **0.4931**             | **0.4871**             | **0.3986**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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