Model: zenml/finetuned-snowflake-arctic-embed-m-v1.5
Author: zenml
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:36, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, en, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-m-v1.5, base_model:finetune:Snowflake/snowflake-arctic-embed-m-v1.5, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-m-v1.5
Card metadata: language: en
license: apache-2.0
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:36, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-m-v1.5
widget: source_sentence: How can I connect a GCP Image Builder to resources using ZenML?
sentences: _run.steps[step_name]
    whylogs_step.visualize()if __name__ == "__main__":
    visualize_statistics("data_loader")
    visualize_statistics("train_data_profiler", "test_data_profiler")

PreviousEvidentlyNextDevelop a custom data validator

Last updated 1 month ago, Implement a custom integration

Creating an external integration and contributing to ZenML

PreviousContribute to ZenMLNextOverview

Last updated 4 months ago, --connector <CONNECTOR_ID>

Example Command Output$ zenml image-builder connect gcp-image-builder --connector gcp-generic
Successfully connected image builder `gcp-image-builder` to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓
┃             CONNECTOR ID             │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE  │ RESOURCE NAMES ┃
┠──────────────────────────────────────┼────────────────┼────────────────┼────────────────┼────────────────┨
┃ bfdb657d-d808-47e7-9974-9ba6e4919d83 │ gcp-generic    │ 🔵 gcp         │ 🔵 gcp-generic │ zenml-core     ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛

As a final step, you can use the GCP Image Builder in a ZenML Stack:

# Register and set a stack with the new image builder
zenml stack register <STACK_NAME> -i <IMAGE_BUILDER_NAME> ... --set

When you register the GCP Image Builder, you can generate a GCP Service Account Key, save it to a local file and then reference it in the Image Builder configuration.

This method has the advantage that you don't need to install and configure the GCP CLI on your host, but it's still not as secure as using a GCP Service Connector and the stack component configuration is not portable to other hosts.

For this method, you need to create a user-managed GCP service account, and grant it privileges to access the Cloud Build API and to run Cloud Builder jobs (e.g. the Cloud Build Editor IAM role.

With the service account key downloaded to a local file, you can register the GCP Image Builder as follows:

zenml image-builder register <IMAGE_BUILDER_NAME> \
    --flavor=gcp \
    --project=<GCP_PROJECT_ID> \
    --service_account_path=<PATH_TO_SERVICE_ACCOUNT_KEY> \
    --cloud_builder_image=<BUILDER_IMAGE_NAME> \
    --network=<DOCKER_NETWORK> \
    --build_timeout=<BUILD_TIMEOUT_IN_SECONDS>, source_sentence: How do I register and activate a ZenML stack with a new GCP Image Builder while ensuring proper authentication?
sentences: oad the returned whylogs profile to WhyLabs, e.g.:import pandas as pd
from whylogs.core import DatasetProfileView
import whylogs as why
from zenml import step
from zenml.integrations.whylogs.flavors.whylogs_data_validator_flavor import (
    WhylogsDataValidatorSettings,
)

whylogs_settings = WhylogsDataValidatorSettings(
    enable_whylabs=True, dataset_id="<WHYLABS_DATASET_ID>"
)

@step(
    settings={
        "data_validator": whylogs_settings
    }
)
def data_profiler(
        dataset: pd.DataFrame,
) -> DatasetProfileView:
    """Custom data profiler step with whylogs

Args:
        dataset: a Pandas DataFrame

Returns:
        Whylogs Profile generated for the dataset
    """

# validation pre-processing (e.g. dataset preparation) can take place here

results = why.log(dataset)
    profile = results.profile()

# validation post-processing (e.g. interpret results, take actions) can happen here

return profile.view()

Visualizing whylogs Profiles

You can view visualizations of the whylogs profiles generated by your pipeline steps directly in the ZenML dashboard by clicking on the respective artifact in the pipeline run DAG.

Alternatively, if you are running inside a Jupyter notebook, you can load and render the whylogs profiles using the artifact.visualize() method, e.g.:

from zenml.client import Client

def visualize_statistics(
    step_name: str, reference_step_name: Optional[str] = None
) -> None:
    """Helper function to visualize whylogs statistics from step artifacts.

Args:
        step_name: step that generated and returned a whylogs profile
        reference_step_name: an optional second step that generated a whylogs
            profile to use for data drift visualization where two whylogs
            profiles are required.
    """
    pipe = Client().get_pipeline(pipeline="data_profiling_pipeline")
    whylogs_step = pipe.last_run.steps[step_name]
    whylogs_step.visualize(), ogsDataValidatorSettings,
)
from zenml import step@step(
    settings={
        "data_validator": WhylogsDataValidatorSettings(
            enable_whylabs=True, dataset_id="model-1"
        )
    }
)
def data_loader() -> Tuple[
    Annotated[pd.DataFrame, "data"],
    Annotated[DatasetProfileView, "profile"]
]:
    """Load the diabetes dataset."""
    X, y = datasets.load_diabetes(return_X_y=True, as_frame=True)

# merge X and y together
    df = pd.merge(X, y, left_index=True, right_index=True)

profile = why.log(pandas=df).profile().view()
    return df, profile

How do you use it?

Whylogs's profiling functions take in a pandas.DataFrame dataset generate a DatasetProfileView object containing all the relevant information extracted from the dataset.

There are three ways you can use whylogs in your ZenML pipelines that allow different levels of flexibility:

instantiate, configure and insert the standard WhylogsProfilerStep shipped with ZenML into your pipelines. This is the easiest way and the recommended approach, but can only be customized through the supported step configuration parameters.

call the data validation methods provided by the whylogs Data Validator in your custom step implementation. This method allows for more flexibility concerning what can happen in the pipeline step, but you are still limited to the functionality implemented in the Data Validator.

use the whylogs library directly in your custom step implementation. This gives you complete freedom in how you are using whylogs's features.

You can visualize whylogs profiles in Jupyter notebooks or view them directly in the ZenML dashboard.

The whylogs standard step,  build to finish. More information: Build Timeout.We can register the image builder and use it in our active stack:

zenml image-builder register <IMAGE_BUILDER_NAME> \
    --flavor=gcp \
    --cloud_builder_image=<BUILDER_IMAGE_NAME> \
    --network=<DOCKER_NETWORK> \
    --build_timeout=<BUILD_TIMEOUT_IN_SECONDS>

# Register and activate a stack with the new image builder
zenml stack register <STACK_NAME> -i <IMAGE_BUILDER_NAME> ... --set

You also need to set up authentication required to access the Cloud Build GCP services.

Authentication Methods

Integrating and using a GCP Image Builder in your pipelines is not possible without employing some form of authentication. If you're looking for a quick way to get started locally, you can use the Local Authentication method. However, the recommended way to authenticate to the GCP cloud platform is through a GCP Service Connector. This is particularly useful if you are configuring ZenML stacks that combine the GCP Image Builder with other remote stack components also running in GCP.

This method uses the implicit GCP authentication available in the environment where the ZenML code is running. On your local machine, this is the quickest way to configure a GCP Image Builder. You don't need to supply credentials explicitly when you register the GCP Image Builder, as it leverages the local credentials and configuration that the Google Cloud CLI stores on your local machine. However, you will need to install and set up the Google Cloud CLI on your machine as a prerequisite, as covered in the Google Cloud documentation , before you register the GCP Image Builder.

Stacks using the GCP Image Builder set up with local authentication are not portable across environments. To make ZenML pipelines fully portable, it is recommended to use a GCP Service Connector to authenticate your GCP Image Builder to the GCP cloud platform., source_sentence: How can I register and set a stack with a new image builder using ZenML?
sentences: ZenML - Bridging the gap between ML & Ops

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Powered by GitBook, > \
    --build_timeout=<BUILD_TIMEOUT_IN_SECONDS># Register and set a stack with the new image builder
zenml stack register <STACK_NAME> -i <IMAGE_BUILDER_NAME> ... --set

Caveats

As described in this Google Cloud Build documentation page, Google Cloud Build uses containers to execute the build steps which are automatically attached to a network called cloudbuild that provides some Application Default Credentials (ADC), that allow the container to be authenticated and therefore use other GCP services.

By default, the GCP Image Builder is executing the build command of the ZenML Pipeline Docker image with the option --network=cloudbuild, so the ADC provided by the cloudbuild network can also be used in the build. This is useful if you want to install a private dependency from a GCP Artifact Registry, but you will also need to use a custom base parent image with the keyrings.google-artifactregistry-auth installed, so pip can connect and authenticate in the private artifact registry to download the dependency.

FROM zenmldocker/zenml:latest

RUN pip install keyrings.google-artifactregistry-auth

The above Dockerfile uses zenmldocker/zenml:latest as a base image, but is recommended to change the tag to specify the ZenML version and Python version like 0.33.0-py3.10.

PreviousKaniko Image BuilderNextDevelop a Custom Image Builder

Last updated 21 days ago, res Spark to handle the resource configuration."""def _backend_configuration(
            self,
            spark_config: SparkConf,
            step_config: "StepConfiguration",
    ) -> None:
        """Configures Spark to handle backends like YARN, Mesos or Kubernetes."""

def _io_configuration(
            self,
            spark_config: SparkConf
    ) -> None:
        """Configures Spark to handle different input/output sources."""

def _additional_configuration(
            self,
            spark_config: SparkConf
    ) -> None:
        """Appends the user-defined configuration parameters."""

def _launch_spark_job(
            self,
            spark_config: SparkConf,
            entrypoint_command: List[str]
    ) -> None:
        """Generates and executes a spark-submit command."""

def launch(
            self,
            info: "StepRunInfo",
            entrypoint_command: List[str],
    ) -> None:
        """Launches the step on Spark."""

Under the base configuration, you will see the main configuration parameters:

master is the master URL for the cluster where Spark will run. You might see different schemes for this URL with varying cluster managers such as Mesos, YARN, or Kubernetes.

deploy_mode can either be 'cluster' (default) or 'client' and it decides where the driver node of the application will run.

submit_args is the JSON string of a dictionary, which will be used to define additional parameters if required ( Spark has a wide variety of parameters, thus including them all in a single class was deemed unnecessary.).

In addition to this configuration, the launch method of the step operator gets additional configuration parameters from the DockerSettings and ResourceSettings. As a result, the overall configuration happens in 4 base methods:

_resource_configuration translates the ZenML ResourceSettings object to Spark's own resource configuration.

_backend_configuration is responsible for cluster-manager-specific configuration., source_sentence: How can I install ZenML with support for a local dashboard, and what precautions should I take when installing on a Mac with Apple Silicon?
sentences:  visit our PyPi package page.

Running with Dockerzenml is also available as a Docker image hosted publicly on DockerHub. Use the following command to get started in a bash environment with zenml available:

docker run -it zenmldocker/zenml /bin/bash

If you would like to run the ZenML server with Docker:

docker run -it -d -p 8080:8080 zenmldocker/zenml-server

Deploying the server

Though ZenML can run entirely as a pip package on a local system, complete with the dashboard. You can do this easily:

pip install "zenml[server]"
zenml up  # opens the dashboard locally

However, advanced ZenML features are dependent on a centrally-deployed ZenML server accessible to other MLOps stack components. You can read more about it here.

For the deployment of ZenML, you have the option to either self-host it or register for a free ZenML Pro account.

PreviousIntroductionNextCore concepts

Last updated 20 days ago, Evaluation and metrics

Track how your RAG pipeline improves using evaluation and metrics.

PreviousBasic RAG inference pipelineNextEvaluation in 65 lines of code

Last updated 4 months ago, 🧙Installation

Installing ZenML and getting started.

ZenML is a Python package that can be installed directly via pip:

pip install zenml

Note that ZenML currently supports Python 3.8, 3.9, 3.10, and 3.11. Please make sure that you are using a supported Python version.

Install with the dashboard

ZenML comes bundled with a web dashboard that lives inside a sister repository. In order to get access to the dashboard locally, you need to launch the ZenML Server and Dashboard locally. For this, you need to install the optional dependencies for the ZenML Server:

pip install "zenml[server]"

We highly encourage you to install ZenML in a virtual environment. At ZenML, We like to use virtualenvwrapper or pyenv-virtualenv to manage our Python virtual environments.

Installing onto MacOS with Apple Silicon (M1, M2)

A change in how forking works on Macs running on Apple Silicon means that you should set the following environment variable which will ensure that your connections to the server remain unbroken:

export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES

You can read more about this here. This environment variable is needed if you are working with a local server on your Mac, but if you're just using ZenML as a client / CLI and connecting to a deployed server then you don't need to set it.

Nightly builds

ZenML also publishes nightly builds under the zenml-nightly package name. These are built from the latest develop branch (to which work ready for release is published) and are not guaranteed to be stable. To install the nightly build, run:

pip install zenml-nightly

Verifying installations

Once the installation is completed, you can check whether the installation was successful either through Bash:

zenml version

or through Python:

import zenml

print(zenml.__version__)

If you would like to learn more about the current release, please visit our PyPi package page.

Running with Docker, source_sentence: How does the KubernetesSparkStepOperator utilize the PipelineDockerImageBuilder class to manage Docker images for Spark jobs on Kubernetes?
sentences: ZenML - Bridging the gap between ML & Ops

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Powered by GitBook, nsible for cluster-manager-specific configuration._io_configuration is a critical method. Even though we have materializers, Spark might require additional packages and configuration to work with a specific filesystem. This method is used as an interface to provide this configuration.

_additional_configuration takes the submit_args, converts, and appends them to the overall configuration.

Once the configuration is completed, _launch_spark_job comes into play. This takes the completed configuration and runs a Spark job on the given master URL with the specified deploy_mode. By default, this is achieved by creating and executing a spark-submit command.

Warning

In its first iteration, the pre-configuration with _io_configuration method is only effective when it is paired with an S3ArtifactStore (which has an authentication secret). When used with other artifact store flavors, you might be required to provide additional configuration through the submit_args.

Stack Component: KubernetesSparkStepOperator

The KubernetesSparkStepOperator is implemented by subclassing the base SparkStepOperator and uses the PipelineDockerImageBuilder class to build and push the required Docker images.

from typing import Optional

from zenml.integrations.spark.step_operators.spark_step_operator import (
    SparkStepOperatorConfig
)

class KubernetesSparkStepOperatorConfig(SparkStepOperatorConfig):
    """Config for the Kubernetes Spark step operator."""

namespace: Optional[str] = None
    service_account: Optional[str] = None

from pyspark.conf import SparkConf

from zenml.utils.pipeline_docker_image_builder import PipelineDockerImageBuilder
from zenml.integrations.spark.step_operators.spark_step_operator import (
    SparkStepOperator
)

class KubernetesSparkStepOperator(SparkStepOperator):
    """Step operator which runs Steps with Spark on Kubernetes.""", ngs/python/Dockerfile -u 0 build

Configuring RBACAdditionally, you may need to create the several resources in Kubernetes in order to give Spark access to edit/manage your driver executor pods.

To do so, create a file called rbac.yaml with the following content:

apiVersion: v1
kind: Namespace
metadata:
  name: spark-namespace
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: spark-service-account
  namespace: spark-namespace
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: spark-role
  namespace: spark-namespace
subjects:
  - kind: ServiceAccount
    name: spark-service-account
    namespace: spark-namespace
roleRef:
  kind: ClusterRole
  name: 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, run

zenml 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
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
model-index: name: zenml/finetuned-snowflake-arctic-embed-m-v1.5
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 384
type: dim_384
metrics: type: cosine_accuracy@1
value: 0.75
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 1
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.75
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3333333333333333
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.75
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 1
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.875
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8333333333333334
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8333333333333334
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 256
type: dim_256
metrics: type: cosine_accuracy@1
value: 0.75
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.75
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.75
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.25
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.75
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.75
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8576691395183482
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8125
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8125
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 128
type: dim_128
metrics: type: cosine_accuracy@1
value: 0.75
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.75
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.75
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.25
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.75
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.75
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.8576691395183482
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8125
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8125
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 64
type: dim_64
metrics: type: cosine_accuracy@1
value: 0.75
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 1
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.75
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3333333333333333
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.75
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 1
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.875
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8333333333333334
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8333333333333334
name: Cosine Map@100
verified: no
Likes: 1 | All-time downloads: 226
Created: 2024-10-25 07:57:07