--- 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: What are the abstract methods provided for managing model servers in ZenML's BaseModelDeployerFlavor class? sentences: - "quired for your GCP Image Builder by running e.g.:zenml service-connector list-resources\ \ --resource-type gcp-generic\n\nExample Command Output\n\nThe following 'gcp-generic'\ \ resources can be accessed by service connectors that you have configured:\n\ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓\n\ ┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE\ \ TYPE │ RESOURCE NAMES ┃\n┠──────────────────────────────────────┼────────────────┼────────────────┼────────────────┼────────────────┨\n\ ┃ bfdb657d-d808-47e7-9974-9ba6e4919d83 │ gcp-generic │ \U0001F535 gcp \ \ │ \U0001F535 gcp-generic │ zenml-core ┃\n┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛\n\ \nAfter having set up or decided on a GCP Service Connector to use to authenticate\ \ to GCP, you can register the GCP Image Builder as follows:\n\nzenml image-builder\ \ register \\\n --flavor=gcp \\\n --cloud_builder_image=\ \ \\\n --network= \\\n --build_timeout=\n\ \n# Connect the GCP Image Builder to GCP via a GCP Service Connector\nzenml image-builder\ \ connect -i\n\nA non-interactive version that connects the\ \ GCP Image Builder to a target GCP Service Connector:\n\nzenml image-builder\ \ connect --connector \n\nExample Command Output" - '🧙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' - ":\n \"\"\"Abstract method to deploy a model.\"\"\"@staticmethod\n @abstractmethod\n\ \ def get_model_server_info(\n service: BaseService,\n ) -> Dict[str,\ \ Optional[str]]:\n \"\"\"Give implementation-specific way to extract relevant\ \ model server\n properties for the user.\"\"\"\n\n@abstractmethod\n \ \ def perform_stop_model(\n self,\n service: BaseService,\n \ \ timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,\n force: bool\ \ = False,\n ) -> BaseService:\n \"\"\"Abstract method to stop a model\ \ server.\"\"\"\n\n@abstractmethod\n def perform_start_model(\n self,\n\ \ service: BaseService,\n timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,\n\ \ ) -> BaseService:\n \"\"\"Abstract method to start a model server.\"\ \"\"\n\n@abstractmethod\n def perform_delete_model(\n self,\n \ \ service: BaseService,\n timeout: int = DEFAULT_DEPLOYMENT_START_STOP_TIMEOUT,\n\ \ force: bool = False,\n ) -> None:\n \"\"\"Abstract method to\ \ delete a model server.\"\"\"\n\nclass BaseModelDeployerFlavor(Flavor):\n \ \ \"\"\"Base class for model deployer flavors.\"\"\"\n\n@property\n @abstractmethod\n\ \ def name(self):\n \"\"\"Returns the name of the flavor.\"\"\"\n\n\ @property\n def type(self) -> StackComponentType:\n \"\"\"Returns the\ \ flavor type.\n\nReturns:\n The flavor type.\n \"\"\"\n \ \ return StackComponentType.MODEL_DEPLOYER\n\n@property\n def config_class(self)\ \ -> Type[BaseModelDeployerConfig]:\n \"\"\"Returns `BaseModelDeployerConfig`\ \ config class.\n\nReturns:\n The config class.\n \"\"\"\ \n return BaseModelDeployerConfig\n\n@property\n @abstractmethod\n \ \ def implementation_class(self) -> Type[BaseModelDeployer]:\n \"\"\"\ The class that implements the model deployer.\"\"\"\n\nThis 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 .\n\nBuilding your own model deployers" - source_sentence: How can you successfully connect the image builder `gcp-image-builder` to the resources using a connector ID? sentences: - 'ZenML - Bridging the gap between ML & Ops Legacy Docs Bleeding EdgeLegacy Docs0.67.0 🧙‍♂️Find older version our docs Powered by GitBook' - 'Google Cloud Image Builder Building container images with Google Cloud Build The Google Cloud image builder is an image builder flavor provided by the ZenML gcp integration that uses Google Cloud Build to build container images. When to use it You should use the Google Cloud image builder if: you''re unable to install or use Docker on your client machine. you''re already using GCP. your stack is mainly composed of other Google Cloud components such as the GCS Artifact Store or the Vertex Orchestrator. How to deploy it Would you like to skip ahead and deploy a full ZenML cloud stack already, including the Google Cloud image builder? Check out the in-browser stack deployment wizard, the stack registration wizard, or the ZenML GCP Terraform module for a shortcut on how to deploy & register this stack component. In order to use the ZenML Google Cloud image builder you need to enable Google Cloud Build relevant APIs on the Google Cloud project. How to use it To use the Google Cloud image builder, we need: The ZenML gcp integration installed. If you haven''t done so, run: zenml integration install gcp A GCP Artifact Store where the build context will be uploaded, so Google Cloud Build can access it. A GCP container registry where the built image will be pushed. Optionally, the GCP project ID in which you want to run the build and a service account with the needed permissions to run the build. If not provided, then the project ID and credentials will be inferred from the environment. Optionally, you can change: the Docker image used by Google Cloud Build to execute the steps to build and push the Docker image. By default, the builder image will be ''gcr.io/cloud-builders/docker''. The network to which the container used to build the ZenML pipeline Docker image will be attached. More information: Cloud build network. The build timeout for the build, and for the blocking operation waiting for the build to finish. More information: Build Timeout.' - "--connector \n\nExample Command Output$ zenml image-builder connect\ \ gcp-image-builder --connector gcp-generic\nSuccessfully connected image builder\ \ `gcp-image-builder` to the following resources:\n┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓\n\ ┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE\ \ TYPE │ RESOURCE NAMES ┃\n┠──────────────────────────────────────┼────────────────┼────────────────┼────────────────┼────────────────┨\n\ ┃ bfdb657d-d808-47e7-9974-9ba6e4919d83 │ gcp-generic │ \U0001F535 gcp \ \ │ \U0001F535 gcp-generic │ zenml-core ┃\n┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛\n\ \nAs a final step, you can use the GCP Image Builder in a ZenML Stack:\n\n# Register\ \ and set a stack with the new image builder\nzenml stack register \ \ -i ... --set\n\nWhen 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.\n\nThis 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.\n\nFor 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.\n\nWith the service account key downloaded to a local file, you can\ \ register the GCP Image Builder as follows:\n\nzenml image-builder register \ \ \\\n --flavor=gcp \\\n --project= \\\n --service_account_path=\ \ \\\n --cloud_builder_image= \\\n --network=\ \ \\\n --build_timeout=" - source_sentence: How do I finetune embeddings using Sentence Transformers in ZenML? sentences: - "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.\n\n_additional_configuration takes\ \ the submit_args, converts, and appends them to the overall configuration.\n\n\ 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.\n\nWarning\n\nIn 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.\n\ \nStack Component: KubernetesSparkStepOperator\n\nThe KubernetesSparkStepOperator\ \ is implemented by subclassing the base SparkStepOperator and uses the PipelineDockerImageBuilder\ \ class to build and push the required Docker images.\n\nfrom typing import Optional\n\ \nfrom zenml.integrations.spark.step_operators.spark_step_operator import (\n\ \ SparkStepOperatorConfig\n)\n\nclass KubernetesSparkStepOperatorConfig(SparkStepOperatorConfig):\n\ \ \"\"\"Config for the Kubernetes Spark step operator.\"\"\"\n\nnamespace:\ \ Optional[str] = None\n service_account: Optional[str] = None\n\nfrom pyspark.conf\ \ import SparkConf\n\nfrom zenml.utils.pipeline_docker_image_builder import PipelineDockerImageBuilder\n\ from zenml.integrations.spark.step_operators.spark_step_operator import (\n \ \ SparkStepOperator\n)\n\nclass KubernetesSparkStepOperator(SparkStepOperator):\n\ \ \"\"\"Step operator which runs Steps with Spark on Kubernetes.\"\"\"" - 'Finetuning embeddings with Sentence Transformers Finetune embeddings with Sentence Transformers. PreviousSynthetic data generationNextEvaluating finetuned embeddings Last updated 1 month ago' - 'Whylogs How to collect and visualize statistics to track changes in your pipelines'' data with whylogs/WhyLabs profiling. The whylogs/WhyLabs Data Validator flavor provided with the ZenML integration uses whylogs and WhyLabs to generate and track data profiles, highly accurate descriptive representations of your data. The profiles can be used to implement automated corrective actions in your pipelines, or to render interactive representations for further visual interpretation, evaluation and documentation. When would you want to use it? Whylogs is an open-source library that analyzes your data and creates statistical summaries called whylogs profiles. Whylogs profiles can be processed in your pipelines and visualized locally or uploaded to the WhyLabs platform, where more in depth analysis can be carried out. Even though whylogs also supports other data types, the ZenML whylogs integration currently only works with tabular data in pandas.DataFrame format. You should use the whylogs/WhyLabs Data Validator when you need the following data validation features that are possible with whylogs and WhyLabs: Data Quality: validate data quality in model inputs or in a data pipeline Data Drift: detect data drift in model input features Model Drift: Detect training-serving skew, concept drift, and model performance degradation You should consider one of the other Data Validator flavors if you need a different set of data validation features. How do you deploy it? The whylogs Data Validator flavor is included in the whylogs ZenML integration, you need to install it on your local machine to be able to register a whylogs Data Validator and add it to your stack: zenml integration install whylogs -y If you don''t need to connect to the WhyLabs platform to upload and store the generated whylogs data profiles, the Data Validator stack component does not require any configuration parameters. Adding it to a stack is as simple as running e.g.:' - source_sentence: What is the purpose of ZenML in the context of ML and Ops? sentences: - "> \\\n --build_timeout=# Register and set a stack\ \ with the new image builder\nzenml stack register -i \ \ ... --set\n\nCaveats\n\nAs 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.\n\nBy 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.\n\nFROM zenmldocker/zenml:latest\n\ \nRUN pip install keyrings.google-artifactregistry-auth\n\nThe 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.\n\ \nPreviousKaniko Image BuilderNextDevelop a Custom Image Builder\n\nLast updated\ \ 21 days ago" - "Control caching behavior\n\nBy default steps in ZenML pipelines are cached whenever\ \ code and parameters stay unchanged.\n\n@step(enable_cache=True) # set cache\ \ behavior at step level\ndef load_data(parameter: int) -> dict:\n ...\n\n\ @step(enable_cache=False) # settings at step level override pipeline level\ndef\ \ train_model(data: dict) -> None:\n ...\n\n@pipeline(enable_cache=True) #\ \ set cache behavior at step level\ndef simple_ml_pipeline(parameter: int):\n\ \ ...\n\nCaching only happens when code and parameters stay the same.\n\nLike\ \ many other step and pipeline settings, you can also change this afterward:\n\ \n# Same as passing it in the step decorator\nmy_step.configure(enable_cache=...)\n\ \n# Same as passing it in the pipeline decorator\nmy_pipeline.configure(enable_cache=...)\n\ \nFind out here how to configure this in a YAML file\n\nPreviousStep output typing\ \ and annotationNextSchedule a pipeline\n\nLast updated 4 months ago" - 'ZenML - Bridging the gap between ML & Ops Legacy Docs Bleeding EdgeLegacy Docs0.67.0 🧙‍♂️Find older version our docs Powered by GitBook' - source_sentence: How does ZenML facilitate the flow of data between steps in a pipeline? sentences: - "tainer_registry \\\n -i local_builder \\\n --setOnce 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:\n\nfrom zenml\ \ import step\n\n@step(step_operator=)\ndef step_on_spark(...)\ \ -> ...:\n \"\"\"Some step that should run with Spark on Kubernetes.\"\"\"\ \n ...\n\nAfter 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.\n\ \nInstead of hardcoding a step operator name, you can also use the Client to dynamically\ \ use the step operator of your active stack:\n\nfrom zenml.client import Client\n\ \nstep_operator = Client().active_stack.step_operator\n\n@step(step_operator=step_operator.name)\n\ def step_on_spark(...) -> ...:\n ...\n\nAdditional configuration\n\nFor additional\ \ configuration of the Spark step operator, you can pass SparkStepOperatorSettings\ \ when defining or running your pipeline. Check out the SDK docs for a full list\ \ of available attributes and this docs page for more information on how to specify\ \ settings.\n\nPreviousKubernetesNextDevelop a Custom Step Operator\n\nLast updated\ \ 4 months ago" - "\U0001F5C4️Handle Data/Artifacts\n\nStep outputs in ZenML are stored in the artifact\ \ store. This enables caching, lineage and auditability. Using type annotations\ \ helps with transparency, passing data between steps, and serializing/des\n\n\ For best results, use type annotations for your outputs. This is good coding practice\ \ for transparency, helps ZenML handle passing data between steps, and also enables\ \ ZenML to serialize and deserialize (referred to as 'materialize' in ZenML) the\ \ data.\n\n@step\ndef load_data(parameter: int) -> Dict[str, Any]:\n\n# do something\ \ with the parameter here\n\ntraining_data = [[1, 2], [3, 4], [5, 6]]\n labels\ \ = [0, 1, 0]\n return {'features': training_data, 'labels': labels}\n\n@step\n\ def train_model(data: Dict[str, Any]) -> None:\n total_features = sum(map(sum,\ \ data['features']))\n total_labels = sum(data['labels'])\n \n # Train\ \ some model here\n \n print(f\"Trained model using {len(data['features'])}\ \ data points. \"\n f\"Feature sum is {total_features}, label sum is\ \ {total_labels}\")\n\n@pipeline \ndef simple_ml_pipeline(parameter: int):\n\ \ dataset = load_data(parameter=parameter) # Get the output \n train_model(dataset)\ \ # Pipe the previous step output into the downstream step\n\nIn this code, we\ \ define two steps: load_data and train_model. The load_data step takes an integer\ \ parameter and returns a dictionary containing training data and labels. The\ \ train_model step receives the dictionary from load_data, extracts the features\ \ and labels, and trains a model (not shown here).\n\nFinally, we define a pipeline\ \ simple_ml_pipeline that chains the load_data and train_model steps together.\ \ The output from load_data is passed as input to train_model, demonstrating how\ \ data flows between steps in a ZenML pipeline.\n\nPreviousDisable colorful loggingNextHow\ \ ZenML stores data\n\nLast updated 4 months ago" - "in the ZenML dashboard.\n\nThe whylogs standard stepZenML wraps the whylogs/WhyLabs\ \ functionality in the form of a standard WhylogsProfilerStep step. The only field\ \ in the step config is a dataset_timestamp attribute which is only relevant when\ \ you upload the profiles to WhyLabs that uses this field to group and merge together\ \ profiles belonging to the same dataset. The helper function get_whylogs_profiler_step\ \ used to create an instance of this standard step takes in an optional dataset_id\ \ parameter that is also used only in the context of WhyLabs upload to identify\ \ the model in the context of which the profile is uploaded, e.g.:\n\nfrom zenml.integrations.whylogs.steps\ \ import get_whylogs_profiler_step\n\ntrain_data_profiler = get_whylogs_profiler_step(dataset_id=\"\ model-2\")\ntest_data_profiler = get_whylogs_profiler_step(dataset_id=\"model-3\"\ )\n\nThe step can then be inserted into your pipeline where it can take in a pandas.DataFrame\ \ dataset, e.g.:\n\nfrom zenml import pipeline\n\n@pipeline\ndef data_profiling_pipeline():\n\ \ data, _ = data_loader()\n train, test = data_splitter(data)\n train_data_profiler(train)\n\ \ test_data_profiler(test)\n\ndata_profiling_pipeline()\n\nAs can be seen from\ \ the step definition , the step takes in a dataset and returns a whylogs DatasetProfileView\ \ object:\n\n@step\ndef whylogs_profiler_step(\n dataset: pd.DataFrame,\n \ \ dataset_timestamp: Optional[datetime.datetime] = None,\n) -> DatasetProfileView:\n\ \ ...\n\nYou should consult the official whylogs documentation for more information\ \ on what you can do with the collected profiles.\n\nYou can view the complete\ \ list of configuration parameters in the SDK docs.\n\nThe whylogs Data Validator\n\ \nThe whylogs Data Validator implements the same interface as do all Data Validators,\ \ so this method forces you to maintain some level of compatibility with the overall\ \ Data Validator abstraction, which guarantees an easier migration in case you\ \ decide to switch to another Data Validator." 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: 1.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 1.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.1 name: Cosine Precision@10 - type: cosine_recall@1 value: 1.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 1.0 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 1.0 name: Cosine Mrr@10 - type: cosine_map@100 value: 1.0 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 1.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 1.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.1 name: Cosine Precision@10 - type: cosine_recall@1 value: 1.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 1.0 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 1.0 name: Cosine Mrr@10 - type: cosine_map@100 value: 1.0 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 1.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 1.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.1 name: Cosine Precision@10 - type: cosine_recall@1 value: 1.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 1.0 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 1.0 name: Cosine Mrr@10 - type: cosine_map@100 value: 1.0 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.75 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.75 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.1 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.75 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9077324383928644 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.875 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.875 name: Cosine Map@100 --- # zenml/finetuned-snowflake-arctic-embed-m-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) on the json dataset. 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-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **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-v1.5") # Run inference sentences = [ 'How does ZenML facilitate the flow of data between steps in a pipeline?', '🗄️Handle Data/Artifacts\n\nStep outputs in ZenML are stored in the artifact store. This enables caching, lineage and auditability. Using type annotations helps with transparency, passing data between steps, and serializing/des\n\nFor best results, use type annotations for your outputs. This is good coding practice for transparency, helps ZenML handle passing data between steps, and also enables ZenML to serialize and deserialize (referred to as \'materialize\' in ZenML) the data.\n\n@step\ndef load_data(parameter: int) -> Dict[str, Any]:\n\n# do something with the parameter here\n\ntraining_data = [[1, 2], [3, 4], [5, 6]]\n labels = [0, 1, 0]\n return {\'features\': training_data, \'labels\': labels}\n\n@step\ndef train_model(data: Dict[str, Any]) -> None:\n total_features = sum(map(sum, data[\'features\']))\n total_labels = sum(data[\'labels\'])\n \n # Train some model here\n \n print(f"Trained model using {len(data[\'features\'])} data points. "\n f"Feature sum is {total_features}, label sum is {total_labels}")\n\n@pipeline \ndef simple_ml_pipeline(parameter: int):\n dataset = load_data(parameter=parameter) # Get the output \n train_model(dataset) # Pipe the previous step output into the downstream step\n\nIn this code, we define two steps: load_data and train_model. The load_data step takes an integer parameter and returns a dictionary containing training data and labels. The train_model step receives the dictionary from load_data, extracts the features and labels, and trains a model (not shown here).\n\nFinally, we define a pipeline simple_ml_pipeline that chains the load_data and train_model steps together. The output from load_data is passed as input to train_model, demonstrating how data flows between steps in a ZenML pipeline.\n\nPreviousDisable colorful loggingNextHow ZenML stores data\n\nLast updated 4 months ago', 'in the ZenML dashboard.\n\nThe whylogs standard stepZenML wraps the whylogs/WhyLabs functionality in the form of a standard WhylogsProfilerStep step. The only field in the step config is a dataset_timestamp attribute which is only relevant when you upload the profiles to WhyLabs that uses this field to group and merge together profiles belonging to the same dataset. The helper function get_whylogs_profiler_step used to create an instance of this standard step takes in an optional dataset_id parameter that is also used only in the context of WhyLabs upload to identify the model in the context of which the profile is uploaded, e.g.:\n\nfrom zenml.integrations.whylogs.steps import get_whylogs_profiler_step\n\ntrain_data_profiler = get_whylogs_profiler_step(dataset_id="model-2")\ntest_data_profiler = get_whylogs_profiler_step(dataset_id="model-3")\n\nThe step can then be inserted into your pipeline where it can take in a pandas.DataFrame dataset, e.g.:\n\nfrom zenml import pipeline\n\n@pipeline\ndef data_profiling_pipeline():\n data, _ = data_loader()\n train, test = data_splitter(data)\n train_data_profiler(train)\n test_data_profiler(test)\n\ndata_profiling_pipeline()\n\nAs can be seen from the step definition , the step takes in a dataset and returns a whylogs DatasetProfileView object:\n\n@step\ndef whylogs_profiler_step(\n dataset: pd.DataFrame,\n dataset_timestamp: Optional[datetime.datetime] = None,\n) -> DatasetProfileView:\n ...\n\nYou should consult the official whylogs documentation for more information on what you can do with the collected profiles.\n\nYou can view the complete list of configuration parameters in the SDK docs.\n\nThe whylogs Data Validator\n\nThe whylogs Data Validator implements the same interface as do all Data Validators, so this method forces you to maintain some level of compatibility with the overall Data Validator abstraction, which guarantees an easier migration in case you decide to switch to another Data Validator.', ] 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] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_384` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:--------| | cosine_accuracy@1 | 1.0 | | cosine_accuracy@3 | 1.0 | | cosine_accuracy@5 | 1.0 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 1.0 | | cosine_precision@3 | 0.3333 | | cosine_precision@5 | 0.2 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 1.0 | | cosine_recall@3 | 1.0 | | cosine_recall@5 | 1.0 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 1.0 | | cosine_mrr@10 | 1.0 | | **cosine_map@100** | **1.0** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:--------| | cosine_accuracy@1 | 1.0 | | cosine_accuracy@3 | 1.0 | | cosine_accuracy@5 | 1.0 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 1.0 | | cosine_precision@3 | 0.3333 | | cosine_precision@5 | 0.2 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 1.0 | | cosine_recall@3 | 1.0 | | cosine_recall@5 | 1.0 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 1.0 | | cosine_mrr@10 | 1.0 | | **cosine_map@100** | **1.0** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:--------| | cosine_accuracy@1 | 1.0 | | cosine_accuracy@3 | 1.0 | | cosine_accuracy@5 | 1.0 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 1.0 | | cosine_precision@3 | 0.3333 | | cosine_precision@5 | 0.2 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 1.0 | | cosine_recall@3 | 1.0 | | cosine_recall@5 | 1.0 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 1.0 | | cosine_mrr@10 | 1.0 | | **cosine_map@100** | **1.0** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.75 | | cosine_accuracy@3 | 1.0 | | cosine_accuracy@5 | 1.0 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.75 | | cosine_precision@3 | 0.3333 | | cosine_precision@5 | 0.2 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.75 | | cosine_recall@3 | 1.0 | | cosine_recall@5 | 1.0 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.9077 | | cosine_mrr@10 | 0.875 | | **cosine_map@100** | **0.875** | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 36 training samples * Columns: positive and anchor * Approximate statistics based on the first 36 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details |
  • min: 13 tokens
  • mean: 23.92 tokens
  • max: 41 tokens
|
  • min: 31 tokens
  • mean: 321.11 tokens
  • max: 512 tokens
| * Samples: | positive | anchor | |:------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | How does ZenML integrate Spark step operators for executing individual steps? | Spark

Executing individual steps on Spark

The spark integration brings two different step operators:

Step Operator: The SparkStepOperator serves as the base class for all the Spark-related step operators.

Step Operator: The KubernetesSparkStepOperator is responsible for launching ZenML steps as Spark applications with Kubernetes as a cluster manager.

Step Operators: SparkStepOperator

A summarized version of the implementation can be summarized in two parts. First, the configuration:

from typing import Optional, Dict, Any
from zenml.step_operators import BaseStepOperatorConfig

class SparkStepOperatorConfig(BaseStepOperatorConfig):
"""Spark step operator config.

Attributes:
master: is the master URL for the cluster. You might see different
schemes for different cluster managers which are supported by Spark
like Mesos, YARN, or Kubernetes. Within the context of this PR,
the implementation supports Kubernetes as a cluster manager.
deploy_mode: can either be 'cluster' (default) or 'client' and it
decides where the driver node of the application will run.
submit_kwargs: is the JSON string of a dict, which will be used
to define additional params if required (Spark has quite a
lot of different parameters, so including them, all in the step
operator was not implemented).
"""

master: str
deploy_mode: str = "cluster"
submit_kwargs: Optional[Dict[str, Any]] = None

and then the implementation:

from typing import List
from pyspark.conf import SparkConf

from zenml.step_operators import BaseStepOperator

class SparkStepOperator(BaseStepOperator):
"""Base class for all Spark-related step operators."""

def _resource_configuration(
self,
spark_config: SparkConf,
resource_configuration: "ResourceSettings",
) -> None:
"""Configures Spark to handle the resource configuration.""" | | How can ZenML be used to finetune LLMs for specific tasks or to improve performance and cost? | Finetuning LLMs with ZenML

Finetune LLMs for specific tasks or to improve performance and cost.

PreviousEvaluating finetuned embeddingsNextSet up a project repository

Last updated 6 months ago | | How can I develop a custom model deployer in ZenML for efficient deployment and management of machine-learning models? | Develop a Custom Model Deployer

Learning how to develop a custom model deployer.

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.

To deploy and manage your trained machine-learning models, ZenML provides a stack component called Model Deployer. This component is responsible for interacting with the deployment tool, framework, or platform.

When present in a stack, the model deployer can also act as a registry for models that are served with ZenML. You can use the model deployer to list all models that are currently deployed for online inference or filtered according to a particular pipeline run or step, or to suspend, resume or delete an external model server managed through ZenML.

Base Abstraction

In ZenML, the base abstraction of the model deployer is built on top of three major criteria:

It needs to ensure efficient deployment and management of models in accordance with the specific requirements of the serving infrastructure, by holding all the stack-related configuration attributes required to interact with the remote model serving tool, service, or platform.

It needs to implement the continuous deployment logic necessary to deploy models in a way that updates an existing model server that is already serving a previous version of the same model instead of creating a new model server for every new model version (see the deploy_model abstract method). This functionality can be consumed directly from ZenML pipeline steps, but it can also be used outside the pipeline to deploy ad-hoc models. It is also usually coupled with a standard model deployer step, implemented by each integration, that hides the details of the deployment process from the user. | * Loss: [MatryoshkaLoss](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`: 4 - `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 - `tf32`: False - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `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 - `torch_empty_cache_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`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: False - `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`: False - `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 - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | dim_384_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 1.0 | 1 | 1.0 | 1.0 | 0.875 | 0.875 | | **2.0** | **3** | **1.0** | **1.0** | **1.0** | **0.875** | | 3.0 | 4 | 1.0 | 1.0 | 1.0 | 0.875 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.10 - Sentence Transformers: 3.2.1 - Transformers: 4.43.1 - PyTorch: 2.5.1+cu124 - Accelerate: 1.0.1 - Datasets: 3.0.2 - 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} } ```