KServe Python SDK

Python SDK for KServe Server and Client.

Installation

KServe Python SDK can be installed by pip or poetry.

pip install

pip install kserve

To install Kserve with storage support

pip install kserve[storage]

Poetry

Install via Poetry.

make dev_install

To install Kserve with storage support

poetry install -E storage

or

poetry install --extras "storage"

KServe Python Server

KServe's python server libraries implement a standardized library that is extended by model serving frameworks such as Scikit Learn, XGBoost and PyTorch. It encapsulates data plane API definitions and storage retrieval for models.

It provides many functionalities, including among others:

• Registering a model and starting the server
• Prediction Handler
• Pre/Post Processing Handler
• Liveness Handler
• Readiness Handlers

It supports the following storage providers:

• Google Cloud Storage with a prefix: "gs://"
  • By default, it uses GOOGLE_APPLICATION_CREDENTIALS environment variable for user authentication.
  • If GOOGLE_APPLICATION_CREDENTIALS is not provided, anonymous client will be used to download the artifacts.
• S3 Compatible Object Storage with a prefix "s3://"
  • By default, it uses S3_ENDPOINT, AWS_ACCESS_KEY_ID, and AWS_SECRET_ACCESS_KEY environment variables for user authentication.
• Azure Blob Storage with the format: "https://{$STORAGE_ACCOUNT_NAME}.blob.core.windows.net/{$CONTAINER}/{$PATH}"
  • By default, it uses anonymous client to download the artifacts.
  • For e.g. https://kfserving.blob.core.windows.net/triton/simple_string/
• Local filesystem either without any prefix or with a prefix "file://". For example:
  • Absolute path: /absolute/path or file:///absolute/path
  • Relative path: relative/path or file://relative/path
  • For local filesystem, we recommended to use relative path without any prefix.
• Persistent Volume Claim (PVC) with the format "pvc://{$pvcname}/[path]".
  • The pvcname is the name of the PVC that contains the model.
  • The [path] is the relative path to the model on the PVC.
  • For e.g. pvc://mypvcname/model/path/on/pvc
• Generic URI, over either HTTP, prefixed with http:// or HTTPS, prefixed with https://. For example:
  • https://<some_url>.com/model.joblib
  • http://<some_url>.com/model.joblib

Metrics

For latency metrics, send a request to /metrics. Prometheus latency histograms are emitted for each of the steps (pre/postprocessing, explain, predict). Additionally, the latencies of each step are logged per request.

KServe Client

Getting Started

KServe's python client interacts with KServe control plane APIs for executing operations on a remote KServe cluster, such as creating, patching and deleting of a InferenceService instance. See the Sample for Python SDK Client to get started.

Documentation for Client API

Please review KServe Client API docs.

Documentation For Models

• KnativeAddressable
• KnativeCondition
• KnativeURL
• KnativeVolatileTime
• NetUrlUserinfo
• V1alpha1InferenceGraph
• V1alpha1InferenceGraphList
• V1alpha1InferenceGraphSpec
• V1alpha1InferenceGraphStatus
• V1alpha1InferenceRouter
• V1alpha1InferenceStep
• V1alpha1InferenceTarget
• V1beta1AlibiExplainerSpec
• V1beta1Batcher
• V1beta1ComponentExtensionSpec
• V1beta1ComponentStatusSpec
• V1beta1CustomExplainer
• V1beta1CustomPredictor
• V1beta1CustomTransformer
• V1beta1ExplainerConfig
• V1beta1ExplainerSpec
• V1beta1ExplainersConfig
• V1beta1InferenceService
• V1beta1InferenceServiceList
• V1beta1InferenceServiceSpec
• V1beta1InferenceServiceStatus
• V1beta1InferenceServicesConfig
• V1beta1IngressConfig
• V1beta1LoggerSpec
• V1beta1ModelSpec
• V1beta1ONNXRuntimeSpec
• V1beta1PodSpec
• V1beta1PredictorConfig
• V1beta1PredictorExtensionSpec
• V1beta1PredictorSpec
• V1beta1PredictorsConfig
• V1beta1SKLearnSpec
• V1beta1TFServingSpec
• V1beta1TorchServeSpec
• V1beta1TrainedModel
• V1beta1TrainedModelList
• V1beta1TrainedModelSpec
• V1beta1TrainedModelStatus
• V1beta1TransformerConfig
• V1beta1TransformerSpec
• V1beta1TransformersConfig
• V1beta1TritonSpec
• V1beta1XGBoostSpec