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KServe Python SDK

Python SDK for KServe Server and Client.


KServe Python SDK can be installed by pip or Setuptools.

pip install

pip install kserve


Install via Setuptools.

python install --user
(or sudo python install to install the package for all users)

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://{$STORAGE_ACCOUNT_NAME}{$CONTAINER}/{$PATH}"
    • By default, it uses anonymous client to download the artifacts.
    • For e.g.
  • Local filesystem either without any prefix or with a prefix "file://". For example:
    • Absolute path: /absolute/path or file:///absolute/path
    • Relative path: relative/path or file://relative/path
    • For local filesystem, we recommended to use relative path without any prefix.
  • Persistent Volume Claim (PVC) with the format "pvc://{$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

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

Class Method Description
KServeClient set_credentials Set Credentials
KServeClient create Create InferenceService
KServeClient get Get or watch the specified InferenceService or all InferenceServices in the namespace
KServeClient patch Patch the specified InferenceService
KServeClient replace Replace the specified InferenceService
KServeClient delete Delete the specified InferenceService
KServeClient wait_isvc_ready Wait for the InferenceService to be ready
KServeClient is_isvc_ready Check if the InferenceService is ready

Documentation For Models