PrimeHub
  • Introduction
  • Installation
  • Tiers and Licenses
  • End-to-End Tutorial
    • 1 - MLOps Introduction and Scoping the Project
    • 2 - Train and Manage the Model
    • 3 - Compare, Register and Deploy the Model
    • 4 - Build the Web Application
    • 5 - Summary
  • User Guide
    • User Portal
    • Notebook
      • Notebook Tips
      • Advanced Settings
      • PrimeHub Notebook Extension
      • Submit Notebook as Job
    • Jobs
      • Job Artifacts
      • Tutorial
        • (Part1) MNIST classifier training
        • (Part2) MNIST classifier training
        • (Advanced) Use Job Submission to Tune Hyperparameters
        • (Advanced) Model Serving by Seldon
        • Job Artifacts Simple Usecase
    • Models
      • Manage and Deploy Model
      • Model Management Configuration
    • Deployments
      • Pre-packaged servers
        • TensorFlow server
        • PyTorch server
        • SKLearn server
        • Customize Pre-packaged Server
        • Run Pre-packaged Server Locally
      • Package from Language Wrapper
        • Model Image for Python
        • Model Image for R
        • Reusable Base Image
      • Prediction APIs
      • Model URI
      • Tutorial
        • Model by Pre-packaged Server
        • Model by Pre-packaged Server (PHFS)
        • Model by Image built from Language Wrapper
    • Shared Files
    • Datasets
    • Apps
      • Label Studio
      • MATLAB
      • MLflow
      • Streamlit
      • Tutorial
        • Create Your Own App
        • Create an MLflow server
        • Label Dataset by Label Studio
        • Code Server
    • Group Admin
      • Images
      • Settings
    • Generate an PrimeHub API Token
    • Python SDK
    • SSH Server Feature
      • VSCode SSH Notebook Remotely
      • Generate SSH Key Pair
      • Permission Denied
      • Connection Refused
    • Advanced Tutorial
      • Labeling the data
      • Notebook as a Job
      • Custom build the Seldon server
      • PrimeHub SDK/CLI Tools
  • Administrator Guide
    • Admin Portal
      • Create User
      • Create Group
      • Assign Group Admin
      • Create/Plan Instance Type
      • Add InfuseAI Image
      • Add Image
      • Build Image
      • Gitsync Secret for GitHub
      • Pull Secret for GitLab
    • System Settings
    • User Management
    • Group Management
    • Instance Type Management
      • NodeSelector
      • Toleration
    • Image Management
      • Custom Image Guideline
    • Volume Management
      • Upload Server
    • Secret Management
    • App Settings
    • Notebooks Admin
    • Usage Reports
  • Reference
    • Jupyter Images
      • repo2docker image
      • RStudio image
    • InfuseAI Images List
    • Roadmap
  • Developer Guide
    • GitHub
    • Design
      • PrimeHub File System (PHFS)
      • PrimeHub Store
      • Log Persistence
      • PrimeHub Apps
      • Admission
      • Notebook with kernel process
      • JupyterHub
      • Image Builder
      • Volume Upload
      • Job Scheduler
      • Job Submission
      • Job Monitoring
      • Install Helper
      • User Portal
      • Meta Chart
      • PrimeHub Usage
      • Job Artifact
      • PrimeHub Apps
    • Concept
      • Architecture
      • Data Model
      • CRDs
      • GraphQL
      • Persistence Storages
      • Persistence
      • Resources Quota
      • Privilege
    • Configuration
      • How to configure PrimeHub
      • Multiple Jupyter Notebook Kernels
      • Configure SSH Server
      • Configure Job Submission
      • Configure Custom Image Build
      • Configure Model Deployment
      • Setup Self-Signed Certificate for PrimeHub
      • Chart Configuration
      • Configure PrimeHub Store
    • Environment Variables
Powered by GitBook
On this page
  • Model Information
  • Example
  1. User Guide
  2. Deployments
  3. Pre-packaged servers

SKLearn server

PreviousPyTorch serverNextCustomize Pre-packaged Server

Last updated 2 years ago

Model Information

Basic

Property
Description

Model Image

infuseai/sklearn-prepackaged:v0.1.0

Input

ndarray

Output

ndarray

Repository

Model URI Structure

<model uri>
└── model.joblib
  1. model.joblib: The model file should be saved by the joblib format. For more information, please see scikit-learn document

How It Works

You can check the detailed code in the . Here, we demonstrate by the pseudo-code.

Load the model

def __init__(self, model_uri):
    model_file = load_from_model_uri(model_uri)
    self._joblib = joblib.load(model_file)

Predict

def predict(self, X):
    return self._joblib.predict_proba(X)

Example

Property
Description

Model Image

infuseai/sklearn-prepackaged:v0.1.0

Model URI

gs://primehub-models/sklearn/iris

Test Request

curl -X POST http://localhost:5000/api/v1.0/predictions \
    -H 'Content-Type: application/json' \
    -d '{ "data": {"tensor": {"shape": [1, 4], "values": [5.3, 3.5, 1.4, 0.2]}} }'

Test Result

{"data":{"names":["t:0","t:1","t:2"],"tensor":{"shape":[1,3],"values":[0.8700986370655746,0.1298937698872714,7.593047154034911e-06]}},"meta":{}}

The example uses the

Model persistence
Github
scikit-learn iris dataset
Link