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
  • Introduction
  • Screenshots
  • Usage
  • External Dependencies
  1. User Guide
  2. Apps

Streamlit

PreviousMLflowNextTutorial

Last updated 2 years ago

Enterprise Applicable to Enterprise EditionCommunity Applicable to Community Edition

Introduction

Streamlit turns data scripts into shareable web apps in minutes. All in Python. All for free. No front‑end experience required.

Property
Description

App Image

Official Website

Screenshots

Usage

  1. Create a Streamlit app

  2. In the create page, fill the FILE_PATH variable. The server is run as the command streamlit run ${FILE_PATH}. You can fill a Streamlit python from:

    • Local file (e.g. /project/<group-name>/path/to/your/file)

  3. Open the Streamlit server you just created

  4. You can see the Streamlit dashboard

External Dependencies

You can manage external dependencies by adding:

When initializing, Streamlit will look for requirements files in the same directory of FILE_PATH and install external dependencies. Your Streamlit app is ready while the "You can now view your Streamlit app in your browser." shows up in the app logs.

If there are a lots of dependencies, it may take some time to install while starting your app. Any change not related to dependencies should show up immediately. Remember to restart your app after adding new dependencies.

Web URL (e.g. )

requirements.txt for Python dependencies managed by pip()

packages.txt for Debian dependencies managed by apt-get()

https://raw.githubusercontent.com/streamlit/streamlit-example/master/streamlit_app.py
docs
docs
infuseai/streamlit
https://streamlit.io/