Boostcamp CV Serving For Python

Overview

Boostcamp-CV-Serving

Prerequisites

  • MySQL
  • GCP Cloud Storage
    • GCP key file
  • Sentry
  • Streamlit Cloud
  • Secrets: .streamlit/secrets.toml
      #DO NOT SHARE THIS INFORMATION!!!!
      [mysql]
      host = <YOUR_HOST>
      port = 3306
      database = <YOUR_DATABASE>
      user = <YOUR_USER>
      password = <YOUR_PASSWORD>
    
      [gcp]
      project_id = <YOUR_PROJECT_ID>
      private_key_id = <YOUR_PROJECT_KEY>
      private_key = <YOUR_PRIVATE_KEY>
      client_email = <YOUR_CLIENT_EMAIL>
      client_id = <YOUR_CLIENT_ID>
      bucket = <YOUR_BUCKET>
    
      [sentry]
      sentry_url = <YOUR_SENTRY_URL>
    

Installation

Local Environmnet

  1. Add secrets.toml into .streamlit folder with the above information.
  2. Initialize Database
    1. python init_database.py
  3. Run following commands
    pip install -r requirements.txt
    streamlit run main.py
    

Streamlit Cloud Environment

  1. Sign up for https://streamlit.io/cloud using Github account.
  2. Click Deploy app.
  3. Choose Github repository and main python file.
  4. Copy and Paste the secrets by clicking the advanced setting button.
  5. Deploy
Owner
Jungwon Seo
CodeThief
Jungwon Seo
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Codes accompanying the paper "Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning" (NeurIPS 2021 Spotlight

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E-RAFT: Dense Optical Flow from Event Cameras

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[PNAS2021] The neural architecture of language: Integrative modeling converges on predictive processing

The neural architecture of language: Integrative modeling converges on predictive processing Code accompanying the paper The neural architecture of la

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ACSC Automatic extrinsic calibration for non-repetitive scanning solid-state LiDAR and camera systems. System Architecture 1. Dependency Tested with U

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Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination

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TimeLens: Event-based Video Frame Interpolation This repository is about the High Speed Event and RGB (HS-ERGB) dataset, used in the 2021 CVPR paper T

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MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift

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