Time-Series-Library Docker & Self-Hosted Deployment

GitHub

A Library for Advanced Deep Time Series Models for General Time Series Analysis.

12,997 stars Python Python 2 files · ~7,177 tokens #deep-learning#time-series#time-series-analysis
RAW Compose
1. Pull Image (DOCKERHUB) docker pull library/tslib
2. Docker Run (1-Click Launch) docker run -d --name time-series-library --restart always tslib
3. Podman Run podman run -d --name time-series-library --restart always tslib
4. Skopeo Inspect (Zero-Pull Manifest) skopeo inspect docker://docker.io/library/tslib:latest
5. ORAS Pull (OCI Artifact) oras pull tslib
6. Compose Deployment docker compose up -d
docker-compose.yml
Download .yml 
services:
  dev_tslib:
    image: tslib
    build:
      context: .
      target: tslib
      # args:
      #   http_proxy: "http://192.168.8.135:7897" #optional, Modify it to your agent address
      #   https_proxy: "http://192.168.8.135:7897" #optional, Modify it to your agent address
    container_name: tslib
    shm_size: 8gb
    tty: true
    restart: always
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility
      # - http_proxy=http://192.168.8.135:7897 #optional, Modify it to your agent address
      # - https_proxy=http://192.168.8.135:7897 #optional, Modify it to your agent address
    # ports:
    #   - "8888:8888"
    #   - "6006:6006"
    volumes:
      - workspace_data:/workspace
    working_dir: /workspace
volumes:
  workspace_data: