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Installing Jan using Docker

Pre-requisites​

note

Supported OS: Linux, WSL2 Docker

  • Docker Engine and Docker Compose are required to run Jan in Docker mode. Follow the instructions below to get started with Docker Engine on Ubuntu.
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh ./get-docker.sh --dry-run
  • If you intend to run Jan in GPU mode, you need to install nvidia-driver and nvidia-docker2. Follow the instruction here for installation.

Run Jan in Docker Mode​

Docker compose ProfileDescription
cpu-fsRun Jan in CPU mode with default file system
cpu-s3fsRun Jan in CPU mode with S3 file system
gpu-fsRun Jan in GPU mode with default file system
gpu-s3fsRun Jan in GPU mode with S3 file system
Environment VariableDescription
S3_BUCKET_NAMES3 bucket name - leave blank for default file system
AWS_ACCESS_KEY_IDAWS access key ID - leave blank for default file system
AWS_SECRET_ACCESS_KEYAWS secret access key - leave blank for default file system
AWS_ENDPOINTAWS endpoint URL - leave blank for default file system
AWS_REGIONAWS region - leave blank for default file system
API_BASE_URLJan Server URL, please modify it as your public ip address or domain name default http://localhost:1377

  • Option 1: Run Jan in CPU mode

    # cpu mode with default file system
    docker compose --profile cpu-fs up -d

    # cpu mode with S3 file system
    docker compose --profile cpu-s3fs up -d

  • Option 2: Run Jan in GPU mode

    • Step 1: Check CUDA compatibility with your NVIDIA driver by running nvidia-smi and check the CUDA version in the output
    nvidia-smi

    # Output
    +---------------------------------------------------------------------------------------+
    | NVIDIA-SMI 531.18                 Driver Version: 531.18       CUDA Version: 12.1     |
    |-----------------------------------------+----------------------+----------------------+
    | GPU  Name                      TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
    | Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
    |                                         |                      |               MIG M. |
    |=========================================+======================+======================|
    |   0  NVIDIA GeForce RTX 4070 Ti    WDDM | 00000000:01:00.0  On |                  N/A |
    |  0%   44C    P8               16W / 285W|   1481MiB / 12282MiB |      2%      Default |
    |                                         |                      |                  N/A |
    +-----------------------------------------+----------------------+----------------------+
    |   1  NVIDIA GeForce GTX 1660 Ti    WDDM | 00000000:02:00.0 Off |                  N/A |
    |  0%   49C    P8               14W / 120W|      0MiB /  6144MiB |      0%      Default |
    |                                         |                      |                  N/A |
    +-----------------------------------------+----------------------+----------------------+
    |   2  NVIDIA GeForce GTX 1660 Ti    WDDM | 00000000:05:00.0 Off |                  N/A |
    | 29%   38C    P8               11W / 120W|      0MiB /  6144MiB |      0%      Default |
    |                                         |                      |                  N/A |
    +-----------------------------------------+----------------------+----------------------+

    +---------------------------------------------------------------------------------------+
    | Processes:                                                                            |
    |  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
    |        ID   ID                                                             Usage      |
    |=======================================================================================|

    • Step 2: Visit NVIDIA NGC Catalog and find the smallest minor version of image tag that matches your CUDA version (e.g., 12.1 -> 12.1.0)

    • Step 3: Update the Dockerfile.gpu line number 5 with the latest minor version of the image tag from step 2 (e.g. change FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04 AS base to FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS base)

    • Step 4: Run command to start Jan in GPU mode

      # GPU mode with default file system
      docker compose --profile gpu-fs up -d

      # GPU mode with S3 file system
      docker compose --profile gpu-s3fs up -d

This will start the web server and you can access Jan at http://localhost:3000.

warning
  • RAG feature is not supported in Docker mode with s3fs yet.