ollama LINUX

### Ollama on Linux Installation and Usage #### Prerequisites Before installing Ollama, ensure that Docker is installed on the Ubuntu system as it serves as a fundamental component for deploying applications like Ollama[^1]. The process involves updating package lists and installing necessary packages to support Docker. To update the package list and install required dependencies: ```bash sudo apt-get update && sudo apt-get upgrade -y ``` Install Docker using the official guide or through convenience scripts provided by Docker itself. After ensuring Docker's successful installation, proceed with setting up Ollama-specific components such as LightRAG and bge-base-zh-v1.5 models which are essential parts of an advanced AI application setup. #### Installing Ollama Components via Docker For deploying Ollama along with its associated modules (LightRAG and bge-base-zh-v1.5), one can utilize Docker containers. This method encapsulates all dependencies within isolated environments making deployment straightforward across different systems without worrying about conflicting software versions. Create a `Dockerfile` tailored towards configuring these elements inside a container image: ```dockerfile FROM ubuntu:latest # Install prerequisites RUN apt-get update && \ apt-get install -y python3-pip git curl wget build-essential # Clone repository containing model configurations/scripts WORKDIR /app RUN git clone https://github.com/example-repo.git . # Setup Python environment & install specific version requirements COPY requirements.txt . RUN pip3 install --no-cache-dir -r requirements.txt # Download pre-trained models into designated directories RUN mkdir models; cd models && \ wget "https://example-model-download-link/model-lightRAG.tar.gz" && \ tar zxvf model-lightRAG.tar.gz && rm *.tar.gz RUN mkdir chinese_models; cd chinese_models && \ wget "https://example-chinese-model-download-link/bge-base-zh-v1.5.tar.gz" && \ tar zxvf bge-base-zh-v1.5.tar.gz && rm *.tar.gz CMD ["python", "./start_ollama_service.py"] ``` This example assumes there exists a GitHub repository holding configuration files needed for running services related to Ollama alongside any custom startup script (`start_ollama_service.py`). Adjust URLs according to actual sources where models reside publicly accessible online. Build this Docker image locally after saving above content into file named `Dockerfile`, then run created images following standard practices documented under Docker documentation resources. #### Running Services Using Docker Compose Alternatively, consider employing Docker Compose when managing multiple interconnected services simultaneously including databases, web servers, etc., together forming complex distributed architectures around core functionalities offered by Ollama platform. Prepare a YAML formatted definition specifying service definitions within single-file manifest called `docker-compose.yml`. Example snippet illustrating basic structure might look similar to below but should be adapted based upon project specifics. ```yaml version: '3' services: ollama-service: build: ./ ports: - "8080:80" volumes: - ./data:/var/lib/datastore depends_on: - db db: image: postgres:alpine environment: POSTGRES_USER: user POSTGRES_PASSWORD: password POSTGRES_DB: dbname ``` After preparing both `Dockerfile` and `docker-compose.yml`, execute commands from terminal session initiating entire stack at once while automatically handling dependency resolution between individual pieces involved during initialization phase. Use command line tooling supplied natively with Docker suite products to start projects defined previously: ```bash docker-compose up -d ``` The `-d` flag runs processes detached mode allowing background execution until explicitly stopped later on demand. #### Additional Tools Integration Incorporating additional tools may enhance productivity further depending upon workflow preferences. For instance, integrating NoMachine remote desktop solution facilitates graphical interface access over network connections securely[^3], useful particularly when dealing with GUI-based administration tasks remotely located machines hosting heavy computational workloads unsuitable directly interacting physically due geographical constraints present today’s globalized world.