deepseekhelp2.txt

New Project:  Would the following info be the best way to use local models of ai on my pc? https://www.docker.com/blog/llm-docker-for-local-and-hugging-face-hosting/
ChatGPT said:

I can't access the web right now, but I can still help you determine if that Docker-based method is the best way to run local AI models on your PC.
Evaluating Docker for Local AI Models

The article you mentioned likely discusses using Docker to run Large Language Models (LLMs) locally or host them via Hugging Face. Docker can be a great option, but whether it's the best depends on your hardware, technical expertise, and intended use case.
Pros of Using Docker for Local AI Models

✅ Isolation & Ease of Setup – Docker containers package dependencies, preventing conflicts with your local environment.
✅ Portability – You can run models across different OSes without modifying the setup.
✅ Hugging Face Integration – Likely supports transformers and Text Generation Inference (TGI), making deployment easier.
✅ Resource Allocation Control – Docker can be set up to limit CPU/GPU/RAM usage for better performance tuning.
Cons of Using Docker for Local AI Models

❌ Overhead – Running models in Docker adds system resource overhead compared to running directly on bare metal.
❌ Learning Curve – If you're new to Docker, setting up models, configuring GPUs (via CUDA), and handling networking can be complex.
❌ Not Always Necessary – If you're just running a local model for personal use, you might not need the overhead of containerization.
Alternative Ways to Run AI Models Locally

Depending on your needs, here are some other approaches:

1️⃣ Direct Installation (Best for Maximum Performance)

    Use Ollama (for easy LLM hosting) → https://ollama.ai/
    Use GPTQ-for-LLaMa or AutoGPTQ for optimized quantized models
    Use KoboldAI or LM Studio for AI chatbot fine-tuning
    Ideal for local AI assistants, coding copilots, or chatbot applications

2️⃣ Local Web UI (Best for User-Friendly Setup)

    Use Text Generation Web UI (easy UI for models like LLaMa, Mistral, and GPTQ)
    Use KoboldAI (great for creative writing and storytelling)
    These give GUI-based control over models instead of CLI/Docker setups.

3️⃣ Docker Approach (Best for Scalability & Deployment)

    If you're deploying an AI model to multiple devices or as an API, Docker is great
    Can integrate with Hugging Face TGI, Triton Inference Server, or FastAPI
    Best suited for developers, self-hosting AI services, or custom AI chatbot deployments

Which One is Best for You?

    If you just want to run a local chatbot or LLM for fun → Use Ollama, LM Studio, or Text Generation Web UI
    If you want a controlled, isolated, and scalable AI hosting environment → Docker is a solid choice
    If you have a powerful GPU and want max performance → Use bare-metal installs with GPTQ-for-LLaMa

Let me know what your hardware setup is (CPU, GPU, RAM), and what exactly you want to do with local AI, and I can give you the best setup plan! 🚀
2/2
You said:
And then this is what deepseek recommended:

1. Core AI Stack Installation
A. Open WebUI (Chat Interface)
bash
Copy

docker run -d \
  --name open-webui \
  -p 3000:3000 \
  -v /home/jeremy/ai-data:/app/backend/data \
  --gpus=all \
  --restart always \
  ghcr.io/open-webui/open-webui:main

    Access at: http://localhost:3000

    First sign-up: Use any email (no verification needed)

B. Model Registry (Ollama)
bash
Copy

# Already running on port 11435 via previous setup
# Test API endpoint:
curl http://localhost:11435/api/tags

2. Install Key Models
A. Coding Model (DeepSeek-Coder 33B Quantized)
bash
Copy

docker exec ollama ollama pull deepseek-coder:33b-q4_k_m

B. General Purpose (Llama3 8B)
bash
Copy

docker exec ollama ollama pull llama3:8b

C. Medical/Technical (Meditron 7B)
bash
Copy

docker exec ollama ollama pull meditron:7b

3. Configure Open WebUI

    Connect to Ollama:

        In Open WebUI → Settings → Ollama Base URL → http://host.docker.internal:11435

    Add Models:

        WebUI → "Pull Model" → Enter mistral:7b, deepseek-coder:33b-q4_k_m, etc.

    Test Chat:

        Select model → "How to implement quicksort in Python?"

4. Advanced Setup - Jupyter Lab Integration
bash
Copy

# Install Jupyter in WSL
pip install jupyterlab
mkdir ~/notebooks && cd ~/notebooks

# Start Jupyter with Ollama API access
jupyter lab --ip=0.0.0.0 --port=8888 --NotebookApp.token='' --allow-root

Access at: http://localhost:8888
Sample Notebook Cell:
python
Copy

import requests
response = requests.post(
    "http://localhost:11435/api/generate",
    json={
        "model": "deepseek-coder:33b-q4_k_m",
        "prompt": "Write a Python async websocket server",
        "stream": False
    }
)
print(response.json()['response'])

5. Performance Monitoring Dashboard
bash
Copy

docker run -d \
  --name=docker-gpu \
  -p 3001:3000 \
  -v /var/run/docker.sock:/var/run/docker.sock \
  --restart unless-stopped \
  nicolargo/glances:latest-full

Access GPU stats at: http://localhost:3001
6. Scheduled Maintenance
bash
Copy

# Auto-update models weekly
(crontab -l ; echo "0 3 * * 0 docker exec ollama ollama pull --all") | crontab -

# Cleanup old containers/images
docker system prune -af --filter "until=24h"

Troubleshooting Toolkit
Issue	Command
Model Not Responding	docker restart ollama open-webui
Low Token Rates	nvidia-smi --query-gpu=utilization.gpu --format=csv
WebUI Login Issues	docker exec open-webui python3 -m app.scripts.reset_admin
Next-Level Optimization

    Custom Quantization:
    bash
    Copy

    docker exec ollama ollama create deepseek-coder:33b-custom -f Modelfile
    # Modelfile:
    FROM deepseek-coder:33b
    PARAMETER num_gpu 45
    PARAMETER quantize q4_k_m

    Fine-Tuning:
    Use Axolotl with your dataset:
    bash
    Copy

    git clone https://github.com/OpenAccess-AI-Collective/axolotl
    cd axolotl && pip install -e .

Let me know which component you want to deep-dive into next! 🚀
ChatGPT said:

DeepSeek's recommendation is a comprehensive approach for setting up and running AI models locally using Docker. This setup involves several layers, each tailored to optimize the AI environment on your machine. Let's break down the key components and how they interact to enhance your AI development and deployment process:
Core AI Stack Installation

    Open WebUI: Provides a user-friendly chat interface to interact with your models. It's set up to run continuously and use GPUs, which is excellent for performance.
    Model Registry (Ollama): Acts as a central hub for your AI models, allowing easy access and management. The test API endpoint ensures it's functioning before you proceed further.

Install Key Models

    Coding Model (DeepSeek-Coder 33B Quantized): This is a powerful coding model, great for software development tasks.
    General Purpose (Llama3 8B): A versatile model that can handle a variety of tasks.
    Medical/Technical (Meditron 7B): Specialized for medical and technical queries, perfect for niche applications.

Configure Open WebUI

    Connection and Model Management: Easy integration with Ollama to pull and manage models directly from the UI, which simplifies workflows.
    Testing: Quick testing setup to validate the model's implementation, crucial for debugging and setup validation.

Advanced Setup - Jupyter Lab Integration

    Local Jupyter Lab Setup: Allows for experimental and development work with direct API access to models, ideal for researchers and developers.
    Sample Notebook Cell: Demonstrates how to use the API for generating text, useful for quick prototyping.

Performance Monitoring Dashboard

    Real-time Monitoring: Tracks GPU and overall system performance, essential for maintaining efficiency and diagnosing issues in resource-intensive tasks.

Scheduled Maintenance

    Automated Updates and Cleanups: Keeps the system and models up-to-date and efficient, reducing manual overhead and ensuring the best performance.

Next-Level Optimization

    Custom Quantization: Allows for the creation of a custom-tailored model that matches your specific hardware configuration, optimizing performance.
    Fine-Tuning: The use of Axolotl for training models on specific datasets to improve model accuracy and response quality.

Key Advantages of this Setup

    Comprehensive Control: From model management to performance monitoring, you have tools at your disposal to control every aspect of your AI environment.
    Scalability: Easy to add new models or upgrade existing ones.
    Efficiency: Automated processes for maintenance and updates save time and ensure your system runs smoothly.
    Customizability: Ability to fine-tune and customize models to your specific needs.

Considerations

    Complexity: The setup requires familiarity with Docker, command-line operations, and potentially some troubleshooting.
    Resource Requirements: Depending on the models and the number of tasks, this setup can be resource-intensive. Ensure your hardware is capable of handling the demands.

This setup by DeepSeek is ideal if you're looking for a robust, scalable, and efficient local AI environment. It's especially suitable for developers, researchers, or businesses that need to run and manage multiple AI models effectively.

If there's a specific area within this setup you'd like more detailed information on, such as further customization, model management, or specific use cases, feel free to ask! 🚀
You said:
I'm wanting the best information possible for setting up a variety of ai models locally on my pc that are the best ones my pc can run for a variety of different situations. I have a 13700kf, a 4090 and 32gb ram 6000mhz.

What is recommended out of the options you gave earlier, that website, and this that deepseek proposed?
ChatGPT said:

With your powerful PC configuration—a 13700KF CPU, a 4090 GPU, and 32GB of RAM at 6000MHz—you're well-equipped to handle most advanced AI models for a variety of tasks. Considering the options from DeepSeek, Docker configurations, and local model management, here's a tailored recommendation to set up and run a variety of AI models locally:
Recommended Setup
1. Core Environment Setup

    Docker: Use Docker for managing containers as it provides a clean, isolated environment for each AI model. This setup will allow you to efficiently switch between different AI tasks without compromising your system’s stability.
    Model Registry (Ollama): It's crucial for managing various models. It will let you store, retrieve, and manage different AI models with ease, which is essential for a versatile setup like yours.

2. AI Models to Install

Given your hardware capabilities and the need for a variety of models:

    DeepSeek-Coder 33B Quantized: This model will be highly effective for coding-related tasks, utilizing your GPU’s capabilities for fast computation.
    Llama3 8B: A general-purpose model that can handle a broad range of queries and tasks.
    Meditron 7B: Specialized for medical and technical queries, leveraging your powerful hardware for tasks that require high accuracy and detail.

3. Advanced Tools and Integrations

    Jupyter Lab: Set up Jupyter Lab for interactive development and testing. It integrates well with Docker and will allow you to run notebooks directly interacting with your AI models.
    Performance Monitoring Dashboard: Use tools like Glances to monitor your GPU and overall system performance. This will help in optimizing resource usage and ensuring that your setup remains efficient under load.

4. Optimization and Maintenance

    Custom Quantization and Fine-Tuning: Utilize the capability to customize and fine-tune your models based on the specific needs of your projects. This can significantly enhance performance by optimizing the models to better utilize the 4090 GPU and high-speed RAM.
    Scheduled Maintenance: Set up cron jobs for regular updates and cleanup. This will help in keeping the models and the Docker environment fresh and efficient.

Additional Recommendations

    Explore Larger Models: Given your hardware, you might also explore larger models available from vendors like OpenAI (e.g., GPT-3 or newer versions) or other Hugging Face models that could benefit from your high-performance setup.
    Security and Data Handling: Ensure that your setup has adequate security measures, especially if you're handling sensitive data. This includes secure APIs and possibly encrypted data storage.
    Backup and Recovery: Implement robust backup and recovery processes, ensuring that you can recover your environment and models in case of hardware or software failure.

Conclusion

The proposal from DeepSeek combined with Docker's flexible container management and the use of Jupyter for interactive development provides a powerful, flexible, and efficient environment for running a variety of AI models. This setup will not only leverage your powerful PC's capabilities but also ensure that it remains manageable and scalable as you explore different AI applications.
You said:
Good. Will you give me a complete step by step guide that details each step i need to perform in order to get everything going and working for what you just recommended?
ChatGPT said:

Sure! Here’s a complete step-by-step guide tailored to your hardware for setting up and running a variety of AI models locally using Docker, with Jupyter Lab integration for an interactive development environment.
Step-by-Step Guide for AI Model Setup on a High-Performance PC
Step 1: Install Docker

Ensure Docker is installed and running on your system. You can download it from Docker's official website.

    Windows Installation:

    Download the installer from the Docker Hub website and follow the setup wizard.

Step 2: Model Registry Setup with Ollama

Set up Ollama for model management. This step involves running a container that will serve as your model registry.

    Run Ollama Container:

docker run -d \
    --name ollama \
    -p 11435:11435 \
    --restart always \
    ghcr.io/your-username/ollama:latest

Verify Ollama is Running:

    curl http://localhost:11435/api/tags

Step 3: Install Key AI Models

Pull necessary AI models into your local Ollama registry. This will allow you to use these models locally with ease.

    Coding Model:

docker exec ollama ollama pull deepseek-coder:33b-q4_k_m

General Purpose Model:

docker exec ollama ollama pull llama3:8b

Medical/Technical Model:

    docker exec ollama ollama pull meditron:7b

Step 4: Set Up Open WebUI

This web interface will allow you to interact with your models via a user-friendly interface.

    Run Open WebUI:

docker run -d \
    --name open-webui \
    -p 3000:3000 \
    -v /home/jeremy/ai-data:/app/backend/data \
    --gpus all \
    --restart always \
    ghcr.io/open-webui/open-webui:main

Access WebUI:

    Access at: http://localhost:3000

Step 5: Jupyter Lab Setup for Interactive Development

Jupyter Lab will be your interactive development environment to run and test AI models directly.

    Install Jupyter Lab:

pip install jupyterlab
mkdir ~/notebooks && cd ~/notebooks

Start Jupyter Lab:

jupyter lab --ip=0.0.0.0 --port=8888 --NotebookApp.token='' --allow-root

Access Jupyter Lab:

    Access at: http://localhost:8888

Step 6: Performance Monitoring Setup

Monitor your system's performance, especially focusing on GPU utilization.

    Run Glances for Monitoring:

docker run -d \
    --name=docker-gpu \
    -p 3001:3000 \
    -v /var/run/docker.sock:/var/run/docker.sock \
    --restart unless-stopped \
    nicolargo/glances:latest-full

Access GPU Stats:

    Access at: http://localhost:3001

Step 7: Scheduled Maintenance and Updates

Automate updates and maintenance to keep your system efficient and up-to-date.

    Setup Cron Jobs:

(crontab -l ; echo "0 3 * * 0 docker exec ollama ollama pull --all") | crontab -

Cleanup System:

    docker system prune -af --filter "until=24h"

Troubleshooting and Optimization

Keep these commands handy for troubleshooting and optimizing your setup.

    Restart Model or WebUI:

docker restart ollama open-webui

Check GPU Utilization:

nvidia-smi --query-gpu=utilization.gpu --format=csv

Reset WebUI Login:

    docker exec open-webui python3 -m app.scripts.reset_admin

This guide provides a comprehensive setup for running a variety of AI models tailored to your powerful PC specs. If you encounter any issues or need further customization, feel free to ask!
You said:
When it says bash, does that mean run it in my ubuntu window on windows?
ChatGPT said:

Yes, when you see "bash" in the instructions, it typically suggests that the commands are meant to be run in a Bash shell, which is the default shell in most Linux distributions, including Ubuntu. Since you're using Windows, the easiest way to use Bash is through the Windows Subsystem for Linux (WSL).

Here’s a brief guide to get started with Bash in WSL on Windows:

    Install WSL (if not already installed):
        Open PowerShell as Administrator and run:

        wsl --install

    This command installs the Ubuntu distribution by default, along with the WSL infrastructure.

    Launch WSL:
        After installation, you can launch the Ubuntu shell by searching for "Ubuntu" in the Start menu or by typing wsl in your command prompt or PowerShell.

    Run Bash Commands:
        Once in the Ubuntu shell, you can run the provided Bash commands as you would in a standard Linux environment.

Make sure you have WSL2 and Docker Desktop configured to use WSL2, which is crucial for running Docker commands smoothly in the Windows environment. This setup will provide you with a Linux-like environment to execute the commands for setting up and managing Docker containers, as indicated in the instructions.
You said:
jeremy@Vagabond:~$ docker run -d \
    --name ollama \
    -p 11435:11435 \
    --restart always \
    ghcr.io/your-username/ollama:latest
Unable to find image 'ghcr.io/your-username/ollama:latest' locally
docker: Error response from daemon: failed to resolve reference "ghcr.io/your-username/ollama:latest": failed to authorize: failed to fetch anonymous token: unexpected status from GET request to https://ghcr.io/token?scope=repository%3Ayour-username%2Follama%3Apull&service=ghcr.io: 403 Forbidden.
See 'docker run --help'.
jeremy@Vagabond:~$
ChatGPT said:

The error message you're seeing indicates that Docker is unable to find the specified image ghcr.io/your-username/ollama:latest because "your-username" is likely a placeholder, and you need to replace it with the actual username or repository where the Docker image is hosted.

Here's how you can correct this issue:

    Correct Image Path: Ensure you replace your-username with the actual GitHub username or organization under which the ollama Docker image is hosted. For example, if the repository belongs to an organization named "ai-models," the command should look like:

docker run -d \
   --name ollama \
   -p 11435:11435 \
   --restart always \
   ghcr.io/ai-models/ollama:latest

Check Image Availability: Before running the Docker command, make sure the Docker image ollama:latest is actually available in the GitHub Container Registry under the specified path. You can check this by visiting the GitHub repository for the image or using Docker commands to search for available tags.

Docker Login (if necessary): If the repository is private, you will need to log in to GitHub Container Registry using your GitHub credentials. You can do this by running:

    echo $CR_PAT | docker login ghcr.io -u USERNAME --password-stdin

    Replace $CR_PAT with your personal access token and USERNAME with your GitHub username.

    Use the Correct Tag: Make sure that the tag latest exists for the ollama image in the repository. If a different tag is more appropriate (like a specific version number), use that instead.

If you are unsure where the ollama image should come from or if the instructions provided to you were incorrect or incomplete, you might need to check back with the source of your instructions or find the correct repository where the Docker image is hosted. If ollama is a hypothetical or example container, you will need to use a real image that is suited for your needs.
You said:
jeremy@Vagabond:~$ docker run -d \
   --name ollama \
   -p 11435:11435 \
   --restart always \
   ghcr.io/ai-models/ollama:latest
Unable to find image 'ghcr.io/ai-models/ollama:latest' locally
docker: Error response from daemon: failed to resolve reference "ghcr.io/ai-models/ollama:latest": failed to authorize: failed to fetch anonymous token: unexpected status from GET request to https://ghcr.io/token?scope=repository%3Aai-models%2Follama%3Apull&service=ghcr.io: 403 Forbidden.
See 'docker run --help'.
jeremy@Vagabond:~$
ChatGPT said:

The error message "403 Forbidden" suggests that the Docker image ghcr.io/ai-models/ollama:latest is either private, or there may be an issue with the permissions set on the GitHub Container Registry. Here are a few steps you can take to troubleshoot and resolve this issue:

    Confirm Repository Access:
        Ensure that the repository ghcr.io/ai-models/ollama is public or that you have access to it if it's private.
        If the repository is private, you will need access rights to pull the image.

    Login to GitHub Container Registry:
        If you haven’t logged into GitHub Container Registry from your command line, you'll need to do so, especially if the repository is private. Here’s how you can login:

        echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

        Replace YOUR_GITHUB_PERSONAL_ACCESS_TOKEN with your personal access token that has the appropriate scopes for accessing GitHub Container Registry, and YOUR_GITHUB_USERNAME with your GitHub username.

    Verify Personal Access Token:
        Ensure that your GitHub personal access token has the correct scopes. You need at least read:packages to pull container images from private repositories.

    Check the Image Name and Tag:
        Double-check the image name and tag. It's possible that there might be a typo, or the tag latest might not exist. Verify this information on the GitHub repository or with the person or documentation that provided you with the command.

    Contact Repository Owner:
        If you are unsure about the access rights or the status of the repository, consider reaching out to the repository owner to confirm whether the Docker image is intended to be accessible and if there are any specific instructions you need to follow.

By following these steps, you should be able to resolve the issue with pulling the Docker image. If the problem persists after checking these aspects, it might be worth exploring alternative Docker images or repositories for your needs.
You said:
Login to GitHub Container Registry:

    If you haven’t logged into GitHub Container Registry from your command line, you'll need to do so, especially if the repository is private. Here’s how you can login:

    echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

    Replace YOUR_GITHUB_PERSONAL_ACCESS_TOKEN with your personal access token that has the appropriate scopes for accessing GitHub Container Registry, and YOUR_GITHUB_USERNAME with your GitHub username.

Verify Personal Access Token:

How do I get this or find this info?
ChatGPT said:

To get a GitHub Personal Access Token (PAT) and use it to log into the GitHub Container Registry from your command line, follow these steps:
Step 1: Create a Personal Access Token

    Go to GitHub:
        Open your web browser and go to GitHub.
        Log in with your GitHub credentials.

    Access Settings:
        Once logged in, click on your profile picture in the upper right corner, and then select Settings from the dropdown menu.

    Developer Settings:
        On the Settings page, scroll down to the bottom of the sidebar and click on Developer settings.

    Personal Access Tokens:
        Click on Personal access tokens under the Access tokens section.

    Generate New Token:
        Click on Generate new token.
        Give your token a name under the Note field to remember the purpose of this token (e.g., Docker Registry Access).
        Set the expiration for your token as you see fit.

    Select Scopes:
        Scroll to select scopes. You'll need at least the read:packages scope to pull Docker images. If you plan to push or update packages, also select write:packages and delete:packages.
        Optionally, if you're using this token for broader purposes, carefully select the scopes that match your needs.

    Generate Token:
        Once you’ve selected the appropriate scopes, click on Generate token at the bottom of the page.

Step 2: Use the Token with Docker

    Copy the Token:
        Make sure to copy your new personal access token. You won’t be able to see it again once you navigate away from the page.

    Login to Docker:
        Open your terminal.
        Replace YOUR_GITHUB_PERSONAL_ACCESS_TOKEN with the token you just copied and YOUR_GITHUB_USERNAME with your GitHub username.
        Run the command:

        echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

Important Notes

    Security: Keep your personal access token secure. Treat it like a password.
    Visibility: If you're using this token on shared systems, ensure that the command history or scripts don't expose your token.

By following these steps, you’ll be able to generate a personal access token and use it to authenticate with GitHub's Container Registry, enabling you to pull or push Docker images as required.
You said:
Developer settings brings me here



GitHub Apps
No GitHub Apps

Want to build something that integrates with and extends GitHub? Register a new GitHub App to get started developing on the GitHub API.
View documentation 
ChatGPT said:

It looks like you're in the wrong section for creating a Personal Access Token. Let me guide you through the correct steps again to ensure you're in the right place:
Correct Steps to Create a GitHub Personal Access Token

    Navigate to Your GitHub Profile Settings:
        Open your browser and log in to GitHub.
        Click on your profile photo in the upper right corner of the GitHub page.
        Select Settings from the dropdown menu.

    Access Personal Access Tokens:
        In the left sidebar of your settings page, scroll down to find the Access Tokens section. If you don't see "Access Tokens" directly, you might need to click on Developer settings first.
        Inside Developer settings, click on Personal access tokens. This is different from GitHub Apps and focuses on personal API access.

    Generate New Token:
        Click on Generate new token.
        Give your token a descriptive name in the Note field.
        Choose an expiration for your token.
        Select the necessary scopes. For Docker operations, you’ll need at least read:packages to pull images. If you also want to push images, select write:packages.

    Generate and Copy Your Token:
        After setting up your token settings, click the Generate token button at the bottom of the page.
        Important: Copy your new personal access token. You won’t be able to see it again once you leave this page.

If you are still having trouble finding the right page for Personal Access Tokens, you might want to use the direct URL to the token generation page: GitHub Personal Access Tokens. This link will take you directly to the page where you can create or manage your tokens.

After obtaining the token, follow the previous instructions to log in to the GitHub Container Registry using Docker in your terminal.
You said:
fine grained tokens or classic?
ChatGPT said:

For your use case involving Docker and accessing GitHub Container Registry, you can choose Classic tokens. Classic tokens provide a straightforward approach for setting scopes manually, which is suitable if you need to perform specific actions such as pulling and pushing Docker images.

Here's a brief comparison to help you decide:
Classic Tokens

    Scope Selection: Manually select the scopes you need.
    Use Case: Good for broader or more general API access when you know exactly which scopes you require.

Fine-Grained Tokens

    Customizable Permissions: Allows you to set more detailed and specific permissions.
    Use Case: Best when you need highly specific permissions for enhanced security, especially for applications that require strict access control.

Given that you're setting up Docker to pull from a GitHub repository, the classic token with the right scopes (read:packages and possibly write:packages) should be sufficient and simpler to manage.
You said:
jeremy@Vagabond:~$ docker run -d \
    --name ollama \
    -p 11435:11435 \
    --restart always \
    ghcr.io/jeremysmyth/ollama:latest
Unable to find image 'ghcr.io/jeremysmyth/ollama:latest' locally
docker: Error response from daemon: failed to resolve reference "ghcr.io/jeremysmyth/ollama:latest": ghcr.io/jeremysmyth/ollama:latest: not found.
See 'docker run --help'.
jeremy@Vagabond:~$
ChatGPT said:

It appears there's an issue with the Docker image you're trying to pull from the GitHub Container Registry. The error message indicates that the image ghcr.io/jeremysmyth/ollama:latest could not be found. This could be due to a few reasons:

    Image Does Not Exist: The repository or tag might be incorrectly specified or does not exist. Ensure that the image name and tag are correctly spelled and that the image has been pushed to your GitHub Container Registry.

    Privacy Settings: If the repository is private, make sure your Docker client is logged into GitHub Container Registry with the correct credentials that have access to this repository.

    Typographical Error: Double-check for any typographical errors in the repository or tag name.

Steps to Resolve:

    Verify the Repository: Make sure the repository ghcr.io/jeremysmyth/ollama:latest actually exists under your GitHub account. You can do this by logging into GitHub and checking your GitHub Container Registry for the repository.

    Check the Image Tag: Ensure that the latest tag is available for the ollama repository. If not, you may need to use a specific version tag that exists.

    Docker Login: If you haven't already, ensure that you are logged in to the GitHub Container Registry from your Docker client:

echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

Replace YOUR_GITHUB_PERSONAL_ACCESS_TOKEN with your personal access token, and YOUR_GITHUB_USERNAME with your GitHub username.

Push the Image: If the image is supposed to exist but doesn't, you might need to push the image to the registry:

    docker push ghcr.io/jeremysmyth/ollama:latest

If you are sure the repository should exist and is properly tagged, try pulling the image again after ensuring that you are logged in correctly. If the problem persists, double-check your repository settings and visibility in GitHub.
You said:
whats ghcr.io
ChatGPT said:

ghcr.io stands for GitHub Container Registry, which is a service provided by GitHub to store and manage Docker container images and other OCI (Open Container Initiative) images directly linked to GitHub repositories. It allows developers to publish, manage, and retrieve container images, which can be integrated into their GitHub workflows and accessed privately or publicly depending on the repository's settings.

Here are some key features and benefits of using GitHub Container Registry (GHCR):

    Integration with GitHub: GHCR is tightly integrated with GitHub, providing an easy and seamless experience for managing your project's container images alongside your source code.

    Access Control: You can control who can access and manage the images through GitHub's built-in access controls and permissions system, which aligns with the permissions for your repositories.

    Visibility and Management: Images can be made private or public to suit different needs, and you can manage these settings directly from your GitHub repository settings.

    Efficient Workflows: GHCR supports GitHub Actions, allowing you to automate workflows for building, pushing, and deploying container images directly from your GitHub repositories.

    Security and Reliability: The registry offers advanced security features such as container scanning for vulnerabilities, which helps maintain the security standards of your Docker images.

When you see a reference to ghcr.io in a Docker command, it's typically referring to the registry where the Docker image is hosted. This means when you pull, push, or run Docker images with ghcr.io in the command, you're interacting with images hosted on GitHub's Container Registry. For example:

docker pull ghcr.io/username/repository:tag

This command would pull a Docker image named repository with the tag tag from the user username's GitHub Container Registry.
You said:
jeremy@Vagabond:~$ echo "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JeremySmyth" | docker login ghcr.io -u YOUR_GITHUB_USER
NAME --password-stdin
Error response from daemon: Get "https://ghcr.io/v2/": denied: denied
jeremy@Vagabond:~$
ChatGPT said:

It looks like you encountered an error while trying to log into the GitHub Container Registry. This error typically indicates a problem with the authentication process. Here are a few steps to troubleshoot and resolve this issue:

    Correct Username Placeholder: It appears you used YOUR_GITHUB_USER literally in your command. You should replace YOUR_GITHUB_USER with your actual GitHub username. Make sure there are no typographical errors or placeholder text left in the command.

    Verify the Personal Access Token: Ensure that the personal access token you are using is correct and has the necessary permissions. It should have at least the read:packages scope to pull container images and write:packages if you intend to push images. Also, ensure there are no extra characters or spaces in your token.

    Use the Correct Command: Ensure your command syntax is correct without any typos. Here's how you should format it:

    echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

    Replace YOUR_GITHUB_PERSONAL_ACCESS_TOKEN with your actual token and YOUR_GITHUB_USERNAME with your actual GitHub username.

Here's an example of what the command might look like with placeholders replaced:

echo "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" | docker login ghcr.io -u JeremySmyth --password-stdin

Make sure there are no additional characters or errors in the token or username. If you continue to experience issues, double-check your token's permissions and validity in your GitHub account's settings under Developer settings > Personal access tokens.
You said:
jeremy@Vagabond:~$ docker run -d \
    --name ollama \
    -p 11435:11435 \
    --restart always \
    ghcr.io/jeremysmyth216/ollama
Unable to find image 'ghcr.io/jeremysmyth216/ollama:latest' locally
docker: Error response from daemon: failed to resolve reference "ghcr.io/jeremysmyth216/ollama:latest": ghcr.io/jeremysmyth216/ollama:latest: not found.
See 'docker run --help'.
jeremy@Vagabond:~$

This is the repository right????
https://github.com/JeremySmyth216/ollama
ChatGPT said:

The error you're encountering suggests that the Docker image you're trying to pull (ghcr.io/jeremysmyth216/ollama:latest) does not exist on the GitHub Container Registry under your account or is not set to public access. Here are a few steps you can take to resolve this issue:

    Verify the Repository Name: Ensure that the repository name in the URL matches exactly with what's on GitHub, including case sensitivity.

    Check for Published Images: Go to the GitHub repository, click on the "Packages" section (usually found on the right side or under the repository's name tab on GitHub) and make sure there's a published Docker image available. If no packages are listed, then no Docker images have been published to this repository.

    Repository Visibility and Access: Ensure that the repository and its packages are public. If they're private, you'll need the appropriate access rights to pull the image.

    Tag the Image Correctly: If you have pushed an image to this repository, make sure it is tagged as latest or specify the correct tag in your Docker command. If you're not sure what tags are available, you can check this in the "Packages" section of your GitHub repository.

    Check Docker Login: Ensure that you are logged into the GitHub Container Registry from your command line. If not, you will need to log in using your GitHub username and a personal access token with the appropriate permissions (read:packages at least).

    Docker Command Correctness: Ensure your Docker command is correctly formatted. It should include the tag if it's not latest, or you should ensure that there is a latest tag available. Here's an example command:

    docker run -d --name ollama -p 11435:11435 --restart always ghcr.io/jeremysmyth216/ollama:latest

If you've checked all the above and the image still cannot be found, it could be that the image hasn't been pushed yet or there was an error during the push process. In that case, you might need to push the Docker image to the repository first. Here’s a basic guide on how to push a Docker image to GitHub Container Registry:

    Tag your local image:

docker tag local-image:tag ghcr.io/jeremysmyth216/ollama:latest

Push the tagged image:

    docker push ghcr.io/jeremysmyth216/ollama:latest

Make sure you replace local-image:tag with your actual local image name and tag.
You said:
INFO[0000] It is recommended that docker daemon be restarted.
Failed to restart docker.service: Unit docker.service not found.
jeremy@Vagabond:~$
ChatGPT said:

The message you're seeing suggests that Docker might not be installed correctly or the Docker service isn't recognized on your system. Let's ensure that Docker is properly installed and the service is set up to run.

Here’s how you can troubleshoot and resolve this issue:
1. Verify Docker Installation

First, check if Docker is installed correctly by running:

docker --version

This should display the version of Docker installed on your system. If you get an error or it states that Docker is not installed, you will need to install Docker.
2. Install Docker (if necessary)

If Docker is not installed, you can install it on Ubuntu (assuming you are using Ubuntu based on your use of Bash and typical commands for Linux environments) by following these steps:

sudo apt update
sudo apt install docker.io

3. Start and Enable Docker Service

After installation, make sure the Docker service is started and enabled to run at boot:

sudo systemctl start docker
sudo systemctl enable docker

4. Check Docker Service Status

Verify that the Docker service is active and running:

sudo systemctl status docker

You should see output indicating that the service is active. If there are any errors, the status output should give you clues about what might be going wrong.
5. Restart Docker Service (if Docker was already installed)

If Docker was installed but not running, or if you needed to start the service manually as mentioned above, you can restart it to apply any pending changes or to refresh the service:

sudo systemctl restart docker

6. Running Docker Commands without Sudo

To run Docker commands without prefixing with sudo, add your username to the Docker group:

sudo usermod -aG docker ${USER}

You will need to log out and log back in for this to take effect, or you can use the following command to apply group changes immediately:

newgrp docker

7. Retry Your Docker Run Command

After ensuring Docker is correctly installed and running, try your Docker command again:

docker run -d --name ollama -p 11435:11435 --restart always ghcr.io/jeremysmyth216/ollama:latest

These steps should help you get Docker running properly on your system. If you continue to encounter problems, there may be specific configuration issues or additional steps needed based on your system's setup or the version of your operating system.
You said:
jeremy@Vagabond:~$ docker --version
Docker version 27.5.1, build 9f9e405
jeremy@Vagabond:~$ sudo systemctl start docker
sudo systemctl enable docker
Failed to start docker.service: Unit docker.service not found.
Failed to enable unit: Unit file docker.service does not exist.
jeremy@Vagabond:~$
ChatGPT said:

It appears that Docker is installed on your system, but the service isn't recognized, which suggests there may be an issue with how Docker is set up or linked with the system services. Here's how you can address this issue:
Reinstall Docker

Since the Docker service isn't being recognized, a clean reinstall might help fix any broken links or configurations:

    Uninstall Docker:

sudo apt-get remove docker docker-engine docker.io containerd runc

Remove Docker directories: This step ensures that any residual configuration files are removed.

sudo rm -rf /var/lib/docker
sudo rm -rf /var/lib/containerd

Update the package index:

sudo apt update

Install Docker using the official Docker repository: This method is generally more reliable than installing from the default Ubuntu repository because it ensures you get the latest version directly from Docker.

    Set up the repository:

sudo apt-get install \
  apt-transport-https \
  ca-certificates \
  curl \
  software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository \
  "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) \
  stable"

Install Docker CE:

    sudo apt update
    sudo apt-get install docker-ce docker-ce-cli containerd.io

Start and enable Docker service:

sudo systemctl start docker
sudo systemctl enable docker

Verify Docker is running:

    sudo systemctl status docker

This approach ensures you have the latest version of Docker and that it's installed correctly with all necessary dependencies. If the problem was due to a corrupted installation or outdated version, these steps should resolve it.

If you still encounter issues after this, it might be worth checking for any system-specific configurations or security settings that could be interfering with Docker's operation.
You said:
jeremy@Vagabond:~$ sudo apt-get remove docker docker-engine docker.io containerd runc
Reading package lists... Done
Building dependency tree... Done
Reading state information... Done
Package 'docker' is not installed, so not removed
E: Unable to locate package docker-engine
jeremy@Vagabond:~$
ChatGPT said:

It looks like the specific Docker packages you tried to remove weren't found on your system, which might indicate that Docker wasn't installed through the package manager or it's a different version/package name than expected.
Next Steps to Install Docker:

Since it seems Docker might not have been installed or there are discrepancies in how it's installed, you should try installing Docker from the official repository directly. This will ensure you have the most recent and compatible version of Docker for your system.

    Update your package manager and install prerequisites:

sudo apt update
sudo apt install apt-transport-https ca-certificates curl software-properties-common

Add Docker’s official GPG key:

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

Verify that you now have the key with the fingerprint: Search for the last 8 characters of the fingerprint.

sudo apt-key fingerprint 0EBFCD88

Set up the stable repository:

sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"

Update apt package index:

sudo apt update

Install the latest version of Docker CE (Community Edition):

sudo apt install docker-ce

Ensure Docker starts on boot:

sudo systemctl enable docker

Check Docker's status to confirm it's running:

sudo systemctl status docker

Run a test image to confirm Docker is installed correctly:

    sudo docker run hello-world

This sequence ensures Docker is installed from its official sources, which should help avoid any issues with package dependencies or incorrect versions that could cause the system to not recognize Docker commands.

After completing these steps, if you still face issues with Docker, you might want to check your system's compatibility or configuration settings that might be affecting Docker's installation or operation.
You said:
JeremySmyth (JeremySmyth216)

Which one  of these is my username with github?
ChatGPT said:

In the context of GitHub and the URL you provided earlier (https://github.com/JeremySmyth216/ollama), "JeremySmyth216" is your GitHub username. This is the username that appears in your repository URLs and is used to identify your account on GitHub. When you need to authenticate or reference your account, such as when logging into Docker with GitHub's Container Registry, you should use "JeremySmyth216".
You said:
echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

Do i leave password alone or change something or add something
ChatGPT said:

In the command you mentioned:

echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

You need to replace YOUR_GITHUB_PERSONAL_ACCESS_TOKEN with your actual GitHub Personal Access Token that you created earlier, and YOUR_GITHUB_USERNAME with your GitHub username, which is "JeremySmyth216". Here is how you should modify the command:

echo "ghp_YourActualTokenHere" | docker login ghcr.io -u JeremySmyth216 --password-stdin

Make sure to replace "ghp_YourActualTokenHere" with the actual token that you generated from your GitHub account. This token is used as the password for logging into GitHub's Container Registry (GHCR.io) via Docker.

Here's what each part means:

    echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN": This part outputs your personal access token, ensuring it's read as the input for the Docker login command.
    |: This is a pipe. It takes the output from the command on the left (your token) and uses it as the input for the command on the right.
    docker login ghcr.io: This is the command to log in to the GitHub Container Registry.
    -u YOUR_GITHUB_USERNAME: Specifies your GitHub username.
    --password-stdin: Tells Docker to expect the password through standard input (stdin), which is security best practice compared to passing the password directly in the command line.

Ensure your token and username are correctly entered to avoid errors during login.
You said:
i never have anything pop up to ask me for password
ChatGPT said:

That's expected behavior in the command you're using. When you use the --password-stdin flag in the Docker login command, it doesn't prompt you for a password because it's expecting the password (or in this case, your GitHub Personal Access Token) to be provided via the standard input (stdin), which is what the echo command is doing.

Here’s what happens step-by-step in your command:

    Echo Command: The echo command prints your personal access token. It's like typing your password but, instead of you typing, it's automatically provided by the echo.

    Pipe (|): This takes the output of the echo command (your token) and sends it directly as input to the next command.

    Docker Login Command: The docker login command with the --password-stdin option reads the password from its standard input, which is the token provided by the echo command.

Since the password (token) is piped directly from echo to docker login, there’s no need for an interactive password prompt to appear. This method is often used in scripts to automate processes without manual intervention, making it more secure than typing or embedding passwords directly in scripts or commands, where they might be exposed.

If everything is set up correctly, you should just see a response indicating whether the login was successful or not. If there's an issue, the error message should give clues about what went wrong, such as a typo in the token or username.
You said:
jeremy@Vagabond:~$ docker run -d \
   --name ollama \
   -p 11435:11435 \
   --restart always \
   ghcr.io/jeremysmyth216/ollama:latest
Unable to find image 'ghcr.io/jeremysmyth216/ollama:latest' locally
docker: Error response from daemon: manifest unknown

Run 'docker run --help' for more information
jeremy@Vagabond:~$

What's wrong with this command
ChatGPT said:

The error message "manifest unknown" typically indicates that the Docker image tag you are trying to pull does not exist on the registry. Here are a few steps to troubleshoot and resolve this issue:

    Check Image Repository Name and Tag: Ensure that the repository name and tag are correctly specified. It seems like you're using ghcr.io/jeremysmyth216/ollama:latest. Confirm that this repository and the latest tag indeed exist on GitHub's Container Registry under your account.

    Visibility and Access:
        Private Repository: If the repository is private, ensure you have logged in to the GitHub Container Registry (ghcr.io) with credentials that have access to this repository.
        Public Repository: If it's meant to be public, verify through the GitHub web interface that the repository is public and the image is correctly tagged.

    Correct Image Name: Double-check the repository name. Ensure that there's no typo and it exactly matches what you've pushed to the GitHub Container Registry. Sometimes, errors can occur if the image was pushed with a different tag or under a different repository name.

    Docker Login: Ensure that you're logged in to ghcr.io with the correct credentials. Since you've already tried logging in, it might be good to check if the login was successful or if there might have been an issue with the personal access token used.

    You can test your login status with:

    docker login ghcr.io

    Verify the Image on GitHub:
        Go to your GitHub account.
        Navigate to the 'Packages' section of your profile.
        Look for the ollama package and check if the latest tag is available.

If all these are confirmed and you still face issues, it could be related to how the image was pushed or an issue with GitHub Container Registry at the moment.
You said:
where do i look for the repository and the container registry and the image?
ChatGPT said:

To find the repository, image, and verify its presence in the GitHub Container Registry, you'll need to follow these steps on GitHub:
1. Log into GitHub

Start by logging into your GitHub account at github.com.
2. Navigate to your Packages

    Directly from URL: You can go directly to the packages area by using the URL github.com/JeremySmyth216?tab=packages (replace JeremySmyth216 with your actual GitHub username if it's different).
    Through GitHub Interface:
        Click on your profile picture in the upper right corner.
        Select "Your packages" from the dropdown menu.

3. Viewing the Specific Package

In the packages section:

    You will see all the container images you've published.
    Click on the package named ollama to open it. This is where your Docker images are stored.

4. Check Tags

    Within the package details, there should be a list of version tags that have been pushed to this repository. Check if there is a tag named latest or any other specific tag you are referencing in your Docker command.

Confirm Visibility Settings

    On the package page, you can also see the visibility (public or private) and manage permissions if necessary.

Verify Tags and Pushing Status

If you don't see the expected tags, or if the package doesn't exist:

    Ensure you have pushed the Docker image correctly from your local machine using commands like:

    docker tag local-image:tagname ghcr.io/jeremysmyth216/ollama:latest
    docker push ghcr.io/jeremysmyth216/ollama:latest

    Make sure you replace local-image:tagname with the correct local image name and tag you used before pushing.

5. Local Docker Check

You can also list the images and tags on your local machine to confirm what you have available to push:

docker images

This command will show all Docker images stored locally, including their tags.

These steps should help you locate your Docker image on GitHub and verify its status and availability. If the image and tags are correctly set up on GitHub, and you're still facing issues, double-check your Docker login credentials and network settings.
You said:
https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry

Does this site help with what needs to be done or changed so i can get this working?
ChatGPT said:

Yes, the GitHub documentation link you provided is an excellent resource for understanding how to work with the GitHub Container Registry. It covers a range of topics that are essential for managing Docker containers and images on GitHub, including:

    Authenticating with the GitHub Container Registry: This section provides detailed instructions on how to authenticate using a personal access token, which is crucial for pushing and pulling images from private repositories.

    Pushing and Pulling Docker Images: It includes step-by-step guidance on how to tag your local images correctly and push them to GitHub, as well as how to pull images from the registry. This can help you ensure that the commands you use are correct and that your images are properly tagged and pushed.

    Managing Docker Images: This part of the documentation helps you understand how to list, manage, and delete images from the GitHub Container Registry. It's useful for maintaining your repository and ensuring it only contains relevant and up-to-date images.

    Setting up GitHub Actions with the Container Registry: If you're interested in automating your Docker workflows, this section provides information on integrating GitHub Actions with your Docker images stored in the GitHub Container Registry.

    Access Permissions and Visibility: The documentation also explains how to manage access to your Docker images, which is important if you are working in a team or need to control who can access your images.

If you are encountering issues with pulling or pushing images, referring to this documentation will provide you with the necessary information to troubleshoot common problems, such as authentication issues, tagging mistakes, or configuration errors. It's a good idea to review the sections relevant to your current task to ensure that you are following the recommended practices and using the correct commands.
You said:
I want you to use the site to help fix my problems
ChatGPT said:

It looks like there was an error with my browsing tool. Let's try a different approach to help solve your issue with setting up the GitHub Container Registry.

You can use the GitHub documentation to find detailed steps to ensure everything is set up correctly. Specifically, you'll want to ensure you:

    Have the correct permissions to access the container registry.
    Have created a personal access token with the appropriate scopes (like read:packages or write:packages).
    Use the correct image name in your docker run command. Make sure the image is actually pushed to your GitHub Container Registry under your account.

Visit the GitHub Docs on the Container Registry for comprehensive guidance on each step. This should cover everything from logging in to pushing and pulling images.

If you're still encountering issues, consider checking your repository settings to make sure the container registry is enabled and properly configured. Also, confirm the exact name of the Docker image you're trying to run to ensure there are no typos or mismatches.
You said:
githubgpt.txt
Document

It seems like the GitHub Container Registry (GHCR) documentation provides detailed information about managing Docker and OCI images, which is critical for your use case. Here are the steps to resolve the issues you're encountering based on the information from your file and the GitHub documentation:
1. Verify Repository Name

Ensure that the repository name you are using in your Docker command matches exactly with the one on GitHub, including the case sensitivity. It appears there may have been an issue with how the repository name was entered or the repository might not be set up to host a container.
2. Docker Login

You mentioned logging in with a personal access token but encountered issues. Here are the steps to ensure proper login:

    Generate a new personal access token on GitHub with the appropriate permissions (read:packages and write:packages).
    Log in using the command: echo "<TOKEN>" | docker login ghcr.io -u <USERNAME> --password-stdin

Make sure to replace <TOKEN> with your personal access token and <USERNAME> with your GitHub username.
3. Push or Pull Images

Once logged in, you should be able to push or pull images. If you are trying to pull an image, ensure that it exists:

    Check the GitHub repository for an existing GitHub Actions workflow that pushes the Docker image to GHCR.
    Verify the tag of the image you are trying to pull. The tag must exist in the registry.

4. GitHub Actions for CI/CD

If you intend to automate Docker builds and pushes to GHCR, setting up GitHub Actions might be necessary. This can automate the process whenever you push changes to your repository.
5. Troubleshooting Permissions

If there are permission issues, ensure that your GitHub token has the correct scopes and that your GitHub repository settings allow your user account to access the packages.
6. Access Control

GitHub offers fine-grained access control to packages. Ensure that your Docker image repository on GitHub is either public or that your user account has explicit access to interact with the private repository.

If these steps do not resolve the issue, reviewing the specific error messages and checking the configuration for typos or misconfigurations would be necessary. Additionally, make sure Docker is correctly installed and running on your system, as some of your errors suggested possible issues with Docker's installation.
You said:
Yes. So with the knowledge from that site, help me with my issues

jeremy@Vagabond:~$ newgrp docker
jeremy@Vagabond:~$ docker run -d --name ollama -p 11435:11435 --restart always ghcr.io/jeremysmyth216/ollama:latest
Unable to find image 'ghcr.io/jeremysmyth216/ollama:latest' locally
docker: Error response from daemon: manifest unknown

Run 'docker run --help' for more information
jeremy@Vagabond:~$ echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin
Error response from daemon: Get "https://ghcr.io/v2/": denied: denied
jeremy@Vagabond:~$ echo "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" | docker login ghcr.io -u jeremysmyth216 --password-stdin
Login Succeeded
jeremy@Vagabond:~$ docker run -d \
   --name ollama \
   -p 11435:11435 \
   --restart always \
   ghcr.io/jeremysmyth216/ollama:latest
Unable to find image 'ghcr.io/jeremysmyth216/ollama:latest' locally
docker: Error response from daemon: manifest unknown

Run 'docker run --help' for more information
jeremy@Vagabond:~$ docker run --help
Usage:  docker run [OPTIONS] IMAGE [COMMAND] [ARG...]

Create and run a new container from an image

Aliases:
  docker container run, docker run

Options:
      --add-host list                    Add a custom host-to-IP mapping (host:ip)
      --annotation map                   Add an annotation to the container (passed through to the
                                         OCI runtime) (default map[])
  -a, --attach list                      Attach to STDIN, STDOUT or STDERR
      --blkio-weight uint16              Block IO (relative weight), between 10 and 1000, or 0 to
                                         disable (default 0)
      --blkio-weight-device list         Block IO weight (relative device weight) (default [])
      --cap-add list                     Add Linux capabilities
      --cap-drop list                    Drop Linux capabilities
      --cgroup-parent string             Optional parent cgroup for the container
      --cgroupns string                  Cgroup namespace to use (host|private)
                                         'host':    Run the container in the Docker host's cgroup
                                         namespace
                                         'private': Run the container in its own private cgroup
                                         namespace
                                         '':        Use the cgroup namespace as configured by the
                                                    default-cgroupns-mode option on the daemon
                                         (default)
      --cidfile string                   Write the container ID to the file
      --cpu-period int                   Limit CPU CFS (Completely Fair Scheduler) period
      --cpu-quota int                    Limit CPU CFS (Completely Fair Scheduler) quota
      --cpu-rt-period int                Limit CPU real-time period in microseconds
      --cpu-rt-runtime int               Limit CPU real-time runtime in microseconds
  -c, --cpu-shares int                   CPU shares (relative weight)
      --cpus decimal                     Number of CPUs
      --cpuset-cpus string               CPUs in which to allow execution (0-3, 0,1)
      --cpuset-mems string               MEMs in which to allow execution (0-3, 0,1)
  -d, --detach                           Run container in background and print container ID
      --detach-keys string               Override the key sequence for detaching a container
      --device list                      Add a host device to the container
      --device-cgroup-rule list          Add a rule to the cgroup allowed devices list
      --device-read-bps list             Limit read rate (bytes per second) from a device (default [])
      --device-read-iops list            Limit read rate (IO per second) from a device (default [])
      --device-write-bps list            Limit write rate (bytes per second) to a device (default [])
      --device-write-iops list           Limit write rate (IO per second) to a device (default [])
      --disable-content-trust            Skip image verification (default true)
      --dns list                         Set custom DNS servers
      --dns-option list                  Set DNS options
      --dns-search list                  Set custom DNS search domains
      --domainname string                Container NIS domain name
      --entrypoint string                Overwrite the default ENTRYPOINT of the image
  -e, --env list                         Set environment variables
      --env-file list                    Read in a file of environment variables
      --expose list                      Expose a port or a range of ports
      --gpus gpu-request                 GPU devices to add to the container ('all' to pass all GPUs)
      --group-add list                   Add additional groups to join
      --health-cmd string                Command to run to check health
      --health-interval duration         Time between running the check (ms|s|m|h) (default 0s)
      --health-retries int               Consecutive failures needed to report unhealthy
      --health-start-interval duration   Time between running the check during the start period
                                         (ms|s|m|h) (default 0s)
      --health-start-period duration     Start period for the container to initialize before
                                         starting health-retries countdown (ms|s|m|h) (default 0s)
      --health-timeout duration          Maximum time to allow one check to run (ms|s|m|h) (default 0s)
      --help                             Print usage
  -h, --hostname string                  Container host name
      --init                             Run an init inside the container that forwards signals
                                         and reaps processes
  -i, --interactive                      Keep STDIN open even if not attached
      --ip string                        IPv4 address (e.g., 172.30.100.104)
      --ip6 string                       IPv6 address (e.g., 2001:db8::33)
      --ipc string                       IPC mode to use
      --isolation string                 Container isolation technology
      --kernel-memory bytes              Kernel memory limit
  -l, --label list                       Set meta data on a container
      --label-file list                  Read in a line delimited file of labels
      --link list                        Add link to another container
      --link-local-ip list               Container IPv4/IPv6 link-local addresses
      --log-driver string                Logging driver for the container
      --log-opt list                     Log driver options
      --mac-address string               Container MAC address (e.g., 92:d0:c6:0a:29:33)
  -m, --memory bytes                     Memory limit
      --memory-reservation bytes         Memory soft limit
      --memory-swap bytes                Swap limit equal to memory plus swap: '-1' to enable
                                         unlimited swap
      --memory-swappiness int            Tune container memory swappiness (0 to 100) (default -1)
      --mount mount                      Attach a filesystem mount to the container
      --name string                      Assign a name to the container
      --network network                  Connect a container to a network
      --network-alias list               Add network-scoped alias for the container
      --no-healthcheck                   Disable any container-specified HEALTHCHECK
      --oom-kill-disable                 Disable OOM Killer
      --oom-score-adj int                Tune host's OOM preferences (-1000 to 1000)
      --pid string                       PID namespace to use
      --pids-limit int                   Tune container pids limit (set -1 for unlimited)
      --platform string                  Set platform if server is multi-platform capable
      --privileged                       Give extended privileges to this container
  -p, --publish list                     Publish a container's port(s) to the host
  -P, --publish-all                      Publish all exposed ports to random ports
      --pull string                      Pull image before running ("always", "missing", "never")
                                         (default "missing")
  -q, --quiet                            Suppress the pull output
      --read-only                        Mount the container's root filesystem as read only
      --restart string                   Restart policy to apply when a container exits (default "no")
      --rm                               Automatically remove the container and its associated
                                         anonymous volumes when it exits
      --runtime string                   Runtime to use for this container
      --security-opt list                Security Options
      --shm-size bytes                   Size of /dev/shm
      --sig-proxy                        Proxy received signals to the process (default true)
      --stop-signal string               Signal to stop the container
      --stop-timeout int                 Timeout (in seconds) to stop a container
      --storage-opt list                 Storage driver options for the container
      --sysctl map                       Sysctl options (default map[])
      --tmpfs list                       Mount a tmpfs directory
  -t, --tty                              Allocate a pseudo-TTY
      --ulimit ulimit                    Ulimit options (default [])
  -u, --user string                      Username or UID (format: <name|uid>[:<group|gid>])
      --userns string                    User namespace to use
      --uts string                       UTS namespace to use
  -v, --volume list                      Bind mount a volume
      --volume-driver string             Optional volume driver for the container
      --volumes-from list                Mount volumes from the specified container(s)
  -w, --workdir string                   Working directory inside the container
jeremy@Vagabond:~$ export CR_PAT=YOUR_TOKEN
jeremy@Vagabond:~$ export CR_PAT=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
jeremy@Vagabond:~$ $ echo $CR_PAT | docker login ghcr.io -u jeremysmyth216 --password-stdin
$: command not found
Error: Cannot perform an interactive login from a non TTY device
jeremy@Vagabond:~$
ChatGPT said:

It seems you are still encountering issues with the Docker command to pull the image from GitHub Container Registry (GHCR). The error "manifest unknown" typically indicates that the Docker image tag specified does not exist in the repository. Here's how you can resolve this and some additional steps to check and ensure everything is configured correctly:
Step 1: Verify Image and Tag

    Go to Your GitHub Repository: Navigate to your GitHub repository where you have your Docker images stored.
    Check for Published Images: Look under the "Packages" section of your GitHub repository to confirm that the Docker image ollama is indeed published and note the exact tag used. The tag should match exactly what you are using in your Docker command (including case sensitivity).

Step 2: Correct Docker Login Command

It appears there was a slight mistake in your Docker login command. Here’s the correct way to use your token for login:

echo "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" | docker login ghcr.io -u jeremysmyth216 --password-stdin

Make sure to replace the placeholder with your actual personal access token.
Step 3: Pull the Image

If the image and tag are correctly noted and you are logged in, try pulling the image again. Ensure you are using the correct tag as listed in the GHCR under your repository:

docker pull ghcr.io/jeremysmyth216/ollama:latest

Then, try running your Docker container:

docker run -d \
   --name ollama \
   -p 11435:11435 \
   --restart always \
   ghcr.io/jeremysmyth216/ollama:latest

Step 4: Ensure Docker Daemon is Running

Make sure that the Docker service is running on your machine. Since you encountered issues with starting the Docker service, here’s how you can reinstall Docker and ensure it starts correctly:

sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io
sudo systemctl start docker
sudo systemctl enable docker

Step 5: Docker Configuration

Ensure your Docker is configured to use GHCR:

    Verify Docker’s daemon.json for any misconfigurations that might be restricting access to GHCR.

Troubleshooting

    If the Docker command still fails, consider checking the Docker logs for more detailed error information.
    Ensure that there are no network issues or firewall settings that might be blocking Docker from accessing GHCR.

These steps should help you in successfully pulling the image from GHCR and running your Docker container. If there are still issues, checking the detailed logs and ensuring all settings (network, Docker daemon, GHCR permissions) are correct would be the next steps.
You said:
So how do i turn this page into putting the image, whatever that is, where it needs to be for me to pull it

https://github.com/JeremySmyth216/ollama
ChatGPT said:

To put an image into the GitHub Container Registry (GHCR) so that you can pull it from your Docker setup, you need to follow these steps. This involves packaging your Docker image and pushing it to your repository on GitHub, which then allows you to pull it from any system.
Step 1: Create a Docker Image

First, you need a Dockerfile to define what goes into your image. This might include your application, dependencies, libraries, and other files.

    Create a Dockerfile (if you haven’t already). Here’s a simple example:

# Use an official Python runtime as a parent image
FROM python:3.8-slim

# Set the working directory in the container
WORKDIR /app

# Copy the current directory contents into the container at /app
ADD . /app

# Install any needed packages specified in requirements.txt
RUN pip install --trusted-host pypi.python.org -r requirements.txt

# Make port 80 available to the world outside this container
EXPOSE 80

# Define environment variable
ENV NAME World

# Run app.py when the container launches
CMD ["python", "app.py"]

Customize this to fit the application you are deploying.

Build your Docker image:

    docker build -t jeremysmyth216/ollama:latest .

Step 2: Log in to GitHub Container Registry

Use your GitHub token to log in to the GitHub Container Registry. Make sure your token has the correct scopes (write:packages and read:packages).

echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

Step 3: Tag Your Docker Image for GHCR

Your image needs to be tagged with the GHCR repository path.

docker tag jeremysmyth216/ollama:latest ghcr.io/jeremysmyth216/ollama:latest

Step 4: Push the Docker Image to GHCR

Now push your tagged image to GitHub.

docker push ghcr.io/jeremysmyth216/ollama:latest

Step 5: Verify the Image in GitHub

    Go to your GitHub repository.
    Click on the "Packages" section which is generally on the right side of the repository page.
    You should see your ollama package listed there.

Step 6: Pull the Image from GHCR

Once the image is confirmed to be in your GHCR, you can pull it from any Docker environment using the same login credentials and the following command:

docker pull ghcr.io/jeremysmyth216/ollama:latest

Additional Tips

    Ensure that your GitHub repository is configured to handle GitHub Packages and that container image support is enabled.
    Check the visibility of your package: GitHub packages can be set to either public or private, depending on your needs. If it's set to private, you'll need to authenticate to pull it even if you are on your own machine.

By following these steps, you will have your Docker image hosted in GHCR and available to pull down to any machine authenticated with your GitHub credentials.
You said:
Now, how do i comebine this knowledge with the info way earlier you told me to do with setting up the ai language models on my pc
ChatGPT said:

Combining the knowledge of managing Docker images with GHCR and setting up AI language models on your PC involves several steps. The goal is to ensure your Docker environment is ready to run the AI models you're interested in, such as those from Hugging Face or any other custom models you might want to utilize. Here’s a step-by-step guide integrating these elements:
Step 1: Prepare Your Docker Environment

Assuming you now have your Docker image in the GitHub Container Registry, the first step is to set up your Docker environment properly.

    Install Docker if it's not already installed. You've run into issues with Docker commands earlier, so make sure Docker is properly installed and running:

sudo apt update
sudo apt install docker-ce docker-ce-cli containerd.io
sudo systemctl enable --now docker
sudo usermod -aG docker $USER
newgrp docker  # Refreshes your group membership

Pull your custom Docker image (if you have pushed it to GHCR):

    docker pull ghcr.io/jeremysmyth216/ollama:latest

Step 2: Set Up AI Models

Next, set up the Docker container that will manage the AI models. This involves using the Docker image you created, which might be configured to run specific AI models or environments.

    Run your Docker container:

    docker run -d \
       --name ollama \
       -p 11435:11435 \
       --restart always \
       ghcr.io/jeremysmyth216/ollama:latest

Step 3: Install and Configure AI Models

You'll need to install key AI models and configure them according to your needs. Here’s how you could manage AI models using Ollama as a registry for models:

    Install key models (replace ollama pull with your specific commands if using a different setup):

docker exec ollama ollama pull deepseek-coder:33b-q4_k_m
docker exec ollama ollama pull llama3:8b
docker exec ollama ollama pull meditron:7b

Configure the environment to connect and use these models. If using an Open WebUI or similar:

    # Access WebUI at http://localhost:3000
    # Configure Ollama in WebUI settings

Step 4: Integrate with Jupyter Lab or Other Tools

If you plan to use Jupyter Lab for interactive programming with these models:

    Install and start Jupyter Lab:

    pip install jupyterlab
    mkdir ~/notebooks && cd ~/notebooks
    jupyter lab --ip=0.0.0.0 --port=8888 --NotebookApp.token='' --allow-root

    Access Jupyter Lab at http://localhost:8888 and start using your models via API calls or direct integration.

Step 5: Monitoring and Maintenance

Set up monitoring tools to keep track of your Docker containers and AI models’ performance.

    Use Docker tools or third-party applications to monitor your system’s resource usage and performance.

    Regularly update your models and Docker images:

    docker exec ollama ollama pull --all  # Regularly update all models

Step 6: Troubleshoot and Optimize

Based on the issues you encounter (like API access issues, model loading errors, etc.), troubleshoot and make necessary adjustments.

    Use logs to diagnose issues: docker logs ollama
    Restart services if needed: docker restart ollama

By following these integrated steps, you will have a robust environment to run and manage various AI language models locally on your PC, tailored to your specific needs and leveraging your powerful hardware setup.
You said:
I need to take this website, https://github.com/ollama/ollama/releases/tag/v0.5.13, and do all of this with it to my github, jeremysmyth216

Step 1: Create a Docker Image

First, you need a Dockerfile to define what goes into your image. This might include your application, dependencies, libraries, and other files.

    Create a Dockerfile (if you haven’t already). Here’s a simple example:

# Use an official Python runtime as a parent image
FROM python:3.8-slim

# Set the working directory in the container
WORKDIR /app

# Copy the current directory contents into the container at /app
ADD . /app

# Install any needed packages specified in requirements.txt
RUN pip install --trusted-host pypi.python.org -r requirements.txt

# Make port 80 available to the world outside this container
EXPOSE 80

# Define environment variable
ENV NAME World

# Run app.py when the container launches
CMD ["python", "app.py"]

Customize this to fit the application you are deploying.

Build your Docker image:

    docker build -t jeremysmyth216/ollama:latest .

Step 2: Log in to GitHub Container Registry

Use your GitHub token to log in to the GitHub Container Registry. Make sure your token has the correct scopes (write:packages and read:packages).

echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

Step 3: Tag Your Docker Image for GHCR

Your image needs to be tagged with the GHCR repository path.

docker tag jeremysmyth216/ollama:latest ghcr.io/jeremysmyth216/ollama:latest

Step 4: Push the Docker Image to GHCR

Now push your tagged image to GitHub.

docker push ghcr.io/jeremysmyth216/ollama:latest

Step 5: Verify the Image in GitHub

    Go to your GitHub repository.
    Click on the "Packages" section which is generally on the right side of the repository page.
    You should see your ollama package listed there.

Step 6: Pull the Image from GHCR

Once the image is confirmed to be in your GHCR, you can pull it from any Docker environment using the same login credentials and the following command:

docker pull ghcr.io/jeremysmyth216/ollama:latest

Additional Tips

    Ensure that your GitHub repository is configured to handle GitHub Packages and that container image support is enabled.
    Check the visibility of your package: GitHub packages can be set to either public or private, depending on your needs. If it's set to private, you'll need to authenticate to pull it even if you are on your own machine.

By following these steps, you will have your Docker image hosted in GHCR and available to pull down to any machine authenticated with your GitHub credentials.
ChatGPT said:

It seems I can't directly access the specific page for downloading the source code from GitHub. You can manually download the source code for version 0.5.13 of Ollama by following these steps:

    Go to the Ollama GitHub repository.
    Look for the release marked "v0.5.13".
    Under this release, you will typically find "Source code (zip)" or "Source code (tar.gz)".
    Click on either of these links to download the source code to your machine.

Once you've downloaded the source code, you can proceed with creating a Docker image as previously described, customizing it as needed for the application you are deploying. This involves writing a Dockerfile, building the image, and then pushing it to your GitHub Container Registry.
You said:
It's downloaded, in zip
ChatGPT said:

Great! Now that you have the source code downloaded as a zip file, you'll need to extract it and set up your Docker environment. Here’s how you can proceed:
Step 1: Prepare the Dockerfile

    Extract the Zip File: Locate the downloaded zip file and extract it to a preferred directory.
    Create or Update the Dockerfile: Navigate into the extracted directory. If there’s an existing Dockerfile, you might not need to create a new one. If not, create a Dockerfile that fits the requirements of the Ollama project or your customization needs. Here’s an example Dockerfile based on the simple setup you mentioned earlier:

# Use an official Python runtime as a parent image
FROM python:3.8-slim

# Set the working directory in the container
WORKDIR /app

# Copy the current directory contents into the container at /app
ADD . /app

# Install any needed packages specified in requirements.txt
RUN pip install --trusted-host pypi.python.org -r requirements.txt

# Make port 80 available to the world outside this container
EXPOSE 80

# Define environment variable
ENV NAME World

# Run app.py when the container launches
CMD ["python", "app.py"]

Step 2: Build Your Docker Image

Open your terminal, navigate to the directory containing your Dockerfile, and run the following command to build your Docker image:

docker build -t jeremysmyth216/ollama:latest .

Step 3: Log in to GitHub Container Registry

Make sure you’re logged into the GitHub Container Registry using your personal access token with the appropriate permissions:

echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

Step 4: Tag Your Docker Image for GHCR

Tag the newly created Docker image to match the GitHub Container Registry path:

docker tag jeremysmyth216/ollama:latest ghcr.io/jeremysmyth216/ollama:latest

Step 5: Push the Docker Image to GHCR

Push your Docker image to the GitHub Container Registry:

docker push ghcr.io/jeremysmyth216/ollama:latest

Step 6: Verify the Image in GitHub

    Visit your GitHub repository.
    Navigate to the "Packages" section to see if your Docker image is listed.

Step 7: Pull the Image from GHCR

Once confirmed, you can pull the image on any machine where you are authenticated:

docker pull ghcr.io/jeremysmyth216/ollama:latest

This setup will ensure that your Docker image is built from the source code you downloaded, hosted on GitHub Container Registry, and ready to be deployed or used as needed.
You said:
# Use an official Python runtime as a parent image
FROM python:3.8-slim

# Set the working directory in the container
WORKDIR /app

# Copy the current directory contents into the container at /app
ADD . /app

# Install any needed packages specified in requirements.txt
RUN pip install --trusted-host pypi.python.org -r requirements.txt

# Make port 80 available to the world outside this container
EXPOSE 80

# Define environment variable
ENV NAME World

# Run app.py when the container launches
CMD ["python", "app.py"]

explain this like im five in steps
ChatGPT said:

Sure, let's break down what each part of this Dockerfile is doing in very simple terms, like explaining it to a five-year-old:
Dockerfile Explained for Kids

    Using a Python Picture:
        Think of this as choosing your favorite coloring book as the base. Here, we pick a special Python coloring book that already has some colors (Python 3.8, but a slim version that doesn’t have too many extra pages).

FROM python:3.8-slim

Picking a Playroom:

    Decide where in your house (container) you want to play and put all your toys (files). We pick a room called /app.

WORKDIR /app

Bringing Your Toys into the Playroom:

    Now, take all your toys that are outside and put them into the playroom you chose. This means we take all the files from where the Dockerfile is and put them inside the /app room in our container.

ADD . /app

Getting New Toys:

    Sometimes you need new toys to play with, which aren't already in your room. So, we look at a list (requirements.txt) that tells us which new toys (software packages) we need to buy and bring into the room. We make sure to get these toys from a trusted toy store (pypi.python.org).

RUN pip install --trusted-host pypi.python.org -r requirements.txt

Open the Window:

    We open a window in the playroom so that you can talk to people outside (other computers). We make sure the window is big enough (port 80) for your voice to go out.

EXPOSE 80

Naming Your World:

    You decide to name your imaginary world “World” because that’s a fun name! This is like a secret code to help remember how we set up our playroom.

ENV NAME World

What to Do When You Start Playing:

    Finally, when you go into the room, you decide to start playing a specific game (app.py). This tells everyone that when they come into your playroom, they should start playing this game.

    CMD ["python", "app.py"]

What Happens When You Follow This Plan?

    Build a Toy House (Container): When someone tells the computer to build this Docker toy house using your plan (Dockerfile), it knows exactly which coloring book to use, where to put your toys, which new toys to get, how to open the window, what name to remember, and what game to play!
    Show to Friends: You can show this special toy house to any of your friends anywhere in the world by telling them how to build one just like yours using your plan (Dockerfile).

And that's how you build a fun and special toy house (container) using your Dockerfile plan!
You said:
do i do this in powershell or what
ChatGPT said:

To create and work with Docker images as described in the Dockerfile, you'll need to use a command line tool where Docker commands are supported. Here’s how you can proceed based on your setup:
Windows Setup

    PowerShell or Command Prompt: Both PowerShell and Command Prompt work well for Docker commands on Windows. PowerShell is more powerful for scripting and has more features, so it's often preferred for more complex tasks.

    Windows Subsystem for Linux (WSL): If you're comfortable with Linux commands and have WSL set up, you can also use it to manage Docker containers. It provides a Linux-like environment on your Windows machine.

Steps to Use Docker in PowerShell

    Open PowerShell: You can search for PowerShell in your Windows search bar and open it. It's recommended to run it as an administrator for Docker commands.

    Run Docker Commands: Once in PowerShell, you can start entering the Docker commands as described in your Dockerfile and Docker documentation. For example, to build your Docker image, you would navigate to the directory containing your Dockerfile and then run:

    docker build -t jeremysmyth216/ollama:latest .

    Check Docker is Running: Before running any Docker commands, ensure Docker Desktop is running on your system. You can start it from the Start Menu.

Using Docker in WSL

If you prefer using Linux commands and have WSL installed:

    Open your Linux distribution via the Start Menu (e.g., Ubuntu).
    Run Docker Commands: You can execute the same Docker commands in the WSL terminal.

Summary

You can choose either PowerShell or WSL based on what you are comfortable with. Both will support Docker commands as long as Docker Desktop is running on your Windows system. For simplicity and direct compatibility with most Docker documentation, PowerShell is a straightforward choice if you are not as familiar with Linux commands.
You said:
jeremy@Vagabond:~$ docker build -t jeremysmyth216/ollama:latest
ERROR: "docker buildx build" requires exactly 1 argument.
See 'docker buildx build --help'.

Usage:  docker buildx build [OPTIONS] PATH | URL | -

Start a build
jeremy@Vagabond:~$
ChatGPT said:

It looks like you missed specifying the directory where the Dockerfile is located. The docker build command needs to know where to find the Dockerfile to build the image. By default, it looks in the current directory, but you need to ensure you include the dot (.) at the end of your command to indicate the current directory, or specify the path where the Dockerfile is located if it's in a different directory.

Here’s how you can correct this:

    Navigate to the Directory: Make sure you are in the directory where your Dockerfile is located. You can check the contents of your current directory using:

ls

This command will list all files and directories, and you should see your Dockerfile listed there.

Run the Docker Build Command: Assuming your Dockerfile is in the current directory and you are in that directory, execute:

    docker build -t jeremysmyth216/ollama:latest .

    Notice the dot (.) at the end of the command, which represents the current directory.

If your Dockerfile is in a different folder, replace the dot with the path to that folder. For example:

docker build -t jeremysmyth216/ollama:latest /path/to/folder

Make sure Docker is running on your system before you execute these commands. If you're using a virtual environment like WSL, ensure Docker's daemon is accessible from within WSL or that you've integrated WSL with Docker Desktop on your Windows.
You said:
How do in i do that last paragraph
ChatGPT said:

To ensure Docker is properly set up and running on your system, especially if you are using Windows Subsystem for Linux (WSL), follow these steps:
1. Check if Docker is Running:

First, verify that Docker is running on your system. You can check this by running the following command in your terminal or PowerShell:

docker info

If Docker is running, you should see a bunch of system information about Docker; if not, you might get an error stating that it can’t connect to the Docker daemon.
2. Start Docker:

If Docker isn't running, you can start it by searching for Docker Desktop in your Windows search bar and clicking on it to run the application.
3. Integrate Docker with WSL (if using WSL):

If you are using WSL and Docker Desktop on Windows, you need to ensure they are integrated so you can run Docker commands from within WSL seamlessly.

    Open Docker Desktop: Go to your system tray, right-click the Docker icon, and select 'Settings' or 'Preferences' on macOS.

    Go to Settings → Resources → WSL Integration: Make sure that the WSL Integration option is enabled, and your specific WSL version (e.g., Ubuntu) is checked. This will allow Docker Desktop to work with your Linux distribution in WSL.

    Apply & Restart: Save the changes and restart Docker Desktop to apply these settings.

4. Verify Integration:

To verify that Docker is properly integrated with WSL, open your WSL terminal and type:

docker ps

This command lists all running Docker containers and should work without errors if Docker is properly integrated.

By following these steps, Docker should be operational within your WSL environment, allowing you to build, run, and manage Docker containers directly from WSL.
You said:
jeremy@Vagabond:~$ docker ps
CONTAINER ID   IMAGE     COMMAND   CREATED   STATUS    PORTS     NAMES
jeremy@Vagabond:~$
ChatGPT said:

Great! Seeing no errors and just a blank list from the docker ps command indicates that Docker is running correctly on your system but you currently have no containers running. This is the expected output if you haven't run any Docker containers yet, or if all containers are stopped.

Now you can proceed with Docker operations such as building images, running containers, and managing Docker resources.

If you need to run a specific container or return to setting up and using your Docker images, just let me know what you want to do next!
You said:
I need to do this but idk how and i dont have a container like you said

Step 1: Prepare the Dockerfile

    Extract the Zip File: Locate the downloaded zip file and extract it to a preferred directory.
    Create or Update the Dockerfile: Navigate into the extracted directory. If there’s an existing Dockerfile, you might not need to create a new one. If not, create a Dockerfile that fits the requirements of the Ollama project or your customization needs. Here’s an example Dockerfile based on the simple setup you mentioned earlier:

# Use an official Python runtime as a parent image
FROM python:3.8-slim

# Set the working directory in the container
WORKDIR /app

# Copy the current directory contents into the container at /app
ADD . /app

# Install any needed packages specified in requirements.txt
RUN pip install --trusted-host pypi.python.org -r requirements.txt

# Make port 80 available to the world outside this container
EXPOSE 80

# Define environment variable
ENV NAME World

# Run app.py when the container launches
CMD ["python", "app.py"]

Step 2: Build Your Docker Image

Open your terminal, navigate to the directory containing your Dockerfile, and run the following command to build your Docker image:

docker build -t jeremysmyth216/ollama:latest .

Step 3: Log in to GitHub Container Registry

Make sure you’re logged into the GitHub Container Registry using your personal access token with the appropriate permissions:

echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

Step 4: Tag Your Docker Image for GHCR

Tag the newly created Docker image to match the GitHub Container Registry path:

docker tag jeremysmyth216/ollama:latest ghcr.io/jeremysmyth216/ollama:latest

Step 5: Push the Docker Image to GHCR

Push your Docker image to the GitHub Container Registry:

docker push ghcr.io/jeremysmyth216/ollama:latest

Step 6: Verify the Image in GitHub

    Visit your GitHub repository.
    Navigate to the "Packages" section to see if your Docker image is listed.

Step 7: Pull the Image from GHCR

Once confirmed, you can pull the image on any machine where you are authenticated:

docker pull ghcr.io/jeremysmyth216/ollama:latest

This setup will ensure that your Docker image is built from the source code you downloaded, hosted on GitHub Container Registry, and ready to be deployed or used as needed.
ChatGPT said:

To start, you need to prepare the environment for building your Docker image. Here's a step-by-step guide tailored for you:
Step 1: Prepare the Dockerfile

    Extract the Zip File: First, locate the downloaded zip file of the Ollama project on your machine. Use a tool like WinRAR, 7-Zip, or the built-in Windows extractor to unzip the file to a preferred directory.

    Create or Update the Dockerfile:
        Open a terminal (like PowerShell or your Ubuntu terminal).
        Navigate to the directory where you extracted the zip file.
        Check if there’s an existing Dockerfile. If it exists, you can modify it if necessary. If it doesn’t exist, you will create one.
        Use a text editor to create or modify the Dockerfile. You can use nano in Ubuntu terminal by typing nano Dockerfile and then entering the content you provided.

    Here’s what each part of the Dockerfile means:
        FROM python:3.8-slim: Starts with a Python 3.8 image that is slimmed down to reduce size.
        WORKDIR /app: Sets /app as the directory where all commands will be run inside the container.
        ADD . /app: Adds your current directory (where the Dockerfile is located) into /app in the container.
        RUN pip install --trusted-host pypi.python.org -r requirements.txt: Installs Python packages listed in requirements.txt.
        EXPOSE 80: Makes port 80 available to the host and other containers.
        ENV NAME World: Sets an environment variable NAME with the value World.
        CMD ["python", "app.py"]: The command that runs when the container starts. Here, it runs app.py with Python.

Step 2: Build Your Docker Image

After creating or updating your Dockerfile:

    Run the following command in your terminal (make sure you're in the directory with the Dockerfile):

    docker build -t jeremysmyth216/ollama:latest .

Step 3: Log in to GitHub Container Registry

    Make sure you are logged into GHCR. If you are not logged in or your token has expired, run:

    echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

Step 4: Tag Your Docker Image for GHCR

    Tag the newly created Docker image to match the GitHub Container Registry path:

    docker tag jeremysmyth216/ollama:latest ghcr.io/jeremysmyth216/ollama:latest

Step 5: Push the Docker Image to GHCR

    Push your Docker image to GitHub:

    docker push ghcr.io/jeremysmyth216/ollama:latest

Step 6: Verify the Image in GitHub

    Visit your GitHub repository.
    Navigate to the "Packages" section to see if your Docker image is listed.

Step 7: Pull the Image from GHCR

    Once confirmed, you can pull the image on any machine where you are authenticated:

    docker pull ghcr.io/jeremysmyth216/ollama:latest

This setup ensures that your Docker image is built from the source code you downloaded, hosted on GitHub Container Registry, and ready to be deployed or used as needed.
You said:
this is ollamas dockerfile

# vim: filetype=dockerfile

ARG FLAVOR=${TARGETARCH}

ARG ROCMVERSION=6.3.3
ARG JETPACK5VERSION=r35.4.1
ARG JETPACK6VERSION=r36.4.0
ARG CMAKEVERSION=3.31.2

# CUDA v11 requires gcc v10.  v10.3 has regressions, so the rockylinux 8.5 AppStream has the latest compatible version
FROM --platform=linux/amd64 rocm/dev-almalinux-8:${ROCMVERSION}-complete AS base-amd64
RUN yum install -y yum-utils \
    && yum-config-manager --add-repo https://dl.rockylinux.org/vault/rocky/8.5/AppStream/\$basearch/os/ \
    && rpm --import https://dl.rockylinux.org/pub/rocky/RPM-GPG-KEY-Rocky-8 \
    && dnf install -y yum-utils ccache gcc-toolset-10-gcc-10.2.1-8.2.el8 gcc-toolset-10-gcc-c++-10.2.1-8.2.el8 gcc-toolset-10-binutils-2.35-11.el8 \
    && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
ENV PATH=/opt/rh/gcc-toolset-10/root/usr/bin:$PATH

FROM --platform=linux/arm64 almalinux:8 AS base-arm64
# install epel-release for ccache
RUN yum install -y yum-utils epel-release \
    && dnf install -y clang ccache \
    && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo
ENV CC=clang CXX=clang++

FROM base-${TARGETARCH} AS base
ARG CMAKEVERSION
RUN curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
COPY CMakeLists.txt CMakePresets.json .
COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
ENV LDFLAGS=-s

FROM base AS cpu
RUN dnf install -y gcc-toolset-11-gcc gcc-toolset-11-gcc-c++
ENV PATH=/opt/rh/gcc-toolset-11/root/usr/bin:$PATH
RUN --mount=type=cache,target=/root/.ccache \
    cmake --preset 'CPU' \
        && cmake --build --parallel --preset 'CPU' \
        && cmake --install build --component CPU --strip --parallel 8

FROM base AS cuda-11
ARG CUDA11VERSION=11.3
RUN dnf install -y cuda-toolkit-${CUDA11VERSION//./-}
ENV PATH=/usr/local/cuda-11/bin:$PATH
RUN --mount=type=cache,target=/root/.ccache \
    cmake --preset 'CUDA 11' \
        && cmake --build --parallel --preset 'CUDA 11' \
        && cmake --install build --component CUDA --strip --parallel 8

FROM base AS cuda-12
ARG CUDA12VERSION=12.8
RUN dnf install -y cuda-toolkit-${CUDA12VERSION//./-}
ENV PATH=/usr/local/cuda-12/bin:$PATH
RUN --mount=type=cache,target=/root/.ccache \
    cmake --preset 'CUDA 12' \
        && cmake --build --parallel --preset 'CUDA 12' \
        && cmake --install build --component CUDA --strip --parallel 8

FROM base AS rocm-6
ENV PATH=/opt/rocm/hcc/bin:/opt/rocm/hip/bin:/opt/rocm/bin:/opt/rocm/hcc/bin:$PATH
RUN --mount=type=cache,target=/root/.ccache \
    cmake --preset 'ROCm 6' \
        && cmake --build --parallel --preset 'ROCm 6' \
        && cmake --install build --component HIP --strip --parallel 8

FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK5VERSION} AS jetpack-5
ARG CMAKEVERSION
RUN apt-get update && apt-get install -y curl ccache \
    && curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
COPY CMakeLists.txt CMakePresets.json .
COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
RUN --mount=type=cache,target=/root/.ccache \
    cmake --preset 'JetPack 5' \
        && cmake --build --parallel --preset 'JetPack 5' \
        && cmake --install build --component CUDA --strip --parallel 8

FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK6VERSION} AS jetpack-6
ARG CMAKEVERSION
RUN apt-get update && apt-get install -y curl ccache \
    && curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
COPY CMakeLists.txt CMakePresets.json .
COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
RUN --mount=type=cache,target=/root/.ccache \
    cmake --preset 'JetPack 6' \
        && cmake --build --parallel --preset 'JetPack 6' \
        && cmake --install build --component CUDA --strip --parallel 8

FROM base AS build
WORKDIR /go/src/github.com/ollama/ollama
COPY go.mod go.sum .
RUN curl -fsSL https://golang.org/dl/go$(awk '/^go/ { print $2 }' go.mod).linux-$(case $(uname -m) in x86_64) echo amd64 ;; aarch64) echo arm64 ;; esac).tar.gz | tar xz -C /usr/local
ENV PATH=/usr/local/go/bin:$PATH
RUN go mod download
COPY . .
ARG GOFLAGS="'-ldflags=-w -s'"
ENV CGO_ENABLED=1
RUN --mount=type=cache,target=/root/.cache/go-build \
    go build -trimpath -buildmode=pie -o /bin/ollama .

FROM --platform=linux/amd64 scratch AS amd64
COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12

FROM --platform=linux/arm64 scratch AS arm64
COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
COPY --from=jetpack-5 dist/lib/ollama/cuda_v11 lib/ollama/cuda_jetpack5
COPY --from=jetpack-6 dist/lib/ollama/cuda_v12 lib/ollama/cuda_jetpack6

FROM scratch AS rocm
COPY --from=rocm-6 dist/lib/ollama/rocm /lib/ollama/rocm

FROM ${FLAVOR} AS archive
COPY --from=cpu dist/lib/ollama /lib/ollama
COPY --from=build /bin/ollama /bin/ollama

FROM ubuntu:20.04
RUN apt-get update \
    && apt-get install -y ca-certificates \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*
COPY --from=archive /bin /usr/bin
ENV PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
COPY --from=archive /lib/ollama /usr/lib/ollama
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
ENV NVIDIA_VISIBLE_DEVICES=all
ENV OLLAMA_HOST=0.0.0.0:11434
EXPOSE 11434
ENTRYPOINT ["/bin/ollama"]
CMD ["serve"]
ChatGPT said:

Ollama's Dockerfile is quite sophisticated and employs multi-stage builds, meaning it uses multiple FROM statements to create separate stages of the build process. Each stage can be used to generate specific parts of the final image, and only the necessary components are moved to the final image. This approach optimizes the build process and minimizes the size of the final Docker image. Here's a simplified explanation of the key components in this Dockerfile, broken down for better understanding:

    Base Images:
        It defines different base images for different architectures (AMD64 and ARM64) using the FROM instruction. These base images are built on top of distributions like Almalinux and specialized images like NVIDIA’s L4T Jetpack.
        For AMD64, it uses ROCm's Almalinux-based images with installed development tools.

    Installing Dependencies:
        It installs various packages and tools necessary for the build process, such as GCC, CUDA, and others, depending on the architecture and purpose of the build stage.

    Environment Setup:
        It sets environment variables required for the build process and the final environment of the application.

    Building Process:
        Uses cmake to configure and build the applications. There are presets specified for different configurations like CPU, CUDA versions, and ROCm.
        The build process is optimized with caches to speed up the compilation.

    Final Stages:
        Different final stages are prepared for different platforms. For example, there are stages for just CUDA 11, CUDA 12, and ROCm.
        It also prepares different stages for NVIDIA Jetpack versions which are tailored for NVIDIA's ARM64 platforms.

    Final Image:
        The final Docker image is based on Ubuntu 20.04.
        It copies the necessary binaries and libraries from the build stages into the final image.
        Sets up environment variables to ensure the application can access GPU resources.
        Defines the entry point and command for the container to start the Ollama service.

This Dockerfile is configured to handle a complex build process that supports multiple hardware architectures and configurations, making it suitable for a wide range of deployment environments.
How to Use This Dockerfile

To use this Dockerfile, you would follow a process similar to:

    Prepare Your Environment:
        Make sure Docker is installed and running on your machine.
        Clone or download the Ollama source code to your local machine.

    Build the Docker Image:
        Navigate to the directory containing the Dockerfile.
        Run the build command tailored to your needs, specifying any build arguments as needed. For example:

    docker build --build-arg TARGETARCH=amd64 -t yourusername/ollama:latest .

Run Your Container:

    Once the image is built, you can run it using Docker:

        docker run -d --name myollama -p 11434:11434 yourusername/ollama:latest

This setup would allow you to deploy a highly customized AI service that can leverage GPU acceleration on both AMD and NVIDIA hardware, as well as offer support for advanced compute operations on ARM-based devices.
You said:
what do i edit this dockerfile with in order to do what i need to do with creating a image or whatever. i currently have no continers or anything in my cocker
ChatGPT said:

To edit and utilize the Dockerfile you have for creating a Docker image of Ollama, you'll follow these general steps:
Step 1: Prepare the Environment

    Choose an Editor:
        Use any text editor you're comfortable with to edit the Dockerfile. Common choices include Visual Studio Code, Sublime Text, or even simpler editors like Notepad++ or Nano (for command line usage).

    Setup Directory:
        Create a directory on your computer where you will store the Dockerfile and any other necessary files (like the source code of the application you are containerizing).

Step 2: Edit the Dockerfile

    Open the Dockerfile in your chosen editor.
    Make necessary changes or customizations to the Dockerfile. This might include:
        Changing the base image if you have different requirements.
        Modifying installation commands or adding new ones to install dependencies required by your application.
        Adjusting environment variables to match your configuration needs.
    Save the changes.

Step 3: Build the Docker Image

    Open a terminal or command prompt.
    Navigate to the directory containing your Dockerfile:

cd path/to/your/dockerfile

Run the Docker build command:

    docker build -t yourusername/ollama:latest .

        yourusername/ollama:latest is the tag you give to your Docker image. Replace yourusername with your DockerHub username or any name you prefer.
        The . at the end of the command tells Docker that the current directory is the context for the build and contains the Dockerfile.

Step 4: Verify the Image Creation

    After the build process completes, check if the image is created successfully by listing all Docker images:

    docker images

Step 5: Run Your Container

    Once the image is built, you can run it to create a container:

    docker run -d --name myollama -p 11434:11434 yourusername/ollama:latest

        This command runs the container in detached mode (-d), names it myollama, maps port 11434 of the container to port 11434 on your host, and uses the image tagged yourusername/ollama:latest.

Step 6: Check Running Containers

    To see if your container is running properly, use:

    docker ps

    This command lists all currently running containers.

By following these steps, you edit the Dockerfile, build a Docker image from it, and run a container based on that image. If you need specific changes in the Dockerfile related to the application logic or the environment setup, those should be clearly planned before the editing step.
You said:
Help me

The error you're encountering suggests that the Docker image you're trying to pull (ghcr.io/jeremysmyth216/ollama:latest) does not exist on the GitHub Container Registry under your account or is not set to public access. Here are a few steps you can take to resolve this issue:

    Verify the Repository Name: Ensure that the repository name in the URL matches exactly with what's on GitHub, including case sensitivity.

    Check for Published Images: Go to the GitHub repository, click on the "Packages" section (usually found on the right side or under the repository's name tab on GitHub) and make sure there's a published Docker image available. If no packages are listed, then no Docker images have been published to this repository.

    Repository Visibility and Access: Ensure that the repository and its packages are public. If they're private, you'll need the appropriate access rights to pull the image.

    Tag the Image Correctly: If you have pushed an image to this repository, make sure it is tagged as latest or specify the correct tag in your Docker command. If you're not sure what tags are available, you can check this in the "Packages" section of your GitHub repository.

    Check Docker Login: Ensure that you are logged into the GitHub Container Registry from your command line. If not, you will need to log in using your GitHub username and a personal access token with the appropriate permissions (read:packages at least).

    Docker Command Correctness: Ensure your Docker command is correctly formatted. It should include the tag if it's not latest, or you should ensure that there is a latest tag available. Here's an example command:

    docker run -d --name ollama -p 11435:11435 --restart always ghcr.io/jeremysmyth216/ollama:latest

If you've checked all the above and the image still cannot be found, it could be that the image hasn't been pushed yet or there was an error during the push process. In that case, you might need to push the Docker image to the repository first. Here’s a basic guide on how to push a Docker image to GitHub Container Registry:

    Tag your local image:

docker tag local-image:tag ghcr.io/jeremysmyth216/ollama:latest

Push the tagged image:

    docker push ghcr.io/jeremysmyth216/ollama:latest

Make sure you replace local-image:tag with your actual local image name and tag.
You said:
INFO[0000] It is recommended that docker daemon be restarted.
Failed to restart docker.service: Unit docker.service not found.
jeremy@Vagabond:~$
ChatGPT said:

The message you're seeing suggests that Docker might not be installed correctly or the Docker service isn't recognized on your system. Let's ensure that Docker is properly installed and the service is set up to run.

Here’s how you can troubleshoot and resolve this issue:
1. Verify Docker Installation

First, check if Docker is installed correctly by running:

docker --version

This should display the version of Docker installed on your system. If you get an error or it states that Docker is not installed, you will need to install Docker.
2. Install Docker (if necessary)

If Docker is not installed, you can install it on Ubuntu (assuming you are using Ubuntu based on your use of Bash and typical commands for Linux environments) by following these steps:

sudo apt update
sudo apt install docker.io

3. Start and Enable Docker Service

After installation, make sure the Docker service is started and enabled to run at boot:

sudo systemctl start docker
sudo systemctl enable docker

4. Check Docker Service Status

Verify that the Docker service is active and running:

sudo systemctl status docker

You should see output indicating that the service is active. If there are any errors, the status output should give you clues about what might be going wrong.
5. Restart Docker Service (if Docker was already installed)

If Docker was installed but not running, or if you needed to start the service manually as mentioned above, you can restart it to apply any pending changes or to refresh the service:

sudo systemctl restart docker

6. Running Docker Commands without Sudo

To run Docker commands without prefixing with sudo, add your username to the Docker group:

sudo usermod -aG docker ${USER}

You will need to log out and log back in for this to take effect, or you can use the following command to apply group changes immediately:

newgrp docker

7. Retry Your Docker Run Command

After ensuring Docker is correctly installed and running, try your Docker command again:

docker run -d --name ollama -p 11435:11435 --restart always ghcr.io/jeremysmyth216/ollama:latest

These steps should help you get Docker running properly on your system. If you continue to encounter problems, there may be specific configuration issues or additional steps needed based on your system's setup or the version of your operating system.
You said:
jeremy@Vagabond:~$ docker --version
Docker version 27.5.1, build 9f9e405
jeremy@Vagabond:~$ sudo systemctl start docker
sudo systemctl enable docker
Failed to start docker.service: Unit docker.service not found.
Failed to enable unit: Unit file docker.service does not exist.
jeremy@Vagabond:~$
ChatGPT said:

It appears that Docker is installed on your system, but the service isn't recognized, which suggests there may be an issue with how Docker is set up or linked with the system services. Here's how you can address this issue:
Reinstall Docker

Since the Docker service isn't being recognized, a clean reinstall might help fix any broken links or configurations:

    Uninstall Docker:

sudo apt-get remove docker docker-engine docker.io containerd runc

Remove Docker directories: This step ensures that any residual configuration files are removed.

sudo rm -rf /var/lib/docker
sudo rm -rf /var/lib/containerd

Update the package index:

sudo apt update

Install Docker using the official Docker repository: This method is generally more reliable than installing from the default Ubuntu repository because it ensures you get the latest version directly from Docker.

    Set up the repository:

sudo apt-get install \
  apt-transport-https \
  ca-certificates \
  curl \
  software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository \
  "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) \
  stable"

Install Docker CE:

    sudo apt update
    sudo apt-get install docker-ce docker-ce-cli containerd.io

Start and enable Docker service:

sudo systemctl start docker
sudo systemctl enable docker

Verify Docker is running:

    sudo systemctl status docker

This approach ensures you have the latest version of Docker and that it's installed correctly with all necessary dependencies. If the problem was due to a corrupted installation or outdated version, these steps should resolve it.

If you still encounter issues after this, it might be worth checking for any system-specific configurations or security settings that could be interfering with Docker's operation.
You said:
jeremy@Vagabond:~$ sudo apt-get remove docker docker-engine docker.io containerd runc
Reading package lists... Done
Building dependency tree... Done
Reading state information... Done
Package 'docker' is not installed, so not removed
E: Unable to locate package docker-engine
jeremy@Vagabond:~$
ChatGPT said:

It looks like the specific Docker packages you tried to remove weren't found on your system, which might indicate that Docker wasn't installed through the package manager or it's a different version/package name than expected.
Next Steps to Install Docker:

Since it seems Docker might not have been installed or there are discrepancies in how it's installed, you should try installing Docker from the official repository directly. This will ensure you have the most recent and compatible version of Docker for your system.

    Update your package manager and install prerequisites:

sudo apt update
sudo apt install apt-transport-https ca-certificates curl software-properties-common

Add Docker’s official GPG key:

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

Verify that you now have the key with the fingerprint: Search for the last 8 characters of the fingerprint.

sudo apt-key fingerprint 0EBFCD88

Set up the stable repository:

sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"

Update apt package index:

sudo apt update

Install the latest version of Docker CE (Community Edition):

sudo apt install docker-ce

Ensure Docker starts on boot:

sudo systemctl enable docker

Check Docker's status to confirm it's running:

sudo systemctl status docker

Run a test image to confirm Docker is installed correctly:

    sudo docker run hello-world

This sequence ensures Docker is installed from its official sources, which should help avoid any issues with package dependencies or incorrect versions that could cause the system to not recognize Docker commands.

After completing these steps, if you still face issues with Docker, you might want to check your system's compatibility or configuration settings that might be affecting Docker's installation or operation.
You said:
JeremySmyth (JeremySmyth216)

Which one  of these is my username with github?
ChatGPT said:

In the context of GitHub and the URL you provided earlier (https://github.com/JeremySmyth216/ollama), "JeremySmyth216" is your GitHub username. This is the username that appears in your repository URLs and is used to identify your account on GitHub. When you need to authenticate or reference your account, such as when logging into Docker with GitHub's Container Registry, you should use "JeremySmyth216".
You said:
echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

Do i leave password alone or change something or add something
ChatGPT said:

In the command you mentioned:

echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

You need to replace YOUR_GITHUB_PERSONAL_ACCESS_TOKEN with your actual GitHub Personal Access Token that you created earlier, and YOUR_GITHUB_USERNAME with your GitHub username, which is "JeremySmyth216". Here is how you should modify the command:

echo "ghp_YourActualTokenHere" | docker login ghcr.io -u JeremySmyth216 --password-stdin

Make sure to replace "ghp_YourActualTokenHere" with the actual token that you generated from your GitHub account. This token is used as the password for logging into GitHub's Container Registry (GHCR.io) via Docker.

Here's what each part means:

    echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN": This part outputs your personal access token, ensuring it's read as the input for the Docker login command.
    |: This is a pipe. It takes the output from the command on the left (your token) and uses it as the input for the command on the right.
    docker login ghcr.io: This is the command to log in to the GitHub Container Registry.
    -u YOUR_GITHUB_USERNAME: Specifies your GitHub username.
    --password-stdin: Tells Docker to expect the password through standard input (stdin), which is security best practice compared to passing the password directly in the command line.

Ensure your token and username are correctly entered to avoid errors during login.
You said:
i never have anything pop up to ask me for password
ChatGPT said:

That's expected behavior in the command you're using. When you use the --password-stdin flag in the Docker login command, it doesn't prompt you for a password because it's expecting the password (or in this case, your GitHub Personal Access Token) to be provided via the standard input (stdin), which is what the echo command is doing.

Here’s what happens step-by-step in your command:

    Echo Command: The echo command prints your personal access token. It's like typing your password but, instead of you typing, it's automatically provided by the echo.

    Pipe (|): This takes the output of the echo command (your token) and sends it directly as input to the next command.

    Docker Login Command: The docker login command with the --password-stdin option reads the password from its standard input, which is the token provided by the echo command.

Since the password (token) is piped directly from echo to docker login, there’s no need for an interactive password prompt to appear. This method is often used in scripts to automate processes without manual intervention, making it more secure than typing or embedding passwords directly in scripts or commands, where they might be exposed.

If everything is set up correctly, you should just see a response indicating whether the login was successful or not. If there's an issue, the error message should give clues about what went wrong, such as a typo in the token or username.
You said:
jeremy@Vagabond:~$ docker run -d \
   --name ollama \
   -p 11435:11435 \
   --restart always \
   ghcr.io/jeremysmyth216/ollama:latest
Unable to find image 'ghcr.io/jeremysmyth216/ollama:latest' locally
docker: Error response from daemon: manifest unknown

Run 'docker run --help' for more information
jeremy@Vagabond:~$

What's wrong with this command
ChatGPT said:

The error message "manifest unknown" typically indicates that the Docker image tag you are trying to pull does not exist on the registry. Here are a few steps to troubleshoot and resolve this issue:

    Check Image Repository Name and Tag: Ensure that the repository name and tag are correctly specified. It seems like you're using ghcr.io/jeremysmyth216/ollama:latest. Confirm that this repository and the latest tag indeed exist on GitHub's Container Registry under your account.

    Visibility and Access:
        Private Repository: If the repository is private, ensure you have logged in to the GitHub Container Registry (ghcr.io) with credentials that have access to this repository.
        Public Repository: If it's meant to be public, verify through the GitHub web interface that the repository is public and the image is correctly tagged.

    Correct Image Name: Double-check the repository name. Ensure that there's no typo and it exactly matches what you've pushed to the GitHub Container Registry. Sometimes, errors can occur if the image was pushed with a different tag or under a different repository name.

    Docker Login: Ensure that you're logged in to ghcr.io with the correct credentials. Since you've already tried logging in, it might be good to check if the login was successful or if there might have been an issue with the personal access token used.

    You can test your login status with:

    docker login ghcr.io

    Verify the Image on GitHub:
        Go to your GitHub account.
        Navigate to the 'Packages' section of your profile.
        Look for the ollama package and check if the latest tag is available.

If all these are confirmed and you still face issues, it could be related to how the image was pushed or an issue with GitHub Container Registry at the moment.
You said:
where do i look for the repository and the container registry and the image?
ChatGPT said:

To find the repository, image, and verify its presence in the GitHub Container Registry, you'll need to follow these steps on GitHub:
1. Log into GitHub

Start by logging into your GitHub account at github.com.
2. Navigate to your Packages

    Directly from URL: You can go directly to the packages area by using the URL github.com/JeremySmyth216?tab=packages (replace JeremySmyth216 with your actual GitHub username if it's different).
    Through GitHub Interface:
        Click on your profile picture in the upper right corner.
        Select "Your packages" from the dropdown menu.

3. Viewing the Specific Package

In the packages section:

    You will see all the container images you've published.
    Click on the package named ollama to open it. This is where your Docker images are stored.

4. Check Tags

    Within the package details, there should be a list of version tags that have been pushed to this repository. Check if there is a tag named latest or any other specific tag you are referencing in your Docker command.

Confirm Visibility Settings

    On the package page, you can also see the visibility (public or private) and manage permissions if necessary.

Verify Tags and Pushing Status

If you don't see the expected tags, or if the package doesn't exist:

    Ensure you have pushed the Docker image correctly from your local machine using commands like:

    docker tag local-image:tagname ghcr.io/jeremysmyth216/ollama:latest
    docker push ghcr.io/jeremysmyth216/ollama:latest

    Make sure you replace local-image:tagname with the correct local image name and tag you used before pushing.

5. Local Docker Check

You can also list the images and tags on your local machine to confirm what you have available to push:

docker images

This command will show all Docker images stored locally, including their tags.

These steps should help you locate your Docker image on GitHub and verify its status and availability. If the image and tags are correctly set up on GitHub, and you're still facing issues, double-check your Docker login credentials and network settings.
You said:
https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry

Does this site help with what needs to be done or changed so i can get this working?
ChatGPT said:

Yes, the GitHub documentation link you provided is an excellent resource for understanding how to work with the GitHub Container Registry. It covers a range of topics that are essential for managing Docker containers and images on GitHub, including:

    Authenticating with the GitHub Container Registry: This section provides detailed instructions on how to authenticate using a personal access token, which is crucial for pushing and pulling images from private repositories.

    Pushing and Pulling Docker Images: It includes step-by-step guidance on how to tag your local images correctly and push them to GitHub, as well as how to pull images from the registry. This can help you ensure that the commands you use are correct and that your images are properly tagged and pushed.

    Managing Docker Images: This part of the documentation helps you understand how to list, manage, and delete images from the GitHub Container Registry. It's useful for maintaining your repository and ensuring it only contains relevant and up-to-date images.

    Setting up GitHub Actions with the Container Registry: If you're interested in automating your Docker workflows, this section provides information on integrating GitHub Actions with your Docker images stored in the GitHub Container Registry.

    Access Permissions and Visibility: The documentation also explains how to manage access to your Docker images, which is important if you are working in a team or need to control who can access your images.

If you are encountering issues with pulling or pushing images, referring to this documentation will provide you with the necessary information to troubleshoot common problems, such as authentication issues, tagging mistakes, or configuration errors. It's a good idea to review the sections relevant to your current task to ensure that you are following the recommended practices and using the correct commands.
You said:
I want you to use the site to help fix my problems
ChatGPT said:

It looks like there was an error with my browsing tool. Let's try a different approach to help solve your issue with setting up the GitHub Container Registry.

You can use the GitHub documentation to find detailed steps to ensure everything is set up correctly. Specifically, you'll want to ensure you:

    Have the correct permissions to access the container registry.
    Have created a personal access token with the appropriate scopes (like read:packages or write:packages).
    Use the correct image name in your docker run command. Make sure the image is actually pushed to your GitHub Container Registry under your account.

Visit the GitHub Docs on the Container Registry for comprehensive guidance on each step. This should cover everything from logging in to pushing and pulling images.

If you're still encountering issues, consider checking your repository settings to make sure the container registry is enabled and properly configured. Also, confirm the exact name of the Docker image you're trying to run to ensure there are no typos or mismatches.
You said:
githubgpt.txt
Document

It seems like the GitHub Container Registry (GHCR) documentation provides detailed information about managing Docker and OCI images, which is critical for your use case. Here are the steps to resolve the issues you're encountering based on the information from your file and the GitHub documentation:
1. Verify Repository Name

Ensure that the repository name you are using in your Docker command matches exactly with the one on GitHub, including the case sensitivity. It appears there may have been an issue with how the repository name was entered or the repository might not be set up to host a container.
2. Docker Login

You mentioned logging in with a personal access token but encountered issues. Here are the steps to ensure proper login:

    Generate a new personal access token on GitHub with the appropriate permissions (read:packages and write:packages).
    Log in using the command: echo "<TOKEN>" | docker login ghcr.io -u <USERNAME> --password-stdin

Make sure to replace <TOKEN> with your personal access token and <USERNAME> with your GitHub username.
3. Push or Pull Images

Once logged in, you should be able to push or pull images. If you are trying to pull an image, ensure that it exists:

    Check the GitHub repository for an existing GitHub Actions workflow that pushes the Docker image to GHCR.
    Verify the tag of the image you are trying to pull. The tag must exist in the registry.

4. GitHub Actions for CI/CD

If you intend to automate Docker builds and pushes to GHCR, setting up GitHub Actions might be necessary. This can automate the process whenever you push changes to your repository.
5. Troubleshooting Permissions

If there are permission issues, ensure that your GitHub token has the correct scopes and that your GitHub repository settings allow your user account to access the packages.
6. Access Control

GitHub offers fine-grained access control to packages. Ensure that your Docker image repository on GitHub is either public or that your user account has explicit access to interact with the private repository.

If these steps do not resolve the issue, reviewing the specific error messages and checking the configuration for typos or misconfigurations would be necessary. Additionally, make sure Docker is correctly installed and running on your system, as some of your errors suggested possible issues with Docker's installation.
You said:
Yes. So with the knowledge from that site, help me with my issues

jeremy@Vagabond:~$ newgrp docker
jeremy@Vagabond:~$ docker run -d --name ollama -p 11435:11435 --restart always ghcr.io/jeremysmyth216/ollama:latest
Unable to find image 'ghcr.io/jeremysmyth216/ollama:latest' locally
docker: Error response from daemon: manifest unknown

Run 'docker run --help' for more information
jeremy@Vagabond:~$ echo "YOUR_GITHUB_PERSONAL_ACCESS_TOKEN" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin
Error response from daemon: Get "https://ghcr.io/v2/": denied: denied
jeremy@Vagabond:~$ echo "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" | docker login ghcr.io -u jeremysmyth216 --password-stdin
Login Succeeded
jeremy@Vagabond:~$ docker run -d \
   --name ollama \
   -p 11435:11435 \
   --restart always \
   ghcr.io/jeremysmyth216/ollama:latest
Unable to find image 'ghcr.io/jeremysmyth216/ollama:latest' locally
docker: Error response from daemon: manifest unknown