Hybrid Local/Cloud-VM Setups & Solutions for Resource Intensive Python Projects

Unrooted Android Local Termux Linux Userland PRoot Container of Debian GNU/Linux & Oracle Cloud VM of Debian GNU /Linux

Setting up a hybrid environment that allows your unrooted local Debian PRoot on Termux to interact seamlessly with a cloud virtual machine (VM) is a great way to combine convenience with processing power. This approach enables you to develop and test code locally on your Android tablet whilst offloading resource-intensive tasks to a cloud VM when necessary.

There a a number of ways you can achieve this:

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1. Establish Secure Remote Access Between Local and Cloud Environments

SSH Connection

• Install OpenSSH Client on Termux:

  Make sure you have the SSH client installed in your Termux environment:

    pkg install openssh
  

• Connect to Your Cloud VM:

  From your Termux terminal, you can establish an SSH connection to your cloud VM:

    ssh username@your_cloud_vm_ip
  

• Set Up SSH Key-Based Authentication:

  For secure and password-less login:

  • Generate an SSH key pair on your tablet:

      ssh-keygen -t rsa -b 4096
    

  • Copy your public key to the cloud VM:

      ssh-copy-id username@your_cloud_vm_ip
    

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2. Synchronize Code Between Local and Cloud Environments

Option A: Use Git for Version Control

• Initialize a Git Repository Locally:

  In your project directory on the tablet:

    git init
  

• Use a Remote Repository:

  • Host your repository on platforms like GitHub or GitLab.

  • Alternatively, set up a bare Git repository on your cloud VM.

• Workflow:

  • Commit and push changes from your local environment.

  • Pull the latest code on the cloud VM when you need to run tasks there.

Option B: Use rsync for Direct Synchronization

• Install rsync:

  Ensure rsync is installed on both local and cloud environments:

    apt-get install rsync
  

• Sync Your Project:

  From your tablet, synchronize your project directory to the cloud VM:

    rsync -avz /path/to/local/project/ username@your_cloud_vm_ip:/path/to/remote/project/
  

• Automate with Scripts:

  Create a Bash script to automate syncing whenever you make changes.

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3. Remote Development Using SSHFS

• Mount the Cloud Filesystem Locally:

  • Install sshfs on Termux:

      pkg install sshfs
    

  • Create a Mount Point:

      mkdir ~/cloud_project
    

  • Mount the Remote Directory:

      sshfs username@your_cloud_vm_ip:/path/to/remote/project ~/cloud_project
    

• Advantages:

  • Edit files locally with your preferred tools.

  • Changes are reflected directly on the cloud VM.

• Unmount When Finished:

    fusermount -u ~/cloud_project
  

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4. Use Jupyter Notebooks Hosted on the Cloud VM

Set Up Jupyter Notebook on Cloud VM

• Install Jupyter on the Cloud VM:

    pip install jupyter
  

• Launch Jupyter Notebook:

  Run Jupyter Notebook on the cloud, binding it to all IP addresses:

    jupyter notebook --ip=0.0.0.0 --no-browser
  

Access Jupyter Notebook from Your Tablet

• Set Up an SSH Tunnel:

  From your Termux terminal:

    ssh -N -f -L localhost:8888:localhost:8888 username@your_cloud_vm_ip
  

• Connect via Browser:

  Open a web browser on your tablet and navigate to http://localhost:8888. You can now interact with the Jupyter Notebook running on the cloud VM.

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5. Offload Resource-Intensive Tasks to the Cloud VM

Remote Execution of Scripts

• Use SSH to Run Scripts on the Cloud VM:

  Execute commands or scripts remotely:

    ssh username@your_cloud_vm_ip 'python /path/to/remote/project/script.py'
  

• Automate with Python Libraries:

  • Fabric:

    Install Fabric in your local environment:

      pip install fabric
    

    Use Fabric to execute remote tasks:

      from fabric import Connection
    
      with Connection('username@your_cloud_vm_ip') as c:
          c.run('python /path/to/remote/project/script.py')
    

Use Remote Jupyter Notebooks for Data Processing

• Develop notebooks locally and run them on the cloud VM to utilize greater computational resources.

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6. Synchronize Environments with Virtual Environments

Using conda Environments

• Export Environment from Local Machine:

    conda env export > environment.yml
  

• Recreate Environment on Cloud VM:

    conda env create -f environment.yml
  

Using pip and virtualenv

• Create Requirements File Locally:

    pip freeze > requirements.txt
  

• Install Requirements on Cloud VM:

    pip install -r requirements.txt
  

• Benefits:

  • Ensures consistent environments across local and cloud setups.

  • Avoids dependency conflicts and version mismatches.

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7. Clarify the Role of vcpkg

• Understanding vcpkg:

  • vcpkg is a package manager for C and C++ libraries, primarily used for managing dependencies in C/C++ projects.

• Alternative Solutions:

  • Since Docker is not available to an unrooted Android device, focus on using Python virtual environments (virtualenv, conda).

  • These tools help isolate your project's Python dependencies without requiring root access or containerization.

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8. Utilize Remote Development Tools

VS Code with Remote Development Extensions

• Run VS Code Server (code-server) on Cloud VM:

  • Install code-server on the cloud VM to access VS Code via a web browser.

  • Installation instructions can be found on the code-server repository.

• Access from Your Tablet:

  • Use your tablet's browser to interact with the remote VS Code instance.

  • This provides a rich development environment connected directly to your cloud resources.

Emacs, Neovim or Vim Over SSH

• Use Command-Line Editors:

  • Access powerful editors like Vim, Neovim or Emacs directly on the cloud VM via SSH.

  • Customize these editors with plugins to enhance productivity.

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9. Implement a Workflow for Project Management

Develop Locally, Execute Remotely

• Local Development:

  • Write and test code on your tablet for immediate feedback.

  • Use lightweight datasets and test cases to ensure functionality.

• Remote Execution:

  • For tasks that require more resources (e.g., large data processing, model training), run the code on the cloud VM.

  • This approach balances convenience with performance.

Automation Scripts

• Create Bash or Python Scripts:

  • Automate the process of syncing code, running remote scripts, and retrieving results.

  • Example Bash script to sync code and execute remotely:

      #!/bin/bash
      rsync -avz /path/to/local/project/ username@your_cloud_vm_ip:/path/to/remote/project/
      ssh username@your_cloud_vm_ip 'python /path/to/remote/project/main.py'
      scp username@your_cloud_vm_ip:/path/to/remote/project/output/* /path/to/local/project/output/
    

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10. Manage Data and Outputs Efficiently

Data Storage Best Practices

• Store Large Data on Cloud VM:

  • Keep big datasets on the cloud to avoid transferring large files over the network.

• Transfer Only Necessary Files:

  • Download results or processed data as needed.

  • Compress files before transfer to reduce bandwidth usage.

Use Cloud Storage Services

• Leverage Services Like AWS S3 or Google Cloud Storage:

  • Store and retrieve data directly from your scripts.

  • Can be accessed from both local and cloud environments if needed.

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11. Ensure Security and Cost Efficiency

Security Measures

• Use Strong SSH Passwords and Keys:

  • Protect access to your cloud VM with strong authentication practices.

• Keep Software Updated:

  • Regularly update packages to patch security vulnerabilities.

• Configure Firewalls:

  • Use the cloud provider's firewall settings to restrict access to essential ports only (e.g., SSH, Jupyter Notebook port).

Cost Management

• Monitor Cloud Resource Usage:

  • Be aware of CPU, memory, and storage usage to manage costs.

  • Shut down or resize the VM when not in use.

• Use Spot Instances or Reserved Instances:

  • Depending on the cloud provider, these options can reduce costs for compute resources.

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Additional Considerations

Overcoming Limitations Without Docker

• Use Ansible for Configuration Management:

  • Automate the setup of your cloud VM environment using Ansible playbooks.

  • This ensures consistency and saves time when provisioning new VMs.

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By implementing these strategies, you can create a hybrid development environment that leverages both your local setup on the Android tablet and the computational power of a cloud VM. This setup allows you to:

• Develop and test code locally with convenience.

• Offload heavy computations to the cloud.

• Maintain consistency across environments.

• Optimize costs and resources effectively.

Oracle Cloud VM of Debian GNU/Linux

Oracle Cloud does offer a free tier that includes a VM instance. You can use this to run a Debian VM. Here’s a quick guide to get you started:

1. Sign up for an Oracle Cloud account: You’ll need to create an account and verify your email.

2. Navigate to the Free Tier: Once you’re in, look for the free tier options and select a VM instance.

3. Create a VM: Choose Debian as your OS and configure the VM according to your needs.

4. Launch the VM: Start the VM and access it via SSH.

Got it! Let's walk through the steps to set up a Debian VM on Oracle Cloud:

Step 1: Log in to Oracle Cloud

1. Go to the Oracle Cloud Console: Open your web browser and go to the Oracle Cloud Console.

2. Log in: Use the credentials from your Oracle Cloud account to log in.

Step 2: Create a Virtual Machine

1. Select Compute: In the Oracle Cloud Console, click on the "Compute" section.

2. Create Instance: Click on "Instances" and then "Create Instance".

3. Choose Free Tier: Select the "Always Free" tier to use the free resources.

4. Select Image: Choose "Oracle Linux" as the image (since Debian isn't directly available, we'll convert it later).

5. Configure Instance: Set the instance name, shape (e.g., VM.Standard.E2.2), and other configurations as needed.

6. Add SSH Key: Add your SSH public key to the instance for secure access.

Step 3: Launch the Instance

1. Review and Create: Review the instance details and click "Create".

2. Wait for Instance: Wait for the instance to be created and go to the "Instances" page to find your new VM.

Step 4: Connect to the Instance

1. Find Public IP: Go to the "Details" page of your instance and note the public IP address.

2. SSH into the Instance: Open your terminal and use the following command to connect:

    ssh opc@<public_ip_address>
   

   Replace <public_ip_address> with the actual IP address of your instance.

Step 5: Install Debian

1. Update Packages: Once logged in, update the existing packages:

    sudo yum update -y
   

2. Install Debian: Download the Debian ISO and install it:

    wget https://cdimage.debian.org/debian-cd/current/amd64/iso-cd/debian-11.2.0-amd64-netinst.iso
    sudo qemu-kvm -cdrom debian-11.2.0-amd64-netinst.iso -boot d
   

3. Follow Installation Steps: Follow the on-screen instructions to install Debian.

Step 6: Verify Installation

1. Check Debian Version: Once installed, check the Debian version:

    cat /etc/debian_version
   

2. Install Additional Packages: Install any additional packages you need using apt-get.

That should get you up and running with a Debian VM on Oracle Cloud! If you run into any issues or need further assistance, feel free to ask. Happy cloud computing! 🌥️💻

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Setting Up a Hybrid Development Environment with Ansible, Git, SSHFS, and Jupyter Labs

1. Set Up Oracle Cloud VM

1. Log in to Oracle Cloud: Access the Oracle Cloud Console and log in with your credentials.

2. Create a New VM Instance:

   • Go to the Compute section and create a new instance.

   • Select the "Always Free" tier.

   • Choose Debian as your operating system (if Debian is unavailable, choose a compatible Linux distribution like Ubuntu).

   • Configure the instance name, shape, and other settings.

   • Add your SSH public key for secure access.

   • Review and create the instance.

2. Provision the VM with Ansible

1. Create an Ansible Inventory File:

    [oracle_vms]
    cloud-vm ansible_host=<your_vm_ip> ansible_user=<your_vm_user>
   

2. Create an Ansible Playbook:

    - hosts: oracle_vms
      become: yes
      tasks:
        - name: Update and upgrade apt packages
          apt:
            update_cache: yes
            upgrade: dist
   
        - name: Install required packages
          apt:
            name:
              - python3
              - python3-pip
              - git
              - build-essential
              - jupyterlab
            state: present
   
        - name: Install vcpkg
          git:
            repo: https://github.com/microsoft/vcpkg.git
            dest: /home/<your_vm_user>/vcpkg
          register: vcpkg_cloned
   
        - name: Bootstrap vcpkg
          command: ./bootstrap-vcpkg.sh
          args:
            chdir: /home/<your_vm_user>/vcpkg
          when: vcpkg_cloned.changed
   

3. Run the Ansible Playbook:

    ansible-playbook -i inventory setup_vm.yml
   

3. Set Up SSHFS on Termux

1. Install SSHFS:

    pkg install sshfs
   

2. Create a Mount Point:

    mkdir -p /mnt/remote
   

3. Mount the Remote Filesystem:

    sshfs user@cloud-vm:/remote/directory /mnt/remote
   

4. Set Up Git for Version Control

1. Initialize Git in Your Project:

    git init
   

2. Add Your Files:

    git add .
   

3. Commit Changes:

    git commit -m "Initial commit"
   

4. Create a Repository on GitHub:

   • Create a new repository on GitHub.

   • Link your local project to the remote repository:

       git remote add origin https://github.com/your-username/your-repo.git
       git push -u origin master
     

5. Install Conda or Python Virtual Environment

1. Install Miniconda:

   • Follow the installation instructions for your system.

2. Create a Conda Environment:

    conda create -n myenv python=3.8
    conda activate myenv
   

3. Install Python Packages:

    pip install <your-required-packages>
   

6. Set Up Jupyter Labs

1. Start Jupyter Labs Locally:

   • Ensure your current working directory is the mounted project directory:

       cd /mnt/remote/your_project_directory
     

   • Start Jupyter Labs:

       jupyter lab --no-browser --port=8888
     

2. Access Jupyter Labs in Your Browser:

   • Open your browser and navigate to http://localhost:8888 to access Jupyter Labs running on your VM, but seamlessly available through your local mount.

7. Automate Syncing with SSHFS

• By mounting the VM locally to your project directory, you seamlessly integrate development and execution environments.

• Note: The mount point uses the VM's Debian environment for execution. Your local Python version does not affect the mounted environment. Ensure you manage dependencies appropriately in both environments.

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This guide ensures you utilize the full power of your cloud VM while keeping your workflow efficient and straightforward. Happy coding and computing! 🚀

Integrating Conda and Jupyter Labs

Conda and Jupyter Labs actually work quite well together and don't have inherent conflicts. In fact, managing your Jupyter Lab environments with Conda is a common practice due to its ease of handling dependencies. Let's break it down:

Using Conda and Jupyter Labs Together

1. Create a Conda Environment:

    conda create -n myenv python=3.8
    conda activate myenv
   

2. Install Jupyter Labs in the Conda Environment:

    conda install -c conda-forge jupyterlab
   

3. Start Jupyter Labs:

   • While inside your Conda environment, start Jupyter Labs:

       jupyter lab --no-browser --port=8888
     

This approach ensures that Jupyter Labs runs with the specific Python environment and dependencies defined by Conda, keeping your project setup clean and organized.

Jupyter Labs and GitHub

Jupyter Labs doesn’t directly push to GitHub, but you can use Jupyter Lab extensions or the terminal within Jupyter Labs to manage your Git operations:

1. Use the Terminal:

   • Open a terminal in Jupyter Labs and use standard Git commands:

       git add .
       git commit -m "Commit message"
       git push origin master
     

2. Jupyter Lab Git Extensions:

   • Install the jupyterlab-git extension for a GUI to manage Git operations:

       jupyter labextension install @jupyterlab/git
       pip install jupyterlab-git
       jupyter serverextension enable --py jupyterlab_git
     

   • This adds a Git panel to Jupyter Labs where you can perform Git operations.

Putting It All Together

1. Mount the VM Locally:

   • Using SSHFS, mount the remote filesystem to your local directory.

2. Activate Conda Environment:

   • Ensure you’re working within the mounted directory:

       cd /mnt/remote/your_project_directory
       conda activate myenv
     

3. Start Jupyter Labs:

   • Start Jupyter Labs from the mounted directory:

       jupyter lab --no-browser --port=8888
     

4. Use Git:

   • Use the terminal within Jupyter Labs or the Git extension to manage your code with GitHub.

By integrating Conda and Jupyter Labs, and leveraging GitHub for version control, you create a robust and organized development environment.

Steps to Publish Jupyter Notebooks on GitHub

Publishing Jupyter Notebooks with your GitHub project is straightforward and a great way to share your work as well as to, as they say, keep apples-with-apples. Here’s a step-by-step guide to get you set up:

1. Create or Navigate to Your GitHub Repository:

   • If you haven't already, create a new repository on GitHub where your project will reside.

   • Navigate to your repository’s local directory:

       cd /mnt/remote/your_project_directory
       git init
     

2. Create a Jupyter Notebook:

   • Open Jupyter Labs:

       jupyter lab --no-browser --port=8888
     

   • Create a new Jupyter Notebook or open an existing one. Save it within your project directory.

3. Save and Commit Your Notebook:

   • In Jupyter Labs, save your notebook.

   • Use the terminal or Jupyter Lab Git extension to stage and commit the notebook:

       git add your_notebook.ipynb
       git commit -m "Add initial Jupyter Notebook"
     

4. Push to GitHub:

   • Push your changes to your GitHub repository:

       git remote add origin https://github.com/your-username/your-repo.git
       git push -u origin master
     

5. Update Regularly:

   • Continue working on your notebooks, saving, committing, and pushing changes as you progress:

       git add your_notebook.ipynb
       git commit -m "Update Jupyter Notebook"
       git push
     

Benefits of Publishing on GitHub

• Version Control: Track changes over time and revert to previous versions if needed.

• Collaboration: Others can view, comment, and contribute to your project.

• Visibility: Showcase your work and share it with the broader community.

• Backup: Safeguard your work against data loss.

By following these steps, you ensure that your Jupyter Notebooks are well-integrated with your GitHub project, making your development process smoother and more organized.