gpt dev drive config instructions jan 4 2025

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Step 1: Setting Up Development Tools on the Dev Drive

You’ll need to configure your commonly used tools (like Python, VSCode, WSL, and Docker) to store their files, projects, or data on the Dev Drive.

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1. Python (venv or Conda)

Using venv:

1. Open a terminal (Command Prompt or PowerShell).

2. Create a Python virtual environment directly on the Dev Drive:

   python -m venv D:\DevProjects\MyEnv

3. Activate the environment:

   D:\DevProjects\MyEnv\Scripts\activate

4. Install required packages:

   pip install numpy pandas matplotlib

5. Store all your Python scripts and projects in D:\DevProjects.

Using Conda:

1. Create a folder on the Dev Drive for Conda environments (e.g., D:\CondaEnvs).

2. Edit Conda’s configuration:

   conda config --add envs_dirs D:\CondaEnvs
   conda config --add pkgs_dirs D:\CondaPkgs

3. Create a new Conda environment:

   conda create --name myenv python=3.9

4. Activate the environment:

   conda activate myenv

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2. VSCode

1. Open VSCode.
2. Set the default workspace location:
   • Go to File > Preferences > Settings.
   • Search for default location.
   • Set the default workspace location to a folder on the Dev Drive (e.g., D:\DevProjects).
3. Open existing projects:
   • Move your current projects to D:\DevProjects.
   • Open them in VSCode by selecting File > Open Folder and navigating to the Dev Drive.

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3. WSL (Windows Subsystem for Linux)

1. Mount the Dev Drive in WSL:

   • Open WSL and create a mount point:

     sudo mkdir /mnt/dev

   • Mount the drive:

     sudo mount -t drvfs D: /mnt/dev

2. Access the Dev Drive in WSL by navigating to /mnt/dev.
3. Store Linux-based projects on /mnt/dev.

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4. Docker

1. Create a folder on the Dev Drive for Docker data (e.g., D:\DockerData).
2. Edit Docker’s configuration:

   • Open or create a file at C:\ProgramData\Docker\config\daemon.json.

   • Add the following:

     {
       "data-root": "D:\\DockerData"
     }

   • Save the file and restart Docker.

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5. Dataset Storage

• For machine learning or data science tasks, store large datasets (e.g., .csv, .h5) in a dedicated folder on the Dev Drive, such as D:\Datasets.

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Step 2: Configuring NVIDIA and GPU for Use with the Dev Drive

If you want to use the Dev Drive for GPU-related tasks like deep learning or CUDA-based workflows, follow these steps:

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1. Install NVIDIA Drivers and CUDA Toolkit

1. Install NVIDIA Drivers:
   • Ensure your NVIDIA GPU drivers are up to date. You can download them from: https://www.nvidia.com/Download/index.aspx
2. Install the CUDA Toolkit:
   • Download and install the latest CUDA Toolkit from: https://developer.nvidia.com/cuda-downloads
   • During installation, set the toolkit’s temporary installation paths to your Dev Drive (if it asks).

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2. Verify GPU Access

1. Open a terminal (Command Prompt or WSL).

2. Run the following to verify CUDA is working:

   nvidia-smi

   This should show your GPU details.

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3. Configure Deep Learning Libraries to Use the Dev Drive

TensorFlow or PyTorch (Python):

1. Store datasets and model files on the Dev Drive:

   import torch
   
   # Example: Loading a dataset from the Dev Drive
   dataset_path = "D:/Datasets/my_dataset"
   model_path = "D:/Models/my_model.pth"
   
   # Save/load PyTorch models
   torch.save(model.state_dict(), model_path)
   model.load_state_dict(torch.load(model_path))

2. When using frameworks like PyTorch or TensorFlow (if not disabled), ensure your GPU is detected:

   import torch
   print(torch.cuda.is_available())

CUDA-C++:

1. Create CUDA projects on the Dev Drive:
   • Set the default project location in Visual Studio or another IDE to D:\DevProjects.
2. Ensure your CUDA .cu files and compiled binaries are stored in D:\.

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4. Set Up Cache and Temporary Data on the Dev Drive

• You can configure tools like NVIDIA’s cuDNN, or libraries like PyTorch and TensorFlow, to store cache or temporary data on the Dev Drive. For example:

  • PyTorch Cache: Set the environment variable:

    TORCH_HOME=D:\TorchCache

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Step 3: Test Everything

1. Run a small Python script using your GPU and confirm it’s using the Dev Drive for datasets and models.
2. For WSL, ensure your projects are accessible in /mnt/dev.
3. Verify Docker containers are using the Dev Drive for storage.

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Conclusion

By configuring your development tools and GPU workflows to use the Dev Drive, you’ll have an optimized environment for coding, data science, and deep learning. If at any point you’re unhappy with the setup, you can wipe and reformat the drive to start fresh.

Let me know if you need specific step-by-step guidance for any tool or library!