Increase IDX project storage space

General
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I know most of you are already asking the same, but I really need it. The libraries I’m trying to install is not small and I’m running out of space very fast. I need at least 30-50GB, and that’s if I decide not to go wild and add more features to my project which I hope to. It would be cool if I was able to have an option in where the project could be edited locally or at the very least on google drive. I’m still a little of a beginner so take it easy on me lol.

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Hi @ic3c0ld - we are now publicly testing increased disk storage!

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Hi kirupa, I am also running out of storage space!

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Hello. I am constantly running out of space in Firebase Studio while installing CrewAI. What should I do to ensure I have that 100GB?

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It’s totally understandable to feel overwhelmed by storage limitations when working with large Python libraries, especially as a beginner! Many popular data science and machine learning libraries like TensorFlow, PyTorch, and even comprehensive data analysis packages can quickly eat up disk space.
The good news is that there are excellent solutions available that provide ample storage and allow for flexible development, including options that integrate with Google Drive. Here’s a breakdown of your best bets, keeping your need for 30-50GB and ease of use in mind:
Best Options for Your Needs

  • Cloud-Based Development Environments (Recommended)
    These are your best bet for large projects as they provide dedicated virtual machines with substantial storage and computational power. They eliminate the need for local installations of huge libraries and often offer collaborative features.
    • Google Colaboratory (Colab):
      • Pros: It’s free (with some limitations), deeply integrated with Google Drive, offers free GPU/TPU access (which is fantastic for many large libraries), and is a Jupyter Notebook environment, which is very beginner-friendly for interactive coding. You can easily load data from and save results to Google Drive.
      • Cons: The free tier has session limits (e.g., your environment might reset after a certain period of inactivity or continuous use), and while it offers storage, it’s not a permanent, dedicated file system like a full VM. For very large datasets or complex project structures that need to persist across sessions without re-uploading, it can be a bit cumbersome. However, it’s generally good for projects up to a few tens of GBs if you manage your files well (e.g., using Google Drive for persistent storage).
      • How it helps: You can mount your Google Drive directly in Colab, allowing you to access and save files directly to your Drive storage. This helps immensely with the “editing locally or on Google Drive” aspect. For your large libraries, they’ll be installed on the Colab instance, and you won’t need to store them on your local machine.
    • GitHub Codespaces:
      • Pros: A powerful cloud-based development environment that can be spun up directly from your GitHub repository. It provides a full VS Code experience in your browser, offers customizable environments (you can define your dependencies in a devcontainer.json file), and can provide significant storage (often 30GB+ in various tiers). It’s excellent for structured projects and team collaboration.
      • Cons: While there’s a free tier for personal use, larger or more frequent usage will incur costs.
      • How it helps: You get a persistent development environment with generous storage, allowing you to install all your large libraries once and have them available. You can keep your project files in your GitHub repository, and since Codespaces integrates directly with GitHub, it’s a very streamlined workflow.
    • Visual Studio Code Remote Development (with SSH/Dev Containers):
      • Pros: If you have access to a remote server (e.g., a cloud VM like Google Compute Engine, AWS EC2, or a dedicated server) with plenty of storage, VS Code’s remote development features are incredibly powerful. You can connect to this remote machine and use VS Code locally as if you were working directly on the server. This gives you complete control over your environment and virtually unlimited storage (limited by your server’s disk space).
      • Cons: Requires setting up and managing a remote server, which can be a steeper learning curve for beginners compared to fully managed cloud IDEs. It also incurs costs for the server.
      • How it helps: This is the “editing locally” part you mentioned, but applied to a remote server. You get the familiar VS Code interface, but all computations and storage happen on the powerful cloud machine.
    • Other Cloud IDEs/Platforms (worth exploring if the above don’t fit perfectly):
      • Replit: Very beginner-friendly, supports many languages, and has decent free tier resources. Might not offer 50GB readily in the free tier, but good for smaller to medium projects.
      • PythonAnywhere: A cloud-based Python development and hosting environment. Good for web apps and scripts, but storage might be limited for very large library installations in the free tier.
      • Deepnote / Kaggle Notebooks: Similar to Google Colab, often with a focus on data science and machine learning. They provide Jupyter environments and can handle large datasets, often with some free tiers and paid options for more resources. Kaggle Notebooks are tightly integrated with datasets used in Kaggle competitions.
  • Using Google Drive for Project Files (with a cloud IDE or local setup)
    While you can’t install Python libraries directly onto Google Drive (libraries need to be installed in a file system that can execute code), you can definitely store your project files (code, data, models, etc.) on Google Drive and access them from your development environment.
    • With Google Colab: As mentioned, Colab has built-in features to mount your Google Drive. This means your Python scripts can read from and write to your Drive. This is ideal for storing your large datasets and code.
    • With a Local IDE (like VS Code or PyCharm) and Google Drive Sync: You can sync your Google Drive to your local machine using Google Drive for Desktop. This allows you to work on your project files locally. The challenge here is still the library installation on your local machine, which will consume your local disk space. This is where cloud IDEs shine, as the libraries are installed in the cloud, not on your computer.
      Recommended Approach for a Beginner
      Given your situation, I’d suggest starting with a combination of:
  • Google Colaboratory (Colab):

    • For core development, running code, and utilizing GPUs/TPUs.

    • Mount your Google Drive: This is crucial. In a Colab notebook, you can execute a cell with:
      from google.colab import drive
      drive.mount(‘/content/drive’)

      This will prompt you to authenticate your Google account, and then your Google Drive will be accessible at /content/drive.

    • Install large libraries on the Colab instance: When you need a large library (e.g., pip install tensorflow), it will be installed in the Colab environment. This installation is temporary for the session but doesn’t consume your local disk space.

    • Store your project files (code, data) on Google Drive: Organize your project in a folder on your Google Drive (e.g., My Drive/MyPythonProject). Your Colab notebooks can then easily access files like /content/drive/MyDrive/MyPythonProject/my_script.py or /content/drive/MyDrive/MyPythonProject/data/large_dataset.csv.

  • A Local IDE (like VS Code or PyCharm Community Edition) with Google Drive Sync:
    • For local editing of your code files: If you prefer the familiarity and features of a local IDE for writing and editing your Python scripts, you can keep your project code files in a folder that is synced to Google Drive via Google Drive for Desktop.
    • Why this works: You write code locally, save it, and it syncs to Drive. When you go to Colab, you open your notebook and load the code from Drive, then run it in the cloud environment where the large libraries are installed.
      This hybrid approach allows you to:
  • Offload heavy library installations to a cloud environment.
  • Utilize significant computational resources (GPUs/TPUs) for free (or low cost).
  • Keep your project files organized and accessible through Google Drive.
  • Edit your code using your preferred local editor, leveraging Google Drive for synchronization.
    Key Things to Remember as a Beginner:
  • Virtual Environments: Even in cloud environments, it’s good practice to understand virtual environments (though Colab manages its own environment per session). When you eventually move to local development or more complex remote setups, venv or conda are essential for isolating project dependencies.
  • Session Limits in Free Tiers: Be aware that free cloud environments like Colab have usage limits. If your project requires very long computation times or continuous availability, you might eventually need to consider paid options.
  • Data Management: For extremely large datasets (hundreds of GBs or TBs), you might need to explore cloud storage solutions like Google Cloud Storage (GCS) or Amazon S3, which are optimized for large-scale data and integrate with cloud compute services. However, for 30-50GB, Google Drive can work well, especially with Colab.
    Start with Google Colab and Google Drive integration. It’s the most straightforward and cost-effective way to get started with large Python projects without quickly running out of local disk space. Good luck with your project!
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Hello Kirupa
I also ran out of disk space and can’t build a completed project. Is there any way I can get more space for the blitzplaygames-12746850 project? Thanks