Fix VS Code Jupyter/Colab Dataset Directory Issue

Introduction

Hey guys! Are you wrestling with VS Code when your Jupyter or Colab extension just refuses to see your local dataset directory during model training? You're not alone! This issue can be a real headache, especially when you're trying to get your AI models up and running. Whether you're building a Convolutional Neural Network (CNN) or a Support Vector Machine (SVM), having your environment throw a FileNotFoundError can halt your progress. This article dives into why this happens, how to troubleshoot it, and some potential solutions to get your model training back on track. We'll cover everything from checking your environment settings to ensuring your paths are correctly configured. Let’s get started and squash this bug together!

Problem Description

The core problem is that when you're training an AI model—be it a CNN or an SVM—the local directory containing your dataset mysteriously goes undetected by the extension or environment you're using. The folder is right there on your machine, but the system stubbornly insists that the path is missing. As a result, the training process grinds to a halt before it even begins. It’s like trying to convince your computer that yes, the file does exist, even though it's staring right at it! This frustrating issue prevents you from loading your data and moving forward with your machine-learning tasks, turning what should be a smooth training session into a debugging marathon. Understanding the root cause and how to address it is essential for maintaining productivity and keeping your projects on schedule.

Environment Details

Before we dive into solutions, let's nail down the specifics of the environment where this issue crops up. Here’s a breakdown of the key components:

• VS Code Version: 1.106.3
• Colab Extension Version: 0.0.1
• Jupyter Extension Version: 2025.9.1
• Operating System: Windows

Knowing these details helps in pinpointing whether the problem lies within a specific version of an extension or the interaction between different components. For instance, compatibility issues between the Colab or Jupyter extension and VS Code itself could be the culprit. Similarly, the operating system can play a role, as file path handling and permissions might differ across platforms. By having a clear picture of your environment, you can better target your troubleshooting efforts and search for solutions that are relevant to your setup.

Steps to Reproduce

To really understand and solve this problem, it's crucial to be able to reproduce it consistently. Here’s how you can recreate the issue:

1. Open your AI/ML project in VS Code: Start by opening the project directory in VS Code where your AI or machine learning code resides.

2. Run a notebook for CNN or SVM training: Open a Jupyter notebook that contains the code for training your CNN or SVM model. This notebook should include the data loading and model training steps.

3. Set the dataset directory using a local path: Define the path to your dataset directory using a local path in your notebook. For example:

   DATASET_DIR = "dataset/training"
   

4. Execute the training cell: Run the cell in your notebook that starts the training process. This is where the code attempts to access the dataset directory.

5. Observe the error: An error message will appear, indicating that the folder cannot be found, even though it exists locally. This usually manifests as a FileNotFoundError.

By following these steps, you can reliably reproduce the issue and test any potential solutions you come across.

Expected Behavior

Ideally, when you set the dataset directory in your notebook, the Jupyter or Colab extension should seamlessly access that directory and load the dataset without any hiccups. The training process should proceed normally, utilizing the data to train your model. In short, you expect the extension to play nice and do its job without throwing mysterious errors. The notebook should find the dataset and commence training as intended, allowing you to focus on refining your model and analyzing results rather than battling file path issues. This smooth, uninterrupted workflow is what makes these extensions so valuable for data science and machine learning tasks.

Actual Behavior

Unfortunately, the actual behavior is far from ideal. The training process grinds to a halt because the dataset path is consistently treated as nonexistent. You'll likely encounter error messages such as:

FileNotFoundError: [Errno 2] No such file or directory: 'dataset/training'

This error indicates that the system is unable to locate the specified directory, even though it is physically present on your machine. This discrepancy between what exists and what the environment recognizes is the core of the problem. It suggests that there's an issue with how the extension is interpreting or accessing the file path, leading to the frustrating error and preventing your model from training.

Additional Context

It's important to note that this issue seems to be specific to running notebooks through extensions (Colab/Jupyter) inside VS Code. When the same code is executed directly using the local Python interpreter, it works without any problems. This suggests that the issue is not with the code itself, but rather with the way the extensions handle file paths or interact with the operating system. This distinction is crucial for narrowing down the source of the problem and focusing on solutions that address the extension-specific behavior. It also highlights the importance of considering the environment when troubleshooting, as the same code can behave differently depending on how it's executed.

Analyzing the Logs

To gain deeper insights, let's examine the logs from the Colab and Jupyter extensions. These logs can often provide clues about what's going wrong behind the scenes.

Colab Extension Logs

[2025-11-30T09:19:08.219Z] [Info] Visual Studio Code: 1.106.3
[2025-11-30T09:19:08.219Z] [Info] Remote: N/A
[2025-11-30T09:19:08.219Z] [Info] App Host: desktop
[2025-11-30T09:19:08.219Z] [Info] Jupyter extension version: 2025.9.1

These logs provide basic information about the VS Code environment and the versions of the extensions being used. They confirm that the Colab extension is aware of the Jupyter extension and the VS Code version. However, they don't directly indicate any specific errors or issues related to file path resolution.

Jupyter Extension Logs

16:16:24.648 [info] Starting Kernel (Python 3 (ipykernel)) for 'd:\Vito FIle\Tugas Kuliah\Aplikasi\Aplikasi Machine learning\prediksi cuaca by image\dataset_training.ipynb' (disableUI=false)
16:16:26.678 [info] https://8080-gpu-t4-s-25v4of2y16h82-b.us-west1-0.prod.colab.dev/: Kernel started: 93edffc9-61f1-4da9-bc13-90292cb5bae3
16:16:27.487 [error] Error in websocket Error: Server sent no subprotocol
    at ClientRequest.<anonymous> (~\.vscode\extensions\ms-toolsai.jupyter-2025.9.1-win32-x64\dist\extension.node.js:24:28335)
    at ClientRequest.emit (node:events:519:28)
    at TLSSocket.socketOnData (node:_http_client:598:11)
    at TLSSocket.emit (node:events:519:28)
    at addChunk (node:internal/streams/readable:561:12)
    at readableAddChunkPushByteMode (node:internal/streams/readable:512:3)
    at TLSSocket.Readable.push (node:internal/streams/readable:392:5)
    at TLSWrap.onStreamRead (node:internal/stream_base_commons:189:23)
    at TLSWrap.callbackTrampoline (node:internal/async_hooks:130:17)
16:16:28.073 [info] Started session for kernel startUsingRemoteKernelSpec:3d384103ffd129831f90d34dc31fa268bda73053dc5871ea7663d61e7475dd2e..python3.usr\bin\python3.\.-m#colab_kernel_launcher
16:16:28.538 [info] Kernel successfully started

The Jupyter extension logs reveal some interesting details. The key takeaway here is the Error in websocket: Server sent no subprotocol. This error suggests a potential issue with the communication between the VS Code extension and the Jupyter kernel, which could be related to how file paths are being resolved or transmitted. While it doesn't directly point to a file path problem, it indicates a disruption in the communication channel that could indirectly affect the extension's ability to locate the dataset directory correctly. The websocket error might be a symptom of a deeper configuration or compatibility issue.

Possible Solutions

Okay, guys, let's brainstorm some solutions to tackle this pesky problem. Here are a few ideas you can try:

1. Check File Paths:

   • Absolute Paths: Instead of relative paths (e.g., dataset/training), try using absolute paths (e.g., C:\path\to\your\project\dataset\training). This can eliminate ambiguity and ensure the extension knows exactly where to look.
   • Path Separators: Ensure you're using the correct path separators for your operating system. Windows uses backslashes (\), while Linux and macOS use forward slashes (/).
   • Typos: Double-check for any typos in your file path. Even a small mistake can cause the system to fail to locate the directory.

2. Verify Working Directory:

   • Make sure your working directory in VS Code is set correctly. The extension might be looking for the dataset relative to the wrong directory. You can set the working directory in VS Code settings or by using the os.chdir() command in your notebook.

3. Extension Configuration:

   • Update Extensions: Ensure your Colab and Jupyter extensions are up to date. Outdated extensions can sometimes have bugs that are fixed in newer versions.
   • Extension Settings: Check the settings for the Colab and Jupyter extensions in VS Code. There might be settings related to file path resolution or working directories that you can adjust.

4. Python Environment:

   • Virtual Environments: If you're using virtual environments, make sure the correct environment is activated in VS Code. The extension might be using a different Python interpreter than the one you expect.

5. Permissions:

   • File Permissions: Ensure that your user account has the necessary permissions to access the dataset directory. Sometimes, file permissions can restrict access to certain directories.

6. Try a Different Extension:

   • As a workaround, you can try an alternative extension or even switch to a different environment temporarily to see if the issue persists. This can help you determine whether the problem is specific to the Colab/Jupyter extension in VS Code.

Conclusion

Alright, folks, we've covered a lot of ground in this article, from identifying the problem to exploring potential solutions. By meticulously checking your file paths, verifying your working directory, and tweaking your extension settings, you should be well-equipped to tackle the "VS Code Jupyter/Colab Extension Fails to Locate Local Dataset Directory During Model Training" issue. Remember to keep your extensions updated, ensure your Python environment is correctly configured, and always double-check those file permissions. With a bit of patience and these troubleshooting steps, you'll be back to training your AI models in no time! Happy coding!