Solving notimplementederror: loading a dataset cached in a localfilesystem is not supported.

Table of Contents

The Hugging Face ‘datasets’ library has changed the way data scientists and machine learning developers access and manage datasets. One of the common issues that users face is the `NotImplementedError: loading a dataset cached in a LocalFileSystem is not supported`.

This kind of error can really be frustrating when we are in the middle of a project. In this blog let us understand the cause of this error and figure out how to solve it.

Why Does notimplementederror: loading a dataset cached in a localfilesystem is not supported Occur?

When we update or have made changes in the ‘fsspec’ library, we may see an error as the ‘fsspec’ library is responsible for handling various file systems in Python. When we have breaking changes or updates in ‘fsspec’ it can lead to incompatibilities with how the `datasets` library accessed cached datasets.

We may find ourselves unable to load datasets which we had previously accessed without issues.

Another reason would be you have not updated ‘datasets’ library.

Causes leading to the notimplementederror: loading a dataset cached in a localfilesystem is not supported error

Version Mismatch

One of the most common causes of “notimplementederror: loading a dataset cached in a localfilesystem is not supported” Error is mismatch between versions of the ‘datasets’ and ‘fsspec’ libraries.

If we have recently updated one without updating the other then we may encounter this issue.

Using Cached Datasets

The error usually comes when trying to load datasets that have been cached locally. If our workflow relies heavily on cached datasets for efficiency, running into this error can disrupt our progress.

Environment Changes

Changing our development environment knowing or unknowing from one Python version to another or updating packages globally can also trigger this error.

Steps to Resolve the “notimplementederror: loading a dataset cached in a localfilesystem is not supported” error.

Update the Datasets Library

The first step in troubleshooting the error is to check whether we have the latest version of the ‘datasets’ library. The Hugging Face team frequently releases updates that include bug fixes and improvements.

We can use the following command to update our ‘datasets’ library in command prompt.

pip install -U datasets

This command will fetch the latest version from PyPI (Python Package Index) and install it.

Check fsspec Version

Even after updating `datasets` if we are not able to solve the error then we should check our version of `fsspec`. In some cases specific versions of `fsspec` may cause compatibility problems with the `datasets` library. We can check our current version of ‘fsspec’ library by running:

pip show fsspec

If we are using a newer version that might be causing issues then we should consider downgrading to a stable version known to work well with `datasets` previously.

For example:

pip install fsspec==2023.9.2

Restart Your Environment

Sometimes after making updates or changes to our libraries we have to restart our Python environment. By restarting the Python environment we can make sure that all changes have taken place and there are no lingering references to old versions of libraries.

If we are using Jupyter Notebook or any interactive environment we can simply restart the kernel. If we are working in a terminal we should close and reopen it.

Verify Your Dataset Loading Code

Errors can also come from how we are attempting to load datasets rather than from library compatibility issues. Below is the sample code which follows best practices as shown in the Hugging Face documentation.

from datasets import load_dataset
dataset = load_dataset("your_dataset_name")

We have to make sure that we are specifying valid dataset names and parameters according to the latest documentation.

Tips for Avoiding notimplementederror: loading a dataset cached in a localfilesystem is not supported error.

Use Virtual Environments : One of the best practices for a developer is to create a virtual environment which helps us to prevent conflicts between different projects and their dependencies.
Creating a virtual environment allows us to manage dependencies on a per project basis without affecting our global Python environment.
Regularly Update Libraries and maintain compatible libraries with each other: We should review release notes for major libraries like `datasets` and `fsspec`. Update ‘datasets’ library and check if ‘fsspec’ library is compatible with it. If we get error we should switch back to previous setup.

Conclusion

Whenever you get “notimplementederror: loading a dataset cached in a localfilesystem is not supported” error while working with Hugging Face’s datasets, you can follow our end to end steps to solve the error.

Here is the checklist you can follow to the “notimplementederror: loading a dataset cached in a localfilesystem is not supported” error.

Update ‘datasets’ library
Check for ‘fsspec’ library,
whether it is compatible with current setup
Restart the environment
Verify dataset loading code for paths and other parameters

You can also read our other both on how to solve “Fatal Python Error: init_import_size: Failed to Import the Site Module” error and “Notimplementederror: cannot copy out of meta tensor; no data!” error in Python.