How much size reduction can I expect when converting pickle to joblib?

Size reduction depends on your file contents. Files with large NumPy arrays typically see 30 to 80 percent reduction at compression level 3. Files with mostly small Python objects see smaller but still meaningful reductions.

What file formats does the Pickle to Joblib Converter support?

The tool accepts .pickle and .pck files as input and outputs compressed .joblib files that can be loaded with joblib.load().

Pickle to Joblib Converter – Free Online Tool / Compress .pickle Files Instantly

How It Works

Upload your pickle file

Drag and drop or browse to select your .pickle or .pck files. Upload multiple files at once for batch conversion.
Load dependencies (if needed)

If your pickle was created from a custom class, upload the .py module file in the dependencies section before converting.
Download compressed joblib

The tool re-serializes your data with joblib compression level 3 and shows the size reduction. Download the .joblib file instantly.

Why Convert Pickle to Joblib?

Smaller Files

Joblib's compression typically reduces file sizes by 30–80%, especially for NumPy-heavy objects. Less disk space, faster transfers.

Faster Loading

Joblib uses memory-mapped loading for large NumPy arrays, meaning joblib.load() can be significantly faster than pickle.load().

Drop-in Replacement

Switching is trivial — just replace pickle.load() with joblib.load() and pickle.dump() with joblib.dump(). Same API, better results.

Custom Class Support

Got pickles made from custom classes like rationale_CNN? Upload the .py source and the tool handles the rest automatically.

Using the Output

After downloading your .joblib file, load it in Python:

import joblib

# Load your converted file
model = joblib.load("your_model.joblib")

# That's it, use it exactly like a pickle!
result = model.predict(data)

Install joblib if you don't have it: pip install joblib

Frequently Asked Questions

What is the difference between pickle and joblib?

Joblib is a replacement for pickle that is significantly more efficient on objects containing large NumPy arrays. It also provides built-in compression, reducing file sizes by 30–80% compared to plain pickle serialization. The resulting .joblib files can be loaded with joblib.load() just like you'd use pickle.load().

How much size reduction can I expect?

Size reduction depends on your file contents. Files with large NumPy arrays typically see 30–80% reduction at compression level 3. Files with mostly small Python objects will see smaller but still meaningful reductions.

What file formats are supported?

The tool accepts .pickle and .pck files as input. Output is always a compressed .joblib file that can be loaded with joblib.load('your_file.joblib').

How do I fix "No module named X" errors?

If your pickle file was created from a class defined in a custom Python module, upload the corresponding .py file in the Custom Dependencies section before converting. The tool will load it so the object deserializes correctly.

Is the Pickle to Joblib Converter free?

Yes, the tool is completely free and open source under the AGPL-3.0 license. No signup or account is required. You can also self-host it from the GitHub repository.

Is my data safe?

All processing happens in-memory. Temporary files are created only during conversion and are immediately deleted afterward. No uploaded data is stored, logged, or shared with any third party.

Can I run this locally or self-host it?

Absolutely. The source code is available on GitHub. Clone the repo, install the dependencies, and run it with Streamlit. Since all processing is local, your data never leaves your machine.

What compression level does the tool use?

The tool uses joblib's compression level 3 (out of 0–9), which provides a strong balance between compression ratio and speed. Higher levels would give slightly smaller files but take noticeably longer to process.