Top 10 Python Machine‑Learning Libraries of 2021 (Including Notable Domestic Projects)

In 2021 AI advanced rapidly, and Python remained the dominant language for implementing new algorithms. Many companies and research groups released high‑performance open‑source libraries, some of which are Chinese projects that have gained significant attention.

1. Awkward Array

Awkward Array handles nested, variable‑length data such as lists, records, mixed types, and missing values, offering an interface similar to NumPy. It claims speed and memory advantages over NumPy and can operate on arrays of different lengths directly. https://pypi.org/project/awkward/

2. Jupytext

Jupytext is a plugin that synchronises Jupyter Notebooks with plain‑text formats (Markdown or script files), enabling version control, editing, merging, and refactoring in any favourite IDE. It also supports Q&A checks within notebooks. The project has over 5k stars on GitHub: https://github.com/mwouts/jupytext

3. Gradio

Gradio is a lightweight UI library, even lighter than Streamlit, allowing users to create interactive demos for models in a browser—drag‑and‑drop images, paste text, record audio, etc. By setting share=True in launch(), a shareable link is generated. The repository has more than 4.5k stars: https://github.com/gradio-app/gradio

4. Hub

Hub excels at data management and preprocessing. It can handle any type and size of data, storing it in the cloud for seamless access across machines. Binary‑compressed data can be stored anywhere and retrieved on demand, enabling TB‑scale processing without massive local storage. It provides APIs for common tools (e.g., PyTorch), data versioning, and conversion. GitHub stars: 4.1k – https://github.com/activeloopai/Hub

5. AugLy

Developed by Facebook, AugLy is a data‑augmentation library supporting audio, text, image, and video, offering over 100 augmentation methods. It handles data types beyond image‑only libraries, allows adding text/emoji overlays, and assists with copy‑detection, hate‑speech detection, and copyright checks. GitHub stars: 4.1k – https://github.com/facebookresearch/AugLy

6. Evidently

Evidently generates interactive visual reports and JSON summaries of model performance from Pandas DataFrames or CSV files, usable inside Jupyter Notebooks. It provides six report types: data drift, numeric target drift, categorical target drift, regression performance, classification performance, and probabilistic classification performance. GitHub stars: 1.8k – https://github.com/evidentlyai/evidently

7. YOLOX

YOLOX is an anchor‑free version of the YOLO object‑detection algorithm, offering a simpler design and better performance, bridging research and industry needs. The project has amassed over 5.2k stars: https://github.com/Megvii-BaseDetection/YOLOX

8. LightSeq

LightSeq, created by ByteDance, is a high‑speed inference engine supporting models such as BERT, GPT, and Transformer, outperforming FasterTransformer. It is praised for its ease of use and broad model support. GitHub stars: 1.9k – https://github.com/bytedance/lightseq

9. Greykite

Greykite, built by LinkedIn for time‑series forecasting (e.g., COVID‑19 recovery speed), offers comprehensive functionality, an intuitive UI, fast predictions, and strong scalability. It includes three main algorithms: Silverkite, Facebook Prophet, and Auto‑ARIMA. GitHub stars: 1.4k – https://github.com/linkedin/greykite

10. Jina and Finetuner

Jina is a neural‑search framework that lets anyone build scalable deep‑learning search applications within minutes. Finetuner works with Jina to tune neural networks for optimal search performance, targeting users with little experience. The combined project has over 1.4k stars: https://github.com/jina-ai/finetuner

For more details, see the original article at https://tryolabs.com/blog/2021/12/21/top-python-libraries-2021 .