notebook | BestHub

Architecture Digest

Jul 4, 2023 · Fundamentals

JupyterLab 4.0: New Features, Performance Boosts, and Extension Management

JupyterLab 4.0 introduces a flexible multi‑document interface, customizable layouts, an integrated file browser, modular extensibility, built‑in terminal, significant performance improvements, a separate real‑time collaboration package, and a PyPI‑based extension manager, while noting that AI‑assisted coding tools may now outpace it.

IDEJupyterLabdata science

0 likes · 4 min read

JupyterLab 4.0: New Features, Performance Boosts, and Extension Management

Cloud Native Technology Community

Jun 28, 2023 · Artificial Intelligence

Building and Deploying Custom Large Language Models with Alauda Cloud‑Native MLOps

This article explains how enterprises can use the Alauda MLOps platform to quickly set up, fine‑tune, and deploy private large language models on cloud‑native infrastructure, covering notebook preparation, GPU allocation, model download, inference service creation, distributed training pipelines, and Docker image building.

Fine-tuningaicloud-native

0 likes · 9 min read

Building and Deploying Custom Large Language Models with Alauda Cloud‑Native MLOps

Python Programming Learning Circle

Aug 13, 2021 · Fundamentals

JupyterLab 3.0: New Features, Installation Options, and Extension Improvements

JupyterLab 3.0 introduces a visual debugger, directory extension, multilingual UI, enhanced simple‑mode, better mobile support, and pre‑built extensions, while offering three installation methods (pip, mamba, conda) and streamlined workflows for extension authors, making the notebook environment more powerful and user‑friendly.

DataScienceInstallationJupyterLab

0 likes · 6 min read

JupyterLab 3.0: New Features, Installation Options, and Extension Improvements

Python Programming Learning Circle

Jun 11, 2021 · Fundamentals

10 Jupyter Lab Extensions to Boost Your Productivity

This article introduces ten essential Jupyter Lab extensions—including a debugger, table of contents, DrawIO integration, execution timer, spreadsheet viewer, system monitor, AI code completion, variable inspector, Matplotlib and Plotly support—explaining their features, installation commands, and how they can significantly boost data scientists' productivity.

JupyterLabProductivityPython

0 likes · 7 min read

10 Jupyter Lab Extensions to Boost Your Productivity

Tencent Advertising Technology

May 4, 2020 · Artificial Intelligence

How to Use TI-ONE Notebook for the 2020 Tencent Advertising Algorithm Competition

This tutorial walks through creating a TI-ONE Notebook instance, importing competition data, training a TensorFlow model, and uploading results, providing a complete end‑to‑end workflow for participants of the 2020 Tencent Advertising Algorithm Competition.

TI-ONETensorFlowdata-import

0 likes · 8 min read

How to Use TI-ONE Notebook for the 2020 Tencent Advertising Algorithm Competition

Python Programming Learning Circle

Apr 3, 2020 · Fundamentals

Choosing the Right Python IDE: Insights from Tencent Engineers

This article surveys the preferences of Tencent engineers on Python IDEs, discussing how personal habits, project requirements, and specific use‑cases such as data science, web development, or lightweight editing influence the choice among tools like Notebook, Spyder, PyCharm, VS Code, Vim, and the built‑in IDLE.

IDEPyCharmVSCode

0 likes · 5 min read

Choosing the Right Python IDE: Insights from Tencent Engineers