The article details a step‑by‑step local deployment of DeepSeek‑OCR‑2, covering GPU memory requirements, accuracy on complex tables, long inference times, dependency hurdles like GCC, GLIBC and flash‑attn, and provides concrete solutions using conda environments and symlinks.
On isolated internal networks where the system GCC is stuck at version 4.8.5, the article shows how to bypass system upgrades by using Conda to install GCC 9 in a virtual environment, create symlinks, and compile AI projects like vLLM without root access.
This guide walks you through configuring a high‑performance AI inference server on Oracle Linux, covering hardware specs, NVIDIA driver and CUDA installation, Conda environment setup, Xinference deployment, service startup, and example model loading commands, all with clear code snippets and images.
This step‑by‑step tutorial walks beginners through creating a new Python project in PyCharm, covering project location selection, Conda interpreter configuration, isolated environment creation, Python version choice, Conda executable setup, opening the project, exploring the directory structure, writing and running a simple program, and where to download the free Community edition.
The author walks through installing OpenManus, configuring it to use DeepSeek (and an Ollama‑based vision model), runs a sample financial data query, and reports that the system is slow, sometimes inaccurate, and still requires further optimization.
The article introduces Python 3.13, explains how to install it via conda, and details major updates such as an improved interactive REPL, the optional removal of the Global Interpreter Lock, and a collection of miscellaneous language and tooling enhancements.
This article explains why the distutils.util module may be missing during Python library installation on a Huawei laptop, and provides step‑by‑step commands using pip or conda to resolve the issue and successfully install packages like pandas.
This article explains the concept and importance of Python virtual environments, compares three popular tools—pythonvenv, virtualenv, and conda—provides step‑by‑step commands for creating and activating environments on different operating systems, and includes a practical API automation code example demonstrating their use.
This article provides a comprehensive overview of Python package management tools—including PyPI, setuptools, pip, virtualenv, and conda—explaining their purposes, core features, and basic usage commands for creating, sharing, and managing Python packages and environments.
This article explains how to use pip and Conda to configure, list, freeze, and clean up Python project dependencies, manage virtual environments, and handle cross‑language packages, providing practical commands and tools for reliable environment and dependency management.
This article explains how to handle Python environment and dependency management using pip tools such as pip list, pip freeze, pipdeptree, pip‑autoremove, and the conda ecosystem for creating isolated virtual environments, exporting configurations, and integrating with IDEs.
This guide explains how to reliably manage Python project dependencies and environments using pip tools (pip list, pip freeze, pipdeptree, pip‑autoremove) and Conda (installation, virtual environments, environment export/import, IDE integration), helping you avoid common pitfalls as projects scale.
This article explains how to manage Python project dependencies using pip and conda, covering basic pip commands, dependency trees with pipdeptree, clean removal with pip‑autoremove, and creating, activating, and exporting isolated environments with Conda, including practical examples and best‑practice recommendations.
This article explains the purpose of Python virtual environments, illustrates common scenarios requiring separate environments, and provides step‑by‑step Conda commands for listing, creating, activating environments, and installing packages within them to ensure project isolation.
This article provides a step‑by‑step tutorial on what Anaconda and Conda are, how to install Anaconda on Linux, manage Python environments, handle packages, configure mirror sources, use Miniconda, the graphical UI, and run Jupyter Notebook, while also discussing their pros and cons.
This comprehensive guide explains what Anaconda is, compares it with Conda, Pip, and Virtualenv, and provides step‑by‑step installation instructions for macOS, Windows, and Linux, along with detailed commands for managing environments, packages, and updates using conda and pip.
This guide walks you through installing Anaconda, checking and updating conda, creating virtual environments with various options, fixing common package‑collection errors, managing packages via conda and pip, switching pip to a faster mirror, and integrating the environments with PyCharm.
Learn step‑by‑step how to install Miniconda on Ubuntu, transfer the installer via WinSCP, configure the base environment, avoid common pitfalls, and use essential conda commands to create, activate, deactivate, and manage Python virtual environments and packages.
This guide shows two methods to confirm that Anaconda is installed correctly on Windows 10 and provides step‑by‑step instructions for adding Anaconda and its Scripts folder to the system PATH to resolve the “conda is not recognized” issue.
This article explains why Python 2.7 reached end‑of‑life in 2020, outlines the benefits and incompatibilities of moving to Python 3, and offers practical guidance for a smooth migration, including using official porting tools and managing dual environments with conda.
Learn step‑by‑step how to install Anaconda parcels on a CDH cluster to create isolated Python 3 environments, set up a local Conda package repository with Nginx, use wget and bandersnatch to mirror packages, and configure private pip sources for Windows and Linux nodes.
This article explains why Anaconda is a convenient tool for managing Python versions and packages, walks through its installation on Windows, shows how to create and activate virtual environments, install third‑party libraries with conda, and integrate the environment with PyCharm for smoother development.
