conda-doctor

View report for the currently active environment

$ conda doctor

$ conda doctor [[-n|--name]] [environment_name]

$ conda doctor [[-p|--prefix]] [path/to/environment]

$ conda doctor [[-v|--verbose]]

conda-doctor diagnose [OPTIONS]
conda-doctor --version
conda-doctor --help

--help
    Display a help message for the command and exit. This applies to both the main command and subcommands like diagnose.

--version
    Show the program's version number and exit.

diagnose
    The primary subcommand to initiate the diagnostic process, running a suite of checks on your conda setup.

--json
    Output results of the diagnostic checks in JSON format, suitable for programmatic parsing or integration with other tools.

--verbose, -v
    Increase output verbosity to display more detailed information about the checks being performed and their outcomes.

--quiet, -q
    Suppress standard output, showing only warnings and errors to minimize console noise.

--name NAME, -n NAME
    Specify the name of a particular conda environment to diagnose, rather than the active or default one.

--prefix PATH, -p PATH
    Specify the file path to a specific conda environment to diagnose. This is an alternative to using --name.

--experimental
    Include experimental diagnostic checks which might not be fully stable or vetted, but can offer deeper insights.

--list-checks
    List all available diagnostic checks that conda-doctor can perform, without actually executing them.

--channel CHANNEL_URL
    Specify a custom channel URL (e.g., a private or test channel) to be included in network connectivity tests to ensure accessibility.

--solver SOLVER_TYPE
    Specify a solver type (e.g., 'libmamba', 'classic') to include in solver tests, verifying its functionality and configuration.

conda-doctor is a powerful diagnostic utility designed to help users identify and resolve common problems within their conda environments. It performs a series of comprehensive checks on various aspects of the conda setup, including system PATH configuration, file and directory permissions, available disk space, network connectivity, conda configuration settings, and the integrity of installed packages and channels.

By systematically examining these components, conda-doctor can pinpoint the root causes of issues such as failed installations, environment activation problems, or unexpected package behavior. Upon completion, it provides a detailed report outlining any detected problems and often suggests specific actions or commands to rectify them. This tool is invaluable for maintaining a healthy conda ecosystem and efficiently troubleshooting errors, making it an essential utility for anyone regularly working with conda environments on Linux systems. It aims to empower users to self-diagnose and fix issues, reducing the need for extensive manual debugging.

conda-doctor requires a functional conda installation to operate. While it identifies common issues and suggests fixes, users are ultimately responsible for understanding and applying these solutions. Some diagnostic checks may require active network connectivity. It might not detect highly specific, esoteric, or custom system configuration issues that fall outside its predefined checks.

The most common invocation of the command is simply conda-doctor diagnose, which runs a comprehensive set of default checks on the active conda environment or the base environment if no specific environment is activated.

The diagnostic report categorizes each check's status as PASS, FAIL, or WARN. For checks resulting in FAIL or WARN, conda-doctor provides a concise explanation of the identified problem and often includes specific recommendations or commands to resolve the issue, guiding the user towards a solution.

conda-doctor emerged as a dedicated tool within the conda ecosystem to address the growing complexity of environment management and the frequent support requests related to common installation or configuration issues. It was developed to empower users with self-service diagnostics, shifting from manual debugging to automated problem identification and recommended solutions. Its introduction aimed to streamline troubleshooting and improve the overall user experience by providing clear, actionable insights into environment health and promoting a more self-sufficient approach to maintaining conda environments.

conda(1), conda env(1), conda install(1), which(1), pip(1)