Error Atlas

Summary The CondaDependencyError: failed to resolve package dependencies occurs when Conda’s solver cannot find a set of compatible package versions that meet all requirements. It often arises from conflicting version constraints, mixed channels (like defaults and conda-forge), or outdated metadata. The solution is to relax constraints, update Conda, or broaden your available repositories. Context Conda uses a dependency solver to ensure every installed package is compatible with the others in your environment. When two or more dependencies have incompatible version requirements — or when metadata is outdated — the solver fails. This issue is especially common when using strict version pins, old YAML environment files, or mixing channels with divergent dependency trees. Probable Cause Strict version pins such as python=3.9.0 or numpy=1.21.0 blocking compatible builds. Mixing the defaults and conda-forge channels without priority control. Outdated or corrupted Con...

Summary The CondaKeyError: key 'pkgs_dirs' not found occurs when Conda tries to read a configuration key that’s missing, invalid, or misformatted in your .condarc file. This file controls Conda’s settings such as channel sources, package directories, and environment paths. The fix is to correct or recreate the .condarc configuration using valid YAML syntax. Context Conda reads configuration data from a YAML file called .condarc , which defines parameters like channels , pkgs_dirs , and envs_dirs . If this file becomes corrupted, edited incorrectly, or missing required keys, Conda cannot locate its package cache directories and raises a CondaKeyError . This typically happens after manual edits, removal of configuration keys, or upgrading between major Conda versions. Probable Cause Corrupted or incomplete .condarc configuration file. YAML syntax errors (missing dashes or indentation issues). Manual deletion of pkgs_dirs or envs_dirs keys. Outdated Cond...

Summary The CondaIndexError: invalid index file or corrupt repodata.json occurs when Conda fails to read the metadata index ( repodata.json ) from a repository channel. This file lists all available packages and dependencies, so when it’s incomplete, corrupted, or outdated, Conda can’t resolve package requests. The fix is to clean the cache and force Conda to re-download a fresh index. Context Every Conda channel maintains a repodata.json file — a JSON index that describes all packages available for that repository. When Conda installs or updates a package, it fetches and caches this metadata. If the file becomes damaged or truncated (due to interrupted downloads, proxy issues, or local corruption), Conda can’t parse it, triggering the error. Cleaning and rebuilding the cache usually resolves it. Probable Cause Corrupted or partially downloaded repodata.json file in Conda’s cache. Interrupted or incomplete network connection during metadata retrieval. Outdated inde...

Summary The CondaVerificationError: The package appears to be corrupted indicates that Conda detected a mismatch between a package’s actual files and its expected checksum or metadata. This typically means the package was incompletely downloaded, altered, or damaged after installation. The fix is to remove the corrupted package, clear the cache, and reinstall it to restore integrity. Context Each Conda package includes a checksum (e.g., MD5 or SHA256) used to verify that files remain unaltered. When these checksums fail verification, Conda raises this error to prevent using potentially broken or unsafe binaries. The issue often arises after network interruptions, antivirus interference, or manual edits inside the pkgs/ directory. A clean reinstall resolves nearly all occurrences. Probable Cause Interrupted or incomplete download of a package tarball. Corrupted files in Conda’s pkgs/ cache directory. File tampering or antivirus scanning during installation. Outda...

Summary The CondaEnvRemoveError: failed to remove environment appears when Conda cannot delete an environment folder because some files are still open, locked, or protected. This happens when processes like Jupyter, VS Code, or Spyder are using the environment, or when file permissions prevent deletion. Closing active sessions, deactivating the environment, and manually deleting its folder typically resolve the issue. Context When you remove an environment with conda remove -n <env> --all , Conda must delete all files and metadata associated with that environment. If any process holds a file handle inside that directory (e.g., a Jupyter kernel, Python shell, or IDE), Conda cannot complete the operation. In some cases, antivirus software or Windows file indexing also locks files, causing permission errors or incomplete removal attempts. Probable Cause The environment is still active or being used by another process. File locks or residual processes from Jupyter, VS...

Summary The CondaEnvExistsError: environment already exists appears when Conda tries to create a new environment with a name or path that’s already in use. This typically happens if an environment with the same name already exists in ~/anaconda3/envs/ (or equivalent path). The solution is to either delete the existing environment, rename the new one, or force an overwrite if appropriate. Context Each Conda environment must have a unique name or directory path. When you run conda create -n myenv python=3.11 , Conda checks whether myenv already exists in the environments directory. If it does, Conda raises this error to prevent overwriting. The issue is common when repeating a setup command, importing from YAML files that reuse existing names, or when a previous creation process was interrupted, leaving residual files behind. Probable Cause An environment with the same name already exists. Previous environment creation was interrupted, leaving a partial folder. A YAM...

Summary The CondaYAMLError: invalid YAML file or parsing error occurs when Conda fails to read your environment.yml due to incorrect YAML syntax. YAML is strict about indentation, spacing, and list structure. This error prevents environment creation until the file’s format is corrected. Ensuring proper indentation (spaces only), correct colons, and consistent structure resolves the issue immediately. Context Conda environment files ( environment.yml ) use the YAML format to describe the name, dependencies, and channels of an environment. YAML is indentation-sensitive, and even a single tab or missing space can make it invalid. This error typically appears after manually editing or exporting an environment file from a different operating system, or when copying snippets from websites with altered spacing. Probable Cause Mixing tabs and spaces in indentation. Missing or misplaced colons ( : ) in key-value pairs. Incorrect list syntax (missing - before dependencies). ...

Summary The CondaFileNotFoundError: environment.yml not found error occurs when Conda cannot locate the environment definition file specified in your command. This usually happens if you’re running the command from the wrong directory, the file name is incorrect (e.g., environment.yaml instead of environment.yml ), or the file simply doesn’t exist. Ensure the file path and name are correct, or specify its full location with the -f option. Context The environment.yml file defines all dependencies, channels, and metadata for a Conda environment. When you run conda env create -f environment.yml , Conda expects to find this file in your current working directory unless you explicitly provide another path. If the file is missing, misnamed, or inaccessible, Conda raises this error and stops execution. It’s a common issue when cloning projects from GitHub or moving between directories without verifying paths. Probable Cause The file environment.yml doesn’t exist in your curre...

Summary The CondaError: Missing write permissions to Conda packages directory occurs when Conda cannot write to its package cache or environment directories. This typically happens on system-wide or multi-user installations, where directories like /opt/anaconda3/pkgs or C:\ProgramData\Anaconda3\pkgs are protected. Running Conda with elevated privileges or configuring it to use user-owned directories resolves the issue safely. Context Conda stores downloaded package archives in a global cache ( pkgs/ ) shared across environments. When that cache or the environment location is read-only, Conda fails to link or unpack new packages. This problem often affects users of shared systems, managed corporate setups, or environments created under administrative privileges. On Windows, group policies and antivirus locks can also block access; on Linux and macOS, system installs in /opt/ or /usr/local/ are frequent culprits. Probable Cause System-wide Conda installation without writ...

Summary The CondaError: Could not open lock file / permission denied message appears when Conda cannot write to its lock file during installation, update, or environment modification. This usually happens because another Conda process is already running, a stale lock file remains from a previous session, or the user lacks write permissions. Closing other sessions, removing old locks, or adjusting permissions typically resolves the problem. Context Conda uses lock files to ensure that only one process modifies an environment at a time. When you install or update packages, Conda locks the target directory to avoid simultaneous writes that could corrupt environments. If another Conda instance holds the lock — or if a previous process was terminated unexpectedly — the lock remains in place, blocking future operations. The same error can occur when the filesystem is read-only or when permissions restrict file creation inside Conda directories. Probable Cause Another Conda proce...

Summary The CondaUpgradeError: cannot update base environment error occurs when Conda prevents modification of its protected base environment. This typically happens when attempting to run conda update --all inside the base environment or when permissions are restricted on system-wide installations. To fix it, update Conda itself with conda update -n base conda or create a new environment instead of upgrading everything in base. Context The base environment in Conda contains the package manager’s own runtime and dependencies. Updating all packages within it can easily cause dependency conflicts or break Conda itself. For this reason, recent Conda versions block full upgrades to base, especially on managed or corporate systems where write access is limited. This protection ensures stability of the package manager and avoids corrupting shared installations. Instead, Conda encourages environment isolation — creating new environments for different Python versions or dependencies. ...

Summary The CondaError: Cannot link a source that does not exist occurs when Conda tries to create links from its package cache ( pkgs/ ) to an environment, but the required package files are missing or corrupted. This typically happens after interrupted installations, antivirus interference, or manual deletion of cached files. Cleaning the Conda cache and reinstalling the affected packages resolves the issue. Context Conda stores all downloaded packages in a central cache folder ( pkgs/ ) and links them into individual environments when needed. During installation, Conda validates and links binaries from this cache. If files are missing, incomplete, or locked by the operating system, the linking process fails. The problem is common after forced shutdowns, cancelled installs, or partial upgrades. It can appear across all platforms — Windows, macOS, and Linux — especially when filesystem permissions or antivirus programs interfere with file operations. Probable Cause Incomp...

Summary The CondaValueError: Key 'channels' is not a list error means Conda cannot parse your configuration file ( .condarc ) because the channels key is incorrectly formatted. Conda expects this key to be a YAML list, but the file defines it as a single string or with invalid indentation. Correcting the syntax or recreating the configuration file resolves the issue immediately. Context Conda uses a YAML-based configuration file called .condarc to define settings like channels, proxies, and environment paths. YAML requires lists to be expressed with dashes under a key (e.g., channels: followed by indented entries). When channels is written as a string (e.g., channels: defaults ) or formatted incorrectly, Conda throws a parsing error and stops reading configuration values. This usually happens after manual edits or copying configurations between systems. Probable Cause Improper YAML syntax in the .condarc file. channels defined as a single string instead of...

Summary The CondaSSLError: OpenSSL SSL_connect: Connection reset by peer message means that Conda failed to establish a secure HTTPS connection during the SSL handshake. This typically occurs because of outdated or untrusted certificates, restrictive proxies, or OpenSSL mismatches. Updating Conda’s SSL libraries and certificate packages, or temporarily disabling SSL verification for testing, usually resolves the issue. Context Conda relies on the OpenSSL library to perform encrypted communication with package repositories like repo.anaconda.com . During an SSL handshake, both client and server exchange certificates to verify identity. When this process fails — for example, due to expired certificates, proxy interception, or system clock errors — Conda throws an SSL-related error and aborts. This is common on corporate networks or older Conda installations where the bundled OpenSSL or certifi package is outdated. Probable Cause Outdated or invalid SSL certificates preventi...

Summary The Conda error Downloaded bytes did not match Content-Length means the downloaded package file size doesn’t match what the repository server expected. This integrity check prevents Conda from installing incomplete or corrupted packages. The issue is usually caused by unstable internet connections, proxies, or cached partial downloads. Clearing Conda’s cache and retrying typically fixes it. Context Conda verifies the size and hash of every downloaded package to ensure data integrity. When the number of bytes actually received doesn’t match the server’s Content-Length header, Conda halts the installation and flags a corruption. The error often arises after interrupted downloads, proxy interference, or outdated cache files in the pkgs/ directory. It’s a safety measure to prevent broken or partial installations that could destabilize environments. Probable Cause Unstable or interrupted network connection during download. Proxy, VPN, or firewall truncating or buff...

Summary The Conda error PackageNotFoundError occurs when Conda cannot locate a requested package in any of the currently configured channels. This often happens when using a minimal Conda setup (e.g., Miniconda) without conda-forge enabled, or when the package name or version is invalid. The fix is to add missing channels, search for the package explicitly, or install it via pip if it’s not hosted on Conda. Context When installing or updating packages, Conda queries a set of channels defined in your configuration file ( .condarc ). If none of these sources provide the requested package, Conda raises a PackagesNotFoundError . This issue commonly arises when creating environments from incomplete YAML files, using outdated Conda versions, or attempting to install OS-specific packages on an unsupported platform. The problem affects all major operating systems and Conda distributions (Anaconda, Miniconda, WSL). Probable Cause The package isn’t hosted on any active channels in ...

Summary The Conda error EnvironmentNotWritableError indicates that you’re trying to modify or install packages in an environment you don’t have write permission for. This usually happens in system-wide or shared environments installed under administrator or root ownership. The solution is to work inside a user-owned environment, adjust folder permissions, or use elevated privileges only when necessary. Context Conda environments are directories containing isolated Python installations and dependencies. If an environment is installed in a system path — such as /opt/anaconda3/envs/ on macOS/Linux or C:\ProgramData\Anaconda3\envs\ on Windows — normal users may lack permission to modify it. Attempts to install, update, or remove packages will fail with EnvironmentNotWritableError or PermissionError: [Errno 13] Permission denied . This problem is common in shared or corporate setups, or when Conda was installed globally. The cleanest fix is to create a user-owned environment in yo...

Summary The Conda error UnsatisfiableError: The following specifications were found to be incompatible means that Conda’s dependency solver cannot find a set of package versions that work together. This usually happens when version pins conflict, when mixing incompatible channels, or when packages for your OS or Python version don’t exist. The fix is to relax version constraints, update Conda, use consistent channels, and rebuild the environment cleanly. Context When Conda installs or updates packages, it resolves all dependencies recursively across configured channels. If even one dependency can’t match version constraints for your platform, Conda throws an UnsatisfiableError . This often happens in complex environments with strict pins (like numpy=1.21.0 or python=3.6 ), or when combining packages from defaults and conda-forge , which may use different build stacks (e.g., compilers, GLIBC versions). It can also appear if metadata is outdated or when certain versions simply d...

Summary The Conda error ResolvePackageNotFound occurs when Conda cannot locate one or more packages listed in your install or environment file. It means those packages are not present in the currently configured channels. The solution is to add missing repositories like conda-forge , correct any typos or platform-specific dependencies, or install unavailable packages using pip . Context When creating or updating an environment, Conda resolves dependencies by checking all configured channels listed in your .condarc file or YAML specification. If a required package or version cannot be found in any of those sources, Conda stops with a ResolvePackageNotFound error. This commonly happens when using environment files shared between different operating systems, such as including Windows-only packages like pywin32 in Linux setups. The error also appears when using Miniconda without enabling the conda-forge channel or when metadata is outdated. Probable Cause The package is un...

Summary The Conda error CondaHTTPError: HTTP 000 CONNECTION FAILED means that Conda couldn’t establish a connection to the package repository. This can be due to unstable internet, misconfigured proxy settings, SSL verification issues, or temporary server downtime. The fix involves checking your network, verifying SSL and proxy configurations, and retrying the command after cleanup or mirror changes. Context When Conda installs or updates packages, it connects to repositories such as https://repo.anaconda.com via HTTPS. If it can’t reach them, it returns HTTP 000 CONNECTION FAILED , a catch-all error for connection resets, timeouts, or SSL failures. This commonly occurs on restricted networks, behind firewalls or proxies, or when system environment variables interfere with Conda’s own network layer. SSL certificate issues or outdated CA bundles can also prevent proper handshakes. The error appears across Windows, macOS, Linux, and WSL, both in Anaconda and Miniconda environment...

Summary The Git error error: RPC failed; curl 56 Recv failure: Connection reset by peer occurs when the connection between your system and the remote server is interrupted mid-transfer. This is most common during large pushes, network instability, or when pushing files beyond host limits. The fix involves increasing Git’s HTTP buffer, improving compression, ensuring network stability, and using Git LFS for large files. Context Git communicates with remote servers (like GitHub, GitLab, or Bitbucket) over HTTPS or SSH. During large pushes or fetches, Git sends data in compressed chunks using RPC (Remote Procedure Calls). If the network drops, the server times out, or the transfer exceeds configured limits, Git terminates the session with a “curl 56” error — indicating a connection reset. This issue is frequent in large repositories, poor Wi-Fi conditions, VPN environments, or outdated Git versions. It can also appear in CI/CD pipelines or behind proxies that interrupt long HTTP se...