Docker gets error "failed to compute cache key: not found" - runs fine in Visual Studio

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06-10-2024

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Docker Error: "failed to compute cache key: not found" - Debugging and Solutions

Problem: You're running a Docker container in Visual Studio, and it works perfectly. However, when you try to run the same container outside of Visual Studio (using Docker Desktop or the command line), you encounter the error "failed to compute cache key: not found".

Simplified: You're using a tool (Visual Studio) to build your project, and it works seamlessly. But when you try to build the project on its own, it crashes, throwing a confusing error message.

Understanding the Error

This error usually occurs when the Docker build process is unable to locate a specific file or directory that's required to calculate the cache key. This key is crucial for Docker to efficiently manage image builds and optimize the process. If it cannot find the necessary components, it fails to create the cache key and throws the "not found" error.

Let's analyze the scenario:

Imagine you have a .NET project with a Dockerfile that references a specific package.json file for dependencies. This file is present in your project directory, and Visual Studio uses it to build the project.

However, when you run docker build outside of Visual Studio, it might not be able to locate the package.json file because:

1. Incorrect Working Directory: The Docker command might be executing from a different location than where your package.json file actually resides.
2. Missing Context: You might not be providing the correct context (directory containing your Dockerfile) when building the image.
3. Hidden Files: The package.json file could be hidden due to .gitignore settings or other file system configurations.

Debugging and Solutions

Here's a step-by-step guide to troubleshoot and fix the error:

1. Verify the Dockerfile: Carefully examine your Dockerfile to ensure all paths and file references are correct and relative to the context directory. If using absolute paths, make sure they are accessible from outside of Visual Studio.
2. Context Is Key: Double-check the context you are providing when running docker build. The command should look like this: docker build -t my-image . (where "." represents the directory containing your Dockerfile). Ensure this directory is correctly pointing to the root of your project where package.json is located.
3. Hidden Files and Directories: Use ls -a in your terminal to reveal hidden files and directories. This can help you identify if your package.json is hidden by .gitignore settings or other file system configurations. You might need to adjust your gitignore or file system permissions to make the file accessible.
4. Visual Studio Configuration: Check your Visual Studio project settings for any specific configurations related to Docker. For example, make sure the project path is correctly configured, and the Docker context is set properly.
5. Clean Build: Try cleaning your Docker build cache by running docker system prune. This might resolve issues related to corrupted cache files.
6. Docker Desktop Settings: If you are using Docker Desktop, double-check your Dockerfile paths and project directory settings within the application's preferences.

Additional Tips

• Debugging: Utilize the --no-cache flag during the build process to temporarily disable the cache and pinpoint the specific file or directory causing the issue.
• Logging: Add RUN echo "Building from directory: $(pwd)" to your Dockerfile to check the actual working directory during the build process. This can help identify potential path discrepancies.
• Environment Variables: Make sure any environment variables used in your Dockerfile are properly defined in your environment.

By following these steps and understanding the error's origin, you can effectively debug and resolve the "failed to compute cache key: not found" issue and ensure your Docker containers build successfully both within and outside of Visual Studio.

Remember, debugging errors in Docker can be challenging but with careful analysis and troubleshooting, you can get your containers up and running efficiently.