Resolving the scaling_cur_freq & scaling_max_freq Error in Ubuntu Docker Containers

Troubleshooting Frequency Scaling Errors in Ubuntu Docker Containers

When working with Docker containers on an Ubuntu 20.04 base, especially those involving external projects, unexpected errors may arise. One such issue occurs when the system attempts to locate files like scaling_cur_freq and scaling_max_freq but fails, causing execution errors.

This issue can be particularly confusing if you're unfamiliar with frequency scaling mechanisms in Linux or if you're running a proprietary container. Many users encounter this when they try to execute specific commands or start a Docker container.

The core of the problem lies in the interaction between the containerized environment and the host machine’s hardware, particularly CPU frequency scaling features, which aren’t always accessible in containers. Solutions to this are often elusive and scattered across different sources.

In this guide, we will explore why this error happens, whether it’s related to your Docker setup or the underlying Linux environment, and what potential solutions can be applied. We will also discuss a similar issue with Chrome installation on AWS EC2 Linux instances and why their fix may not apply here.

Analyzing the Frequency Scaling Error Solutions

The scripts provided earlier serve to resolve the issue of missing CPU frequency scaling files such as scaling_cur_freq and scaling_max_freq, which are essential for certain processes in Docker containers. These files are typically found in the /sys/devices/system/cpu/cpu0/cpufreq directory, but in containerized environments, especially on Ubuntu 20.04, they may not be available. The bash script addresses this by checking for the existence of these files and creating stubs if they are missing. This ensures the container can proceed with its operations without encountering errors related to these missing system files.

The shell script uses a loop to cycle through the required files, and if any are missing, it creates them using the touch command. This approach is simple yet effective, ensuring that the files are available when needed without requiring extensive modifications to the system. It also allows for the script to be easily adapted for other environments where frequency scaling is not properly configured. By adding logging or additional error-checking features, the script can be further enhanced for production environments.

The Python solution takes a different approach by leveraging the os.path.isfile() method to check if the necessary files exist. If they do not, it logs the error to a file for easier troubleshooting. This method is more suited for situations where detailed logging is required, or where the project might need to integrate into a larger Python-based system. Additionally, Python's modularity and readability make it easier to scale this solution across multiple projects, especially if multiple files need to be checked or created.

Finally, the Dockerfile solution automates the file creation process during the build phase of the Docker container. This ensures that the necessary directories and files are always present before the container starts, avoiding any runtime issues. By utilizing commands like RUN and chmod, the Dockerfile manages permissions and file creation directly within the container's environment. This method is ideal for ensuring consistent deployment across various servers or cloud environments where system configuration might differ. Combining these approaches offers robust solutions for a common containerized Linux issue.

#!/bin/bash
# Check if the required files exist
FREQ_PATH="/sys/devices/system/cpu/cpu0/cpufreq"
REQUIRED_FILES=("scaling_cur_freq" "scaling_max_freq")
# Loop through each file and create a stub if it's missing
for FILE in "${REQUIRED_FILES[@]}"; do
    if [[ ! -f "$FREQ_PATH/$FILE" ]]; then
        echo "File $FILE not found, creating a stub."
        sudo touch "$FREQ_PATH/$FILE"
        echo "Stub created for $FILE."
    else
        echo "$FILE exists."
    fi
done
# End of script

import os
import logging
# Set up logging
logging.basicConfig(filename='freq_check.log', level=logging.INFO)
freq_files = ['/sys/devices/system/cpu/cpu0/cpufreq/scaling_cur_freq',
              '/sys/devices/system/cpu/cpu0/cpufreq/scaling_max_freq']
# Function to check file existence
def check_files():
    for file in freq_files:
        if os.path.isfile(file):
            logging.info(f'{file} exists.')
        else:
            logging.error(f'{file} is missing.')
# Call the function
check_files()

FROM ubuntu:20.04
RUN apt-get update && apt-get install -y sudo
# Create necessary directories and files if they don't exist
RUN mkdir -p /sys/devices/system/cpu/cpu0/cpufreq/
RUN touch /sys/devices/system/cpu/cpu0/cpufreq/scaling_cur_freq
RUN touch /sys/devices/system/cpu/cpu0/cpufreq/scaling_max_freq
# Set permissions to avoid access issues
RUN chmod 644 /sys/devices/system/cpu/cpu0/cpufreq/*
# Ensure the container runs a basic command on start
CMD ["/bin/bash"]

Understanding CPU Frequency Scaling and Container Limitations

Another critical aspect of the scaling_cur_freq and scaling_max_freq issue is how Docker containers handle hardware interactions, particularly with CPU frequency scaling in Linux environments. These scaling files are part of the Linux kernel's CPU governor feature, which adjusts CPU performance dynamically. However, Docker containers often don't have direct access to these hardware resources, leading to missing file errors, as seen in the error log.

In a typical Linux environment, the CPU scaling mechanism can be modified or accessed through the /sys directory. However, within a containerized environment, this access is restricted unless explicitly configured. This limitation is what often causes Docker to fail when projects expect to interact with the host machine’s CPU features. Without proper access or emulation, the container may report that it cannot find critical files like scaling_cur_freq.

To resolve these issues, understanding how Linux handles CPU governors and how Docker isolates hardware resources is crucial. Solutions can range from manually creating file stubs within the container to modifying the Docker runtime configuration to allow more direct hardware access. Developers must be mindful of these limitations when building or deploying containers on systems where direct hardware interaction is necessary.