Fixing LibOpenCL.so.1 Error In VLLM On GPU

Introduction: The libOpenCL.so.1 Problem

Encountering the dreaded libOpenCL.so.1: cannot open shared object file: No such file or directory error when trying to run a VLLM-OpenVINO setup on an Intel Arc A770 GPU can be incredibly frustrating. This error typically signifies that the system cannot find the necessary OpenCL library, which is essential for utilizing the GPU's processing power. As you can see from the provided details, the user has set up a Docker container to run VLLM with OpenVINO for GPU acceleration. However, the application fails to load the OpenCL library, resulting in a runtime error. This means the program cannot access the GPU resources and will likely fall back to CPU processing, which can significantly slow down performance. The goal of this article is to dissect the error, identify the root causes, and provide a comprehensive solution to get your VLLM-OpenVINO setup running smoothly with your Intel Arc A770.

Understanding the Error: libOpenCL.so.1

The error message libOpenCL.so.1: cannot open shared object file: No such file or directory is quite explicit. It's telling us that the system is unable to locate the OpenCL library, specifically the shared object file libOpenCL.so.1. This library is a crucial component for enabling GPU acceleration in applications that use OpenCL. OpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms, including CPUs, GPUs, and other processors. When OpenVINO tries to load the Intel GPU plugin, it relies on this library to interact with the Intel Arc A770 GPU.

This error commonly surfaces in environments where the necessary OpenCL drivers or libraries are not installed correctly, or are not accessible to the application. In a Docker container, this issue can arise because the container lacks the required drivers or the correct paths to these libraries. The user's setup, which includes building a Docker image and running a container, emphasizes the importance of correctly configuring the container environment to ensure that the required libraries are available and accessible to the VLLM application.

Diagnosing the Issue: System Setup and Logs

The user has provided valuable information, including the host system's specifications, Docker commands, and detailed error logs. Let's delve into these details to pinpoint the source of the problem. First, the host system is running Ubuntu 25.10 with an Intel Arc A770 GPU. The inxi -G output confirms that the GPU drivers are loaded. This indicates that the host system itself is capable of utilizing the GPU. Then, the Docker commands reveal the steps taken to build and run the container. The key command here is docker run -it --rm -v ~/llm/models:/llm/models -p 8000:8000 --device /dev/dri:/dev/dri vllm-openvino-env.

This command attempts to pass the host's /dev/dri device to the container, which is a standard method for granting the container access to the GPU. Finally, the error message in the logs provides the precise location where the error occurs: /usr/local/lib/python3.10/dist-packages/openvino/libs/libopenvino_intel_gpu_plugin.so. This is the OpenVINO plugin file for Intel GPUs. The traceback clearly shows the error originating from this location, confirming that the VLLM application, through OpenVINO, is trying to load the Intel GPU plugin but failing due to the missing libOpenCL.so.1.

The user also included the output of clinfo from the host system. This tool lists all available OpenCL platforms and devices. This is important because it validates that the host system has an operational OpenCL environment and the Intel Arc A770 is recognized. If the clinfo output in the container doesn't match, it could indicate that the container's environment is not correctly configured to access the host's OpenCL drivers and devices. Another crucial piece of information is the VLLM log from within the container. Examining this log can provide insights into how VLLM attempts to load the OpenVINO plugin and its reliance on the OpenCL library.

Solution: Ensuring OpenCL Availability in the Docker Container

The primary cause of this problem is the missing or inaccessible libOpenCL.so.1 inside the Docker container. To address this, we need to ensure that the container has access to the correct OpenCL drivers and libraries. There are several ways to accomplish this:

1. Install OpenCL Drivers in the Dockerfile: The most robust solution is to install the necessary OpenCL drivers directly within the Dockerfile. You can achieve this by adding commands that install the Intel OpenCL runtime or the appropriate drivers for your GPU during the image build process. This ensures that the OpenCL library is present when the container runs.

2. Mounting Host Libraries: Another approach is to mount the OpenCL libraries from the host system into the container. This involves using the -v flag in the docker run command to bind the host's OpenCL library path to a path inside the container. This allows the container to access the host's drivers. However, this method might lead to compatibility issues if the container's environment is not aligned with the host's OpenCL version or drivers.

3. Using nvidia-container-toolkit (if applicable): If you are using NVIDIA GPUs, the nvidia-container-toolkit is an excellent tool. Although, in this case, we have an Intel Arc A770, so it's not applicable. However, this toolkit simplifies the process of configuring a container to utilize the host's NVIDIA drivers.

For the Intel Arc A770, you need to make sure the Intel OpenCL runtime or drivers are installed. A sample Dockerfile modification could look like this: You need to adapt this based on the specific OpenCL drivers and packages for your Ubuntu version and Intel Arc A770. The Dockerfile should include these steps:

FROM ubuntu:latest

# Update and install necessary packages
RUN apt-get update && apt-get install -y --no-install-recommends \
    ocl-icd-opencl-dev \
    intel-opencl-icd \
    && rm -rf /var/lib/apt/lists/*

# Copy your application files and dependencies and the rest of your Dockerfile steps

This Dockerfile installs the ocl-icd-opencl-dev and intel-opencl-icd packages. These packages should provide the OpenCL ICD (Installable Client Driver) and the necessary runtime for Intel GPUs. After modifying the Dockerfile, rebuild the image and rerun the container. This approach ensures that the OpenCL drivers are installed and available within the container's environment.

Troubleshooting and Verification

After implementing the solution, it's essential to verify that the issue has been resolved. Here's how to troubleshoot and confirm the fix:

1. Check the Docker Build Logs: During the Docker image build process, carefully examine the logs for any errors related to package installation or library dependencies. These logs can pinpoint issues that might prevent the successful installation of OpenCL drivers.

2. Verify OpenCL Inside the Container: After running the container, execute the clinfo command within the container. This command will list the available OpenCL platforms and devices. Ensure that the output shows the Intel Arc A770 GPU and that the OpenCL environment is correctly initialized. The container's clinfo output should now reflect the host's OpenCL capabilities if correctly configured.

3. Inspect the VLLM Logs: Examine the VLLM logs again after running the application. The error related to libOpenCL.so.1 should be gone. Instead, the logs should indicate that the model is loaded successfully and that the GPU is being utilized for processing. Check if the VLLM logs display the GPU device being used during the inference process.

4. Test the Application: Finally, test the VLLM application by sending requests to it. Verify that the response times are significantly faster than when running on the CPU, confirming that the GPU is actively accelerating the process. If everything is working correctly, the inference should utilize the GPU, and the response times should be considerably improved.

Addressing Potential PIP_EXTRA_INDEX_URL Concerns

The user raised a question about `PIP_EXTRA_INDEX_URL=