Most commonly we see this error when people have installed the driver but not yet rebooted the system, although it is also seen if other packages have been installed after the NVIDIA driver and before the system has been rebooted. This error is usually due to a problem with the installation of the driver. “ NVIDIA-SMI has failed because it couldn’t communicate with the NVIDIA driver” The nvidia-smi command can also show some errors, which can help us determine what the problem might be. The most important bit of software though is the lowly Driver - that bit of code that allows your system to access and utilise the incredibly powerful hardware inside. However, in order to get the most out of the hardware, you also need the right software, which is where tools like IBM’s Watson Machine Learning Community Edition comes in, providing GPU accelerated versions of machine learning and deep learning tools free of charge. The benefits of GPU acceleration when running frameworks like TensorFlow and PyTorch include the huge performance gains, but also the efficiency gains of using the optimal hardware for the job. Some examples cover either Red Hat Enterprise Linux or Ubuntu Linux, but open source alternatives should work the same.Īs GPU acceleration is becoming important for more workloads, particularly for machine learning and deep learning use cases, we’re seeing ever greater adoption of these devices. This covers installations and troubleshooting within Linux environments only. Assumptions: This is for NVIDIA GPUs, and does not cover other brands or other types of accelerator.
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March 2023
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