Last update: June 1st, 2022
The last post about CUDA installation guide was for CUDA 9.2. We went through several types of CUDA installation methods, including the multiple-version CUDA installs. While the guide is still valid for CUDA 9.2, NVIDIA keeps releasing newer versions of CUDA. As a concrete example, when this article was first written in December 2018, the latest CUDA version was CUDA 10, taking the spotlight from CUDA 9.2. If we are about to upgrade to CUDA 10, how can we achieve so? Can we simply upgrade the CUDA toolkit without upgrading the display driver?
Handling CUDA Version Upgrade
CUDA version upgrade itself can be a misleading term because since CUDA 8.0, multiple versions of CUDA can be installed on the same machine. But let’s have a simple scenario where we already have CUDA 9.1 installed and only want to upgrade to CUDA 10. NVIDIA states that each version of CUDA toolkit requires certain minimum NVIDIA display version that should be satisfied. This means that when upgrading to newer version of CUDA toolkit, we need to make sure that the currently installed display driver version is newer/bigger than the minimum compatible display driver version. In other words, standard CUDA upgrade involves two upgrade processes: CUDA (toolkit) upgrade and driver upgrade. The following picture visualizes the standard upgrade process from CUDA 9.1 to CUDA 10: the toolkit is upgraded from 9.1 to 10 and the driver is upgraded from 390 to 410.
In this post, we are about to accomplish something less common: building and installing TensorFlow with CPU support-only on Ubuntu server / desktop / laptop. We are targeting machines with older CPU, as for example those without Advanced Vector Extensions (AVX) support. This kind of setup can be a choice when we are not using TensorFlow to build a new AI model but instead only for obtaining the prediction (inference) served by a trained AI model. Compared with model training, the model inference is less computational intensive. Hence, instead of performing the computation using GPU acceleration, the task can be simply handled by CPU.
tl;dr The WHL file from TensorFlow CPU build is available for download from this Github repository.
Since we will build TensorFlow with CPU support only, the physical server will not need to be equipped with additional graphics card(s) to be mounted on the PCI slot(s). This is different with the case when we build TensorFlow with GPU support. For such case, we need to have at least one external (non built-in) graphics card that supports CUDA. Naturally, running TensorFlow with CPU pertains to be an economical approach to deep learning. Then how about the performance? Some benchmark results have shown that GPU performs better than CPU when performing deep learning tasks, especially for model training. However, this does not mean that TensorFlow CPU cannot be a feasible option. With proper CPU optimization, TensorFlow can exhibit improved performance that is comparable to its GPU counterpart. When cost is a more serious issue, let’s say we can only do the model training and inference in the cloud, leaning towards TensorFlow CPU can be a decision that also makes more sense from financial standpoint. Continue reading
In the previous posts, we’ve walked through the installations and configurations for various components and libraries required for doing deep learning / artificial intelligence on a Ubuntu 16.04 box. The next step is to be productive, crunching codes and solving problems by applying various algorithms. At this stage, visits to StackOverflow, Github or other similar sites become more frequent. And here is when the problem may arise. Not all codes or snippets copied and pasted from such online references can immediately work. One of the reasons is that the code was indeed written for same software, library, or tool but at different version.
Interestingly, software components for machine learning present different way to obtain the versions. These variations can sometimes result in additional time spent to query “ubuntu get xyz version” on the search engine. This is okay for one component, but when the system becomes complex enough (for example machine learning meets big data for ETL), this can turn into a productivity killer due to unjustifiable time taken for navigating the search engine.
Why not build a list for that?
This post summarizes the shell commands used for obtaining the versions of machine learning-related software and libraries. Commands are embodied in categories that reflect the logical / functional unit the software component belongs to. Continue reading
In the recent posts, we have been going through the installation of deep learning framework like Caffe2 and its dependencies, such as CUDA or cuDNN. In this post, we will go few steps back to the very basic prerequisite of setting up a GPU-powered deep learning system: display driver installation. We will specifically focus on NVIDIA display driver installation due to the pervasiveness and robustness of NVIDIA GPUs as deep learning infrastructure.
Before proceeding to the installation, let’s discuss some key terminologies related with the use of NVIDIA GPUs as the computing infrastructure in a deep learning system.
GPU: Graphical / Graphics Processing Unit. A unit of computation, in a form of a small chip on the graphics card, traditionally intended to perform rapid computation for image / graphics rendering and display purpose. A graphics card can contain one or more GPUs while one GPU can be built of hundreds or thousands of cores.
CUDA: A parallel programming model and the implementation as a computing platform developed by NVIDIA to perform computation on the GPUs. CUDA was designed to speed up computation by harnessing the power of the parallel computation utilizing hundreds or thousands of the GPU cores.
CUDA-enabled GPUs: NVIDIA GPUs that support CUDA programming model and implementation
CUDA compute capability: A number that refers to the general specifications and available features especially in terms of parallel computing methods of a CUDA-enabled GPU. The full list of the available features in each compute capability can be seen here.
Note on CUDA compute capability and deep learning:
It is important to note that if you plan to use an NVIDIA GPU for deep learning purpose, you need to make sure that the compute capability of the GPU is at least 3.0 (Kepler architecture). Continue reading
Last update: 12 March 2022
If you are doing deep learning AI research and/or development with GPUs, big chance you will be using graphics card from NVIDIA to perform the deep learning tasks. A vantage point with GPU computing is related with the fact that the graphics card occupies the PCI / PCIe slot. From the frugality point of view, it may be a brilliant idea to scavenge unused graphics cards from the fading PC world and line them up on another unused desktop motherboard to create a somewhat powerful compute node for AI tasks. Maybe not.
With the increasing popularity of container-based deployment, a system architect may consider creating several containers with each running different AI tasks. This means that that the underlying GPU resources should then be shared among the containers. NVIDIA provides a utility called NVIDIA Docker or nvidia-docker2 that enables the containerization of a GPU-accelerated machine. As the name suggests, the utility targets Docker container type. Continue reading