September 16

How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Phase 4: Installation GPU Tensorflow package and execution of the first neural network in python program execution)

Reading Time: 2 minutes

In the serie, “How to use GPU with Tensorflow 1.8 and CUDA 9.2”, we are now in the final phase. This step focusses on the installation of GPU Tensorflow 1.8 and the execution of a python program based on the .

The compilation of Tensorflow is not simple. So, I recommend to reserve around 2 hours to make this task. The compilation of the software is structured into 4 steps:

  • Step 1: Installation of the wheel package created in the previous phase and the keras library.
  • Step 2: Creation of the python program using the Keras library (with the configuration of Tensorflow backend).
  • Step 3: Execution of the program and confirmation that the CUDA cores of the graphical card.

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Category: Artificial Intelligence | 2 Comments on How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Phase 4: Installation GPU Tensorflow package and execution of the first neural network in python program execution)
September 15

How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Phase 3: Compilation of Tensorflow 1.8)

Reading Time: 4 minutes

In the serie, “How to use GPU with Tensorflow 1.8 and CUDA 9.2”, we are now in the third phase. This step is related to the compilation of Tensorflow 1.8 by including the library CUDA. This compilation will enable tensorflow to use the GPU core of the graphical card (384 cores). TensorFlow programs typically run significantly faster on a GPU than on a CPU. Therefore, if your system has a NVIDIA GPU and you need to run performance-critical applications, you should ultimately build and install this version. In the previous sections, we already installed the last driver of the graphical card (MX150), the  library CUDNN, and CUPTI to prepare the laptop for the compilation of Tensorflow 1.8. We also check if the library is correctly installed by using the samples available in the package of CUDA.

The compilation of Tensorflow is not simple. So, I recommend to reserve around 2 hours to make this task. The compilation of the software is structured into 4 steps:

  • Step 1: Download the required packages in the Ubuntu repository
  • Step 2: Installation of Blazel. Blazel is an open-source build and test tool similar to Make, Maven, and Gradle.
  • Step 3: Compilation of Tensorflow from the source.
  • Step 4: Creation of the package whl. This package will be deployed in the phase 4.

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Category: Artificial Intelligence | 1 Comment on How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Phase 3: Compilation of Tensorflow 1.8)
September 13

How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Phase 2: CUDA and cuDNN installation)

Reading Time: 5 minutes

In the serie “How to use GPU with Tensorflow 1.8 and CUDA 9.2”, we are now in the second phase. This step is related to the installation and the configuration of the library CUDA 9.2, the  library CUDNN and CUPTI to prepare the laptop for the compilation of Tensorflow 1.8. We also check if the library is correctly installed by using the samples available in the package of CUDA.

The main attention point in this phase is the driver of NVIDIA freshly installed in the previous phase. During the installation of the CUDA library it’s important to prevent the installer to reinstall the driver because this could lead to a unstable system. So, we need to be attentive to this aspect.

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Category: Artificial Intelligence | 1 Comment on How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Phase 2: CUDA and cuDNN installation)
September 9

How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Phase 1: Installation of the NVIDIA Driver on Ubuntu 18.04)

Reading Time: 3 minutes

In the preview post, “How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Introduction)”, I expressed my interest in using the CUDA cores of my graphical card (MX150) for the acceleration of the calculation of the DNN. In this context, I use Python 3 and the high level neural network Keras with Tensorflow as backend.

The objective of this post is the installation of the proprietary driver of NVIDIA which is compatible with CUDA 9.2

In July 2018, Ubuntu version 18.04 doesn’t provide a driver which is compatible with the version 9.2 of CUDA.  Furthermore, when I made this exercise, NVIDIA has released a new package which was not compatible with the version of the driver (396 instead of 390). It seems that today the situation has changed. I see on the driver page https://www.nvidia.com/object/unix.html a new driver (version 396,54 which was released the 28/08/2018).

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Category: Artificial Intelligence | 1 Comment on How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Phase 1: Installation of the NVIDIA Driver on Ubuntu 18.04)
September 9

How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Introduction)

Reading Time: 4 minutes

Receiving my new laptop T580, I observed in the specifications that there is a NVIDIA graphic card MX150 with 384 CUDA cores.

New challenge will be to use these CUDA cores for the acceleration of the calculation for the Deep Neural network. In this context, I use Python 3 and the high level neural network Keras with Tensorflow as backend

By executing an apt-cache search cuda, I observed that the Lubuntu 18.04 LTS repository has the version 9.1 of CUDA based on the driver 396. However, based on the test realized by Aryal Bibek (see the URL: http://www.python36.com/benchmark-tensorflow-on-cifar10/). This article concludes that the support of CUDA 9.2 on Tensorflow 1.8.0 considerably increases the speed of training: This combination performs up to 37% faster when compared to earlier versions of Tensorflow. Even when compared to the previous versions of CUDA 9 Toolkit with 7.0.5 cuDNN, the latest CUDA 9.2 is around 7-12% faster.

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Category: Artificial Intelligence | 20 Comments on How to use GPU of MX150 with Tensorflow 1.8 CUDA 9.2 (Introduction)
August 1

Installation Docker on Lubuntu 18.04

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I like to use Docker containers to make my test. I observed that the software companies frequently propose a container version of their application to easily test the features or rapidly create a proof of concept to validate a design. Thanks to its portability, the container is becoming the cornerstone of the modern IT platform especially for the application teams; the container combined with an automation of the development pipeline will give to the company a strategic advantage to stay “Time to Market”.

I will install a docker version >1.12 because I want to use the GPU cores of the graphic card’s host fpr the acceleration of the calculation for the deep learning network.

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August 1

On my laptop Thinkpad T580, I would like to better control the fan speed; it’s too noisy!

Reading Time: 3 minutes

In Belgium, the weather is hot and I observe that my fan is really noisy. Normal, isn’t it? the temperature is really high (around 30° and 55° for the CPU)

However, I would like to better control the fan based on the different temperatures like we can do with the Windows application available for the laptop lenovo :https://support.lenovo.com/be/en/solutions/HT077046 but nothing available for Linux.

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Category: T580 | 5 Comments on On my laptop Thinkpad T580, I would like to better control the fan speed; it’s too noisy!