In the previous post, we’ve proceeded with CUDA 9.1 installation on Ubuntu 16.04 LTS. As with other software that evolves, NVIDIA released CUDA 9.2 back in May. It is also safe to assume that CUDA 9.2 will not be final version. Newer version will may come soon or later and here we are left with the bogging question: “How can we upgrade safely without clobbering the currently working system?” Moreover, we may also wonder if there is a mechanism to rollback the change and live with current setup while recognizing that it’s not yet the time to upgrade.
This post will cover three scenarios of CUDA 9.2 installation: 1) fresh installation, 2) install to upgrade by removing old version, 3) install to upgrade and keep multiple versions. Continue reading
What is interesting in the deep learning ecosystem is the plentiful choices of deep learning frameworks. On the other side, of course there is another equation; more options equate to more confusion, especially in choosing the most appropriate framework for the entire gamut of the problems. At the end of the day, instead of using one, we may need to stick with multiple deep learning frameworks with each usage depending on the nature of the problem to solve.
TensorFlow is one of the popular (de facto most popular in terms of Github stars) deep learning frameworks. TensorFlow comes with excellent documentation. This also includes the documentation for installation. If you go to the official documentation page for installation, you will be provided with elaborate installation guide for multiple OS platforms. Then why this post?
The latest version of TensorFlow with GPU support (version 1.8 at the time this post is published) is built against CUDA 9.0. However, NVIDIA has released CUDA 9.1 and there is possibility of newer version release in the near future. Given that TensorFlow is lagging behind the CUDA GA version, the publicly released TensorFlow bundle cannot immediately work on the system having only the latest CUDA version installed. A remedy for this is by installing from source, which can be non-trivial especially for those who are not so familiar with the source build mechanism.
The final system setup after completing the installation steps explained in the posts will be as follows.
|NVIDIA driver version||390.48
|Python install method||virtualenv
Note that the components will be updated in the future. This implies version upgrade for the components. It is expected that this post will still be valid even after version upgrade. Under the circumstances where this post becomes invalid, the content will be updated or another post will be written. Yet, this would be realized with sufficient comments or feedback regarding existing content. Continue reading
In the previous posts, we have gone through the installation processes for deep learning infrastructure, such as Docker, nvidia-docker, CUDA Toolkit and cuDNN. With the infrastructure setup, we may conveniently start delving into deep learning: building, training, and validating deep neural network models, and applying the models into a certain problem domain. Translating deep learning primitives into low level bytecode execution can be an enormous task, especially for practitioners without interests in the deep learning calculus. Fortunately, there are several deep learning frameworks that provide the high level programming interface to assist in performing deep learning tasks.
In this post, we will go through the installation of Caffe2, one of the major deep learning frameworks. Caffe2 is adopted from Caffe, a deep learning framework developed by the Barkeley Vision and Learning Center (BVLC) of UC Berkeley. Caffe2 was started with the aim to improve Caffe especially to better support large-scale distributed model training, mobile deployment, reduced precision computation, new hardware, and flexibility of porting to multiple platforms. Continue reading
CUDA Deep Neural Network (cuDNN) is a library from NVIDIA that provides the GPU-accelerated primitives for deep learning such as convolution, pooling, normalization, activation layers, tensor transformation. With cuDNN, a machine learning researcher or developer can spend less time writing the implementation for low-level GPU performance tuning. The cuDNN library powers major deep learning frameworks such as Caffe, Caffe 2, Tensor Flow, Cognitive Toolkit and PyTorch.
This post summarizes the steps to install cuDNN 7 for Cuda Toolkit 9.1 on Ubuntu 16.04. Installation for different version of cuDNN and Cuda Toolkit may require additional tweak or different steps that are not covered in this post. Continue reading