When developing a deep-learning system, especially during the modeling stage, a lot of trials and errors can be involved in evolving the codebase. The easy remedy to reduce errors will be by using a robust IDE that provides productivity-boosting features such as code completion, method definition, codestyle suggestion, advanced debugging, user-friendly UI, and so forth.
In this article, we will go into more details about Jupyter Notebook installation and configuration on Ubuntu 16.04. However, it’s important to note that the configuration depends on some pre-requisites. This article is the continuation of the previous article about TensorFlow installation. Please make sure you have read the article to understand the pre-requisites, otherwise some steps explained in this article may not work. 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
NVIDIA Collective Communications Library (NCCL) is a library developed to provide parallel computation primitives on multi-GPU and multi-node environment. The idea is to enable GPUs to collectively work to complete certain computing task. This is especially helpful when the computation is complex. With multiple GPUs working together, the task will be completed in less time, rendering a more performing system. People with background or experience in distributed system, such as Hadoop, may immediately relate this concept with similar model applied in the traditional distributed system. Hadoop, for example, supports MapReduce programming model that splits a compute job into chunks that are spread into the slave nodes and collected back by the master to produce the final output. 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
When performing deep learning tasks especially on a single physical machine, there can be a moment where we need to execute tasks in parallel. Suppose that we are evaluating different models. We may need a task to calculate the precision and recall of a certain model while at the same time we are in need for training another model. We can proceed with the sequential operation, doing the tasks one by one. But life will be much easier if the tasks can be done in parallel. A possible route to achieving this is by creating several containers and perform distinct task in each container.
NVIDIA provides a utility called nvidia-docker. The utility enables creation of Docker containers that leverage CUDA GPU computing when being run. Under the hood, nvidia-docker will add a new Docker runtime called nvidia during the installation. By specifying this runtime when invoking a command in a (new) Docker container, the command execution will be accelerated with the GPUs. Continue reading