milishowcase.blogg.se - Install nvidia cuda drivers ubuntu 16.04

#INSTALL NVIDIA CUDA DRIVERS UBUNTU 16.04 HOW TO#
#INSTALL NVIDIA CUDA DRIVERS UBUNTU 16.04 INSTALL#
#INSTALL NVIDIA CUDA DRIVERS UBUNTU 16.04 SOFTWARE#

However it also exists on the bleeding edge. It has significant potential for quantitative trading models, much of which we will be exploring in subsequent artices. It will become clear in subsequent articles why TensorFlow is such a useful library for quant trading research so please bear with me! A Few Words Of Cautionĭeep learning is a rapidly moving field on the cusp of the research frontier. Hence more time can be spent developing quant models rather than fighting with a framework. Ease of Use - Despite the initial learning curve TensorFlow is actually quite straightforward to use, particularly with the newer releases.

#INSTALL NVIDIA CUDA DRIVERS UBUNTU 16.04 SOFTWARE#

It also means that they will be strongly motivated to continually improve the software as they are "eating their own dog food". It is used in their production systems and for leading AI research, as carried out by some of their sub-teams including DeepMind.

Google - TensorFlow is a Google product, albeit an open-source one.

Python - TensorFlow is a Python library and so it can easily talk to all of the other quantitative finance libraries discussed on QuantStart such as NumPy, Pandas and Scikit-Learn.

Popularity - With popularity comes a large community and thus more likelihood of solving errors when they crop up, as well as a larger base of tutorials and textbooks from which to learn.

It has been chosen for all subsequent deep learning articles on QuantStart for the following pragmatic reasons: The intent is simply to describe the installation of TensorFlow, which is emerging as one of the strongest contenders for deep learning model implementation. The focus of this article is not on why framework X is superior to framework Y.

Recently I discussed the advantages and disadvantages of using a desktop deep learning research system versus renting one in the cloud.

#INSTALL NVIDIA CUDA DRIVERS UBUNTU 16.04 HOW TO#

We will also take a look at the common problems that can occur and how to troubleshoot them. The discussion will then turn towards installing TensorFlow against both a CPU and a GPU.

#INSTALL NVIDIA CUDA DRIVERS UBUNTU 16.04 INSTALL#

We will then consider an optimal choice for operating system and install the necessary Python research environment.

We will begin by outlining the advantages of the TensorFlow library along with a few words of caution on the potential difficulty of its intallation. However, this article describes the installation procedure for TensorFlow on a modern Linux desktop system with an affordable, up-to-date consumer-grade GPU, such as those found within Nvidia's GeForce series. It can be accessed remotely at a competitive hourly rate. An example is Amazon's Deep Learning AMI, which comes preinstalled with all necessary dependencies and deep learning software. There are many ways to install TensorFlow, such as making use of a ready-made machine image for a cloud server. Indeed it can still be challenging to get working on certain systems.

Up until recently this reputation was warranted. However it has a reputation for being difficult to install. Hence a framework that removes the low-level implementation details of execution, while providing a high-level API for straightforward model specification-without sacrificing execution accuracy or the ability to scale computation-is very attractive to quant researchers. Either way, experience with C, C++ or Fortran is a must. However, direct programming of GPUs requires knowledge of proprietary languages like Nvidia CUDA or abstraction layers such as OpenCL. This is particularly crucial for deep learning techniques as production-grade models require training on GPUs to make them computationally tractable. Any serious quant trading research with machine learning models necessitates the use of a framework that abstracts away the model implementation from the model specification.