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.
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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.
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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.
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