- #Ubuntu nvidia drivers 18.04 latest version how to#
- #Ubuntu nvidia drivers 18.04 latest version code#
#Ubuntu nvidia drivers 18.04 latest version how to#
Please refer to the Anaconda installation instructions for up to date details on how to install Anaconda on a Linux system. The Linux Python 3.7 installer script can be found here. As of the writing date of this article the latest version includes Python 3.7. To simplify the installation of a Python research environment QuantStart recommends downloading the latest Anaconda Individual distribution. Python Environment PrerequisitesĪs mentioned in our previous installation article it is necessary to have a functional Python3 virtual environment in which to run TensorFlow. In the following sections we will discuss the necessary Python prerequisites, how to install TensorFlow for CPU-only use and how to install all CUDA prerequisites required for TensorFlow GPU use. This article describes how to install TensorFlow on such a workstation where the underlying operating system is Ubuntu 18.04.įor more background on TensorFlow, along with its choice as a deep learning research framework, please see our previous article on the topic. However for certain use cases it is arguably beneficial to train and execute deep learning models on a local custom workstation.
#Ubuntu nvidia drivers 18.04 latest version code#
It is possible to execute TensorFlow code via pre-made cloud machine images on GPU-based cloud instances. We have previously mentioned that there are many ways to install TensorFlow, depending on chosen operating system and available hardware. This means it is now even easier to specify deep learning models within TensorFlow. Keras, a popular library for specifying deep learning models has now been directly incorporated into TensorFlow via the tf.keras high level deep learning API. Since then the situation has improved further. In the previous article on the same topic we discussed how sophisticated quantitative trading research with machine learning requires a robust framework to abstract away the machine learning model specification from the model implementation.Īt the time of the original article the TensorFlow library provided such an abstraction by avoiding the need to write optimised deep learning models in low-level C, C++ or FORTRAN and the CUDA GPU programming model provided by Nvidia.
In this article we will demonstrate how to install a modern deep learning research environment on a Linux machine via the TensorFlow library, which will form the basis of all subsequent deep learning research on QuantStart. This article constitutes the first in a series on the topic of modern machine learning via deep learning as applied to systematic trading research. Earlier in the year we carried out our 2020 QuantStart Content Survey and Advanced Machine Learning & Deep Learning was voted the most popular topic.
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