TensorPort - Documentation

Getting Started

Get started with TensorPort

This guide will get you started with TensorPort. You will learn how to create projects and datasets, and how to run them on TensorPort. Finally, we will take you for a tour around TensorPort's graphical interface and show you how to access TensorBoard right from your project.

To keep things simple, we'll show you how to do all this using a ready-to-go demo model of a self-driving car and a pre-built dataset.

If you prefer video over text, you can also watch this tutorial as a video below. We recommend following the video along with the guide for the best learning experience.

Prerequisites

Before we begin, make sure you have all your gear ready:

  • A TensorPort account. Go create one if you haven't yet!
  • Make sure you have Python installed. TensorPort supports both Python 2.7 and 3.5.
  • Install the TensorPort Python package. It's as easy as pip install tensorport
  • Install Git Large File Storage (LFS). TensorPort uses it to handle large datasets.

For this guide you also need the self-driving car demo:

  • Get the source code from the GitHub repository, or use the command line: git clone https://github.com/tensorport/tensorport-self-driving-demo.git
  • Download the data set.

What's Next

Get a quick overview of how TensorPort is structured.

Overview

Getting Started

Get started with TensorPort