Integrating PyTorch with Tensoboard to Develop Deep Learning Models

AI or deep learning developers today would seek to work on predictive modeling. One of the tools that is popularly used is PyTorch. It’s an open-source deep learning framework developed and maintained by Facebook. In simple words, it’s a mathematical library that allows you to perform efficient computation and automated differentiation on graphical models. Although developing such solutions for production and even for test purposes is very difficult, the modern PyTorch API provides classes and idioms that allow users to easily develop a suite of deep learning models. It’s easier to develop such a solution if you have a fair knowledge of coding in Python and algorithms in coding.

Getting Started with PyTorch

Before installing PyTorch, you need to install Python (3.7 or higher). If you don’t have it installed, you can download and install Python from the download section on the Anaconda page. Once you have installed Python, it will install pip as well.

Alternately, you can install Anaconda using a 64-bit graphical installer for PyTorch 3.x. Click on the installer link and select Run. Anaconda will download and the installer prompt will be presented to you. The default options are usually the same.

To ensure that PyTorch is installed, run the below commands

python

import torch

x = torch.rand(5, 3)

print(x)
 

The output should be like the one below.
 

tensor([[0.4580, 0.1895, 0.3118],

[0.6137, 0.8052, 0.2740],

[0.1879, 0.7243, 0.9168],

[0.1749, 0.2112, 0.4375],

[0.8695, 0.9957, 0.1626]])
 

To check if your system had a valid GPU with CUDA available for PyTorch, run the below command.
 

import torch
 

If CUDA cores are available/enabled for installation, the output will be similar one below.
 

torch.cuda.is_available()
 

You are now all set to use PyTorch with Tensor to develop deep learning models for regression, classification, and predictive modeling tasks, including Multilayer Perceptrons (MLP) and Convolutional Neural Networks (CNN).

Apps4Rent Can Help in Deploying a PyTorch Instance

Apps4Rent offers virtual desktops on the cloud which can be used to configure all apps and tools a developer requires. These virtual/remote desktops are completely scalable based on the resources (CPU, RAM, storage, and GPU) the solution would require. To leverage CUDA cores from GPU, it is suggested to opt for a virtual machine instance with 8 GB GPU memory. Since the virtual/remote desktop is on the cloud, it can be accessed from any local PC/laptop, Windows, or Mac, from anywhere in the world, which has internet connectivity. No configuration change is required on your local PC/laptop. Call, chat or email our virtual/remote desktop specialists, available 24/7 for assistance.