Name: How to take advantage of GPUs and TPUs for your ML project (Coding TensorFlow)
Uploaded: 2020-03-25T07:49:05.000Z
Duration: 4 min 24 s
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I'm Paige, and I'm a Developer Advocate for TensorFlow.

Machine learning techniques like Convolutional Neural Networks,

also called CNNs, and Generative Adversarial Networks, or GANs,

have shown great promise in a diverse range of applications--

to scene reconstruction to speech recognition.

on massive amounts of data, machine learning engineers

often need to use specialized hardware, such as Graphics

Processing Units, GPUs, or Tensor Processing Units, TPUs.

for the portions of the model that can be broken up

Think of these chips as very specialized tools

that can do one particular task extremely well and extremely

tasks that used to take days or even weeks to complete now

The good news is that you can develop deep learning models

on Google Colab using GPU and TPU at no cost.

Let's dive into a notebook and check it out.

To change your runtime in Google Colab, all you have to do

is select Runtime, Change Runtime Type, and then

For this selection, we'll go with GPU, and we'll hit Save.

Let's install TensorFlow 2.0 with GPU support.

We can confirm that TensorFlow can see the GPU by running

Once the command has run, we can see that the device

To observe the TensorFlow speed-up on GPU

relative to CPU, let's use this basic keras model.

is able to train to completion in around 43 seconds.

So you received almost a third of a boost of speed.

you can run these two commands from any Colab notebook.

For TPUs, let's try a bit more interesting of an example.

We'll change the runtime type again, select TPU,

In this Colab notebook, we'll build a two-layer forward LSTM

model, and we'll convert a keras model to its equivalent TPU

version, using the standard keras methods Fit, Predict,

As we scroll down, we can see that we're downloading data,

we're building a data generator with TF logging,

we're checking to see the size of the array coming in

from Project Gutenberg, and we're building our model.

This model will take quite a while to train, but remember,

you're training against every single aspect

After we've cycled through 10 [? epochs ?]

we can start making predictions with our model.

Let's take a look at what a neural network thinks

But this does look like a traditional script.

And you can see that if you start adding layers, adding

nodes, and even adding clusters of TPUs as opposed to just one,

and start generating Shakespeare-like plays

You just learned how to accelerate your machine

learning projects with GPUs and TPUs in Google Colab.

In the next video, I'll walk you through how

So subscribe to the TensorFlow YouTube channel

In the meantime, keep building cool projects with TensorFlow,

and make sure to share them on Twitter with the hashtag

We're looking forward to seeing what you create.

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How to take advantage of GPUs and TPUs for your ML project (Coding TensorFlow)

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