Hi everyone. It's Nono here, and this is a really
quick video on how you can run TensorFlow on your machine inside of Docker, even
if TensorFlow cannot be installed with pip or with Python on your machine. Right? For newcomers, people who don't know
this, Docker is a program that lets us virtualize Linux machines and
other types of machines in our Mac or Windows, and TensorFlow is a machine
learning, a machine learning framework by Google, which if you're here, you
probably know already, all right? Quick reminder, uh, please like the video
if you would like to see more content or you enjoy this video and subscribe
and click on the bell if you're gonna get notified when I upload new videos. All right. We have three commands here. The first one is docker run -it. So Docker and Run basically
just runs a new container using an image that you give it. Here the important part is the
-i flag that is going to create an interactive session for us. You have here python:3.9-slim, which
is the image that we're gonna use on our container and we have bash,
so we start an interactive session. When we have the interactive
session, we'll be able to run pip install tensorflow tensorflow-io. This is TensorFlow and
the TensorFlow IO package. So I can then run the Python
command that I have in the code. Right? All right, so let's actually
take a look at how to do that. I have a window here
and I also have Docker. If I run here... docker run interactively, and I select
the python:3.9-slim image, I'm going to say I want to start a bash um, session,
and I'm gonna actually name this tf so we know what container we're talking about. This gets started really quickly. I already have this Python
image on my machine. In yours it might have to download it. And we can see here that indeed
we have that new container that has a Python image called tf here. We could stop it or we could do whatever. When we exit, we'll see.. Does this actually get stopped? How do we get back to it? If we get stopped, we click
play, we now run here... docker exec -it, the name of
the container, and then what command we want to run on it. All right, so we're back on our container. Now I can check here that I have
Python 3 and that I have pip. And we're gonna straightforward do pip
install tensorflow and tensorflow-io. This is gonna take some time and
I'm gonna explain a few things here. This is installing TensorFlow, not
on your machine, but on a virtualized machine that is actually a Linux machine. Indeed, you can actually see that
it's installing tensorflow_cpu_aws. That's a specific build of TensorFlow
that is meant to run on cloud instances, probably on AWS, right? That's Amazon Web Services. After all these packages have
been resolved, pip is installing them on that container. Remember, this container is running
in this Docker container, and if I delete or stop that container,
TensorFlow is like it's not installed in your machine anymore. If you don't delete the container
after stopping it, you can connect back to it and continue using TensorFlow. All right. So it seems like that's done. So I'm gonna click here on Python.. Verify that the same Python version
is present if I just do Python. So I'm gonna go into Python, I'm
gonna import TensorFlow as tf. We're gonna first print to see what
TensorFlow version I have, and now we can test a few operations here. So for example, tf.constant is gonna
return a constant and convert_to_tensor with a simple Python array is going
to return a tensor shape (3,), right? I think this is it. So this was Nono Martinez Alonso
with a really quick video on how to run TensorFlow on your machine. Remember you can like this video
if you wanna let me know that this was something useful for you and
don't forget to subscribe and click on that bell if you're gonna get
notified when I upload new videos. For those of you who are still there,
I'm gonna show really one quick thing that clarifies everything. I think if I exit again, a Python
and the container, you can see here that that container is still running. I can stop it so we
can stop the container. We can reattach to it and
I'm back at it, right? Uh, but what happens if I now try
to use my Python version or my Mac and try to actually do that? So tensorflow as tf. So TensorFlow is not there. All you have to do is go back to your
container, start it, and once that's started, you just run docker exec -it,
the name of your container, and then you can directly prompt the python3
command to get a Python session. Here we'll have tensorflow
as tf, and you can see that that's being properly imported. All right. It was Nono Martinez Alonso here. Thanks a lot for watching
and I'll see you next time.
