Installing TensorFlow 2

دوره: یادگیری عمیق با TensorFlow / فصل: Setting up the working environment / درس 6

Installing TensorFlow 2

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Finally let’s take a look at the libraries we’ll use in this course.

The good thing about Anaconda is that none pie in pandas along with many other libraries come automatically

with it.

That’s a strong plus because there’s no need to install the main packages separately as with some other

software for programming in Python.

So we’ll be using tensor flow to to ensure that you’ve got the latest version installed.

Things are not as straightforward in this video.

We’ll show you how to create a new environment installed tensor flow upgrade to its latest version and

then add a new kernel to Jupiter.

Ready.

Let’s begin by opening the start menu and searching for the anaconda prompt next.

We should check the environments we have by typing Conda info environments.

As you can see I have four different environments.

The base one python 2 Python 3 and Python three point seven with t F2 installed on it.

In fact the course was prepared in that last environment.

OK let’s see how to create a new environment so you can install tensor flow to we have to write Conda.

Create double dash name.

Then we must include the name of the environment we want to create.

Since you can use Anaconda not only for python but also for other programming languages it’s always

recommended to include Python or simply pi in the name.

Once you’ve done that we can include the version of Python in our case that would be Python 3.

Good.

The good news is when you have more than one environment with Python 3 installed you can add any other

clarification you see fit all right.

TMF To so that I know that tensor flow to is installed there now all my widely used packages including

tensor flow one are in my Python 3 environment.

However I’ll be using this pi 3 TMF To environment only when I need to F2 so I’ll include that in the

name.

Finally we must finish the line with the language we want installed and its version so Python equals

3 as you can see after clicking enter we get the package specifications and all that’s about to be installed

then we are asked if we want to proceed.

Sure we do.

We click the wiki and hit enter again our new environment has been created and python is being installed.

Great time to enter or activate the environment so we can manage it.

So we should write Conda activate and the name of the environment in this case pi 3.

TMF To

at the beginning of the line we see an indication that we are in fact in the new environment know that

it is completely empty.

The only packages it contains are the default ones that come with Anaconda everything you’ve installed

before won’t be included.

Now it’s time to install tensor flow.

We can simply write Conda install tensor flow that should do the trick for you

now to ensure that you’ve got the latest version of tensor flow installed.

We highly recommend that you also upgrade the currently installed tensor flow version the proper command

is Pip install upgrade tensor flow.

Great.

We are almost ready finally we must make sure we see the colonel in Jupiter once we started.

The easiest way to do it is by installing AI pi Colonel similar to what we’ve done so far.

We can use Pip once more pip install AI pi colonel should be sufficient and that’s all

to make sure everything is working.

Let’s open Jupiter

we can choose our preferred Colonel.

In that case it is Python brackets kinda environment pi 3 ti F2 to make sure we’ve worked correctly

we can import tensor flow as T F and then print t f dot version.

The result is exactly as expected we’ve got tensor flow to running.

Good job.

Just one final note what if I change the kernel to my usual Python 3 kernel and then rerun the code.

Well let’s see what happens

the version of tensor flow here is one point one for this shows you that you can maintain different

versions of the same package on different kernels and switch between them seamlessly and that’s something

I regularly use in my daily work.

All right.

All I have to do now is go back to the kernel that will allow me to code in t F2 and I’ll be fully prepared

for tensor flow too.

Thanks for watching.

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