And we're here for Ask TensorFlow.

I'm Paige Bailey, a TensorFlow Developer Advocate,

and I'm here today with Alex Passos.

I'm an engineer on the TensorFlow team.

Excellent. And we're very excited to answer

all of your TensorFlow questions.

So our first question is from Prince Canuma who asked,

"What are the major improvements to TensorFlow Core?"

as part of TensorFlow 2.0.

Do you want to take that one, Alex?

Yeah, thanks for the question, Prince.

I'm very happy to talk about this.

We're changing a lot of things in TensorFlow with TensorFlow 2.0

to make it much, much easier to use.

We're improving the high-level APIs at launch,

we're integrating them with Keras,

we have a new high level API for distributed TensorFlow

that we call the distribution strategy

that let's you write your code once

and then change how it runs on multiple devices,

mutliple CPUs, multiple GPUs,

even multiple TPUs, if you want.

Eager execution is started on by default

which means that interacting with TensorFlow

line by line on a non-Python notebook

or a Colab, or Jupyter is a lot friendlier.

Also, debugging is much easier.

And we're also planning on building out a lot of additional documentation

and tutorials and resources for TensorFlow 2.0,

so very much focusing on making it easier to get started than ever.

Yeah, if you haven't seen the tensorflow.org website,

we completely changed it for this 2.0 process.

It's much cleaner.

All sorts of cool useful contents.

There are places you can just click and run the code.

You should check it out.

Absolutely. Awesome!

So our second question is from Katayoun who asked,

"Is there XGBoost-like functionality within TensorFlow?"

and I am certain that there is.

That's a great question.

We have gradient boosted trees, right?

Yeah, and the gradient boosted trees that were in TensorFlow

contributing in 1.2, they're now in Core TensorFlow.

Excellent, and they're part of the Estimators API, I believe?

- Yes. - Yes, awesome.

So if you want to use boosted trees for all of your machine learning projects,

you're absolutely able to.

And the nice thing too about TensorFlow

is that it isn't just about deep learning,

you can use traditional methods as well.

So, other frameworks might be focused just on creating neural networks,

we give you the ability to do a lot of other things as well.

Yeah, we also have k-means, for example, as an estimator there.

Excellent.

So our next question is from amir who asked-- oh man--

he asked what's going to happen to tf.contrib.slim.

Do you want to take that one?

That is a tough question.

So, tf.contrib.slim was great when it came out,

but I think we have a better higher level API in Core TensorFlow now.

So slim has deprecated.

We'd really encourage you to take code that's using slim

and see if you can do it better and cleaner

using our new Keras higher level APIs.

If you want to keep using slim, it's open source software,

so you can fork it.

And slim relies on a lot of things that are core to TensorFlow 1.0

that we think should not move forward to TensorFlow 2.0.

However, we're not deleting the components that slim depends on,

we're putting them in the tf.compat.v1 package,

so you can still use slim, if you fork it onto your own project,

but we think there are better higher level APIs

for you to use now, right?

Absolutely, and another thing that we want to mention is,

as part of TensorFlow 2.0,

tf.contrib completely is being cut out,

and this is really for the betterment of 2.0 itself.

There was a lot of code that was placed in contrib

that was not supported, that was maybe working

in an earlier version of TensorFlow and then stopped working later.

So really, getting rid of contrib is a good idea.

But it's not that we're getting rid of--

contrib used to be this very useful channel

where you could easily get code into TensorFlow

with less care and overhead

than what to get code inside the Core TensorFlow package.

This is a really important use case and we're not getting rid of it.

Now we have fully open source community-driven special interest groups

to which we contribute but we do not own

and there's SIG add-ons, we have IO,

we have many others where if you feel like

you still need a home for a TensorFlow related project,

odds are we can find a home for it somewhere in our special interest groups.

Absolutely, and there's also an RFC that details the fate of every endpoint

that was created as part of tf.contrib.

So a lot of it has been migrated to the Core API.

Some of it's been migrated to other repos.

So, for example, the tf.contrib.gan

is being migrated to its own repo.

And then also, some functionality,

like SIG to SIG, landed in /addons, and some image operations as well.

Yeah, a good rule of thumb

is that if you just run the TF 2.0 upgrade script on your code,

it will pick any contrib symbols that have moved to Core

and will just rename them for you.

So if any contrib symbols are left on your code after that,

then you should probably search and see what was the new home for that package,

or in the case of slim, you might want to copy it into your code.

So if you're a person who loves using contrib,

there are a lot of different ways to contribute.

That's an excellent question.

Thanks so much for it, amir.

So the next question is from Donstan, who asked,

"Is it possible to use TF 2.0 with Python 3.7?"

Yes! (laughs)

Excellent answer.

We have released bit packages, finally, for Python 3.7.

Finally. (laughs)

This is true facts.

And also, TensorFlow has signed the Python 3 statement,

which if you're not familiar, means that we will not be supporting

Python 2 as of January 1st, 2020,

and it's been a long time coming.

So definitely have Python 3 support,

definitely have Python 3.7 support, in particular, Donstan,

and we will not be supporting Python 2 as of January 1st, 2020.

Yeah, I'm so excited about getting to delete all the Python 2-related code

and being able to use all the cool new stuff with Python 3,

so thanks for the question, Donstan.

Excellent.

And our next one is from Siby, who asked,

"Is it better to use TF 2.0 alpha, or TF 1.13 in Google Colab?"

Wow, this is a good question.

I think you should really use both and probably by the time you see this,

we might have even released 1.14.

I encourage you to try 2.0 alpha

because we're trying really hard to make it much, much easier to use,

especially in an interactive environment like Colab.

But if you're importing modules

or using libraries that rely on TensorFlow 1.x

and have not been updated to TensorFlow 2.x instead,

that will keep working.

And you know, in Colab, you can just,

!pip install whatever version of TensionFlow you want.

Yep, and I believe 1.13 might be the default right now,

but, as Alex mentioned, pip installing 2.0

or whether it's the alpha or an earlier release--

though we recommend the alpha (laughs)--

any package that you choose

is perfectly capable of working in Google Colab.

So, for the next question, Kris asked,

"How does TF 2.0 work with Keras?"

That's an excellent question, Kris.

So we talked about this a little bit before,

but Keras is really a first-class citizen in TensorFlow 2.0.

If you're familiar with the standalone version of Keras,

we've integrated much of that functionality

into something called tf.keras,

so it ships directly with TensorFlow itself.

There's not a one-to-one correlation,

but most of the stuff that you love to use

is included as part of the TensorFlow package.

Yeah, we have all the useful layers, metrics, losses, optimizers,

really all the tools you need

to build your models and train your models,

and the cool thing about tf.keras is that it's integated deeply

with distribution strategy, eager execution,

and other features that are new to TF 2.0,

so, it's a very nice, well-rounded experience

that we really encourage you to try.

In the future, I think, we'll probably externalize this

and move it to the original keras-team/keras,

but for now, the version that's integrated with TensorFlow 2.0

is higher performance, and easier to use.

Excellent, and we also know that the Keras community,

the standalone Keras community, is very strong and very enthusiastic.

So if you'd like to get involved with tf.keras development,

or if you have suggestions, we're starting a special interest group.

So take a look at the community section on the TensorFlow website,

sign up, and send us your questions.

Thanks for the question, Kris.

Thanks so much to everyone for your questions,

and if you have additional ones, we would love to hear them.

Just use the #AskTensorFlow on social media,

and we'll probably answer your question in the next video.

See you next time.

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