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  • LAURENCE MARONEY: Hi, everybody.

  • Laurence Maroney here.

  • I'm at TensorFlow World, and it's a great privilege

  • that I have to chat with Megan Kacholia, vice

  • president of engineering working on TensorFlow.

  • And you just gave a great keynote about TensorFlow

  • and about TensorFlow 2 and some of the new things in it.

  • Could you tell us a little bit about it?

  • MEGAN KACHOLIA: Yeah, we've been working on TensorFlow 2

  • for a little while, all right?

  • We talked about it at the Dev Summit

  • earlier this year in the spring, and then

  • just finalized the release in September, so just last month.

  • One big thing with TensorFlow 2 is

  • we're focused a lot on just usability, all right,

  • making it easier for users to be able to do what they need

  • to do with cleaner, more streamlined APIs, better

  • debugability, with eager mode, just try and make things

  • simpler and easier for folks.

  • LAURENCE MARONEY: One of the things I really

  • liked in your keynote was where you called out

  • like three audiences, right?

  • There was researchers, there was data scientists,

  • and there was developers.

  • I spend most of my time working with developers,

  • but what I'd love to really drill down on

  • is researchers and some of the great stuff

  • that's in TensorFlow 2 for researchers.

  • Could you tell us about that?

  • MEGAN KACHOLIA: Sure.

  • I think sometimes people worry that if they

  • want a lot of control--

  • that's one thing researchers really like,

  • is they want to be able to try out-- especially ML

  • researchers-- they want to be able to try out new things.

  • And they want to make sure they have that control

  • and be able to do that.

  • I think one thing to emphasize is that researchers still have

  • that control with TensorFlow 2.

  • Yes, we have done a lot to try and streamline

  • the high-level APIs, because we want

  • to make it easier for folks to commit at that point.

  • But I know there's a lot of folks who just want

  • to be able to go in deeper.

  • They want to go under the covers.

  • They want to go under the hood and be able to figure out,

  • well, I have this new model type I want to try out.

  • I have some new model architecture

  • I want to play with.

  • And all the things they loved about TensorFlow

  • and being able to do that before, that's all still there.

  • We're just trying to make sure there's also

  • a nice clean high-level surface as well.

  • It's not an either/or situation.

  • There's still both.

  • LAURENCE MARONEY: Yeah.

  • And then, all that great work that we've

  • done to make it easier for software developers who

  • aren't necessarily researchers doesn't preclude that.

  • MEGAN KACHOLIA: Exactly.

  • LAURENCE MARONEY: Everything the researchers

  • loved is still there.

  • MEGAN KACHOLIA: Exactly.

  • LAURENCE MARONEY: So-- and with TensorFlow 2,

  • there's also some state-of-the-art research has

  • gone on, right?

  • MEGAN KACHOLIA: Yes, definitely.

  • I mean, just even showing today the example that I went through

  • from Hugging Face.

  • Hugging Face has done so many cool things

  • in the NLP, the natural language processing space.

  • Everyone's very excited about BERT models right now.

  • I think you hear it everywhere.

  • Everyone's talking about BERT.

  • LAURENCE MARONEY: They just like saying BERT.

  • MEGAN KACHOLIA: It's a fun name.

  • Everyone's talking about BERT right now, and transformers.

  • And you can see even they were able to implement

  • some of the more advanced models in TensorFlow 2.0.

  • And recently, they did it.

  • And it really exciting for us to be

  • able to highlight that and show that no, it

  • wasn't us, the TensorFlow team who

  • has all the intricate knowledge of things.

  • It was the external community being

  • able to use this for really advanced research use cases.

  • LAURENCE MARONEY: And that's a really important thing, right?

  • So it's like we can build what we know, but it's

  • when people are able to take that platform

  • and bring their expertise to it--

  • MEGAN KACHOLIA: Yes, exactly.

  • LAURENCE MARONEY: --and make changes.

  • MEGAN KACHOLIA: And do what they need to do with it.

  • LAURENCE MARONEY: That's what I find particularly inspiring,

  • really, really cool.

  • And one of the other things that you drilled into

  • was performance.

  • You've been working really hard to tweak performance

  • on Tensorflow as well.

  • MEGAN KACHOLIA: Yes.

  • LAURENCE MARONEY: Could you talk a little bit about that?

  • MEGAN KACHOLIA: Yeah, so performance

  • is something that we always pay attention to.

  • I mean, one big thing that came with TensorFlow 2.0 as well was

  • distribution strategy.

  • Make it easy for people to be able to scale things up and not

  • have to worry about how do I set things?

  • How do I do things?

  • Like no, let us handle more for you.

  • So that's something that we focused a lot on,

  • is just how do you get that performance

  • and get it in an easy way so that way users don't

  • have to worry?

  • Now, again, if people want to go under the covers

  • and tinker and tweak every last thing, that's fine.

  • That's all still there, but we want

  • to make sure that it's easy for them

  • to get the performance they need.

  • So I talked some numbers for GPUs,

  • looking at different types of Nvidia GPUs, the 3x performance

  • improvement that we're able to get there with 2.0.

  • LAURENCE MARONEY: Which is amazing.

  • MEGAN KACHOLIA: Which is great.

  • Yeah, it's great, those things.

  • And then in the upcoming releases,

  • we'll have more things around TPUs,

  • as well as Mixed Precision support.

  • LAURENCE MARONEY: Wow.

  • OK, so still working hard.

  • MEGAN KACHOLIA: Yes.

  • It's never-- I mean, it's just like anything else.

  • The work keeps going.

  • LAURENCE MARONEY: Yes, exactly.

  • MEGAN KACHOLIA: There's always new things happening.

  • LAURENCE MARONEY: So I'm preventing

  • the advancement of this performance

  • by having you here to talk to us.

  • Thanks for taking the time.

  • MEGAN KACHOLIA: No, that's all right.

  • LAURENCE MARONEY: But a couple of other things

  • as well for developers, and for tooling around developers,

  • there's TensorBoard, right?

  • MEGAN KACHOLIA: Yes.

  • LAURENCE MARONEY: There's been advances in that.

  • MEGAN KACHOLIA: Yes.

  • LAURENCE MARONEY: Could you--

  • MEGAN KACHOLIA: One big thing with TensorBoard

  • that we announced here at TensorFlow World

  • is hosted TensorBoard.

  • So the whole idea here is that, again, people

  • love to be able to share their experiments.

  • Sometimes it's because they need help.

  • Let me show you.

  • Can you help me see what's going on?

  • Sometimes it's because like, oh, my gosh, look.

  • Look at what I found.

  • Look at the results I got.

  • Let me show someone else.

  • And right now, folks are generally

  • sharing with screenshots.

  • They'll take screenshots of TensorBoard

  • just showing what's going on with their experiment

  • or their current set-up.

  • And instead, we want to make it so folks can actually

  • share their TensorBoard instead of showing

  • a picture of some snapshot in time of their TensorBoard.

  • And that's where hosted TensorBoard comes in.

  • So this is something that we're just starting

  • to preview release for.

  • There's a lot of features we'll be adding

  • over the coming months as we stabilize it and get it to more

  • of the general availability.

  • But I think it's really exciting for folks

  • to be able to take that and then make use of it

  • to more easily share with others,