TensorFlow 2.0 upgrade, Python support, & more! #AskTensorFlow

Key Takeaways

shall we take a look at the first one that came in was from Alpha Arthur Alf Arthur can I ask about any pre-built binary for the or TX 2080 GPU on Ubuntu 16 that is very specific so I like specific questions yes even if I can but so in this case so the pre-built binaries for tensorflow tend to be associated with a specific driver from Nvidia so like the version of CUDA that we support of the version of Cu DNN that we support so my recommendation would be if you're taking a look at any of the pre-built binaries take a look at what driver or what version of the driver you have supported on that specific card I'm not an expert in Nvidia cards although I love them so I don't really know what supported by that card Arthur but if you go over here like on my laptop I have I've called up like some of the what Nvidia say as their tensorflow system requirements and the specific versions of the drivers that they support and the one gotcha and we had this in the last segment as well that I find when working with GPUs is that it's easy for you to go to the driver vendor and download the latest version but that may not be the one that tends flow is built from one to ten flow support so just make sure that they actually match each other yes and you should be good to go even with that particular card yes and if you have warm feelings and excitement about builds in general for tensorflow we have a great special interest group specifically focused on that called cig build big build big build so strongly suggest going to the community section of our github and checking out the the cig build listserv and sort of joining it and joining our weekly stand-ups right so thanks Arthur for that question and then the next question is a really funny one I think that how many times have you been asked this today oh my god at least twelve please oh man and then the other flavor of it is well is this particular symbol that I use all the time is this going to also be supported in terms of flow to zero and if not what has changed I mean people have invested so much time building stuff intense flow one dot X they don't want it to be deprecated they don't want it to go away understandable so how do we how do we answer do minds full of scripts work with tense floaty yes so the sad fact is that it probably not they they would not work with with tensorflow to dotto out of the box but we have created an upgrade utility for you to use it's automatically downloaded with tensorflow to doto whenever you download it for more information on it and what in particular it's doing you can check out this medium blog post that i and my colleague Anna created as well as this upgrade to tensorflow to dotto video goes through and with gifts which is the best communication medium possible shows you how you can use the upgrade script on an end file so any sort of arbitrary Python file or even Jupiter notebooks one of our machine learning gdes created an extension that it allows you to do that as well and it'll give you an export txt file that shows you all of the symbol renames that added keywords and then also some manual changes if you have to make manual changes cool usually you do not so to see this in action we can go and we can go and take a look at this particular text generation example that we have running a Shakespeare well takes all of the corpus of Shakespeare texts okay trains against the Shakespeare text and generates something that the bard could have potentially written you know should he have had access to to deep learning and deep learning resource know you all and will uphold a wildest unyoked humor of your idle tree I did not know you knew Shakespeare oh I actually played Henry the fourth in high school that's amazing I was I love this notebook I was Beatrice Shea and much ado about nothing oh so why were I'm what to do about yes so here's what it looks like in collab form text generation when using an RNN with eager execution you could export the Python file and then to upgrade it got to reconnect the runtime this is true so starting it it looks like the requirements of already we can check to see that we're using tensorflow alpha and then like I mentioned before all you would have to do is preface this with a bang TF upgrade B to the name of the the name of the Python file is text generation I have one upgrade an upgrade shift-enter it does it's it does its upgrading magic and very quickly and tells me all the things that would need to be changed to make it t dot to dot o compatible and creates that file for me off to the side so now if I wanted to if I wanted to run this model it should be able to it should be able to train as it as it would so let's just check to make sure that would be the case and I think a lot of the errors that you're seeing here it's more just named API rather than breaking changes within the API this is true so you can see that you have some renames and some additional keywords sounds good I saw you have some like handy-dandy gifs in there yes absolutely are there any gifts for those of us who don't say Jeff sorry I had to put work out joking well I'm MPV so peanut butter exactly sounds good so I see when it comes to upgrade there are a few little gotchas basically in summary but hopefully this blog post in your video and all the stuff that we're doing will help you get around those garnishes and even even more amazingly the community that you were mentioning before we've had such an interest in testing tensorflow 2.0 and trying it out against historic models that we've formed a weekly testing stand up and also we have a migration support our that's being that's being implemented with the internal support our so if you have an external group to Google that's interested in upgrading your models please join the testing group and and we can get you situated yep and a lot of stuff that we've seen like in Karis models for example Carmel had that great slide where she was training fashion amnael the code is the exact same exactly the same so while there might be stuff changing under the hood it's like the a lot of the surface level code that you're gonna be writing in cameras at least isn't changing yeah if you've used Karis you're probably not going to have any problems yeah yeah so good stuff shall we move on to the next question now we could talk about 200 all day but yes okay we just mentioned Karis and it appears so so I guess I could ask you this question hopefully you know the answer what is the purpose of keeping estimators in Karis as separate api's is there going to be something native to Kara's models that allows a distributed training a la training evaluate okay so the purpose of keeping them I think is there's many purposes right so I think for me the main purpose that I would like to think of though is one that is because a lot of people are using them as including internal Google teams that would tar and feather us if we remove them so it's like and so when it comes to like estimators estimators are really great for large scale training yes right and it's like a lot of time if you're doing a lot of the large scale training it's like really keep going with estimators really great you know like I said when I first started with tents floor I started like with estimators because I couldn't figure out what a node was in a neural network and there were all these concepts that I had to learn and yeah I had just a simple estimator that I could use to do to do like a DNN or something like that so you know they're there for a reason and they're staying for the reason Carris is one of the things that from the point of view of making life easier for developers that we've really been doubling down on intensive flow to and things like we just spoke about that the code is the same between 1 & 2 and it's that the layers API I think makes it super simple for you to design a neural network and in the fact that you can go low level beyond that and like you know that define your own layers really gives you that allows you to drive stick instead of driving automatic salutely one of the beauties of Karason 2.0 is that you have carries the way that you're probably familiar with using it and then if you need to do additional customizations there's a sub classing component and then if you need to go even lower then we have something called TF module and we even exposed some of the the basic most core ops tensorflow as well so really at any sort of level you want to interact with the API you can yep and I think there was another part of the question was then around distributed training sorry it scrolled oh so I can't say it now but there's like there's something called distributed strategy guys in with Karis and word sense flow too and the whole idea behind that is to allow you to be able to distribute your training maybe across multiple GPUs on the same machine maybe across multiple GPUs on different machines yeah maybe across CPU spread all over the place that kind of thing so distribution strategy is really all about that you know to help you with that so estimators in chaos we love them both they're both still there hopefully this is something that will help you so with that question and I think we've got time for just one more absolutely so oh this is a page question this is totally a me question I am the Python person so ask tensorflow when will tensorflow be supported in python 3 7 and hence BX and hence be accessed in anaconda 3 so I can I can certainly answer the Python 3 7 and also I would love to speak a little bit more about support for Python going to answer the 3 7 question I'm going to bounce over to our tensorflow tutto project tracker these are all of the standing issues that we have when doing development for tensorflow 2.0 it's transferring your avatar yes I have filed many issues and all of them are transparent to the public so if you ever want to have a sort of context on where we stand currently and what we have yet to do this project tracker is a great way to understand that but let's take a look at 3/7 and there we go so in process of releasing binaries for Python 3 5 and 3 7 that's issued 25 for 20 and it's going a little bit off the screen for 29 but you can you can take a look at that issue and see that it's currently in progress there's not really an ETA but it's something that we want to have complete by the time that the alpha RC is released yep so so that is wonderful to see there's also a website called Python 3 statement I think it's Python 3 statement com maybe it's dot org there we go so tensorflow has made the commitment that as of January 1st 2020 we no longer support python 2 and we have done that with a plethora of our of our Python community so tensorflow pandas scikit-learn etc we are firmly committed to Python 3 and Python 3 support so so you will you will be getting your Python 3 support and we're we're firmly committed to having that and and and the nice thing about the issue tracker is it's not going to be a big hey we have its you know coming at some random point in the future it'd be case if it's totally transparent you can keep an eye on what we're doing and you can see people commenting and our engineers commenting back with like yeah man I totally ran the thing last night and it's it's almost there one more test yeah that sounds good okay I think that's all we have time for so whatever you do don't forget to hit that subscribe button alright and thank you so much and thanks for being engaged thank you [Music]