What is preventing TF to use GPU when used in native windows?

[eabase]

What is preventing TF to use GPU when used in native windows?

[sgkouzias]

@eabase according to Simon_Au-Yong in a relative discussion on the TensorFlow Forum:

Native Windows is challenging for the TF team to support given their limited resources. Hence them moving to WSL2 is understandable because it’s the best of both worlds, being both a Linux environment and having access to the GPU. The other option would be to boot direct from Linux from say an external SSD.

For professionals coming from other fields who have to work with neural nets Collab would be a quick option. Nothing to fuss about. And if you have the budget and need performance, TPUs are a great choice.

Overall, the best environment to run TF with GPU support is on Linux x86-64 with the appropriate drivers installed.

[mihaimaruseac]

Up to 2022, I was in the team responsible for making TF work on all platforms. Windows, and especially GPU, was the one with the most breakages and also the one with the least expertise. Everyone was focused on Linux and only a team of ~3 was working on the other ecosystems.

Since then, none of those is still in TF and support is very reduced, even for Linux

[sgkouzias]

@mihaimaruseac I am relatively a newbie in the world of TensorFlow but I wonder why valuable TensorFlow team members left the team and why in general the number of members was so radically reduced. I really don't get it..

[eabase]

Hi Guys!
Thank you so much for quick feedback. Not what I was hoping to hear, but nevertheless better than silence.

@sgkouzias
Hi Sotiris,
I wasn't really looking for an answer that involves buying cloud server time. I have my own GPU, and want to put it to full use in all my environments, which is why WSL, is just not enough. I'm a huge fan of *nix, but at the end of the day, people wanna run in windows with whatever HW they have. SO it sounds very strange that "windows" is not supported. Everything else is running fine in windows, even using MSYS, Cygwin built tools etc, so I just don't see the issue here. Which is good, because it's also an incentive to get it working.

@thiagojramos
Hi Thiago,
I would be careful to make assumptions on target audience. You have no idea what other fringe developers are trying to do and what they use. In my own case I am extremely hybrid in the sense that I try to use whatever native windows tools primarily, and then extend using near native tools as Cygwin, MSYS, and only WSL as a last resort, in which case I am not happy, because windows hides it's containers in an incompatible and non-portable way. If I have to use WSL, I much rather prefer using a Virtualbox container where I have full control of everything while being able to backup and deployed on a different machine if necessary. I refuse to use Docker, as it completely shields developers from what is going on in the Linux environment, on the OS level, resulting in absurd communication and issues with app developers who doesn't understand basic *nix based OS principles. (Yeah, sure it is very useful for distributing quick tests and solutions, but there it ends, especially if you need to interact with hardware and bare metal.)

@mihaimaruseac
Hi Mihai, Awesome!
Can you point me in the right direction for the compilation process on windows using a nearly latest TF?
My first thought was that this should be possible using MSYS or MinGW64.

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Rant Warning:
I then got distracted by Nvidia now saying to use their own Python repositories, installing absurd wrapper packages that then redirects the package repos to their own pypi servers. This without even telling the user, while installing pypi.rc/ini files all over your Windows system. It took me an hour to root out all that nasty crap after, because their uninstaller OC doesn't do anything. 👎 JFC, who the heck was thinking this through? Totally senseless! 😡

Then we have the Conda addicts. And to be perfectly honest, I think they need to wake up from their legacy attitude. Since ~3 years back python has reached astronomical levels of user friendliness and cross platform compatibility. So why the heck are people still using Conda? It's just a bunch of complicated CMD, Posh and Shell wrappers to various python and binaries. Again shielding and complexifying what is actually done behind the scenes (in a python environment!) Please leave conda and come back to the warm python reality. 🐍

[mihaimaruseac]

@mihaimaruseac I am relatively a newbie in the world of TensorFlow but I wonder why valuable TensorFlow team members left the team and why in general the number of members was so radically reduced. I really don't get it..

Reorg during pandemic, shift of priorities, old manager and tech lead left, almost no-one who worked on TF pre-2.0 was still left in the team, new members with decision power but with no real knowledge of operating systems and compilers hired on time pressure, Peter principle, promo driven development, JAX, contracting support, Goodhart's law (in multiple instances). Read between the lines, but I cannot be more clear than this as I still have years of career left :)

and so on is simply a strategy that will kill the library long term

JAX is what is the future now from Google. Or PyTorch. If you can use Keras 3 (which is not the default within TF -- or it should not be), you should be backend agnostic, but there's still a lot of work left to do to cover everything.

[sgkouzias]

@mihaimaruseac I am relatively a newbie in the world of TensorFlow but I wonder why valuable TensorFlow team members left the team and why in general the number of members was so radically reduced. I really don't get it..

Reorg during pandemic, shift of priorities, old manager and tech lead left, almost no-one who worked on TF pre-2.0 was still left in the team, new members with decision power but with no real knowledge of operating systems and compilers hired on time pressure, Peter principle, promo driven development, JAX, contracting support, Goodhart's law (in multiple instances). Read between the lines, but I cannot be more clear than this as I still have years of career left :)

and so on is simply a strategy that will kill the library long term

JAX is what is the future now from Google. Or PyTorch. If you can use Keras 3 (which is not the default within TF -- or it should not be), you should be backend agnostic, but there's still a lot of work left to do to cover everything.

@mihaimaruseac thank you so much for the insightful response. I am really grateful and do appreciate your efforts and generally the TensorFlow & Keras team (those who left the team and those who crafted and maintain the new, more inclusive and powerful Keras). I am a fan of the "progressive disclosure of complexity" core principle of Keras. If JAX is the future I will embrace it.

[thiagojramos]

As you can see from the above comment, the only obstacle is the "Desire". Besides, their target audience doesn't seem to be Windows users (let alone home users who need to install this package because it's a requirement for something else).

[sgkouzias]

It looks like the issue is assigned to @Venkat6871 . @Venkat6871 could you provide some useful guidance?

[sgkouzias]

Professor Alvaro Rodriguez writes in a relevant discussion on the TensorFlow Forum:

I have to say, that the drop of GPU support for windows, the lack of documentation and support for cpp, the lack of support and documentation for TensorFlow lite, the lack of TFrecord multi-platform standalone libraries… and so on is simply a strategy that will kill the library long term. Except for very niche projects in large companies.

Other platforms like PyTorch are investing in easy to use multi-platform solutions. If (or when) someone actually puts a solution powerful, stable, easy to access and easy to import and export to other platforms and languages. Private enthusiasts, researchers and academics will drop TensorFlow. And don’t forget that industries rely on specialists who learned in academia and come from research.

In a time of AI revolution, where the technology is more popular than ever, and is being added to literally everything. In my opinion, TensorFlow is neglecting everything outside Python-Linux, dropping an already lacking support for interoperability, and not investing in accessibility.

I’m saying this as a researcher and professor working in a computer science lab in an university. I write this just after investing almost 100 hours trying to simply build TensorFlow-cc to add some basic capabilities to a research project for the European Union, I failed. Also the absolute lack of recent information anywhere about TensorFlow-cc, and the responses I have seen to old threads lets me know most people gave up the same way I’m ready to tell my whole team to abandon TensorFlow and try other solutions.

I have seen others in my lab commenting similar concerns and frustrations. Many of our researchers are already moving away from TensorFlow and soon the whole department will follow.

For context. The Computer Science department is the largest department in my university, and serves the most important IT faculty in the Northwest of Spain.

For us ML is thriving. In addition to the Bachelor degree in Computer Science, where ML is more than present, being the most requested in the entire university. We opened a new one in Data Science, and are opening a new one in Artificial Intelligence. Next year we will be adding new classes and teachers to be able to serve the increasing number of students in two of the three Machine Learning subjects I teach… As far as I know, none of them will learn TensorFlow, none of them are using it in their personal projects and none of them will use it their degrees. They instead will be using Julia, Matlab, OpenCv , PyTorch, Scikit-Learn and other solutions.

Which leaves me to the industry sector. I worked also as a researcher in a public hospital in a project about diabetes, and in a private research center dedicated to laser and manufacturing. They all used TensorFlow, the same my laboratory did. I have been told they are all moving away from it, currently opting for a Scikit-Learn+OpenCv and PyTorch based approach. The reason is in one case the drop of GPU support for windows, and in the other a perceived drop of support combined with lack of interoperability.

The thing is, nobody moves away from a technology they spent years using and learning unless the technology fails them. And once you move away from something because of a problem, if you find a solution somewhere else, you will probably never return.

That is what is happening in TensorFlow. Google has intentionally dropped the ball with support, documentation, accessibility, ease of use, interoperability across languages and interoperability across platforms… so others will raise to the occasion.

Simply put, TensorFlow is becoming the Bing search engine with regards to AI

[mihaimaruseac]

Can you point me in the right direction for the compilation process on windows using a nearly latest TF?

Unfortunately, most files that were there to support compiling on GPU on Windows are no longer in the repo, so there's not really a simple path forward.