story
Looking forward to your feedback as you try it out.
We can do better, however, and we're working on ways to leverage the hardware better (for example, if you have no data-dependent choices in your model we can enqueue kernels in parallel on all GPUs in your machine at once from a single python thread, which will perform much better than explicit python multithreading).
Stay on the lookout as we release new experimental APIs to leverage multiple GPUs and multiple machines.
Chainer started it, was around years earlier, and it still has more users. So Google is not copying PyTorch, it's copying Chainer.
You can't please everybody, as if they listen or not to users people still complain. If both are making effort to improve themselves though, the community has only to benefit from this competitiveness.
Doing image classification, object localization, and homography (given an input image, which of my known template images is matches it and in what orientation).
There's a lot of work being done on this specific part. If you have a standard RNN architecture you want to run, you can probably use the cudnn code in tf.contrib.cudnn to get a super fast implementation.
There is some performance work that needs to be done on properly caching weights between time steps of an RNN if you use a tf.nn.RNNCell. Currently if you want to implement a custom architecture, or a seq2seq decoder, or an RL agent, this is the API you would want to use. Several of the eager benchmarks are based on this API; so that performance will only improve.
I'm hopeful that for the next major release, we'll also have support for eager in tf.contrib.seq2seq.
Eager is actually not as innocent as "open-source projects borrowing the best parts from each other", as some commenters here suggest.
Google is attempting to dominate the machine-learning API and the Python ecosystem for scientific computing.
The company that controls the API influences which apps are built on it and how. Think about how Google bundled Android services on top of Android, and how that posed an existential threat to other companies. That's what's coming for TensorFlow. Many developers are too naive to realize it, or too short-sighted to care.
I wouldn't compare a permissively licensed library to Android services at all.
I didn't compare Tensorflow to Android services. I said that Tensorflow would serve as the basis of a service bundle, much like Android did. Let's come back in a couple years and I'll tell you I told you so.
Insulting the reader
> but Google is not a charity
truism
> They are pouring commercial resources...
As opposed to "non-commercial resources"?
> ... for a reason.
Everything happens for a reason.
> That reason is Google Cloud.
> How do I know this?
Pray tell!
> Because Tensorflow works better on Google Cloud than anywhere else.
This is the only real argument in this conspiracy. And if "anywhere" includes the users' hardware, it's wrong: tensorflow runs flawlessly on any Linux/NVIDIA hardware. Maybe it works better with GCE than AWS, but that would once again fall into that "rather unsurprising" category of factoids.
> Google is making a concerted effort to catch up with AWS in cloud, mostly through machine learning.
This can be re-written as "Google has a cloud offering, which it tries to sell. And right now, machine learning is pretty hot". Throwing a "concerted effort" in there is just trying to jazz it up to something ominous. Which it isn't.
> I didn't compare Tensorflow to Android services. I said that Tensorflow would serve as the basis of a service bundle, much like Android did.
"The basis of a service bundle" actually doesn't sound that scary. Nobody is disputing that Google offers services build on tensorflow. It just isn't any sort of "Trojan horse" conspiracy, and it is somewhat limited by the fact the tensorflow is OSS licensed and could be forked by anybody people suddenly find out it's full of geek soldiers.
It has to be consistent and there has to be one way to do it.
I personally have a 10 message thread with Google cloud support on exporting a Cloud trained model to tensorflow and nobody could figure it out [Case #13619720].
In fact if you dig up the case, then even official support told me that savedmodel needs some freezing using bazel otherwise it doesn't work.
The github page and stackoverflow are full of these. If you can, please take the message to the other side :(
I don't think the cloud guys (where training will happen in distributed mode) talk to the android guys (where models will be used after quantization). There is a huge serialization problem that all of us are currently struggling with.
