[HN Gopher] What's New in TensorFlow 2.10?
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What's New in TensorFlow 2.10?
Author : RafelMri
Score : 34 points
Date : 2022-09-06 18:43 UTC (4 hours ago)
(HTM) web link (blog.tensorflow.org)
(TXT) w3m dump (blog.tensorflow.org)
| amilios wrote:
| Is there any point on which Tensorflow actually wins out against
| Pytorch? My sense is that Pytorch is easier for almost every if
| not every single use case. Not to mention the 1 => 2 transition
| gave me serious Python 2 => 3 vibes with the switch to default
| eager execution etc. Anyone still using Tensorflow care to weigh
| in?
| jphoward wrote:
| I use pytorch the vast majority of the time, but I think there
| are 2 reasons tensorflow remains competitive.
|
| First, is TF's deployment has always been ahead of Pytorch's
| (although the gap is closing, especially as Onnx becomes more
| popular).
|
| However, the more important reason is how amazingly good value
| TPUs are in terms of their RAM and FLOPS. Although pytorch has
| XLA support, it just doesn't work as well as TF on TPU pods.
|
| In Kaggle competitions, when the input data can fit on a
| consumer GPU everyone uses pytorch. When it doesn't, everyone
| uses the free TPUs on the Kaggle platform and reverts to
| tensorflow/keras.
| emehex wrote:
| I think TF is still popular (with beginners, at least) because
| of the keras API (far easier to get started!)
| jpeter wrote:
| And google started using Jax instead of Tensorflow
| suresk wrote:
| I seem to be in a small group that doesn't have a huge
| preference between the two, maybe I'm not doing that much
| advanced stuff?
|
| If I'm just prototyping something myself, I usually reach for
| TF first, mostly because Keras feels like the "right" level of
| abstraction for most stuff. I used to prototype things with
| fastai/pytorch more, but newer developers didn't like how much
| was hidden behind multiple levels of *kwargs and some of the
| dataloader stuff could get tricky if you tried to do anything
| non-standard. I haven't tried PyTorch Lightning.
|
| Besides the deployment story, which is pretty big and others
| have touched on, there are some minor things that feel nice in
| the TF/Keras ecosystem:
|
| - Part of deployment, I guess, but I like that more of the
| preprocessing can happen in the model, vs as a separate step.
| The less transforming of data that has to be done in the
| serving code, the fewer possibilities for things to get out of
| sync and introduce bad data at runtime.
|
| - Keras being able to infer input sizes in layers is nice for
| avoiding a bunch of bookkeeping code to calculate layer sizes.
|
| That said, I feel like maybe the TF ecosystem has more sharp
| edges? I've encountered more than a few of them lately as I've
| been doing work on some recommender models using tfrs. I've
| also run into things like tensorboard logging not working with
| entire classes of layers and causing training to crash.
|
| I'm curious - what are some things about PyTorch that make it
| better for almost every single use case?
| fxtentacle wrote:
| If you want to actually deploy AI models, TFLite is still your
| best option. And for exporting there, you first need things to
| run well inside regular TF.
|
| Also, I often find myself using the TF data pre-processing
| pipeline even from inside PyTorch, because it's just so much
| easier to get excellent processing performance with TF. Not
| sure why, but PyTorch is in many cases "unnecessarily" single-
| threaded.
| learndeeply wrote:
| > If you want to actually deploy AI models
|
| On mobile*.
|
| > I often find myself using the TF data pre-processing
| pipeline even from inside PyTorch
|
| the tf.data pipeline is quite nice, has some neat auto-tuning
| features.
| Sin2x wrote:
| Slowly becoming irrelevant: https://paperswithcode.com/trends
| jorgemf wrote:
| For that statement you would need to show industry graphs, not
| only research
| Sin2x wrote:
| Industry does not exist in a vacuum, it follows research.
|
| Additionally, see Google trends: https://trends.google.com/tr
| ends/explore?date=today%205-y&ge...
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