PyTorch or TensorFlow?

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Should you pick PyTorch or TensorFlow?

You'll learn:
✔️ A brief history of both frameworks
✔️ How they compare in the research community
✔️ How they compare in shipping to production
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⌚️ Timetable:
0:00 - Are there any other frameworks?
0:30 - Google Trends (PyTorch vs TensorFlow)
2:27 - Dimension 1: Ease of development & research
4:22 - Data-driven conclusions
5:55 - Dimension 2: Can we ship it?
7:12 - PyTorch is catching up?
7:45 - So what should I use?

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#pytorch #tensorflow #deeplearning

[TheAIEpiphany]

What's your framework of choice and why?
Write it down in the comments 👇

Note: I tried to make an objective review (data-driven) of both frameworks and their pros and cons.

Although I did write code both in TF 1.x, 2.x as well as in Keras (pure before it became TF's API).

Stay safe and keep (deep) learning!

[cristian-bull]

Thank you for the thorough explanation. Now I am motivated to go to the gym and learn Pytorch.

[joejitsuway]

CommaAI (self-driving) just switched from Tensorflow to Pytorch recently as well.

[zoeythefatgirl]

I find tensorflow is too abstract and sometimes I can build a model with TF but can't understand how it works; I switched to pytorch recently and I find it more straightforward when building a model.

[annicetrazafindratovolahy]

I was using tensorflow but it is really hard to develop DL model with it. Even the GPU configuration is difficult with it. Now I decided to use pytorch. Thank you so much for this clear explaination

[すぬちゃんねる-es]

why is your worldmap on the back flipped

[miguelalba]

I have 2.5 years of experience working with tensorflow 1.x and 2.x (both low-high level APIs), I feel tf had become better with time, handling large and difficult datasets is now more practical compared to 1.x, writing layers in keras gives you some good prototyping speed and flexibility, tf.Module let you handle easier custom things and in general tf has many advantages in production level . Since one year I started with pytorch and I am gonna say at least for me it's the "definitive framework". it feels more native when you write stuff and less buggier than tf also you have more clarity about what's going on and why sometimes things just don't work, for research is a MUST and I feel it should be way way! more adopted in industry. One thing they should improve a bit more is the gap between production and model development!

[paulntalo]

Thank for honest goodness. Am just starting advance data science but was quite sure of which of the two tools to prioritize. But must agree I have found more tutorials using pytorch than TensorFlow especially about new ML models

[ozysjahputera]

I picked up pytorch because it came pre packaged with anaconda3. It is easy enough to learn esp for those who already studied perceptron, mlp, and backpropagation in the past. I installed TF and Keras but have not played with them .... well, just a bit with Keras.

[skviknesh]

After taking a big decision to learn TF and completing a 4hr course, now, thinking - why the heck I didn't watch this earlier?
Thanks so much for such a clear explanation to make decisions bro!

[nikolayandcards]

Glad that I found your channel. Also glad that I choose PyTorch for research. Awesome work! Keep it up!

[brilliantcity]

After researching a lot of resources online. I got a perfect tutorial. Really thank you, brother.

[aamira]

Here are my two cents, I coded an LSTM network using TF (KERAS ) and the exact same network in Pytorch, Not only TF was faster to code, it performed better. No matter what I did Pytorch could not converge ( Performace was erratic ). Yes, I agree Pytorch is good for research and is a lot more explainable. There is a lot of repetitive boilerplate code in Pytorch which I think should be replaced by high-level API like Keras, as part of the framework not an addon like fastai or Pytorch lightning..

[johanneszwilling]

😎 MOST useful video on deciding which! Thx!

PS: Nice touch with the google trends!

[stephonhenry-rerrie]

thanks so much for this explanation. glad to see the charts and graphs there

[benjaminfindon]

can deep learning train on annotated examples. e.g. theres no images .jpg files theres just a csv file with data in it (numbers and categories) can a deep NN train on this or is it nessasary to have the images as well?

[UniverseGames]

"Using backend: pytorch". How can I get rid of such thing when run python code, any help

[SimSim]

In sense of demo usage - when I get tensorflow model I just need to load it. When I get pytorch model I need to have the model defined in the code. This allows many researchers to "cheat". This research community trend of "kinda open code" but "not really" is disturbing. Pytorch is enabler of this behavior although not the cause, many researchers use its complexity to basically close their code or render it useless for anyone with less than a week to investigate what they've written. As "end user" of those models, where I download and try to run existing models released in papers, it looks like a trend made me suffer. With every year it becomes harder and harder to run the code that comes with papers. I do think pytorch is more complex in many senses for demo runs (less for development).

BTW Keras is not dead - it just became tf2.0

[CristianGarcia]

I agree with most said here but you missed the discussion about the high level APIs. If you program in Pytorch + Modules you are NEVER going to be more productive than TF + Keras, you have to use a framework like PytorchLightning or Skorch but these are not as mature or easy to use as Keras. I think the comparison here is only true for the low-level APIs, the intermediate level APIs (Module / Layer) are pretty much the same (Layers have shape inference tho). I think Keras predates TF since it was originally based on Theano so it has a massive gap with the Pytorch equivalents and its officially supported by the TF team.

That said, I am actually more excited about JAX than TF or PT.

[buiucnhan]

It 2022, i need to know if Keras was really dead?