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Most of my training is done using RNN , either LSTM or GRU, and I've found the CPU to be taking much of the load.
I'm looking to put a new system together for testing, and I haven't seen any posts on which Architecture is more conducive to ML with TensorFlow. It seems it boils down to the Intel Core-X series having AVX-512 and AMD not (Specifically looking at i9-7900X vs Threadripper 1950X as they are a similar price).
So the question I have is two-fold:
Does TensorFlow make use of AVX-512 extensions, and
Are those extensions beneficial enough to make up for a 6-core deficiency of the 1950X -> 7900X
~ Are there any other considerations I am not taking into account? Any specialized performance optimizations that TF has made for Intel processors over AMD?
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I saw PyTorch Lightning advertised as PyTorch but for people who don't want to worry so much about the underlying methodology. This narrative is on the PyTorch lightning website but also here for example.
For hardware reasons, does something similar exist for TensorFlow? I have a code example for neural nets here written in PyTorch and PyTorch Lightning but am not sure how to rewrite it in TensorFlow.
Probably the best association would be Keras (formerly separate from but now for some time integrated in TF - you can you Keras as a high level API).
Note that you can also use tensorflow_addons (I personally enjoy working with it) package and other libraries&wrappers that come into the aid of TensorFlow, because since Keras is integrated into TF, you will be also very likely to use them on your Keras code.
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I am using Google Colabs to train my model using GPU.
Even i use the same code, the execution time for an epoch differs significantly.
It differs between 5 minutes and 25 minutes.
I tried to reset the runtime.
I check the memory, it seems same.
I could not find a solution.
I suspect the type of GPU you are using is changing. You can check by running the command nvidia-smi. Here's an example:
A P100 is the fastest GPU available. You might consider subscribing to Colab Pro for priority access to P100s.
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I am new to machine learning and I want to start doing basic regression analysis. I saw that scikit-learn provides a simple way to do this. But why people use tensorflow for regression instead? Thanks!
If the only thing you are doing is regression, scikit-learn is good enough and will definitely do you job. Tensorflow is more a deep learning framework for building deep neural networks.
There're people using Tensorflow to do regression maybe just out of personal interests or they think Tensorflow is more famous or "advanced".
Tensorflow is a deep learning framework and involves far more complex decisions concerning algorithm design.
In the first step, it is recommended to use sklearn, because you will get a first ml model with scikit-learn faster. Later you can use a dl model with tensorflow. :-)
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Our boss found the idea of the paper "A Neural Algorithm of Artistic Style" amazing and think it should attract some of his customers. He decided to set up a server provide the service of style transfer for them.
There are several deep learning architectures with implementations of this idea such as TensorFlow, Torch, caffe, etc. If aims to achieve the best performance, which implementation of these architectures runs fastest? If we configure the algorithm with a considerably good CUDA device such as GeForce GTX 1090 or better, is it possible to finish the task of a VGG model in several seconds? If wish to apply the state of art of the idea to the aforementioned deep learning architectures, are they all applicable?
Checking out some benchmarks: https://github.com/soumith/convnet-benchmarks I'd say that Nervana and Torch are the best frameworks.
If instead of time, we have a look at open source contributions and paper implementations I think torch is the winner.
You can easily find neural-style algorithm implementations in Torch: Neural-Style and Fast Neural-Style
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I have read blogpost about TensorFlow is being open sourced.
In the tutorials and the examples on the TensorFlow website, I see that they are mostly classification problems. (e.g. given an image, classify the number written in it)
I am curious about it the software also suitable for solving problems in recommendation system?
For example, is it good for solving problems on collaborative filtering / content-based filtering?
Tensorflow is great for deep learning, or training large neural nets. Although, it can be used for several other mathematical applications such as PDEs, various classifiers, recommendation systems etc, there doesn't seem to have a lot of support for them as yet.
This reddit thread might be a good place to start for searching libraries which are centred around recommendation systems.