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This is 100% #JuliaLang code. It trains a dense neural network using the #Keras Python library. If you replace the first 3 lines with "import tensorflow as tf; from tensorflow import keras", you can run the exact same code in Python. Talk about excellent interoperability!
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Such an interesting call! I mean, how useful is to try this tool enhancing the speed by using Julia. I would like to know if JuliaML.jl gives or not a better result than you presented?
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Pretty interesting.
what are your thoughts on Julia? Do you think it will take over Python for ML/DL use cases?
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cool. what's the advantage of running this code in julia as opposed to python?
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Good question! This example just shows that you can use any Python library in Julia. This makes it easier to transition from Python to Julia.
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thanks. I am keeping an eye out for julia. One reason I focus on python is the ML ecosystem. Wouldn't natively built julia packages be better? what about compatibility issues?
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Pure Julia code is 10x to 100x faster than pure Python code, in most cases. It can even be faster than Python C modules, like NumPy, in some cases. For example sin.(cos.(exp.(A))) in Julia will iterate over the array A just once, whereas NumPy would create 2 temporary arrays.
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I did not realize that you could use Python in Julia. That's amazing and I'm going to give Julia a much better look now.
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Cool! Check out my Julia tutorial for Python programmers: https://colab.research.google.com/github/ageron/julia_notebooks/blob/master/Julia_for_Pythonistas.ipynb…
Hope you find it useful.
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Can the model be saved and used for future inference in julia just like python?
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I didn’t try it before so I don’t know the performance, is there a difference in performance ?
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That's super interesting! Do you know the performance implications of running this? Will it be at least as fast as the original Python?
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Actually, I think a better question is: can I get performance benefits from mixing Julia and Python, or will everything that interacts with the Python run in the Python interpreter?
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I haven't run any benchmarks on this, but I assume that everything that interacts with Python runs at Python speed, plus a small overhead due to the conversion. But that overhead is small as Python runs in the Julia process itself. The benefit is interoperability, not speed.
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Aha, thanks! So—potentially—we can write nice, fast data transformation code in Julia before feeding the data into a Python training loop.
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Yes, you could do that. However, in the long term Julia solves the two-language problem, where you need one language for high level stuff and another for low level. With Julia, you can write high level code and it will run fast.
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