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I am new to using Colab and trying to speed up my code by utilizing the GPU/TPU runtimes. I have a block of plain Python code without any data science libraries like TensorFlow or PyTorch. I was able to successfully change the runtime to TPU, which I confirmed using torch.cuda.is_available().

I am aware that the GPU and TPU runtimes provided by Colab are designed to accelerate machine learning workloads that leverage frameworks like TensorFlow and PyTorch. For general Python code without those libraries, you are unlikely to see significant performance improvements from using the faster hardware runtimes as a free Colab user. However, is there any way to speed up performance of general Python code, not just libraries like TensorFlow? Any guidance is greatly appreciated.

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    $\begingroup$ GPUs and TPUs cannot be used to speed up normal Python code. To do that, you may look into libraries like numba and cython. $\endgroup$
    – noe
    Commented Oct 23, 2023 at 21:49
  • $\begingroup$ So, is cython code accelerated in Tensor Processing Units (TPU)? $\endgroup$
    – peter
    Commented Nov 15 at 13:31

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As hinted by the comment there are several approaches but only one that really matches the accelleration of a "block of plain Python code" using a GPU/TPU. This is different to how can python code be accellerated in general as you've specifically asked for it accellerated by a GPU/TPU.

If using an Nvidia GPU, python can be accellerated using Nvidia CUDA [1], which is really a set of bindings to CUDA from python and therefore not very pythonic (have to specify types and structures etc).

If the majority of your manipulations are array based you can use higher level libraries such as CuPy [2] (Numpy and SciPy equivalent) but like the other elements in the RAPIDS [3] ecosystem is focused around data science uses cases (DataFrame, Networks etc).

The most accesible way to use CUDA Python is via numba, for which there are several tutorials/videos that show how to convert your code into a vector format such that it can be accelerated by a GPU [4], [5].

My recommendation is understand how to use the numba @jit decorator first [6] before looking to apply GPU based accelleration. It should become apparent that the cost of using python in this way, is that it becomes less pythonic.

[1] https://nvidia.github.io/cuda-python/overview.html

[2] https://cupy.dev/

[3] https://rapids.ai/

[4] https://thedatafrog.com/en/articles/boost-python-gpu/

[5] https://www.kaggle.com/code/harshwalia/1-introduction-to-cuda-python-with-numba

[6] https://numba.pydata.org/numba-doc/latest/user/5minguide.html

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