Does Numpy automatically detect and use GPU?
Question:
I have a few basic questions about using Numpy with GPU (nvidia GTX 1080 Ti). I’m new to GPU, and would like to make sure I’m properly using the GPU to accelerate Numpy/Python. I searched on the internet for a while, but didn’t find a simple tutorial that addressed my questions. I’d appreciate it if someone can give me some pointers:
1) Does Numpy/Python automatically detect the presence of GPU and utilize it to speed up matrix computation (e.g. numpy.multiply, numpy.linalg.inv, … etc)? Or do I have code in a specific way to exploit the GPU for fast computation?
2) Can someone recommend a good tutorial/introductory material on using Numpy/Python with GPU (nvidia’s)?
Thanks a lot!
Answers:
Does Numpy/Python automatically detect the presence of GPU and utilize
it to speed up matrix computation (e.g. numpy.multiply,
numpy.linalg.inv, … etc)?
No.
Or do I have code in a specific way to exploit the GPU for fast
computation?
Yes. Search for Numba, CuPy, Theano, PyTorch or PyCUDA for different paradigms for accelerating Python with GPUs.
No, you can also use CuPy which has a similar interface with numpy. https://cupy.chainer.org/
JAX uses XLA to compile NumPy code to run on GPUs/ TPUs : https://github.com/google/jax
No. Numpy does not use GPU. But you can use CuPy. The syntax of CuPy is quite compatible with NumPy. So, to use GPU, You just need to replace the following line of your code
import numpy as np
with
import cupy as np
That’s all. Go ahead and run your code. One more thing that I think I should mention here is that to install CuPy you first need to install CUDA. Since the objective of your question is to make your computations faster by making use of GPU, I would also suggest you explore PyTorch. With PyTorch, you can do almost everything that you can do with NumPy and much more. The learning curve would also be quite smooth if you are already familiar with NumPy. You can find more details on replacing NumPy with PyTorch here: https://www.youtube.com/watch?v=p3iYN-2XL8w
I have a few basic questions about using Numpy with GPU (nvidia GTX 1080 Ti). I’m new to GPU, and would like to make sure I’m properly using the GPU to accelerate Numpy/Python. I searched on the internet for a while, but didn’t find a simple tutorial that addressed my questions. I’d appreciate it if someone can give me some pointers:
1) Does Numpy/Python automatically detect the presence of GPU and utilize it to speed up matrix computation (e.g. numpy.multiply, numpy.linalg.inv, … etc)? Or do I have code in a specific way to exploit the GPU for fast computation?
2) Can someone recommend a good tutorial/introductory material on using Numpy/Python with GPU (nvidia’s)?
Thanks a lot!
Does Numpy/Python automatically detect the presence of GPU and utilize
it to speed up matrix computation (e.g. numpy.multiply,
numpy.linalg.inv, … etc)?
No.
Or do I have code in a specific way to exploit the GPU for fast
computation?
Yes. Search for Numba, CuPy, Theano, PyTorch or PyCUDA for different paradigms for accelerating Python with GPUs.
No, you can also use CuPy which has a similar interface with numpy. https://cupy.chainer.org/
JAX uses XLA to compile NumPy code to run on GPUs/ TPUs : https://github.com/google/jax
No. Numpy does not use GPU. But you can use CuPy. The syntax of CuPy is quite compatible with NumPy. So, to use GPU, You just need to replace the following line of your code
import numpy as np
with
import cupy as np
That’s all. Go ahead and run your code. One more thing that I think I should mention here is that to install CuPy you first need to install CUDA. Since the objective of your question is to make your computations faster by making use of GPU, I would also suggest you explore PyTorch. With PyTorch, you can do almost everything that you can do with NumPy and much more. The learning curve would also be quite smooth if you are already familiar with NumPy. You can find more details on replacing NumPy with PyTorch here: https://www.youtube.com/watch?v=p3iYN-2XL8w