Numpy is one of the most popular linear algebra libraries right now. There’s also PyTorch - an open source deep learning framework developed by Facebook Research. While the latter is best known for its machine learning capabilities, it can also be used for linear algebra, just like Numpy.
The most important difference between the two frameworks is naming. Numpy calls tensors (high dimensional matrices or vectors) arrays while in PyTorch there’s just called tensors. Everything else is quite similar.
Even if you already know Numpy, there are still a couple of reasons to switch to PyTorch for tensor computation. The main reason is the GPU acceleration. As you’ll see, using a GPU with PyTorch is super easy and super fast. If you do large computations, this is beneficial because it speeds things up a lot.
The other reason is the integration with other parts of the PyTorch framework. Most people use linear algebra for some kind of machine learning nowadays. In this case, using PyTorch is probably a better choice because the data can be used with the rest of the framework.
Numpy is the most commonly used computing framework for linear algebra. A good use case of Numpy is quick experimentation and small projects because Numpy is a light weight framework compared to PyTorch.
Moreover, PyTorch lacks a few advanced features as you’ll read below so it’s strongly recommended to use numpy in those cases.
import numpy as np
Fortunately, using one framework doesn’t exclude the other. You can get the best of both worlds by converting between Numpy arrays and PyTorch tensors.
# Numpy -> PyTorch tensor = torch.from_numpy(np_array) # PyTorch -> Numpy ndarray = tensor.numpy()
zeros = np.zeros((4, 4)) ones = np.ones((4, 4)) random = np.random.random((4, 4))
zeros = torch.zeros(4, 4) ones = torch.ones(4, 4) random = torch.rand(4, 4)
Basic Linear Algebra
# Index item array[0, 0] # returns a float # Index row array[0, :] # returns an array
# Index item torch[0, 0] # returns a tensor # Index row torch[0, :] # returns a tensor
Addition & subtraction
array + array2 array - array2
tensor + tensor2 tensor - tensor2
(Element wise) multiplication
# Element wise array * array # Matrix multiplication array @ array
# Element wise tensor * tensor # Matrix multiplication tensor @ tensor
Shape and dimensions
shap = array.shape num_dim = array.ndim
shape = tensor.shape shape = tensor.size() # equal to `.shape` num_dim = tensor.dim()
new_array = array.reshape((8, 2))
new_tensor = tensor.view(8, 2)
# not natively supported
Inverse and Moore-Pensore inverse
# Inverse np.linalg.inv(array) # Moore Pensore inverse np.linalg.pinv(array)
# Inverse tensor.inverse() # Moore Pensore inverse tensor.pinverse()
These functions return floating point numbers in Numpy where PyTorch returns 1 by 1 tensors.
# Sum array.sum() # Mean array.mean() # Standard Deviation array.std()
# Sum tensor.sum() # Mean tensor.mean() # Standard Deviation tensor.std()
Saving to disk
Saving results to disk is a huge time server. Here’s how you’d do it in Numpy or PyTorch.
np.save('file.npy', array) np.load('file.npy')
torch.save(tensor, 'file') torch.load('file')
Using the GPU
This is where PyTorch really shines. By copying an array to the GPU memory, there’s a huge potential for performance improvements due to heavy parallelization. Note that PyTorch uses CUDA under the hood so only NVIDIA GPUs are supported.
# put the tensor in GPU memory gpu_tensor = tensor.gpu()
In Google Colab I got a 20.9 time speed up in multiplying a 10000 by 10000 matrix by a scaler when using the GPU.
If you do an operation on two arrays, both must be either on the CPU or GPU.
If there’s anything you’d like to see added, tweet me at @rickwierenga.
A huge thanks to Sam Miserendino for proofreading this post!
Thanks for reading
I hope this post was useful to you. If you have any questions or comments, feel free to reach out on Twitter or email me directly at rick_wierenga [at] icloud [dot] com.