Setting up Pytorch Locally

1. Install miniconda

You can use PyCharm for automatic installation, or you can install it manually.

Check if you have Jupyter Notebook and Jupyter Lab.

If the above are all okay, then you can install Pytorch.

2. Create a new conda environment

conda create -n torch-gpu python=3.9
conda activate torch-gpu

3. Install Pytorch

Go to the official website to download the Pytorch installation command https://pytorch.org/ It can be a conda/pip command.

4. Register the kernel to Jupyter Lab

conda install ipykernel
sudo python -m ipykernel install --name=torch-gpu

5. Open Jupyter Lab

jupyter lab

6. Test Pytorch (Apple Silicon M1)

Select the registered kernel, then run the code:

test1.py

import torch
import math
# this ensures that the current MacOS version is at least 12.3+
print(torch.backends.mps.is_available())
# this ensures that the current current PyTorch installation was built with MPS activated.
print(torch.backends.mps.is_built())

test2.py

dtype = torch.float
device = torch.device("mps")
# Create random input and output data
x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype)
y = torch.sin(x)

# Randomly initialize weights
a = torch.randn((), device=device, dtype=dtype)
b = torch.randn((), device=device, dtype=dtype)
c = torch.randn((), device=device, dtype=dtype)
d = torch.randn((), device=device, dtype=dtype)

learning_rate = 1e-6
for t in range(2000):
    # Forward pass: compute predicted y
    y_pred = a + b * x + c * x ** 2 + d * x ** 3

    # Compute and print loss
    loss = (y_pred - y).pow(2).sum().item()
    if t % 100 == 99:
        print(t, loss)

    # Backprop to compute gradients of a, b, c, d with respect to loss
    grad_y_pred = 2.0 * (y_pred - y)
    grad_a = grad_y_pred.sum()
    grad_b = (grad_y_pred * x).sum()
    grad_c = (grad_y_pred * x ** 2).sum()
    grad_d = (grad_y_pred * x ** 3).sum()

    # Update weights using gradient descent
    a -= learning_rate * grad_a
    b -= learning_rate * grad_b
    c -= learning_rate * grad_c
    d -= learning_rate * grad_d


print(f'Result: y = {a.item()} + {b.item()} x + {c.item()} x^2 + {d.item()} x^3')

Setup Local Pytorch