This is the output I got after a couple of seconds: # Update weights using gradient descent a -= learning_rate * grad_a # Backprop to compute gradients of a, b, c, d with respect to loss grad_y_pred = 2.0 * (y_pred - y) # Forward pass: compute predicted y y_pred = a + b * x + c * x ** 2 + d * x ** 3 # Compute and print loss loss = (y_pred - y). Learning_rate = 1e-6 for t in range( 2000): randn((), device =device, dtype =dtype)ĭ = torch. randn((), device =device, dtype =dtype)Ĭ = torch. randn((), device =device, dtype =dtype)ī = torch. # Create random input and output data x = torch. The optional -y flag will accept any prompt for installing additional dependencies: I’m using Anaconda, so the environment creation boils down to issuing a conda create command followed by the environment name and Python version. Let’s start with the virtual environment. This section will show you how to create and activate a new Python virtual environment, how to install PyTorch, and how to install a couple of other necessary data science libraries. But should you run it as is? Probably not, since it’s the best idea to configure a virtual environment first. You can see that PyTorch gives you an installation command. Image 1 - PyTorch installation command (Image by author) I want to install the most recent stable release through Anaconda, so my options look like this: Head over to the official get started page and click on the options that match your environment.įor reference, I’m installing PyTorch on an M1 Pro MacBook. It will differ based on your operating system (Mac, Windows, Linux), Python configuration (Pip, Anaconda), and whether you’re using CUDA or not. Okay, so the first step is to find the correct installation command. Install Additional Libraries (Optional).
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