ec2-macos-init VS Pytorch

I don't think they're using false or duplicate Apple devices for this. I think that it may be likely they are using AWS resources for it: https://aws.amazon.com/ec2/instance-types/mac/

When AWS first came out with these, this was my first thought. People could spin up an EC2 instance and use it for iMessage, and Beeper came to be shortly after this feature went live in AWS.

AWS do have Mac instances you can use, although I'm not sure if you get the full macOS UI experience. https://aws.amazon.com/ec2/instance-types/mac/

If all you really need is to write swift, you could use something like https://github.com/sickcodes/Docker-OSX or https://aws.amazon.com/ec2/instance-types/mac/

AWS https://aws.amazon.com/ec2/instance-types/mac/

I’m pretty sure that terms and conditions thing you’re worrying about is fake news. AWS offer Big Sur instances and they’re charged per second. https://aws.amazon.com/ec2/instance-types/mac/

Have seen that AWS have a mac host option. Perfect for testing environments.

You can also spin up an EC2 instance on AWS, yes: https://aws.amazon.com/ec2/instance-types/mac/

get yourself a VM from AWS, MacStadium, macOS-VM etc.

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():

import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.