ec2-macos-init
Pytorch
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ec2-macos-init | Pytorch | |
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59 | 338 | |
141 | 77,783 | |
0.0% | 2.4% | |
3.6 | 10.0 | |
7 months ago | 7 days ago | |
Go | Python | |
Apache License 2.0 | BSD 1-Clause License |
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ec2-macos-init
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Beeper Mini Is Back
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.
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How can I play with macos e.g. in the cloud? Or any other way? I have windows and never used mac before.
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/
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Should I dual-boot Ventura for daily purposes?
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/
- Is it possible to create/terminate ec2 instances based on certain events?
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Trying Mac
AWS https://aws.amazon.com/ec2/instance-types/mac/
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Cheapest option for IOS development in 2023
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/
- Will this code work on macOS??
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I'm needing to buy a used Mac for testing. What should I know about getting a used Mac?
Have seen that AWS have a mac host option. Perfect for testing environments.
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Is there any way of setting up a free Mac OS VM on Windows 10?
You can also spin up an EC2 instance on AWS, yes: https://aws.amazon.com/ec2/instance-types/mac/
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Which Mac mini should I buy?
get yourself a VM from AWS, MacStadium, macOS-VM etc.
Pytorch
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Einsum in 40 Lines of Python
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
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Library for Machine learning and quantum computing
TensorFlow
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My Favorite DevTools to Build AI/ML Applications!
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.
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penzai: JAX research toolkit for building, editing, and visualizing neural nets
> does PyTorch have a similar concept
of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...
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Tinygrad: Hacked 4090 driver to enable P2P
fyi should work on most 40xx[1]
[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...
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The Elements of Differentiable Programming
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].
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Functions and operators for Dot and Matrix multiplication and Element-wise calculation in PyTorch
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In PyTorch with @, dot() or matmul():
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Building a GPT Model from the Ground Up!
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
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Open Source Ascendant: The Transformation of Software Development in 2024
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.
What are some alternatives?
OSX-KVM - Run macOS on QEMU/KVM. With OpenCore + Monterey + Ventura + Sonoma support now! Only commercial (paid) support is available now to avoid spammy issues. No Mac system is required.
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
greenclip - Simple clipboard manager to be integrated with rofi - Static binary available
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
homebrew-aws - Homebrew is a package manager for macOS which provides easy installation and update management of additional software. This Tap (repository) contains the Formulae that are used in the macOS AMI that AWS offers.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
micromdm - Mobile Device Management server
flax - Flax is a neural network library for JAX that is designed for flexibility.
esxi-unlocker - VMware ESXi macOS
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
ExpansionCards - Reference designs and documentation to create Expansion Cards for the Framework Laptop
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more