nanobench
Pytorch
nanobench | Pytorch | |
---|---|---|
13 | 341 | |
1,320 | 78,205 | |
- | 1.6% | |
0.0 | 10.0 | |
14 days ago | 4 days ago | |
C++ | Python | |
MIT License | BSD 1-Clause License |
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nanobench
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The issue of unit tests and performance measurements (Benchmark)
An alternative is tracking the number of instructions a test executes: https://github.com/martinus/nanobench
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how do you properly benchmark?
Nano bench is a great library with low overhead. https://github.com/martinus/nanobench
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Much Faster than std::string, fmt::format, std::to_chars, std::time and more?
I've done a relatively simple test of taking random doubles (between 0 and 1), converting them to a C string via std::to_chars and then converting that C string back to a double via std::from_chars vs his xeerx::chars_to and got the following results on my machine via nanobench:
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Can you give an example of well-designed C++ code, and explain why you think it is so?
I like https://nanobench.ankerl.com/
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Best accurate way to measure/compare elapsed time in C++
Of course, the best way to benchmark is nanobench: https://nanobench.ankerl.com/
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The 23 year-old C++ developers with three job offers over $500k
I've created robin-hood-hashing and nanobench, and recently made some contributions to Bitcoin and doxygen
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I don’t know which container to use (and at this point I’m too afraid to ask)
Right. Regex runtime construction is known to be slow, so ideally the state machinery construction is built at compile time (boost.xpressive, ctre). Also, boost.regex is faster than most of the std implementations if compile time isn’t possible. And if that’s no good rewrite without regex. Since it sounds like it’s all encapsulated at least it would be easy to measure the options. These days I use this one to compare https://nanobench.ankerl.com/
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I'm writing a microbenchmarking library called "precision" without any macros. What do you guys think of the API?
You can check the API of nanobench which also doesn't use macros, as far as I have used it.
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C++20 std::format is 2x slower than std::fstream?
I've tried again with your latest changes and decided to use https://github.com/martinus/nanobench for a better benchmark and got the following output:
- Nanobench: Fast, Accurate, Single-Header Microbenchmarking Functionality For C++
Pytorch
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Clasificador de imágenes con una red neuronal convolucional (CNN)
PyTorch (https://pytorch.org/)
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AI enthusiasm #9 - A multilingual chatbot📣🈸
torch is a package to manage tensors and dynamic neural networks in python (GitHub)
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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():
What are some alternatives?
benchmark - A microbenchmark support library
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
fast_io - C++20 Concepts IO library which is 10x faster than stdio and iostream
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
robin-hood-hashing - Fast & memory efficient hashtable based on robin hood hashing for C++11/14/17/20
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
curl4cpp - Single header cURL wrapper for C++ around libcURL
flax - Flax is a neural network library for JAX that is designed for flexibility.
ut - C++20 μ(micro)/Unit Testing Framework
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
bench-rest - bench-rest - benchmark REST (HTTP/HTTPS) API's. node.js client module for easy load testing / benchmarking REST API's using a simple structure/DSL can create REST flows with setup and teardown and returns (measured) metrics.
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