Vc
optuna
Vc | optuna | |
---|---|---|
6 | 34 | |
1,420 | 9,714 | |
1.1% | 2.5% | |
6.1 | 9.9 | |
3 months ago | 2 days ago | |
C++ | Python | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Vc
-
The Bitter Truth: Python 3.11 vs Cython vs C++ Performance for Simulations
Most high-performance math libraries perform a lot of vectorization (Eigen, etc) under the hood. And you've got stuff like Klein, Vc (which is reminiscent of std::valarray), etc. Then there's OpenMP's #pragma omp simd (assuming version 4.0 or greater).
-
John "God" Carmack: C++ with a C flavor is still the best (also: Python performance "keeps hitting me in the face")
I personally like the ideas in Parallelism v2 TS, which is available in for libstdc++ 11 onwards. The reference implementation is a library named Vc (afaik Vc is the most popular SIMD library for C++), and this has also been implemented in recent versions of HPX.
-
SPO600 project part 2
First of all about our project, I previously decided to work with VC library.https://github.com/VcDevel/Vc
-
SPO600 project part 1
I've decided to switch to something better, and after a few hours of searching, I found this repository: NSIMD https://github.com/agenium-scale/nsimd FastDifferentialCoding https://github.com/lemire/FastDifferentialCoding VS https://github.com/VcDevel/Vc XSIMD https://github.com/xtensor-stack/xsimd
- Vc 1.4.2 released: portable SIMD programming for C++
-
All C++20 core language features with examples
> - Waiting for Cross-Platform standardized SIMD vector datatypes
which language has standardized SIMD vector datatypes ? most languages don't even have any ability to express SIMD while in C++ I can just use Vc (https://github.com/VcDevel/Vc), nsimd (https://github.com/agenium-scale/nsimd) or one of the other ton of alternatives, and have stuff that JustWorksTM on more architectures than most languages even support
- Using nonstandard extensions, libraries or home-baked solutions to run computations in parallel on many cores or on different processors than the CPU
what are the other native languages with a standardized memory model for atomics ? and, what's the problem with using libraries ? it's not like you're going to use C# or Java's built-in threadpools if you are doing any serious work, no ? Do they even have something as easy to use as https://github.com/taskflow/taskflow ?
- Debugging cross-platform code using couts, cerrs and printfs
because people never use console.log in JS or System.println in C# maybe ?
- Forced to use boost for even quite elementary operations on std::strings.
can you point to non-trivial java projects that do not use Apache Commons ? Also, the boost string algorithms are header-only so you will end up with exactly the same binaries that if it was in some std::string_algorithms namespace:
https://gcc.godbolt.org/z/43vKadbde
optuna
-
Optuna – A Hyperparameter Optimization Framework
I didn’t even know WandB did hyperparameter optimization, I figured it was a neural network visualizer based on 2 minute papers. Didn’t seem like many alternatives out there to Optuna with TPE + persistence in conditional continuous & discrete spaces.
Anyway, it’s doable to make a multi objective decide_to_prune function with Optuna, here’s an example https://github.com/optuna/optuna/issues/3450#issuecomment-19...
- How to test optimal parameters
- FOSS hyperparameter optimization framework to automate hyperparameter search
-
How did you make that?!
The network configuration process is usually not particularly scientific and mostly relies on empirical observation. For some cases, tools like Optuna can be used to automatically find the optimal parameters. In others, on others, you can look for modern studies which explore the effect of this parameter on performance, such as this study (2022), but these are typically very specific to one particular architecture.
-
[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Keras Tuner, Optuna : https://github.com/optuna/optuna ?
- How to tune more than 2 hyperparameters in Grid Search in Python?
-
Suggestion to optimize algo
I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org Optuna - A hyperparameter optimization framework
-
Best practices for training PyTorch model
Research the type of model to get an idea of what hyper parameters to use. I recommend using a hyper parameter optimization library like Optuna to get the best configuration
-
[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
What are some alternatives?
highway - Performance-portable, length-agnostic SIMD with runtime dispatch
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
xsimd - C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE))
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
Eigen
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
blaze
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
MIRACL - MIRACL Cryptographic SDK: Multiprecision Integer and Rational Arithmetic Cryptographic Library is a C software library that is widely regarded by developers as the gold standard open source SDK for elliptic curve cryptography (ECC).
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
GLM - OpenGL Mathematics (GLM)
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python