optuna
xsimd
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| optuna | xsimd | |
|---|---|---|
| 34 | 3 | |
| 9,640 | 2,036 | |
| 3.4% | 2.2% | |
| 9.9 | 8.7 | |
| 5 days ago | 7 days ago | |
| Python | C++ | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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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.
optuna
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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
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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.
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[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?
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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
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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
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[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
xsimd
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GDlog: A GPU-Accelerated Deductive Engine
https://github.com/xtensor-stack/xsimd
GH topics > HashMap:
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SIMD intrinsics and the possibility of a standard library solution
xsimd - 1.6K GH stars
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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
What are some alternatives?
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.
highway - Performance-portable, length-agnostic SIMD with runtime dispatch
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
Vc - SIMD Vector Classes for C++
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
libsimdpp - Portable header-only C++ low level SIMD library
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
nsimd - Agenium Scale vectorization library for CPUs and GPUs
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
FastDifferentialCoding - Fast differential coding functions (using SIMD instructions)
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
VectorizedKernel - Running GPGPU-like kernels on CPU with auto-vectorization for SSE/AVX/AVX512 SIMD Architectures