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cuml | evojax | |
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10 | 11 | |
3,894 | 781 | |
2.0% | 4.2% | |
9.3 | 4.6 | |
3 days ago | 7 months ago | |
C++ | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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.
cuml
- FLaNK Stack Weekly for 13 November 2023
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Is it possible to run Sklearn models on a GPU?
sklearn can't, bit take a look at cuML (https://github.com/rapidsai/cuml ). It uses the same API as sklearn but executes on GPU.
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[P] Looking for state of the art clustering algorithms
As a companion to the other comments, I'd like to mention that the RAPIDS library cuML provides GPU-accelerated versions of quite a few of the algorithms mentioned in this thread (HDBSCAN, UMAP, SVM, PCA, {Exact, Approximate} Nearest Neighbors, DBSCAN, KMeans, etc.).
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Is there a multi regression model that works on GPU?
CuML
- [D] What's your favorite unpopular/forgotten Machine Learning method?
- Machine Learning with PyTorch and Scikit-Learn – The *New* Python ML Book
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What are the advantages and disadvantages of using GPU for machine learning/ deep learning/ scientific computation over the conventional CPU software acceleration?
Did they implement the clustering algorithm themselves? cuML is a GPU-accelerated scikit-learn-like package that covers many of the common ML algorithms.
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Intel Extension for Scikit-Learn
https://github.com/rapidsai/cuml
> cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU equivalents. For details on performance, see the cuML Benchmarks Notebook.
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GPU Based Kernel-PCA
Cython code
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Python Machine Learning Guy getting started with CUDA. What should I be brushing up on?
Take a look at RAPIDS CUML https://github.com/rapidsai/cuml. It's useful for most common ML algorithms. Feel free to create Github issues for feature requests & bugs.
evojax
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[P] EvoTorch 0.4.0 dropped with GPU-accelerated implementations of CMA-ES, MAP-Elites and NSGA-II.
Awesome results! Would love to see a comparison with other accelerated evolution methods (eg https://github.com/google/evojax)
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Online courses on statistics and multi-objective optimization
For which problems do you want to apply multiple-objective optimization? What are the objectives? https://mml-book.github.io/ is only about single objective optimization (including constraints). Multi objective reinfocement learning (https://arxiv.org/pdf/1908.08342.pdf) is not really MO-optimization. Machine learning optimization frameworks like https://github.com/google/evojax support single objective optimization and quality-diversity (MAP-elites), but not MO-optimization.
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Question on how to model a "discontinuous" action space
population based algos dont care about differentiability https://github.com/google/evojax https://github.com/RobertTLange/evosax https://github.com/nnaisense/evotorch https://github.com/uber-research/PyTorch-NEAT
- Should I pursue Evolutionary Strategies?
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Performance of Evolutionary Algorithms for Machine Learning
Googles evojax project shows that evolutionary algorithms may be applied in the machine learning domain. And https://github.com/google/jax provides means to implement these algorithms to be deployed on CPUs/GPUs or even TPUs. But some questions remain unanswered:
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[D] What's your favorite unpopular/forgotten Machine Learning method?
Check out EvoJAX if you haven't seen it! Recently released for neuroevolution
- EvoJAX: Hardware-Accelerated Neuroevolution
- Show HN: EvoJAX: Hardware-Accelerated Neuroevolution
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[P] EvoJAX: Hardware-Accelerated Neuroevolution
Code for https://arxiv.org/abs/2202.05008 found: https://github.com/google/evojax
What are some alternatives?
scikit-learn - scikit-learn: machine learning in Python
evotorch - Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.
scikit-learn-intelex - Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
PyTorch-NEAT
scikit-cuda - Python interface to GPU-powered libraries
fast-cma-es - A Python 3 gradient-free optimization library
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
cudf - cuDF - GPU DataFrame Library
lightseq - LightSeq: A High Performance Library for Sequence Processing and Generation
cuhnsw - CUDA implementation of Hierarchical Navigable Small World Graph algorithm
numba-dpex - Data Parallel Extension for Numba
pdc-dp-means - "Revisiting DP-Means: Fast Scalable Algorithms via Parallelism and Delayed Cluster Creation" [Dinari and Freifeld, UAI 2022]