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ray-project / ray

Star 19k
Open

[train] fix scalability of `JsonLoggerCallback`

6
matthewdeng
matthewdeng commented Jan 6, 2022

Problem: Currently JsonLoggerCallback.handle_result will load in the entirety of the existing results, append the new result, and then rewrite the entire file. This may not scale when running long-running jobs or jobs with large results.

https://github.com/ray-project/ray/blob/4e8f90aca20aa7bb87a4e84039889444824382ca/python/ray/train/callbacks/logging.py#L138-L142

Potential Fix:

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good first issue P2 train
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nni

microsoft / nni

Star 10.9k

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

python data-science machine-learning deep-learning neural-network tensorflow machine-learning-algorithms pytorch distributed feature-extraction hyperparameter-optimization feature-engineering nas bayesian-optimization automl automated-machine-learning model-compression neural-architecture-search deep-neural-network
  • Updated Jan 27, 2022
  • Python

optuna / optuna

Star 5.9k
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awslabs / autogluon

Star 4.1k
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mljar-supervised

mljar / mljar-supervised

Star 1.8k
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microsoft / FLAML

Star 1.7k
Open

samples about conversion to ONNX

sonichi
sonichi commented Jan 25, 2022

In principle it seems getting the parameters from FLAML to C# LightGBM seems to work, but I dont have any metrics yet. The names of parameters are slightly different but documentation is adequate to match them. Microsoft.ML seems to have version 2.3.1 of LightGBM.

Another approach that might be useful, especially for anyone working with .NET, would be having some samples about conversion to ONN

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documentation good first issue help wanted
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amanchadha / coursera-deep-learning-specialization

Star 856

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

deep-learning neural-network coursera recurrent-neural-networks neural-networks hyperparameter-optimization regularization convolutional-neural-networks neural-machine-translation coursera-machine-learning convolutional-neural-network hyperparameter-tuning andrew-ng coursera-assignment cnns rnns andrew-ng-course recurrent-neural-network neural-style-transfer coursera-specialization
  • Updated Jan 23, 2022
  • Jupyter Notebook
Gradient-Free-Optimizers

SimonBlanke / Gradient-Free-Optimizers

Star 836

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

machine-learning optimization constrained-optimization hyperparameter-optimization meta-heuristic simulated-annealing hill-climbing bayesian-optimization nelder-mead random-search particle-swarm-optimization evolution-strategies blackbox-optimization gradient-free-optimization tree-of-parzen-estimator hyperactive
  • Updated Jan 16, 2022
  • Python

LiYangHart / Hyperparameter-Optimization-of-Machine-Learning-Algorithms

Star 783

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

machine-learning deep-learning random-forest optimization svm genetic-algorithm machine-learning-algorithms hyperparameter-optimization artificial-neural-networks grid-search tuning-parameters knn bayesian-optimization hyperparameter-tuning random-search particle-swarm-optimization hpo python-examples python-samples hyperband
  • Updated Aug 19, 2021
  • Jupyter Notebook

guan-yuan / awesome-AutoML-and-Lightweight-Models

Star 745

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

tensorflow pytorch hyperparameter-optimization awesome-list quantization nas automl model-compression neural-architecture-search meta-learning architecture-search quantized-training model-acceleration automated-feature-engineering quantized-neural-network
  • Updated Jun 19, 2021

HDI-Project / ATM

Star 515
Open

Avoid creating redundant datasets

bcyphers
bcyphers commented Jan 31, 2018

If enter_data() is called with the same train_path twice in a row and the data itself hasn't changed, a new Dataset does not need to be created.

We should add a column which stores some kind of hash of the actual data. When a Dataset would be created, if the metadata and data hash are exactly the same as an existing Dataset, nothing should be added to the ModelHub database and the existing

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enhancement help wanted good first issue low priority
Neuraxle

Neuraxio / Neuraxle

Star 494
Open

Code Style: Improve Code Style

1
guillaume-chevalier
guillaume-chevalier commented Jun 28, 2021

Describe the bug
Code could be more conform to pep8 and so forth.

To Reproduce
https://app.codacy.com/gh/Neuraxio/Neuraxle/issues?&filters=W3siaWQiOiJMYW5ndWFnZSIsInZhbHVlcyI6W119LHsiaWQiOiJDYXRlZ29yeSIsInZhbHVlcyI6WyJDb2RlU3R5bGUiXX0seyJpZCI6IkxldmVsIiwidmFsdWVzIjpbXX0seyJpZCI6IlBhdHRlcm4iLCJ2YWx1ZXMiOltdfSx7ImlkIjoiQXV0aG9yIiwidmFsdWVzIjpbXX1d

Expected behavior
Less code st

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good first issue invalid documentation
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