dgl
optuna
| dgl | optuna | |
|---|---|---|
| 4 | 34 | |
| 13,018 | 9,681 | |
| 0.8% | 2.2% | |
| 9.9 | 9.9 | |
| 9 days ago | 7 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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dgl
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
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Detecting Out-of-Distribution Datapoints via Embeddings or Predictions
For trees/graphs, you’ll want a neural net that can take these as inputs for which I’m not sure a standard library exists. One recommendation is to checkout dgl: https://github.com/dmlc/dgl
- Beyond Message Passing: A Physics-Inspired Paradigm for Graph Neural Networks
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[D] Convenient libs to use for new research project at the intersection of GNN and RL.
The best pkg for GCN - https://github.com/dmlc/dgl
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
What are some alternatives?
pytorch_geometric - Graph Neural Network Library for PyTorch
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.
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
torchdrug - A powerful and flexible machine learning platform for drug discovery
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
spektral - Graph Neural Networks with Keras and Tensorflow 2.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
deep_gcns_torch - Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python