SDGym VS DPL

Compare SDGym vs DPL and see what are their differences.

SDGym

Benchmarking synthetic data generation methods. (by sdv-dev)
synthetic-data Benchmark tabular-data synthetic-data-vault sdgym-synthesizers generative-adversarial-network Deep Learning generative-ai generative-models
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DPL

[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks. (by releaunifreiburg)
Benchmark Computer Vision Deep Learning hpo hyperparameter-optimization hyperparameter-tuning large-language-models llm Machine Learning mlp Natural Language Processing power-laws scaling-laws tabular-data Transformer
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SDGym DPL
1 1
242 11
1.2% -
7.8 5.6
2 days ago 6 months ago
Python Python
GNU General Public License v3.0 or later Apache License 2.0
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SDGym

Posts with mentions or reviews of SDGym. We have used some of these posts to build our list of alternatives and similar projects.
  • [D] Synthetic data generation techniques for data privacy
    1 project | /r/MachineLearning | 15 Feb 2022
    I would suggest starting with "differentially private synthetic data generation". These methods utilize differential privacy and mostly protect against membership inference attacks, are very popular in the ML/DL community. I would also suggest reading up on privacy preserving ML methods in general and adversarial attacks against them (membership inference, inversion, reconstruction, property inference), but if you're keen on reading some code, check out sd-gym: https://github.com/sdv-dev/SDGym. The authors have collected implementations for a lot of PPSDG methods. Also I strongly suggest reading McMahan's 2016 paper: https://arxiv.org/abs/1607.00133.

DPL

Posts with mentions or reviews of DPL. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing SDGym and DPL you can also consider the following projects:

Mimesis - Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.

prompttools - Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).

SDV - Synthetic data generation for tabular data

deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

Copulas - A library to model multivariate data using copulas.

tapnet - Tracking Any Point (TAP)

FAST-RIR - This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

autogluon - AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data [Moved to: https://github.com/autogluon/autogluon]

AgileRL - Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.

SDGym vs Mimesis DPL vs prompttools SDGym vs SDV DPL vs deeplake SDGym vs Copulas DPL vs tapnet SDGym vs FAST-RIR DPL vs autogluon DPL vs AgileRL