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PyPOTS

A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values

DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Popularity
Repository stars2,09143,283Highest
7-day net stars——
Fork1945,060Highest
30-day trendInsufficient historyInsufficient history
Maintenance
Last push2026-10-312026-11-02
Maintenance notesNot verifiedNot verified
Repository created2022-03-292020-01-23
License & use
LicenseBSD-3-Clause · PermissiveApache-2.0 · Permissive
PricingPricing not verifiedPricing not verified
Basics
Browse by purposeData analysisData analysis
TypeFrameworks & SDKsFrameworks & SDKs
SubcategoryData science & machine learningData science & machine learning
PlatformNot verifiedNot verified
Cost detailsCheck current official software, hosting, model and infrastructure pricing.Check current official software, hosting, model and infrastructure pricing.
Pricing source——
First collected2026-11-032026-11-03
MetricRepository stars, not unique users or individual-skill installsRepository stars, not unique users or individual-skill installs
LanguagePythonPython
About this projectA Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing valuesDeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Measured at2026-11-02 19:00 UTC2026-11-02 19:02 UTC

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