median-research-group/LibMTL
Some data may be outdated — see provenance labels.
A PyTorch Library for Multi-Task Learning observed · 2026-09-21
Health v2 · maintenance only
38/100
- Activity 13
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1767
- days_rel: n/a
- days_push: 524
- n_releases_24m: 0
Adoption not part of the score
2593 stars · 233 forks observed · 2026-09-21
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
LibMTL is an open-source PyTorch library for Multi-Task Learning (MTL). It provides implementations of many MTL architectures and gradient-based multi-objective optimization methods with a unified API.
Use cases
- train multi-task learning models in pytorch
- compare MTL weighting algorithms like PCGrad and CAGrad
- implement MMoE or PLE architectures
- run multi-objective optimization for deep learning
- benchmark gradient manipulation methods for MTL
When to choose
- you need a unified PyTorch framework for multi-task learning experiments
- you want to evaluate many recent MTL optimization methods without reimplementing them
- you need architectures like MMoE and PLE out of the box
When to avoid
- you need single-task training only
- you work outside PyTorch
- you need production serving rather than research training
Facets
library · maturity active
machine-learning deep-learning machine-learning deep-learning python pytorch multi-task-learning mtl multiobjective-optimization mmoe ple
2 sources
- readme: https://github.com/median-research-group/LibMTL · fetched 2026-09-21 · 1453414a9238
- registry_pypi: https://pypi.org/pypi/libmtl/json · fetched 2026-08-29 · c814a1a392d4
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| median-research-group/LibMTL | main | 38 |
For agents
markdown · JSON · MCP: product_card(name="median-research-group/LibMTL")
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem