pytorch-meta
Extensions and data-loaders for few-shot learning & meta-learning in PyTorch
GraphCanon updated Oct 3, 2026 · GitHub synced Oct 3, 2026
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Decision brief
PyTorch-Meta focuses on facilitating few-shot learning and meta-learning with PyTorch, offering extensions and data-loaders specifically for these tasks.
Good fit when
- When developing models that require handling few-shot learning scenarios where only a small amount of labeled data is available.
- For researchers or developers interested in leveraging pre-built modules and data-loaders to accelerate the prototyping phase in meta-learning projects.
Avoid when
- If your project requires extensive support for traditional deep learning tasks, as PyTorch-Meta does not offer comprehensive utilities beyond few-shot learning and meta-learning.
- For those strictly adhering to a single ecosystem that does not include the PyTorch framework or its specific versions below 1.4.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (1173d since push)
- As of Oct 3, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 3, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install pytorch-metaSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides modules that facilitate the development of few-shot learning and meta-learning models using PyTorch framework.
Capability facts
- Languages
- python
Source: github.language · Oct 3, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 3, 2026)
You can install Torchmeta either using Python's package manager pip, or from source. To avoid any conflict with your existingSource link
Tags
README
Installation You can install Torchmeta either using Python's package manager pip, or from source. To avoid any conflict with your existing Python setup, it is suggested to work in a virtual environment with . To install : Requirements Python 3.6 or above PyTorch 1.4 or above Torc...
For agents
This page has a .md twin and JSON over the API.