PyTorch OO class-based design leads to high LOC and poor maintainability

5/10 Medium

PyTorch's object-oriented class approach results in applications with orders-of-magnitude more lines of code than necessary, negatively impacting both runtime performance and long-term code maintainability. This architectural choice is seen as fundamentally misaligned with the needs of production ML engineering.

PyTorch
Category
architecture
Workaround
none
Stage
build
Freshness
persistent
Scope
framework
Upstream
wontfix
Recurring
Yes
Buyer Type
team
Maintainer
slow

Sources

Collection History

Query: “What are the most common pain points with PyTorch for developers in 2025?”4/4/2026

torch chose an OO or class approach which is not the best approach for ML since it quickly leads to applications with several orders of magnitude higher LOC which severely impacts performance but more importantly maintainability.

Created: 4/4/2026Updated: 4/4/2026