Hacktoberfest 2026: los issues que los mantenedores marcaron para octubre, abiertos y aptos para principiantes. Explorar issues de Hacktoberfest

Consider reorganizing file layout to shorten path lengths

Abierto
#1,032 0 comentarios 0 reacciones 1 asignado Ver en GitHub

@rino20 ya está trabajando en esto.

Desde el 11/1/2023.

Evaluación

Este issue todavía no se ha evaluado.

Descripción

bug

Describe the bug
The current package includes path names upwards of 150 characters such as default_8bit_cluster_preserve_quantize_scheme.py which are in turn relatively short files deep in a hierarchy. On Windows, the default limitation for paths is 260 characters as defined by MAX_PATH. While it is possible to modify this on a per-call basis, by default the Python ecosystem's tooling does not make this process simple for end users and there are a variety of tools still limited to the 260 character MAX_PATH. When you combine the path lengths of this repository with a location in a users' profile such as C:\Users\person-fullname\AppData\Local\Python\environments\deep-learning-environment\Lib\site-packages the limit can be exceeded.

Motivation
I work in a context where users can create environments in arbitrary locations and we want to use this package, the workaround we've found for the time being is to repack this repository as a Python egg and inject that into the environment at runtime, but would prefer to be able to use it as a direct Python dependency. Note that this limitation is specific to Windows, fortunately MacOS defaults to a MAXPATHLEN of 1024 and Linux values are typically 1024 or higher.

System information

TensorFlow version (installed from source or binary): All versions

TensorFlow Model Optimization version (installed from source or binary): All versions, specifically found working with v0.7.3.

Lenguaje dominante
Python
Estrellas
1.6k
Forks
349
Merge medio
3 d 2 h
PR fusionados (30 d)
1

Guía de contribución

Abrir la guía de contribución

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de tensorflow/model-optimization

Todos los issues de tensorflow/model-optimization

Issues similares

Más issues de Python

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.