aws / aws/sagemaker-python-sdk

CodeArtifact login support for FrameworkProcessor Jobs

Cerrado
#4,144 0 comentarios 0 reacciones 0 asignados Ver en GitHub
component: training type: feature request
Lenguaje dominante
Python
Estrellas
2.3k
Forks
1.3k
Merge medio
1 d 22 h
PR fusionados (30 d)
35

Descripción

**Describe the feature you'd like**
We use private Python packages hosted in CodeArtifact for some of our common preprocessing and training code. However, currently there is no way to have a processing job authenticate to CodeArtifact before it installs Python requirements from a `requirements.txt` file.

Currently, we get an authorization token from CodeArtifact, write it to a file, and include that file with our processing/training code that gets pushed to S3 and pulled down in the jobs. This is really just a cleaner way to implement the same thing.

**How would this feature be used? Please describe.**
The simplest way to implement this would be to borrow code from the [sagemaker-training-toolkit](https://github.com/aws/sagemaker-training-toolkit/blob/master/src/sagemaker_training/modules.py#L186-L235) package to parse a CodeArtifact repo URL from a CodeArtifact repo ARN, and then simply write this token as an index into the `runproc.sh` file that gets generated in the [_generate_framework_script()](https://github.com/akuma12/sagemaker-python-sdk/blob/master/src/sagemaker/processing.py#L1830-L1864) method of the FrameworkProcessor class.

Adding an optional `codeartifact_repo_arn` parameter to the `.run()` method would allow it to be passed to the `_generate_framework_script()` method.

Guía de contribución

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Línea de trabajo

Comienza con src/sagemaker/processing.py, especialmente FrameworkProcessor.run() y _generate_framework_script(), y luego compara el análisis de la URL de CodeArtifact en sagemaker-training-toolkit modules.py. Sigue cómo runproc.sh instala requirements.txt. Se considera terminado cuando se pueda pasar un ARN de repositorio opcional y el job generado se autentique en CodeArtifact antes de instalar paquetes privados.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
aws, python
Área
cloud, machine-learning
Tipo de issue
Nueva funcionalidad
Dificultad
3/5
Tiempo estimado
1-2 días
Estado de actividad
Estancado
Claridad
Bien especificado
Aptitud para principiantes
38/100

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