aws / aws/sagemaker-python-sdk
Native GRPC Support for Endpoints
- Langage dominant
- Python
- Étoiles
- 2.3k
- Forks
- 1.3k
- Merge moyen
- 1 j 22 h
- PR mergées (30 j)
- 35
Description
**Describe the feature you'd like**
Add support for making `GRPC` calls to sagemaker endpoints for model serving/batch transform. Currently, only `REST` is supported.
**How would this feature be used? Please describe.**
If one plans to deploy a model in an environment where clients make GRPC calls, it is currently not possible to use sagemaker to deploy models. REST is not an option in some deployment environments.
**Describe alternatives you've considered**
I understand there is the option to serialize the request/response data into recordio/protobuf and make a REST call but if clients are expecting to make GRPC calls, this is not an option.
If production clients will make GRPC calls it is not really worth the effort to implement a REST `serve` entrypoint in images, as it won't match how the model is called in production. This results in two different images and custom frameworks around serving/batch transformation. It would greatly improve the experience if data scientists are able to test a model with GRPC then directly deploy it via sagemaker.
**Additional context**
Getting a feeler out here for if this may be eventually supported. Is this something that could potentially be implemented in the sdk alone or would it require changes to proprietary apis? Basically, wondering if this is something that could be implemented by the community.
Guide de contribution
Ouvrir le guide de contribution
Piste de recherche
L’issue n’identifie pas les fichiers du SDK, les tests ni les points d’entrée pour l’endpoint SageMaker et les chemins batch-transform. Déterminez d’abord si la prise en charge native de GRPC peut être implémentée dans le Python SDK ou si elle nécessite des modifications de l’API propriétaire SageMaker ; le travail sera considéré comme terminé lorsque les clients pourront effectuer des appels GRPC pour le serving des modèles et la transformation par lots.
Rédigé par le modèle d'indexation à partir du texte de l'issue.
Évaluation
- Stack technique
- aws, grpc, python
- Domaine
- api, cloud, machine-learning
- Type d'issue
- Fonctionnalité
- Difficulté
- 5/5
- Temps estimé
- Plus d'une semaine
- Activité
- À l'abandon
- Clarté
- À clarifier
- Accessibilité débutants
- 20/100