Comments (6)
agcopenhaver
commented on August 16, 2024
Bump
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rluria14
commented on August 16, 2024
Bump 2
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laurenyu
commented on August 16, 2024
sorry for the delayed response. what is your use case for wanting this released as a Python library? (we don't have PyPI releases for any of the sagemaker-*-container repositories.)
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rluria14
commented on August 16, 2024
@laurenyu thanks for the response. i would like to add it to conda-forge and need a tarball in order to proceed. could you generate a release on the github page?
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rluria14
commented on August 16, 2024
@laurenyu please let me know when you get a chance. thanks!
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nadiaya
commented on August 16, 2024
We have added two github releases: https://github.com/aws/sagemaker-pytorch-serving-container/releases
And we have released it to pypi:
https://pypi.org/project/sagemaker-pytorch-inference/
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Related Issues (20)
- Serving a model using custom container, instance run of disk HOT 4
- Need for a minimum reproducible example in readme.md
- No model logs from PyTorch 1.10 SageMaker endpoint HOT 2
- Launch TorchServe without repackaging model contents HOT 5
- Batch Inference does not work when using the default handler
- add environment variable "OMP_NUM_THREADS"
- Document how to locally run the container HOT 2
- using cuda enabled pytorch image
- how to use gpu in sagemaker instance HOT 1
- Is this Dockerfile compatible with sagemaker elastic inference
- MMS mode in inference does not support in GPU instance
- [Question] Using model.mar with built-in handler script
- Specify batch size for MME
- Prepend `code_dir` to `sys.path` rather than `append`
- Incorrect reporting of memory utilisation
- Documentation for inference.py `transform_fn`
- Reuse the requirements.txt installation logic from sagemaker-inference-toolkit
- ModuleNotFoundError: Sagemaker only copies entry_point file to /opt/ml/code/ instead of the holy-cloned source code
- Improve debuggability during model load and inference failures
- Zombie process exception HOT 4
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