Comments (3)
I resolved 1.4.0 error.
from
COPY mms-entrypoint.py /usr/local/bin/dockerd-entrypoint.py COPY config.properties /home/model-server
to
COPY ./docker/build_artifacts/mms-entrypoint.py /usr/local/bin/dockerd-entrypoint.py COPY ./docker/build_artifacts/config.properties /home/model-server
1.2.0, 1.3.1 are syntax errors.
You will also need to change your PATH as you did in 1.4.0.
from sagemaker-pytorch-inference-toolkit.
sorry to hear that you had trouble building the images, but glad to see that you were able to resolve the issue. Indeed, our documentation is rather out of date, but the expected workflow is to copy the files under docker/build_artifacts
to the location of the Dockerfile you're using to build an image (example).
from sagemaker-pytorch-inference-toolkit.
the Dockerfiles (and image building) have been moved to https://github.com/aws/deep-learning-containers
from sagemaker-pytorch-inference-toolkit.
Related Issues (20)
- How do I access Custom Attributes from the model during inference? HOT 1
- 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
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