Comments (4)
Sorry for the delay in response.
Unfortunately, requirements.txt support is available only for PyTorch 1.3.1+ with the serving images. Is there a reason you need 1.2.0 specifically?
from sagemaker-pytorch-inference-toolkit.
It is also supported in 1.1.0. I need 1.2.0 because pytorch 1.3.1 gives a higher latency for my model. Can you point me to the code section where this part is implemented? I will look into incorporating this.
from sagemaker-pytorch-inference-toolkit.
you will need to rebuild the Docker image with the updated source in https://github.com/aws/sagemaker-inference-toolkit, which is a dependency of this repository.
alternatively, you could try something like
import sys
sys.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
in your inference script.
I've also reached out to the team responsible for building the PyTorch images to see if they would be able to update the 1.2.0 images.
from sagemaker-pytorch-inference-toolkit.
Hi @alohia, we recently release the PyTorch 1.4.0 containers. Does that work with your inference workflow?
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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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