I am following this tutorial right here: https://aws.amazon.com/blogs/machine-learning/training-and-deploying-models-using-tensorflow-2-with-the-object-detection-api-on-amazon-sagemaker/ and I am trying to build and push tfrecord-processing docker image by executing following command: !sh ./docker/build_and_push.sh $image_name Everything seems to go fine until very end: Step 6/7 : COPY code /opt/program —> 68bc931b454c Step 7/7 : ENTRYPOINT ["python3", "/opt/program/prepare_data.py"] —> Running in 68fa1cac7cae Removing intermediate container ..
We are currently working on creating a Multi-Arm Bandit model for sign up optimization using the Build Your Own workflow that can be found here (basically substituting the model for our own): https://github.com/aws/amazon-sagemaker-examples/tree/master/advanced_functionality/scikit_bring_your_own Our project directory is set up as: Project Directory The issue is that I added some code including the dataclasses library that ..
Is it possible to deploy a pre-trained AI/ML model on AWS Sagemaker without containerizing it and have an endpoint configured for it so that it can be used by Web/Mobile applications for prediction? If yes then please share your thoughts. Source: Docker..
I’m stuck in a situation where Sagemaker is looking for a docker image in ECS registry that I had to remove, and I can’t figure out how to make it forget about it. I had to rebuild a docker with torchserve for Sagemaker. I removed the old one (let’s call it torchserve-old-name, and uploaded a ..
I think it must be a relatively common use case to load a model and invoke an endpoint to call R’s predict(object, newdata, …) function. I wanted to do this with a custom AWS Sagemaker container, using plumber on the R side. This example gives all the details, I think, and this bit of documentation ..
In Amazon SageMaker, I’m trying to deploy a custom created Docker container with a Scikit-Learn model, but deploying keeps giving errors. These are my steps: On my local machine created a script (script.py) and splitted training and test data. The script contains a main section, accepts parameters ‘output-train-dir’, ‘model-dir’, ‘train’ and ‘test’, and contains the ..
I have same issue with Dockerfile while train model in sagemaker at that time training failed with following error. AlgorithmError: CannotStartContainerError. Please make sure the container can be run with ‘docker run train’. Please refer SageMaker documentation for details. It is possible that the Dockerfile’s entrypoint is not properly defined, or missing permissions. My Dockerimages ..
I deployed a model to a SageMaker endpoint for inference. My input data is quite large and I would like to send its S3 URI to the endpoint instead, so that I can download it onto the deployed Docker container. Unfortunately, when I try using the SageMaker SDK to download the data, I get this ..
I am working on aws machine learning in linux system,for that I need to run Dockerfile to train sagemaker model. During sagemaker model train , training failed with following error : AlgorithmError: CannotStartContainerError. Please make sure the container can be run with ‘docker run train’. Please refer SageMaker documentation for details. It is possible that ..
I created a custom docker container to run Catboost on Amazon Sagemaker, followed this demo (https://github.com/aws-samples/sagemaker-byo-catboost-container-demo/blob/master/Catboost_container_for_SageMaker.ipynb). I now want to do hyperparameter tuning with this custom container, but this is not a built-in or pre-built Sagemaker container, so I am not sure if I could or how to create hyperparameter tuning job on Sagemaker with ..