GitHub
github.com › aws › sagemaker-python-sdk
GitHub - aws/sagemaker-python-sdk: A library for training and deploying machine learning models on Amazon SageMaker
A library for training and deploying machine learning models on Amazon SageMaker - aws/sagemaker-python-sdk
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GitHub
github.com › aws › sagemaker-core
GitHub - aws/sagemaker-core
Welcome to the sagemaker-core Python SDK, an SDK designed to provide an object-oriented interface for interacting with Amazon SageMaker resources. It offers full parity with SageMaker APIs, allowing developers to leverage all SageMaker capabilities ...
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Using SageMaker SDK to deploy a open source xgboost model locally
Yeah, that was the issue. Actually, setting sagemaker_session = None worked. More on this issue here: https://github.com/aws/sagemaker-python-sdk/issues/929 More on repost.aws
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August 22, 2021SageMaker Endpoint Debugging
If this is an issue with a specific combination that you notice do submit an issue to the public github for the sagemaker sdk (https://github.com/aws/sagemaker-python-sdk). More on repost.aws
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September 29, 2023python - Deploy a custom pipeline using Sagemaker SDK - Stack Overflow
I have been having a hard time to deploy my locally trained SKlearn model (pipeline with custom code + logistic model) to Sagemaker Endpoint. My Pipeline is as follows: All this custom code ( More on stackoverflow.com
Fixing the Sagemaker SDK - And frustrating open source contribution experiences...
My first issue I meekly said there were some issues and outlined them and the maintainer responded that they wrote it the way they wanted it to work and they still don't see any issues.
Typical get lost kid response.
I had hoped for - some welcoming response, hey those are good ideas, yes let's see what we can figure out, maybe enlist me in fixing some other issues in the backlog. But instead I was driven away, basically if I want anything improved I need to fork it and publish it, I can't be wasting time when people are resolute they don't want any input.
More on reddit.com 13
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October 24, 2019Videos
12:21
LLM Fast Model Loader Using SageMaker Python SDK - YouTube
02:23
How To Get Started With Amazon Sagemaker SDK (You Don't Need ...
40:35
Introduction to AWS SageMaker with Python - YouTube
53:32
AWS Sagemaker tutorial | Build and deploy a Machine Learning API ...
MOSTLY AI SDK on AWS SageMaker Unified Studio
SageMaker Studio: ML Development Overview - AWS
SageMaker
sagemaker.readthedocs.io
Amazon SageMaker Python SDK — sagemaker 2.254.1 ...
Here you’ll find an overview and API documentation for SageMaker Python SDK. The project homepage is in Github: https://github.com/aws/sagemaker-python-sdk, where you can find the SDK source and installation instructions for the library.
GitHub
github.com › aws-samples › amazon-sagemaker-local-mode
GitHub - aws-samples/amazon-sagemaker-local-mode: Amazon SageMaker Local Mode Examples
The local mode in the Amazon SageMaker Python SDK can emulate CPU (single and multi-instance) and GPU (single instance) SageMaker training jobs by changing a single argument in the TensorFlow, PyTorch or MXNet estimators. To do this, it uses Docker compose and NVIDIA Docker.
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AWS
aws.amazon.com › blogs › machine-learning › git-integration-now-available-for-amazon-sagemaker-python-sdk
Git integration now available for the Amazon SageMaker Python SDK | Artificial Intelligence
January 6, 2020 - Git integration is now available in the Amazon SageMaker Python SDK. You no longer have to download scripts from a Git repository for training jobs and hosting models. With this new feature, you can use training scripts stored in Git repos directly ...
GitHub
github.com › aws › sagemaker-experiments
GitHub - aws/sagemaker-experiments: Experiment tracking and metric logging for Amazon SageMaker notebooks and model training.
SageMaker Experiments is an AWS service for tracking machine learning Experiments. The SageMaker Experiments Python SDK is a high-level interface to this service that helps you track Experiment information using Python.
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Go Packages
pkg.go.dev › github.com › aws › aws-sdk-go-v2 › service › sagemaker
sagemaker package - github.com/aws/aws-sdk-go-v2/service/sagemaker - Go Packages
October 31, 2025 - github.com/aws/aws-sdk-go-v2 · Open Source Insights · Package sagemaker provides the API client, operations, and parameter types for Amazon SageMaker Service. Provides APIs for creating and managing SageMaker resources. Other Resources: SageMaker Developer Guide ·
GitHub
github.com › aws › sagemaker-spark
GitHub - aws/sagemaker-spark: A Spark library for Amazon SageMaker.
See the sagemaker-pyspark-sdk for more on installing and running SageMaker PySpark.
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GitHub
github.com › aws › amazon-sagemaker-examples
GitHub - aws/amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Today, Amazon SageMaker is excited to announce the release of SageMaker-Core, a new Python SDK that provides an object-oriented interface for interacting with SageMaker resources such as TrainingJob, Model, and Endpoint. This SDK introduces the resource chaining feature, allowing developers to pass resource objects as parameters, eliminating manual parameter specification and simplifying code management.
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GitHub
github.com › aws › sagemaker-jumpstart-industry-pack
GitHub - aws/sagemaker-jumpstart-industry-pack
The SageMaker JumpStart Industry Python SDK is a client library of Amazon SageMaker JumpStart. The library provides tools for feature engineering, training, and deploying industry-focused machine learning models on SageMaker JumpStart.
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GitHub
github.com › aws › sagemaker-spark › blob › master › sagemaker-pyspark-sdk › README.rst
sagemaker-spark/sagemaker-pyspark-sdk/README.rst at master · aws/sagemaker-spark
The SageMaker PySpark SDK provides a pyspark interface to Amazon SageMaker, allowing customers to train using the Spark Estimator API, host their model on Amazon SageMaker, and make predictions with their model using the Spark Transformer API.
Author aws
GitHub
github.com › aws › sagemaker-inference-toolkit
GitHub - aws/sagemaker-inference-toolkit: Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
November 20, 2025 - Serve machine learning models within a Docker container using Amazon SageMaker.
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GitHub
github.com › aws-samples › amazon-sagemaker-bert-pytorch › blob › master › bert-sm-python-SDK.ipynb
amazon-sagemaker-bert-pytorch/bert-sm-python-SDK.ipynb at master · aws-samples/amazon-sagemaker-bert-pytorch
The Amazon SageMaker Python SDK makes it easier to run a PyTorch script in Amazon SageMaker using its PyTorch estimator. After that, we can use the SageMaker Python SDK to deploy the trained model and run predictions.
Author aws-samples
AWS re:Post
repost.aws › questions › QUknUaw-APSsOjAR5nRbw0pA › sagemaker-endpoint-debugging
SageMaker Endpoint Debugging | AWS re:Post
September 29, 2023 - If this is an issue with a specific combination that you notice do submit an issue to the public github for the sagemaker sdk (https://github.com/aws/sagemaker-python-sdk).
Stack Overflow
stackoverflow.com › questions › 75768789 › deploy-a-custom-pipeline-using-sagemaker-sdk
python - Deploy a custom pipeline using Sagemaker SDK - Stack Overflow
Have tried solutions from AWS Sagemaker SKlearn entry point allow multiple script , from AWS Sagemaker SKlearn entry point allow multiple script and from https://github.com/aws/amazon-sagemaker-examples/issues/725 but none seems to work and always give me a
GitHub
github.com › aws › sagemaker-feature-store-spark
GitHub - aws/sagemaker-feature-store-spark
Contribute to aws/sagemaker-feature-store-spark development by creating an account on GitHub.
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Reddit
reddit.com › r/aws › fixing the sagemaker sdk - and frustrating open source contribution experiences...
r/aws on Reddit: Fixing the Sagemaker SDK - And frustrating open source contribution experiences...
October 24, 2019 - We do have some repositories where the code is done in-house and shared to GitHub - sacrificing a good contributor experience for more use of the dev tools the team are using, so I was expecting that to be the case here. I'm happy that it isn't, the Sagemaker SDK has lots of merged contributor PRs.
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Building off the answer given by @mokugo-devops, I was able to link my existing notebook to my GitHub account.
First, I followed the directions posted in the link provided in his answer to set up my GitHub repo with my AWS account on the CLI, then I used the following command to edit my existing notebook:
aws sagemaker update-notebook-instance \
--notebook-instance-name <value> \
--default-code-repository <saved-github-repo-name-in-AWS>
my notebook instance is now linked my GitHub repo.
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You can actually setup your notebook in SageMaker with a link to GitHub.
You can then launch a Notebook instance that will pull in your Notebook, whilst allowing you to commit the changes that you've made and push to your remote repository.
Further instructions: https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html
AWS
docs.aws.amazon.com › amazon sagemaker › developer guide › machine learning environments offered by amazon sagemaker ai › amazon sagemaker notebook instances › git repositories with sagemaker ai notebook instances › add a git repository to your amazon sagemaker ai account
Add a Git repository to your Amazon SageMaker AI account - Amazon SageMaker AI
To manage your GitHub repositories, easily associate them with your notebook instances, and associate credentials for repositories that require authentication, add the repositories as resources in your Amazon SageMaker AI account. You can view a list of repositories that are stored in your account and details about each repository in the SageMaker AI console and by using the API.