aws-sdk
amazon-sagemaker-examples
aws-sdk | amazon-sagemaker-examples | |
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10 | 17 | |
64 | 9,512 | |
- | 0.8% | |
3.9 | 9.1 | |
3 months ago | 1 day ago | |
Jupyter Notebook | ||
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
aws-sdk
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AWS SDK v3 + DAX
From this GitHub issue and this one from 2021 it seems like AWS just doesn't care about this - so, asking here if anyone knows of a workaround (other than "just keep using v2"... that's not a long-term solution), or if any AWS devs can shed some light on the situation.
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The Truth About CloudWatch Pricing
AWS SDKs
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Amazon ECS Exec to access your Windows containers on Amazon EC2 and AWS Fargate
Please note that ECS Exec is supported via AWS SDKs, AWS CLI, as well as AWS Copilot. In the future, we will enable this capability in the AWS Console. Also, this feature only supports Linux containers (Windows containers support for ECS Exec is not part of this announcement).
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Using AWS for Text Classification Part-1
Set up the AWS CLI and AWS SDKs.
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Tips for scalable workflows on AWS
One common pattern to integrate with AWS from a workflow job is to call additional services using the AWS CLI. Overall, this works well, but there are a few considerations one should note when doing so. First and foremost, a workflow job needs to know where the AWS CLI installed and how to use it. You can do this by either installing the AWS CLI on the host compute and bind mounting it into the container job, or including the AWS CLI as part of the container image. That said, see my notes above on keeping container images small for associated caveats. Second, while the AWS CLI is great for scripting, for more complex operations direct integration via the AWS SDK is a better fit.
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Waiting for things to happen and paginating responses with boto3
I like how you can imagine what the Python implementation that uses this looks like from this structure. If there are no waiters for what you need, you can create an Issue in the AWS SDK repository because the service teams provide those. I tried that for DynamoDB Streams, and I'm curious to see how long it will take them to add that.
- Will AWS SDKs make Terraform useless in the future?
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Sending Emails with SES, Terraform and TypeScript
Amazon Simple Email Service (SES) is a serverless service for sending emails from your applications. Like other AWS services, you can send emails with SES using the AWS REST API or the AWS SDKs. In this article, I want to look at how to send emails using SES with TypeScript specifically.
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The Evolution of AWS from a Cloud-Native Development Perspective: Serverless, Event-Driven, Developer-Friendly, Sustainable
In 2002, Jeff Bezos's so-called API Mandate forced all Amazon teams to expose their data and functionality through service interfaces. Amazon has built AWS around the same principles: every service is programmatically controllable, from starting a virtual machine to accessing a satellite. While this is an essential property of an effective cloud platform, it is not necessarily developer-friendly. By now, AWS has incrementally and significantly improved in this space. Besides using their APIs, we can control services and infrastructure with a unified Command Line Interface (CLI), Software Development Kits (SDK), CloudFormation, and, since July 2019, the AWS Cloud Development Kit (CDK). The CDK had a massive impact on developer productivity and satisfaction. While teams could already access and control services and infrastructure using the AWS SDK and their favorite programming language, infrastructure was primarily defined using incredible amounts of mostly punctuation marks and whitespace, also known as YAML or JSON. CDK — initially only flavored TypeScript and Python — finally gave developers an AWS-native means to define Infrastructure as actual Code. Since its introduction, AWS has added support for more languages, like Java, C#, and Go.
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AWS SSO integration with G suite
Yes, no AWS api to create users/groups: https://github.com/aws/aws-sdk/issues/25
amazon-sagemaker-examples
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Thesis Project Help Using SageMaker Free Tier
I need to use AWS Sagemaker (required, can't use easier services) and my adviser gave me this document to start with: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_langchain_jumpstart.ipynb
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Sagemaker step scaling policy
I'm trying to define a step scaling policy for my sagemaker realtime endpoint, based on this example notebook. I understand that the step scaling policy defines thresholds to provision a different amount of instances, but I am confused because it doesn't seem to specify the metrics to track.
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Working On My Own Generative AI App, Taking ~10 sec to generate image.
Yeah man: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_to_image/Amazon_JumpStart_Text_To_Image.ipynb
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Study Plan to pass exam AWS Machine Learning Specialty exam with tips and advice
It's time to get your hands dirty by solving some ML Use Cases of your own from AWS SageMaker Use Cases repo.
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Using AWS for Text Classification Part-1
Additionally, you can easily deploy pretrained fastText models on their own to live SageMaker endpoints to compute embedding vectors on the fly for use in relevant word-level tasks. See the following GitHub example for more details.
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[D] How to monitor NLP and Object Detection models on AWS Sagemaker?
We are kind of boxed into using Sagemaker at our organization and we need to do a POC for Sagemaker's model monitoring. We noticed that Sagemaker monitoring works best with models that use tabular data/features. There are a lot of example notebooks that demonstrate model monitoring capabilities, but all of the examples are based on tabular data. We are trying to apply Sagemaker's model monitoring and gather metrics from Data Quality, Model Quality, Bias Drift, Feature Attribution Drift, and Explainability and then push those metrics into CloudWatch, similar to what was done in these notebooks: https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker_model_monitor .
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Migrate local Data Science workspaces to SageMaker Studio
Amazon SageMaker provides XGBoost as a built-in algorithm and data science team decided to use it and re-train the model. So, data scientists just need to call built-in version and provide path to data on S3, more detailed description can be found in documentation. Example notebook can be found here.
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What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
Amazon SageMaker provides four new tabular data modeling algorithms: LightGBM, CatBoost, AutoGluon-Tabular and TabTransformer. These popular, state-of-the-art algorithms can be used for both tabular classification and regression tasks. They are available through the SageMaker JumpStart UI inside of SageMaker Studio, as well as through python code using SageMaker Python SDK. To learn how to use these algorithms, you can find SageMaker example notebooks below:
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How InfoJobs (Adevinta) improves NLP model prediction performance with AWS Inferentia and Amazon SageMaker
In this section, we go through an example in which we show you how to compile a BERT model with Neo for AWS Inferentia. We then deploy that model to a SageMaker endpoint. You can find a sample notebook describing the whole process in detail on GitHub.
- amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
What are some alternatives?
botocore - The low-level, core functionality of boto3 and the AWS CLI.
aws-lambda-docker-serverless-inference - Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
amazonka - A comprehensive Amazon Web Services SDK for Haskell.
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
serverless-haskell - Deploying Haskell applications to AWS Lambda with Serverless
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
aws-route53 - A Haskell AWS Route53 client library
sp-api-sdk - Amazon Selling Partner SPI - PHP SDKs
aws-genomics-workflows - Genomics Workflows on AWS
Popular-RL-Algorithms - PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
aws-kinesis-client - A producer/consumer client library for Kinesis
sagemaker-studio-auto-shutdown-extension