A Library for Training and Deploying ML Models on Amazon SageMaker

SageMaker Python SDK

SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.

With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well.

Installing the SageMaker Python SDK

The SageMaker Python SDK is built to PyPI and can be installed with pip as follows:

pip install sagemaker

You can install from source by cloning this repository and running a pip install command in the root directory of the repository:

git clone https://github.com/aws/sagemaker-python-sdk.git
cd sagemaker-python-sdk
pip install .

Supported Operating Systems

SageMaker Python SDK supports Unix/Linux and Mac.

Supported Python Versions

SageMaker Python SDK is tested on:

• Python 3.7
• Python 3.8
• Python 3.9
• Python 3.10

AWS Permissions

As a managed service, Amazon SageMaker performs operations on your behalf on the AWS hardware that is managed by Amazon SageMaker. Amazon SageMaker can perform only operations that the user permits. You can read more about which permissions are necessary in the AWS Documentation.

The SageMaker Python SDK should not require any additional permissions aside from what is required for using SageMaker. However, if you are using an IAM role with a path in it, you should grant permission for iam:GetRole.

Licensing

SageMaker Python SDK is licensed under the Apache 2.0 License. It is copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. The license is available at: http://aws.amazon.com/apache2.0/

Running tests

SageMaker Python SDK has unit tests and integration tests.

You can install the libraries needed to run the tests by running pip install --upgrade .[test] or, for Zsh users: pip install --upgrade .\[test\]

Unit tests

We run unit tests with tox, which is a program that lets you run unit tests for multiple Python versions, and also make sure the code fits our style guidelines. We run tox with all of our supported Python versions, so to run unit tests with the same configuration we do, you need to have interpreters for those Python versions installed.

To run the unit tests with tox, run:

tox tests/unit

Integrations tests

To run the integration tests, the following prerequisites must be met

1. AWS account credentials are available in the environment for the boto3 client to use.
2. The AWS account has an IAM role named SageMakerRole. It should have the AmazonSageMakerFullAccess policy attached as well as a policy with the necessary permissions to use Elastic Inference.

We recommend selectively running just those integration tests you'd like to run. You can filter by individual test function names with:

tox -- -k 'test_i_care_about'

You can also run all of the integration tests by running the following command, which runs them in sequence, which may take a while:

tox -- tests/integ

You can also run them in parallel:

tox -- -n auto tests/integ

Git Hooks

to enable all git hooks in the .githooks directory, run these commands in the repository directory:

find .git/hooks -type l -exec rm {} \;
find .githooks -type f -exec ln -sf ../../{} .git/hooks/ \;

To enable an individual git hook, simply move it from the .githooks/ directory to the .git/hooks/ directory.

Building Sphinx docs

Setup a Python environment, and install the dependencies listed in doc/requirements.txt:

# conda
conda create -n sagemaker python=3.7
conda activate sagemaker
conda install sphinx=3.1.1 sphinx_rtd_theme=0.5.0

# pip
pip install -r doc/requirements.txt

Clone/fork the repo, and install your local version:

pip install --upgrade .

Then cd into the sagemaker-python-sdk/doc directory and run:

make html

You can edit the templates for any of the pages in the docs by editing the .rst files in the doc directory and then running make html again.

Preview the site with a Python web server:

cd _build/html
python -m http.server 8000

View the website by visiting http://localhost:8000

SageMaker SparkML Serving

With SageMaker SparkML Serving, you can now perform predictions against a SparkML Model in SageMaker. In order to host a SparkML model in SageMaker, it should be serialized with MLeap library.

For more information on MLeap, see https://github.com/combust/mleap .

Supported major version of Spark: 3.3 (MLeap version - 0.20.0)

Here is an example on how to create an instance of SparkMLModel class and use deploy() method to create an endpoint which can be used to perform prediction against your trained SparkML Model.

sparkml_model = SparkMLModel(model_data='s3://path/to/model.tar.gz', env={'SAGEMAKER_SPARKML_SCHEMA': schema})
model_name = 'sparkml-model'
endpoint_name = 'sparkml-endpoint'
predictor = sparkml_model.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge', endpoint_name=endpoint_name)

Once the model is deployed, we can invoke the endpoint with a CSV payload like this:

payload = 'field_1,field_2,field_3,field_4,field_5'
predictor.predict(payload)

For more information about the different content-type and Accept formats as well as the structure of the schema that SageMaker SparkML Serving recognizes, please see SageMaker SparkML Serving Container.

For detailed documentation, including the API reference, see Read the Docs.

Download Details:

Author: aws
Source Code: https://github.com/aws/sagemaker-python-sdk 
License: Apache-2.0 license

#machinelearning #python #aws #mxnet #tensorflow 

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A library for training and deploying machine learning models on Amazon SageMaker

SageMaker Python SDK

SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.

With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well.

View Documentation View Github

Installing the SageMaker Python SDK

The SageMaker Python SDK is built to PyPI and can be installed with pip as follows:

pip install sagemaker

You can install from source by cloning this repository and running a pip install command in the root directory of the repository:

git clone https://github.com/aws/sagemaker-python-sdk.git
cd sagemaker-python-sdk
pip install .

#machine learning #models #aws #tensorflow #a library for training and deploying machine learning models on amazon sagemaker #amazon sagemaker

Aws Sagemaker: Train, Test & Deploy Models

AWS Sagemaker is a Machine Learning end to end service that solves the problem of training, tuning, and deploying Machine Learning models.
It provides us with a Jupyter Notebook instance that runs on a virtual machine hosted by Amazon. We can perform all our data analysis and preprocessing in the notebook along with model development and validation as we do on our local machines. Furthermore, it enables us to deploy our model by creating an API endpoint that can be accessed through any web app to obtain results.
Now not wasting any more time, Let’s get started.

#sagemaker #train #test #deploy models

Workshop Alert! Deep Learning Model Deployment & Management

The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.

Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.

Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.

In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.

#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop

Deploy your own model with AWS Sagemaker

Sagemaker is a fully managed machine learning service,which provides you support to build models using built-in-algorithms, with native support for bring-your-own-algorithms and ML frameworks such as Apache MXNet, PyTorch, SparkML, Tensorflow and Scikit-Learn.

In this post, I’ll walk through how to deploy a machine learning model which is built and trained locally using custom algorithm, as a REST API using Sagemaker, lambda and docker.

I’ll break the process in to 5 steps

• Step 1: Building the model and saving the artifacts.
• Step 2: Defining the server and Inference code.
• Step 3: Build a Sagemaker Container.
• Step 4: Creating Model, Endpoint Configuration, and Endpoint.
• Step 5: Invoking the model using a lambda with API Gateway trigger.

Step 1: Building the model and saving the artifacts.

Build the model and serialize the object, which is used for prediction. In this post, I’m using simple Linear Regression, one independent variable.

Once you serialize the python object to pickle file, save that artifact(pickle file) in tar.gz format and upload in a S3 bucket.

Step 2: Defining the server and inference code.

When an endpoint is invoked Sagemaker interacts with the Docker container, which runs the inference code for hosting services and processes the request and returns the response.Containers need to implement a web server that responds to /invocations and /ping on port 8080.

Inference code in the container will receive GET requests from the infrastructure and it should respond to Sagemaker with an HTTP 200 status code and an empty body, which indicates that the container is ready to accept inference requests at invocations endpoint.

And invocations is the endpoint that receives POST requests and responds according to the format specified in the algorithm

To make the model REST API, you need Flask, which is WSGI(Web Server Gateway Interface) application framework, Gunicorn the WSGI server, and **nginx **the reverse-proxy and load balancer.

Code : https://github.com/NareshReddyy/Sagemaker_deploy_own_model.git

Step 3: Sagemaker Container.

Sagemaker uses docker containers extensively. You can put your scripts, algorithms, and inference code for your models in the containers, which includes the runtime, system tools, libraries and other code to deploy your models, which provides flexibility to run your own model.

You create Docker containers from images that are saved in a repository. You build the images from scripted instructions provided in a Dockerfile.

#mlops #sagemaker #aws-sagemaker #deploy-model #sagemaker-rest-api

Deploying trained ML model on Heroku using Flask | End-to-End ML Project Tutorial

The series will cover everything from Data Collection to Model Deployment using Flask Web framework on Heroku!

GitHub Repository: https://github.com/dswh/fuel-consumpt…

Subscribe: https://www.youtube.com/c/DataSciencewithHarshit/featured

#ml #heroku #ml #deploying