AI News, alexandraj777/machine-learning-samples forked from aws-samples/machine-learning-samples

alexandraj777/machine-learning-samples forked from aws-samples/machine-learning-samples

This sample code builds a hyperparameter optimization pipeline for Amazon Machine Learning using the latest AWS SDK for Python (Boto 3).

If want to use SigOpt to optimize your hyperparameters faster and better than tuning by hand, sign up for a free trial on our website and grab your API token from your user profile.

This script relies on a manually specified list of hyperparameters to tune the hyperparameters of a linear binary classification model.

Many machine learning models have exposed parameters, commonly known as hyperparameters (Amazon ML sometimes calls them training parameters), that you choose values for before model training begins.

You'll notice that learning rate is also a hyperparameter of your model, but Amazon ML is automatically selecting a value for it based on your data, so we can't tune it in this example.

In keeping with best practices for hyperparameter optimization, and to prevent over-fitting of the model, this example actually maximizes the average of k-fold cross validated AUC metrics.

This description skips over the details of how cross validation is performed with Amazon ML, because it is described much better in the README for the K-Fold Cross Validation example that formed the basis for this code sample.

To perform hyperparameter optimization the scripts iteratively choose new values of regularization_type and regularization_amount, evaluates a model with these new hyperparameters for every fold of the data, averages the AUC metrics, and records the performance of the assignments.

Each time the script evaluates a model on a new set of hyperparameters it creates k machine learning models, one for each train datasource, and k evaluations, one for each evaluate datasource, on Amazon ML.

API calls via the python SDK will return quickly so that you can build a datasource, machine learning model, and an evaluation while the datasource is still pending!

Amazon SageMaker Automatic Model Tuning: Using Machine Learning for Machine Learning

This feature allows developers and data scientists to save significant time and effort in training and tuning their machine learning models.

A Hyperparameter Tuning job launches multiple training jobs, with different hyperparameter combinations, based on the results of completed training jobs.

developer’s typical machine learning process comprises 4 steps: exploratory data analysis (EDA), model design, model training, and model evaluation.

SageMaker already makes each of those steps easy with access to powerful Jupyter notebook instances, built-in algorithms, and model training within the service.

Focusing on the training portion of the process, we typically work with data and feed it into a model where we evaluate the model’s prediction against our expected result.

We keep a portion of our overall input data, the evaluation data, away from the training data used to train the model.

In many cases after we’ve chosen an algorithm or built a custom model, we will need to search the space of possible hyperparameter configurations of that algorithm for the best results for our input data.

A user only needs to select the hyperparameters to tune, a range for each parameter to explore, and the total number of training jobs to budget.

There are few types of parameters: Now that we have our ranges defined we want to define our success metric and a regular expression for finding that metric in the training job logs.

Hopping back over to our notebook instance, we have a handy analytics object from tuner.analytics() that we can use to visualize the results of the training with some bokeh plots.

To take advantage of automatic model tuning there are really only a few things users have to define: the hyperparameter ranges, the success metric and a regex to find it, the number of jobs to run in parallel, and the maximum number of jobs to run.

Increasing max_parallel_jobs will cause the tuning job to finish much faster but a lower parallelism will generally provide a slightly better final result.

Performing Hyperparameter Optimization with Amazon Machine Learning

This sample code builds a hyperparameter optimization pipeline for Amazon Machine Learning using the latest AWS SDK for Python (Boto 3), and SigOpt's API for Bayesian optimization.

To use SigOpt to optimize your hyperparameters faster and better than tuning by hand, sign up for a free trial on our website and grab your API token from the API tokens page.

Get the samples by cloning this repository: The script hyperparameter_optimization.py uses the SigOpt API to optimally suggest hyperparameters of a linear binary classification model.

Many machine learning models have exposed parameters, commonly known as hyperparameters (Amazon ML sometimes calls them training parameters), that you choose values for before model training begins.

You'll notice that learning rate is also a hyperparameter of your model, but Amazon ML is automatically selecting a value for it based on your data, so we can't tune it in this example.

In keeping with best practices for hyperparameter optimization, and to prevent over-fitting of the model, this example actually maximizes the average of k-fold cross validated AUC metrics.

To perform the hyperparameter optimization the scripts iteratively choose new values of regularization_type and regularization_amount, evaluate a model with these new hyperparameters for every fold of the data, average the AUC metrics, and record the performance of the assignments.

Each time the script evaluates a model on a new set of hyperparameters is creates k machine learning models, one for each train datasource, and k evaluations, one for each evaluate datasource, on Amazon ML.

Since your machine is not doing the heavy computation of training and testing the model, it has great opportunities for parallelization, splitting up the optimization loop between n different threads or processes.

All Things Distributed

Applications based on machine learning (ML) can provide tremendous business value.

Though the AWS Cloud gives you access to the storage and processing power required for ML, the process for building, training, and deploying ML models has unique challenges that often block successful use of this powerful new technology.

After this, there is often a long process of training that includes tuning the knobs and levers, called hyperparameters, that control the different aspects of the training algorithm.

At Amazon Web Services, we've committed to helping you unlock the value of your data through ML, through a set of supporting tools and resources that improve the ML model development experience.

This new managed service enables data scientists and developers to quickly and easily build, train, and deploy ML models without getting mired in the challenges that slow this process down today.

Amazon SageMaker provides the following features: In the hosted notebook environment, Amazon SageMaker takes care of establishing secure network connections in your VPC and launching an ML instance.

This development workspace also comes pre-loaded with the necessary Python libraries and CUDA drivers, attaches an Amazon EBS volume to automatically persist notebook files, and installs TensorFlow, Apache MXNet, and Keras deep learning frameworks.

With high-performance algorithms, distributed computing, managed infrastructure, and hyperparameter optimization, Amazon SageMaker drastically decreases the training time and overall cost of building production systems.

Amazon SageMaker takes care of launching the instances, deploying the model, and setting up the HTTPS endpoint for your application to achieve low latency / high throughput prediction.

In production, Amazon SageMaker manages the compute infrastructure to perform health checks, apply security patches, and conduct other routine maintenance, all with built-in Amazon CloudWatch monitoring and logging.

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