Published inTowards Data Science·May 21Customizing your Cloud Based Machine Learning Training Environment — Part 2Additional solutions for increasing your development flexibility — This is the second part of a two-part post on the topic of customizing your cloud-based AI model training environment. In the first part, a prerequisite for this part, we introduced the conflict that may arise between the desire to use a pre-built specially-designed training environment and the requirement that…Sagemaker7 min readSagemaker7 min read
Published inTowards Data Science·May 21Customizing Your Cloud Based Machine Learning Training Environment — Part 1How to leverage the power of the cloud without compromising development flexibility — Cloud-based machine learning (ML) services offer a great number of conveniences to the AI developer, perhaps none as important as the access they provide to a wide variety of fully provisioned, fully functional, and fully maintained ML training environments. For example, managed training services, such as Amazon SageMaker and Google…Machine Learning8 min readMachine Learning8 min read
Published inTowards Data Science·May 21Tips and Tricks for Upgrading to PyTorch 2.0What to look out for when moving to the all-new “Compiled Mode” — Pytorch19 min readPytorch19 min read
Published inTowards Data Science·Jan 25Streaming Big Data Files from Cloud StorageMethods for efficient consumption of large files — Working with very large files can pose challenges to application developers related to efficient resource management and runtime performance. Text file editors, for example, can be divided into those that can handle large files, and those that make your CPU choke, make your PC freeze, and make you want to…Big Data13 min readBig Data13 min read
Published inTowards Data Science·Jan 24On the Importance of Compressing Big DataWhy and How to Minimize your Data Storage Footprint — “Data is the new oil”, a phrase attributed to Clive Humby, describes the growing reliance of many modern companies on data for their development and success. Companies are collecting massive amounts of data, to the point where units of measurement such as the petabyte, exabyte, and zettabyte, have replaced the…Cloud Storage15 min readCloud Storage15 min read
Published inTowards Data Science·Nov 28, 2022A First Look at AWS TrainiumHarnessing the Power of Dedicated DNN Training Chips — Part 3 — After much anticipation, Amazon EC2 TRN1 instances are now available for public use. Based on the AWS Trainium chip, the TRN1 instance type was specifically designed for accelerating deep learning training. This post is the third part of a series on training deep learning models on dedicated AI chips. In…Deep Learning16 min readDeep Learning16 min read
Published inTowards Data Science·Nov 17, 2022How to Run Machine Learning Hyperparameter Optimization in the Cloud — Part 3Cloud Tuning by Parallelizing Managed Training Jobs — This the final part of a three-part post on the topic of hyperparameter tuning (HPT) machine learning models in the cloud. In the first part we set the stage by introducing the problem and defining a toy model and a training function for our tuning demonstrations. In the second part…Deep Learning8 min readDeep Learning8 min read
Published inTowards Data Science·Nov 17, 2022How to Run Machine Learning Hyperparameter Optimization in the Cloud — Part 2Two Methods for Tuning on a Dedicated Ray Cluster — This is the second part of a three-part post on the topic of hyperparameter tuning (HPT) machine learning models in the cloud. In part 1 we set the stage by introducing the problem and defining a toy model that we will use in our tuning demonstrations. …Deep Learning7 min readDeep Learning7 min read
Published inTowards Data Science·Nov 17, 2022How to Run Machine Learning Hyperparameter Optimization in the Cloud — Part 1Four Alternatives for Cloud Based Tuning — Introduction This is the first of a three-part post in which we will explore some of the ways in which hyperparameter optimization can be performed in a cloud machine learning (ML) environment. Hyperparameter Tuning (HPT): HPT or hyperparameter optimization (HPO) refers to the art of finding the most optimal set of…Hyperparameter Tuning9 min readHyperparameter Tuning9 min read
Published inTowards Data Science·Oct 17, 2022How to Accelerate your PyTorch GPU Training with XLAThe Power of PyTorch/XLA and how Amazon SageMaker Training Compiler Simplifies its use — Deep Learning11 min readDeep Learning11 min read
