article thumbnail

Fine-tune and deploy a summarizer model using the Hugging Face Amazon SageMaker containers bringing your own script

AWS Machine Learning

Pre-trained models and fully managed NLP services have democratised access and adoption of NLP. Amazon Comprehend is a fully managed service that can perform NLP tasks like custom entity recognition, topic modelling, sentiment analysis and more to extract insights from data without the need of any prior ML experience.

Scripts 81
article thumbnail

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning

We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw. For example, to use the RedPajama dataset, use the following command: wget [link] python nemo/scripts/nlp_language_modeling/preprocess_data_for_megatron.py

Scripts 96
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Store your Snowflake account credentials in AWS Secrets Manager.

Scripts 103
article thumbnail

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Provision KMS keys in dev and prod accounts Our first step is to create AWS Key Management Service (AWS KMS) keys in the dev and prod accounts. Create a KMS key in the prod account Go through the same steps in the previous section to create a customer managed KMS key in the prod account. Choose Create key.

Scripts 98
article thumbnail

­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

As feature data grows in size and complexity, data scientists need to be able to efficiently query these feature stores to extract datasets for experimentation, model training, and batch scoring. SageMaker Feature Store consists of an online and an offline mode for managing features.

Scripts 73
article thumbnail

Host the Spark UI on Amazon SageMaker Studio

AWS Machine Learning

Amazon SageMaker offers several ways to run distributed data processing jobs with Apache Spark, a popular distributed computing framework for big data processing. With interactive sessions, you can choose Apache Spark or Ray to easily process large datasets, without worrying about cluster management.

Scripts 67
article thumbnail

Frugality meets Accuracy: Cost-efficient training of GPT NeoX and Pythia models with AWS Trainium

AWS Machine Learning

Training steps To run the training, we use SLURM managed multi-node Amazon Elastic Compute Cloud ( Amazon EC2 ) Trn1 cluster, with each node containing a trn1.32xl instance. To get started with managed AWS Trainium on Amazon SageMaker , see Train your ML Models with AWS Trainium and Amazon SageMaker. He founded StylingAI Inc.,

Scripts 95