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 95
article thumbnail

Contact Center Trends 2024: Our Predictions

Fonolo

We live in an era of big data, AI, and automation, and the trends that matter in CX this year begin with the abilities – and pain points – ushered in by this technology. For example, big data makes things like hyper-personalized customer service possible, but it also puts enormous stress on data security.

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

We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket. 1 with the following additions: The Snowflake Connector for Python to download the data from the Snowflake table to the training instance.

Scripts 102
article thumbnail

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

AWS Machine Learning

Under Advanced Project Options , for Definition , select Pipeline script from SCM. For Script Path , enter Jenkinsfile. upload_file("pipelines/train/scripts/raw_preprocess.py","mammography-severity-model/scripts/raw_preprocess.py") s3_client.Bucket(default_bucket).upload_file("pipelines/train/scripts/evaluate_model.py","mammography-severity-model/scripts/evaluate_model.py")

Scripts 97
article thumbnail

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

AWS Machine Learning

SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table.

Scripts 72
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. install-scripts chmod +x install-history-server.sh./install-history-server.sh script and attach it to an existing SageMaker Studio domain.

Scripts 66
article thumbnail

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

AWS Machine Learning

After downloading the latest Neuron NeMo package, use the provided neox and pythia pre-training and fine-tuning scripts with optimized hyper-parameters and execute the following for a four node training. Before joining AWS, he worked at Baidu research as a distinguished scientist and the head of Baidu Big Data Laboratory.

Scripts 94