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Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA

AWS Machine Learning Blog

Although QLoRA helps optimize memory during fine-tuning, we will use Amazon SageMaker Training to spin up a resilient training cluster, manage orchestration, and monitor the cluster for failures. To take complete advantage of this multi-GPU cluster, we use the recent support of QLoRA and PyTorch FSDP. 24xlarge compute instance.

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

AWS Machine Learning Blog

Walkthrough Download the pre-tokenized Wikipedia dataset as shown: export DATA_DIR=~/examples_datasets/gpt2 mkdir -p ${DATA_DIR} && cd ${DATA_DIR} wget [link] wget [link] aws s3 cp s3://neuron-s3/training_datasets/gpt/wikipedia/my-gpt2_text_document.bin. Each trn1.32xl has 16 accelerators with two workers per accelerator.

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Develop and train large models cost-efficiently with Metaflow and AWS Trainium

AWS Machine Learning Blog

Besides easy access, using Trainium with Metaflow brings a few additional benefits: Infrastructure accessibility Metaflow is known for its developer-friendly APIs that allow ML/AI developers to focus on developing models and applications, and not worry about infrastructure. Complete the following steps: Download the CloudFormation template.

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Citus 12: Schema-based sharding for PostgreSQL

Hacker News

What if you could automatically shard your PostgreSQL database across any number of servers and get industry-leading performance at scale without any special data modelling steps? Schema-based sharding has almost no data modelling restrictions or special steps compared to unsharded PostgreSQL.

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Deploy a Hugging Face (PyAnnote) speaker diarization model on Amazon SageMaker as an asynchronous endpoint

AWS Machine Learning Blog

We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud. Create a model function for accessing PyAnnote speaker diarization from Hugging Face You can use the Hugging Face Hub to access the desired pre-trained PyAnnote speaker diarization model.

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Fine-tune Meta Llama 3.1 models using torchtune on Amazon SageMaker

AWS Machine Learning Blog

You could further optimize the time for training in the following graph by using a SageMaker managed warm pool and accessing pre-downloaded models using Amazon Elastic File System (Amazon EFS). Challenges with fine-tuning LLMs Generative AI models offer many promising business use cases. 8b-lora.yaml on an ml.p4d.24xlarge

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Implement a custom AutoML job using pre-selected algorithms in Amazon SageMaker Automatic Model Tuning

AWS Machine Learning Blog

An AutoML tool applies a combination of different algorithms and various preprocessing techniques to your data. For example, it can scale the data, perform univariate feature selection, conduct PCA at different variance threshold levels, and apply clustering.

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