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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 118
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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. Huan works on AI and Data Science. He has published more than 180 peer-reviewed papers in leading conferences and journals.

Scripts 117
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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

AWS Machine Learning

We use the custom terminology dictionary to compile frequently used terms within video transcription scripts. Yaoqi Zhang is a Senior Big Data Engineer at Mission Cloud. Adrian Martin is a Big Data/Machine Learning Lead Engineer at Mission Cloud. Here’s an example. Cristian Torres is a Sr.

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Run text generation with GPT and Bloom models on Amazon SageMaker JumpStart

AWS Machine Learning

We first fetch any additional packages, as well as scripts to handle training and inference for the selected task. You can use any number of models pre-trained on the same task with a single inference script. Finally, the pre-trained model artifacts are separately fetched with model_uris , which provides flexibility to the platform.

APIs 92
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Generate images from text with the stable diffusion model on Amazon SageMaker JumpStart

AWS Machine Learning

We first fetch any additional packages, as well as scripts to handle training and inference for the selected task. You can use any number of models pre-trained on the same task with a single inference script. Finally, the pre-trained model artifacts are separately fetched with model_uris , which provides flexibility to the platform.

APIs 97
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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning

To create these packages, run the following script found in the root directory: /build_mlops_pkg.sh He entered the big data space in 2013 and continues to explore that area. He is actively working on projects in the ML space and has presented at numerous conferences, including Strata and GlueCon.

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Run image segmentation with Amazon SageMaker JumpStart

AWS Machine Learning

We fetch any additional packages, as well as scripts to handle training and inference for the selected task. You can use any number of models pre-trained for the same task with a single training or inference script. Fine-tune the pre-trained model.

APIs 85