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Scalable training platform with Amazon SageMaker HyperPod for innovation: a video generation case study

AWS Machine Learning

The method is trained on a dataset of video clips and achieves state-of-the-art results on fashion video and human dance synthesis benchmarks, demonstrating its ability to animate arbitrary characters while maintaining appearance consistency and temporal stability. The implementation of AnimateAnyone can be found in this repository.

Scripts 121
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Integrate HyperPod clusters with Active Directory for seamless multi-user login

AWS Machine Learning

To achieve this multi-user environment, you can take advantage of Linux’s user and group mechanism and statically create multiple users on each instance through lifecycle scripts. For more details on how to create HyperPod clusters, refer to Getting started with SageMaker HyperPod and the HyperPod workshop. strip(), pysss.password().AES_256))"

Scripts 114
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Build high-performance ML models using PyTorch 2.0 on AWS – Part 1

AWS Machine Learning

The following figure shows a performance benchmark of fine-tuning a RoBERTa model on Amazon EC2 p4d.24xlarge inference with AWS Graviton processors for details on AWS Graviton-based instance inference performance benchmarks for PyTorch 2.0. Run your DLC container with a model training script to fine-tune the RoBERTa model.

Scripts 82
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Train gigantic models with near-linear scaling using sharded data parallelism on Amazon SageMaker

AWS Machine Learning

To get started, follow Modify a PyTorch Training Script to adapt SMPs’ APIs in your training script. In this section, we only call out a few main steps with code snippets from the ready-to-use training script train_gpt_simple.py. The notebook uses the script data_prep_512.py Benchmarking performance. return loss.

Scripts 76
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New performance improvements in Amazon SageMaker model parallel library

AWS Machine Learning

Finally, we’ll benchmark performance of 13B, 50B, and 100B parameter auto-regressive models and wrap up with future work. A ready-to-use training script for GPT-2 model can be found at train_gpt_simple.py. You can find an example in the same training script train_gpt_simple.py. Benchmarking performance. 24xlarge nodes.

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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

Laying the groundwork: Collecting ground truth data The foundation of any successful agent is high-quality ground truth data—the accurate, real-world observations used as reference for benchmarks and evaluating the performance of a model, algorithm, or system. For examples to get started, check out the Amazon Bedrock Agents GitHub repository.

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The Ultimate Guide to Call Center Training

Fonolo

Include workshops and group activities as much as possible! As part of your formal training plan, schedule time to send staff to conventions, classes, and workshops. To demonstrate the practical aspect of your customer profiles, write up role-play scripts for each profile and have staff act them out. Act it out.