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Large language models (LLMs) are reshaping the landscape of artificial intelligence (AI). Their remarkable generative capabilities have led to widespread application across numerous industries, including content generation, sentiment analysis, chatbot creation, and virtual assistant technology. One such model is Llama 2, developed by Meta and made available through AWS. Llama 2 is an auto-regressive language model that utilizes an optimized transformer architecture, designed for both research and commercial use in English. It is available in various parameter sizes—7 billion, 13 billion, and 70 billion—alongside both pre-trained and fine-tuned versions. For further details on Llama 2 on AWS, check out Llama 2 foundation models from Meta now accessible via Amazon SageMaker JumpStart.
Many users choose to fine-tune or pre-train these Llama 2 models using their own datasets to enhance accuracy for specific applications. However, a significant challenge often encountered is the high cost associated with fine-tuning and training. As organizations endeavor to maximize the potential of LLMs, the need for cost-effective training solutions is increasingly critical. This article explores how to leverage the Neuron distributed training library to fine-tune and continuously pre-train Llama 2 while minimizing training costs using AWS Trainium instances on Amazon SageMaker.
AWS Trainium Instances for Training Workloads
SageMaker offers ml.trn1 and ml.trn1n instances, powered by Trainium accelerators, specifically designed for high-performance deep learning training. These instances deliver up to 50% cost savings compared to similar training-optimized Amazon Elastic Compute Cloud (Amazon EC2) instances. This post focuses on utilizing the ml.trn1.32xlarge Trainium instance type, which is ideal for training large-scale models. The ml.trn1n instances provide double the networking throughput (1,600 Gbps) through Amazon Elastic Fabric Adapter (EFAv2). SageMaker Training supports the availability of both instance types in the US East (N. Virginia), US West (Oregon) and most recently the US East (Ohio) regions. These instances come in On-Demand, Reserved, and Spot options, or as part of a Savings Plan.
For comprehensive insights on Trainium Accelerator chips, refer to Achieve high performance with lowest cost for generative AI inference using AWS Inferentia2 and AWS Trainium on Amazon SageMaker. Additionally, AWS Trainium Customers offers valuable testimonials, while Amazon EC2 Trn1 Instances for High-Performance Model Training are Now Available provides in-depth highlights and specifications.
Utilizing the Neuron Distributed Library with SageMaker
SageMaker is a fully managed service that enables developers, data scientists, and practitioners to build, train, and deploy machine learning (ML) models at scale. SageMaker Training includes features that enhance and simplify the ML training experience, such as managed infrastructure and images for deep learning, automatic model tuning with hyperparameter optimization, and a pay-as-you-go billing structure. This section outlines the benefits of employing SageMaker for distributed training with the Neuron Distributed library—focusing on the managed infrastructure, time-to-train, and cost-to-train advantages, along with its resiliency and recovery capabilities, which are part of the AWS Neuron SDK for deep learning workloads on AWS Inferentia and AWS Trainium instances.
In high-performance computing (HPC) clusters used for deep learning model training, hardware resiliency can pose challenges. While hardware failures during training on a single instance are uncommon, they become more frequent as clusters expand to dozens or hundreds of instances. Regular checkpointing can mitigate wasted compute time, but teams managing their own infrastructure need to monitor workloads closely and be ready to address failures at any time to reduce training delays. SageMaker Training’s managed infrastructure offers several resiliency features that streamline this monitoring and recovery:
- Cluster health checks – Before commencing a training job, SageMaker conducts health checks and ensures communication among the provisioned instances. It replaces any defective instances to ensure the training job starts on a healthy cluster. Health checks are currently available for the TRN1 instance family as well as P* and G* GPU-based types.
- Automatic checkpointing – Checkpoints from a local path (default is /opt/ml/checkpoints) are automatically copied to a user-specified Amazon Simple Storage Service (Amazon S3) location. When training resumes, SageMaker retrieves the saved checkpoints from S3 back to the local directory, allowing the training job to continue from the last saved state.
- Monitoring and tracking training – In the event of a node failure, it’s crucial to identify where the issue occurred. Using PyTorch Neuron, data scientists can monitor training progress via TensorBoard, enabling them to capture the training loss and determine the optimal stopping point for the model.
- Built-in retries and cluster repair – SageMaker can be configured to automatically retry training jobs that fail due to internal server errors. During retries, it replaces any problematic instances and reboots healthy ones, restarting the job for faster completions. Cluster updates are currently enabled for the TRN1 instance family as well as P and G GPU-based types. Practitioners can implement their own retry mechanisms around the client code submitting the jobs to handle other launch errors, such as exceeding account quotas.
For customers working with large clusters of hundreds of instances on a training job, SageMaker Training’s resiliency and recovery features can decrease the total time for model convergence by up to 20% through fewer failures and quicker recovery. Additionally, this enables engineering teams to monitor and respond to failures around the clock. While SageMaker training jobs are suitable for a wide range of training use cases with customizable settings and integration with the AWS ecosystem, Amazon SageMaker HyperPod is specifically optimized for efficient and resilient training of foundation models at scale. For detailed use cases of SageMaker HyperPod, consult the SageMaker HyperPod developer guide.
In this article, we will employ the Neuron Distributed library to continuously pre-train a Llama 2 model using tensor and pipeline parallelism with SageMaker training jobs. To gain further insights into the resiliency and recovery features of SageMaker Training, refer to Training large language models on Amazon SageMaker: Best practices. Additionally, you may find it beneficial to explore discussions on the gender pay gap in another blog post linked here.
Conclusion
In summary, training Llama 2 with AWS Trainium on Amazon SageMaker is a promising strategy for those looking to optimize costs and efficiency in their AI projects, while also benefiting from the robust features offered by SageMaker. For authoritative insights on wage deductions in California, visit this source. This is an excellent resource for those interested in workplace safety and training.