// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Batch Size
The number of training examples utilized in one iteration of a model's training process before its internal parameters are updated.
TECHNICAL DEFINITION
Batch size refers to the number of training examples propagated through the neural network at once during a single forward and backward pass, influencing the stability of the gradient estimate and computational efficiency.
BACKGROUND
A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts, and are a foundational technology behind modern chatbots. Biased or inaccurate training data can make an LLM's output less reliable.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Mini-batch size
- training batch
USAGE NOTE
Choosing an optimal batch size is a hyperparameter tuning task that affects training speed and model convergence.
DEVELOPERS
Organizations developing technology related to Batch Size.
NVIDIA develops GPUs and AI software platforms (like CUDA, TensorRT) that are critical for accelerating AI model training and inference. Batch size is a fundamental parameter in optimizing performance and efficiency on their hardware.
Google develops the TensorFlow framework and TPUs, which are widely used for large-scale AI model training and serving. Their AI infrastructure and tools heavily leverage batching for efficient data processing and model updates.
Meta AI is the primary developer of PyTorch, a popular open-source deep learning framework. PyTorch provides extensive control over batch size, which is crucial for researchers and engineers in developing and optimizing AI models.
Hugging Face provides libraries (like Transformers) and platforms for building, training, and deploying large language models. Their inference solutions heavily rely on batching techniques to optimize throughput and latency for processing prompts.
Azure Machine Learning offers cloud-based services for MLOps, including tools for training, deploying, and managing AI models. Users configure batch sizes for efficient training and inference jobs, especially for large-scale applications.
AWS SageMaker is a fully managed service for machine learning that helps developers build, train, and deploy models quickly. SageMaker supports various batching strategies for both model training and inference endpoints to optimize resource utilization.
Databricks, through its acquisition of MosaicML, focuses on efficient training and deployment of large language models. They develop techniques and platforms that optimize training parameters, including batch size, for improved speed and cost-effectiveness.