Transformers
  • 🌍GET STARTED
    • Transformers
    • Quick tour
    • Installation
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    • Run inference with pipelines
    • Write portable code with AutoClass
    • Preprocess data
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    • Train with a script
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    • Load and train adapters with BOINC AI PEFT
    • Share your model
    • Agents
    • Generation with LLMs
  • 🌍TASK GUIDES
    • 🌍NATURAL LANGUAGE PROCESSING
      • Text classification
      • Token classification
      • Question answering
      • Causal language modeling
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      • Translation
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      • Multiple choice
    • 🌍AUDIO
      • Audio classification
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    • 🌍COMPUTER VISION
      • Image classification
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    • 🌍MULTIMODAL
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    • 🌍GENERATION
      • Customize the generation strategy
    • 🌍PROMPTING
      • Image tasks with IDEFICS
  • 🌍DEVELOPER GUIDES
    • Use fast tokenizers from BOINC AI Tokenizers
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    • Use model-specific APIs
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    • Templates for chat models
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    • Benchmarks
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    • Troubleshoot
  • 🌍PERFORMANCE AND SCALABILITY
    • Overview
    • 🌍EFFICIENT TRAINING TECHNIQUES
      • Methods and tools for efficient training on a single GPU
      • Multiple GPUs and parallelism
      • Efficient training on CPU
      • Distributed CPU training
      • Training on TPUs
      • Training on TPU with TensorFlow
      • Training on Specialized Hardware
      • Custom hardware for training
      • Hyperparameter Search using Trainer API
    • 🌍OPTIMIZING INFERENCE
      • Inference on CPU
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      • Inference on many GPUs
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    • Instantiating a big model
    • Troubleshooting
    • XLA Integration for TensorFlow Models
    • Optimize inference using `torch.compile()`
  • 🌍CONTRIBUTE
    • How to contribute to transformers?
    • How to add a model to BOINC AI Transformers?
    • How to convert a BOINC AI Transformers model to TensorFlow?
    • How to add a pipeline to BOINC AI Transformers?
    • Testing
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  • 🌍CONCEPTUAL GUIDES
    • Philosophy
    • Glossary
    • What BOINC AI Transformers can do
    • How BOINC AI Transformers solve tasks
    • The Transformer model family
    • Summary of the tokenizers
    • Attention mechanisms
    • Padding and truncation
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    • Perplexity of fixed-length models
    • Pipelines for webserver inference
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  • 🌍API
    • 🌍MAIN CLASSES
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      • 🌍VISION MODELS
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      • 🌍AUDIO MODELS
        • Audio Spectrogram Transformer
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      • 🌍MULTIMODAL MODELS
        • ALIGN
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        • IDEFICS
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        • TAPAS
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        • TVLT
        • ViLT
        • Vision Encoder Decoder Models
        • Vision Text Dual Encoder
        • VisualBERT
        • X-CLIP
      • 🌍REINFORCEMENT LEARNING MODELS
        • Decision Transformer
        • Trajectory Transformer
      • 🌍TIME SERIES MODELS
        • Autoformer
        • Informer
        • Time Series Transformer
      • 🌍GRAPH MODELS
        • Graphormer
  • 🌍INTERNAL HELPERS
    • Custom Layers and Utilities
    • Utilities for pipelines
    • Utilities for Tokenizers
    • Utilities for Trainer
    • Utilities for Generation
    • Utilities for Image Processors
    • Utilities for Audio processing
    • General Utilities
    • Utilities for Time Series
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On this page
  • Performance and Scalability
  • Training
  • Inference
  • Training and inference
  • Contribute
  1. PERFORMANCE AND SCALABILITY

Overview

PreviousPERFORMANCE AND SCALABILITYNextEFFICIENT TRAINING TECHNIQUES

Last updated 1 year ago

Performance and Scalability

Training large transformer models and deploying them to production present various challenges. During training, the model may require more GPU memory than available or exhibit slow training speed. In the deployment phase, the model can struggle to handle the required throughput in a production environment.

This documentation aims to assist you in overcoming these challenges and finding the optimal setting for your use-case. The guides are divided into training and inference sections, as each comes with different challenges and solutions. Within each section you’ll find separate guides for different hardware configurations, such as single GPU vs. multi-GPU for training or CPU vs. GPU for inference.

Use this document as your starting point to navigate further to the methods that match your scenario.

Training

Training large transformer models efficiently requires an accelerator such as a GPU or TPU. The most common case is where you have a single GPU. The methods that you can apply to improve training efficiency on a single GPU extend to other setups such as multiple GPU. However, there are also techniques that are specific to multi-GPU or CPU training. We cover them in separate sections.

  • : start here to learn common approaches that can help optimize GPU memory utilization, speed up the training, or both.

  • : explore this section to learn about further optimization methods that apply to a multi-GPU settings, such as data, tensor, and pipeline parallelism.

  • : learn about mixed precision training on CPU.

  • : learn about distributed CPU training.

  • : if you are new to TPUs, refer to this section for an opinionated introduction to training on TPUs and using XLA.

  • : find tips and tricks when building your own deep learning rig.

Inference

Efficient inference with large models in a production environment can be as challenging as training them. In the following sections we go through the steps to run inference on CPU and single/multi-GPU setups.

Training and inference

Here you’ll find techniques, tips and tricks that apply whether you are training a model, or running inference with it.

Contribute

This document is far from being complete and a lot more needs to be added, so if you have additions or corrections to make please don’t hesitate to open a PR or if you aren’t sure start an Issue and we can discuss the details there.

When making contributions that A is better than B, please try to include a reproducible benchmark and/or a link to the source of that information (unless it comes directly from you).

🌍
Methods and tools for efficient training on a single GPU
Multi-GPU training section
CPU training section
Efficient Training on Multiple CPUs
Training on TPU with TensorFlow
Custom hardware for training
Hyperparameter Search using Trainer API
Inference on a single CPU
Inference on a single GPU
Multi-GPU inference
XLA Integration for TensorFlow Models
Instantiating a big model
Troubleshooting performance issues