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philschmid/deep-learning-pytorch-huggingface

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Deep Learning Pytorch Huggingface

This project provides a comprehensive collection of educational resources and technical guides for training, fine-tuning, and deploying machine learning models using PyTorch and Hugging Face. It serves as a practical reference for scaling deep learning workflows, offering structured instructions for managing large-scale architectures across distributed hardware accelerators.

The repository distinguishes itself by focusing on the end-to-end lifecycle of large language models, specifically emphasizing containerized deployment and performance optimization. It details workflows for parameter-efficient fine-tuning and provides technical guidance on reducing memory footprints through weight quantization and speculative decoding techniques.

Beyond core training and deployment, the project covers a broad range of machine learning operations, including environment packaging, performance benchmarking, and the configuration of containerized execution environments. These resources assist in maintaining consistent performance and resource efficiency when hosting generative and multimodal models in production.

Features

  • Hugging Face Implementations - Provides educational resources and tutorials for training, fine-tuning, and deploying models using the transformer ecosystem.
  • Large Language Model Fine-Tuning - Provides structured workflows for fine-tuning pre-trained language models on specialized datasets using instruction tuning.
  • Distributed Training - Implements strategies for scaling model training across multiple hardware accelerators to handle large-scale architectures.
  • Model Inference and Serving - Provides production-ready serving infrastructure for deploying models with optimized precision and speculative decoding.
1,383 stars·262 forks·Jupyter Notebook·MIT·32 views
  • Parameter Efficient Fine-Tuning - Enables memory-efficient model adaptation by updating only a small subset of weights during the fine-tuning process.
  • Large Language Model Deployments - Offers specialized deployment strategies for hosting large-scale language models in containerized environments.
  • Model Fine-Tuning - Provides processes and workflows for optimizing pre-trained models on task-specific datasets using parameter-efficient techniques.
  • Containerized Model Serving - Implements standardized containerized architectures for serving large language models as scalable inference endpoints.
  • Deep Learning Training Pipelines - Provides end-to-end workflows and instructions for managing distributed training pipelines for large-scale architectures.
  • Distributed Training Scaling Utilities - Offers utilities for scaling deep learning training workloads across distributed hardware clusters.
  • Generative Text Inference - Accelerates text generation by implementing parallel token prediction strategies to reduce sequential inference steps.
  • Data Parallelism - Supports distributed training by splitting large model workloads across multiple hardware accelerators using synchronized gradient updates.
  • Performance Benchmarking - Provides benchmarking tools to measure latency and throughput for evaluating model performance under production loads.
  • LLM Deployment Guides - Offers technical guides for the practical deployment and operationalization of large language models in containerized environments.
  • Inference Optimization Techniques - Implements techniques like quantization and speculative decoding to improve the speed and resource efficiency of model inference.
  • Multimodal Deployments - Supports the deployment of multimodal models capable of processing both text and image inputs in scalable inference endpoints.
  • Large Language Model Optimization - Provides methods and utilities for optimizing large language models to improve inference speed and efficiency on constrained hardware.
  • Model Quantization - Provides technical references for reducing model memory footprints through weight quantization techniques.
  • Model Training Optimizers - Optimizes training efficiency through distributed strategies, quantization, and hardware-specific acceleration techniques.
  • Weight Quantization - Reduces model memory footprint and improves inference speed by converting high-precision weights into lower-bit numerical formats.
  • Speculative Decoding - Implements speculative decoding to accelerate text generation by using smaller auxiliary models to predict multiple tokens in parallel.
  • Isolated Execution Environments - Provides containerized environments to ensure consistent execution of deep learning workflows across different hardware and software setups.
  • Deep Learning Experiment Environments - Provides containerized environments that bundle deep learning frameworks to ensure consistent execution across development and production systems.

    • Star history

    Star history chart for philschmid/deep-learning-pytorch-huggingfaceStar history chart for philschmid/deep-learning-pytorch-huggingface

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Frequently asked questions

    What does philschmid/deep-learning-pytorch-huggingface do?

    This project provides a comprehensive collection of educational resources and technical guides for training, fine-tuning, and deploying machine learning models using PyTorch and Hugging Face. It serves as a practical reference for scaling deep learning workflows, offering structured instructions for managing large-scale architectures across distributed hardware accelerators.

    What are the main features of philschmid/deep-learning-pytorch-huggingface?

    The main features of philschmid/deep-learning-pytorch-huggingface are: Hugging Face Implementations, Large Language Model Fine-Tuning, Distributed Training, Model Inference and Serving, Parameter Efficient Fine-Tuning, Large Language Model Deployments, Model Fine-Tuning, Containerized Model Serving.

    Which projects share features with philschmid/deep-learning-pytorch-huggingface?

    Projects with overlapping indexed features include: infrasys-ai/aisystem — AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip… zhaochenyang20/awesome-ml-sys-tutorial — This project provides a comprehensive technical guide and framework for engineering large-scale machine learning… tingsongyu/pytorch-tutorial-2nd — This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It… huggingface/notebooks — This is a collection of Jupyter notebooks that serve as educational guides for training, fine-tuning, and deploying… huggingface/course — This project is an educational course and learning curriculum for implementing and fine-tuning transformer models… hoper-j/ai-guide-and-demos-zh_cn — This project is a comprehensive learning resource and set of demonstrations focused on large language model…

    Projects sharing features with Deep Learning Pytorch Huggingface

    These projects share indexed features with Deep Learning Pytorch Huggingface. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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      AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip architecture to high-level training frameworks. It encompasses the development of AI compiler frameworks, inference engines, and distributed training orchestrators designed to coordinate workloads across a heterogeneous compute stack of CPUs, GPUs, and NPUs. The project focuses on the deep integration of software and hardware, employing software-hardware co-design to align tensor layouts with physical memory structures. It provides specialized capabilities for accelerating Transformer mo

      Jupyter Notebookaiaiinfraaisys
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    • zhaochenyang20/awesome-ml-sys-tutorialzhaochenyang20 avatar

      zhaochenyang20/Awesome-ML-SYS-Tutorial

      5,371View on GitHub↗

      This project provides a comprehensive technical guide and framework for engineering large-scale machine learning systems. It covers the full lifecycle of model development, focusing on the infrastructure and computational principles required to build, train, and serve generative AI models across distributed GPU clusters. The repository distinguishes itself by offering deep-dive tutorials and implementation strategies for complex system challenges. It emphasizes high-performance architectural primitives, such as collective communication orchestration, distributed tensor sharding, and static gr

      Python
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    • tingsongyu/pytorch-tutorial-2ndTingsongYu avatar

      TingsongYu/PyTorch-Tutorial-2nd

      4,555View on GitHub↗

      This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen

      Jupyter Notebookcomputer-visiondeepsortdiffusion-models
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    • huggingface/notebookshuggingface avatar

      huggingface/notebooks

      4,468View on GitHub↗

      This is a collection of Jupyter notebooks that serve as educational guides for training, fine-tuning, and deploying machine learning models within the Hugging Face ecosystem. The notebooks cover the full lifecycle of model development, from loading and configuring pre-trained transformers to packaging trained models for real-time inference via scalable endpoints. The notebooks demonstrate a range of capabilities including diffusion model training and fine-tuning for image generation and editing, transformer model adaptation for natural language processing tasks, and parameter-efficient fine-t

      Jupyter Notebook
      View on GitHub↗4,468
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