zhaochenyang20/Awesome-ML-SYS-Tutorial

Features

• Awesome List - A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.
• Distributed Training Frameworks - Serves as a comprehensive distributed training framework for orchestrating multi-node model training and collective communication.
• Distributed Training - Orchestrates large-scale model training across multiple GPU devices using data and model parallelism.
• Distributed Training Orchestration - Coordinates training and inference actors across GPU clusters using resource-aware placement groups.
• Distributed Training Sharding - Distributes model parameters and optimizer states across multiple GPU devices to enable large-scale model training.
• Machine Learning Systems - Provides a comprehensive framework for engineering and scaling distributed machine learning systems across GPU clusters.
• Large Language Model Configurations - Configures and executes high-performance serving for large language models with support for tensor parallelism.
• Large Language Model Optimization - Provides comprehensive infrastructure and optimization techniques for accelerating large language model inference in production.
• Fully Sharded Data Parallelism - Implements memory-efficient training by partitioning model parameters, gradients, and optimizer states across data-parallel processes.
• Model Serving - Provides a comprehensive inference engine for deploying and serving large language models via web endpoints.
• Reinforcement Learning - Provides technical guides on training workflows, rollout engine design, and memory-efficient weight update mechanisms for large-scale models.
• Generative Inference Engines - Acts as a high-performance generative AI inference engine with support for speculative decoding and hardware-aware execution.
• Training Orchestrators - Coordinates distributed reinforcement learning training cycles and policy updates across GPU clusters.
• Parallelism Integrators - Distributes large-scale model training across multiple GPUs using combinations of tensor, pipeline, and data parallelism.
• Agentic Training Frameworks - Supports training models on complex multi-turn dialogues and multi-step tool-use workflows.
• Distributed Model Checkpointing - Write model states to disk in a sharded format where each worker saves only its local portion to improve efficiency.
• Training Loop Managers - Orchestrates the collection of model experiences and performs multi-epoch parameter updates for actor and critic models.
• Model Inference APIs - Generates content from models using web APIs or offline engines with support for streaming and tensor parallelism.
• Model Inference and Serving - Exposes model inference capabilities via web endpoints and direct engine execution for production use.
• Multi-GPU Distribution - Manages high-speed data exchange between multiple graphics processors to synchronize gradients and model parameters across distributed clusters.
• Model Quantization - Defines hardware-specific quantization parameters and activation data types to optimize model memory usage and inference performance.
• Multimodal Models - Provides technical infrastructure for aligning and processing interleaved multimodal inputs within reasoning engines.
• Prefix Caching - Organizes KV cache in a tree structure to share common prefixes across requests and reduce redundant computation.
• Reinforcement Learning Training Pipelines - Orchestrates the full reinforcement learning training cycle including rollout generation and policy updates.
• Alignment and RLHF - Provides comprehensive reinforcement learning workflows including tool calling and search integration for model alignment.
• Collective Communication Operations - Coordinates high-performance collective communication operations like AllReduce and Broadcast across distributed GPU clusters.
• Machine Learning Tutorials - Offers a comprehensive technical tutorial on the infrastructure and engineering principles required to build large-scale machine learning systems.
• Distributed Inference Orchestrators - Manages multi-node and multi-GPU model execution by coordinating replicas and request scheduling.
• Distributed Training Coordination - Manages the lifecycle of actor, rollout, and reward model workers across distributed GPU clusters.
• Agentic Execution Loops - Executes conversational workflows that support tool calling and state management over multiple turns.
• Multi-turn Interaction Managers - Manages stateful multi-turn dialogues and tool-calling workflows by injecting observations into the model context.
• Compute Graph Captures - Captures GPU operation sequences into static graphs to eliminate kernel launch overhead.
• Distributed Training Scaling Utilities - Documents strategies for parallelizing model training, including tensor parallelism and communication optimization via high-speed backends.
• Expert Parallelism Configurations - Uses all-to-all communication patterns to dispatch tokens to relevant experts and aggregate results across distributed hardware.
• Chunked Prefill Mechanisms - Implements chunked prefill mechanisms to process large input sequences in segments, maintaining attention masking across fragmented computation steps.
• Distributed Gradient Synchronization - Coordinates gradient updates across multiple compute nodes to ensure consistent model training in distributed environments.
• Inference Backends - Provides hardware-agnostic layers for executing models across diverse computing environments.
• Inference Pipeline Orchestrators - Provides a unified framework for chaining sequential inference tasks like audio encoding, reasoning, and synthesis.
• Large-Scale Model Training - Splits large neural network layers across multiple devices to enable training of models exceeding single-processor memory.
• Long Context Training Optimizations - Segments input sequences across GPUs to enable memory-efficient training on long-context tasks.
• Policy Loss Calculators - Computes loss for policy updates using clipped probability ratios to constrain model changes and ensure stable training updates.
• Mixed Precision Training - Implements low-precision training techniques to reduce memory footprint and accelerate reinforcement learning workflows.
• Reward Functions - Integrates custom reward functions for evaluating model outputs with support for asynchronous execution.
• Training Lifecycle Management - Orchestrates the training process by handling checkpointing, logging, and state resumption.
• Model Rollout Executions - Orchestrates asynchronous model generation cycles and manages state transitions between text generation and tool execution.
• Asynchronous Rollout Decoupling - Separates model generation from gradient updates to enable asynchronous training and flexible deployment.
• Request Schedulers - Prioritizes incoming inference requests to optimize batch composition and system throughput.
• Speculative Decoding Strategies - Uses lightweight draft models to generate tokens in parallel, verified by larger target models to improve sampling throughput.
• ML Performance Profilers - Provides profiling and optimization tools to analyze hardware topology and memory patterns for high-performance machine learning.
• Pretrained Sequence Model Loaders - Provides utilities for downloading and initializing pretrained model weights for inference and fine-tuning.
• Asynchronous Training - Decouples rollout generation from gradient updates to enable simultaneous processing and increase training throughput.
• Mixture of Experts - Partitions model experts across multiple devices to enable training and inference of massive models that exceed single-GPU capacity.
• Training Performance Profiling - Records granular timestamps across distributed workers to analyze throughput and resource efficiency during model training.
• Model Quantization - Reduces memory footprint and accelerates inference by applying precision schemes like FP8 or INT8 to model weights.
• Dynamic Weight Updates - Injects new weight tensors into a running distributed engine to update model parameters dynamically without requiring a full restart.
• Weight Offloading - Moves inactive model weights and optimizer states to CPU memory to free GPU capacity for larger batch sizes.
• Multi-GPU Training Utilities - Performs collective operations across multiple GPUs to enable distributed training and data exchange.
• Policy Gradient Optimizers - Updates actor and critic networks based on calculated advantages and performance metrics to improve decision-making.
• Precision Quantization - Converts model weights and activations to FP8 formats to balance numerical precision with hardware efficiency.
• Value Loss Calculators - Minimizes the difference between predicted token values and actual returns using mean squared error to refine value model accuracy.
• Policy Clipping - Updates the actor model using a clipped objective function to prevent large policy shifts during reinforcement learning.
• Reinforcement Learning Training Utilities - Update draft model parameters during reinforcement learning by calculating cross-entropy loss against the target model's hidden states.
• Reinforcement Learning Value Estimators - Implements generalized advantage estimation methods to determine the relative benefit of actions during reinforcement learning.
• Structured Output Parsers - Enforces schemas on model-generated content to ensure reliable data integration and structural compliance.
• Grammar-Constrained Samplers - Pre-calculates vocabulary masks to enforce grammar constraints without blocking GPU sampling pipelines.
• Distributed Tensor Sharding - Handles model parameters as distributed objects to simplify sharding and state management across GPU clusters.
• Distributed Task Orchestration - Utilizes remote task and actor primitives to scale training workloads across multi-node clusters.
• Distributed Inference Clusters - Enables distributed operation by allowing inference servers to communicate across multiple network nodes.
• Resource Allocation - Manages hardware resource distribution and process placement across distributed training and inference clusters.
• AI Model Load Balancers - Distributes incoming requests across multiple language model servers using load balancing algorithms to optimize performance and enable prefix caching.
• Rollout Engines - Manages iterative interaction between a model and an environment by sampling actions and maintaining context until termination.
• Request Batching - Prioritizes and batches incoming requests for prefill and decoding phases using dynamic reordering strategies.
• Paged KV Cache Management - Uses fixed-size blocks to store and manage key-value cache states for improved memory efficiency.
• External Tool Integrations - Parses model-generated tool calls and injects responses back into conversation history for autonomous task execution.
• Activation Recomputation Strategies - Minimizes peak memory usage by recomputing intermediate activations during the backward pass.
• Critic Loss Minimizers - Trains the critic model by minimizing the mean squared error between predicted value estimates and actual returns.
• Custom Tool Definitions - Provides frameworks for defining custom tools by extending base classes with specific action and observation schemas.
• Checkpoint Management - Supports sharded checkpointing to persist model states across multiple ranks without global aggregation.
• Sequence Packing - Combines sequences of varying lengths into continuous vectors to eliminate computational waste from padding.
• Reward Modeling - Calculates token-level rewards by combining divergence scores between models with final-token reward scores from a reward model.
• Custom State Tracking - Maintains request lifecycles and state machines for managing tool calls and conversation history in agentic workflows.
• Data Preprocessing Pipelines - Converts raw datasets into structured formats by generating prompts and parsing ground truth answers for model training.
• Diffusion Pipelines - Executes iterative noise-refinement image generation using optimized pipelines for diffusion models.
• External Tool Execution - Enables asynchronous multi-turn dialogues where models independently invoke external tools within a unified training workflow.
• Output Constraint Engines - Enforces structured output formats like JSON or specific grammars during model inference.
• Generation Accelerators - Increases inference throughput by using smaller draft models to predict tokens in parallel for verification by larger models.
• GPU Kernel Implementations - Records GPU operation sequences into static graphs to eliminate kernel launch overhead during repetitive execution.
• Inference Scaling - Distributes model computation across multiple physical machines to handle large-scale inference tasks.
• Knowledge Retrieval Systems - Implements dense retrieval engines to provide external knowledge context for models during training and inference.
• Sequence Importance Sampling - Corrects for distribution shifts between rollout and training policies by weighting updates based on action probability ratios.
• Inference Optimizations - Optimizes memory and compute efficiency for long-sequence inference using specialized attention hardware kernels.
• Inference Optimization Techniques - Implements speculative decoding, chunked prefill, and zero-overhead scheduling to improve generative model throughput and latency.
• Model Training and Inference Engines - Ensures bitwise-identical operator execution between training and rollout phases using deterministic kernels to eliminate numerical divergence.
• Numerical Consistency Verifiers - Aligns log-probability calculations between training and inference backends to eliminate numerical drift.
• Large Model Optimizations - Constructs large models using deferred allocation to bypass GPU memory limits during initialization.
• Model Compression Suites - Converts high-precision model weights into packed formats to reduce memory footprint and enable single-node execution of large-scale models.
• Attention Kernel Fusion - Optimizes attention computation by fusing projection and attention operations into specialized kernels.
• Performance Profilers - Provides interactive tools to analyze execution bottlenecks, memory usage, and latency during the inference lifecycle.
• Multimodal Agent Capabilities - Enables agents to process and generate non-textual data like images, audio, and video.
• Multimodal Integration Frameworks - Integrates diverse data modalities like text, audio, and visual inputs into unified reasoning pipelines.
• Multimodal Token Interleaving - Processes interleaved multimodal tokens to generate coherent responses while maintaining semantic context.
• Mixed Granularity Quantization - Balances numerical precision and hardware efficiency by applying per-token and per-block quantization.
• Quantized Inference Runtimes - Executes quantized models using hardware-specific acceleration for improved inference performance.
• Reinforcement Learning Data Filters - Selects high-quality training data based on performance metrics to optimize model learning efficiency during reinforcement learning.
• Reinforcement Learning Optimizers - Applies fine-grained loss masking and reward propagation strategies to ensure accurate gradient updates across complex interaction sequences.
• Sequence Parallelism Frameworks - Slices input data across sequence dimensions to support longer response lengths than a single device can process.
• Tool-Augmented Reasoning Engines - Connects large language models to external data and function execution environments for search-augmented reasoning.
• Training Configurations - Loads model-specific configuration parameters into the environment to prepare the system for distributed training tasks.
• Batch Size Scaling - Manages training throughput and memory usage through dynamic token-based batching.
• Asynchronous Prefetchers - Overlaps communication and computation by proactively fetching model layers during forward and backward passes.
• Training and Fine-Tuning - Provides configuration for reinforcement learning strategies including advantage estimation and preference-feedback mechanisms.
• Modular Pipeline Orchestration - Decomposes complex processing tasks into independent, swappable stages to transform raw inputs into final outputs.
• Cache Quantization - Reduces memory usage during long-context generation by storing attention caches in lower-precision formats.
• Compute Throughput Optimizers - Maximizes hardware efficiency through tensor parallelism, activation offloading, and custom fused kernels.
• GPU Operation Batchers - Reduces CPU-to-GPU launch overhead by packaging multiple operations into single executable graphs.
• Distributed Parameter Synchronisation - Coordinates gradient updates and parameter synchronization across multiple compute nodes using collective communication protocols.
• GPU Memory Allocators - Partitions available GPU memory into static and dynamic regions to reserve space for model weights and runtime activations.
• Inference Cache Management - Allocates and manages key-value cache buffers during model inference to optimize memory usage.
• Low-Bit Inference Engines - Minimizes memory bandwidth and latency by executing models with packed low-precision weights.
• FP8 Training Optimization - Optimizes training performance by utilizing 8-bit floating point formats to reduce memory footprint and increase computational throughput.
• Training Workflow Orchestrators - Orchestrates automated pipelines for model training and multi-round parameter updates.
• Agent Lifecycle Management - Orchestrates the execution of agent loops and manages the lifecycle of worker processes and language model servers.
• Attention Mechanisms - Calculates relevance between input sequences using dot products and softmax normalization to determine data prioritization.
• Streaming Attention Kernels - Applies attention masks to retain sink and window tokens for efficient long-sequence processing.
• Audio Tokenization - Converts high-frequency continuous audio waveforms into low-frequency discrete token sequences using vector quantization to enable efficient processing.
• Batch Inference Engines - Executes inference on large collections of input prompts and saves generated responses to structured output files.
• Curriculum Learning Frameworks - Trains models on tasks of increasing difficulty to improve training efficiency and convergence speed.
• Custom Decoding Strategies - Customizes diffusion decoding behavior through external configuration files to decouple algorithm parameters from core inference logic.
• Distributed Layer Synchronizers - Coordinates data movement between independent attention workers and shared MLP layers to maintain state consistency across parallel processing units.
• Distributed Inference Frameworks - Integrates high-performance inference engines to support distributed, multi-turn tool usage and efficient sequence generation.
• Dynamic Parameter Initialization - Defers model parameter creation to CPU or virtual devices to optimize memory usage during startup.
• Encoder-Decoder Architectures - Connects data sequences by matching decoder queries against encoder keys for generation tasks.
• Feature Alignment - Aligns visual embeddings with language model sequences to maintain spatial context across data types.
• Sequence Completion Sampling - Adjusts model generation behavior during training using sampling parameters like temperature and top-p.
• Cross-Attention Mechanisms - Integrates multi-modal conditioning signals into neural network layers using cross-attention mechanisms.
• Diffusion Models - Deploys diffusion-based models via a unified server interface supporting custom decoding algorithms.
• Reinforcement Learning Sampling - Allocates training samples dynamically to focus learning on model weaknesses and improve training efficiency.
• Dynamic Sampling Filters - Applies dynamic filters to training samples to optimize learning efficiency and reward diversity.
• Consistency Enforcement Kernels - Ensures bitwise identical log-probability calculations by standardizing kernels and disabling non-deterministic optimizations.
• Model Compilers - Transforms high-level neural network definitions into optimized, hardware-specific executable code for dynamic workloads.
• Training Configuration Management - Provides configurable templates to preserve reasoning traces and optimize context usage for agentic models.
• Model Inference Accelerators - Boosts token generation speeds during reinforcement learning and inference by integrating speculative decoding and chunked parallel computation.
• Inference Adapters - Separates model training from inference engines to prevent resource contention during high-demand operations.
• Data Preprocessing - Provides utilities for loading, tokenizing, and preprocessing diverse datasets for reinforcement learning and multimodal training.
• Expert Load Balancers - Mitigates computational bottlenecks by managing expert routing to prevent load imbalances.
• Memory Optimization Techniques - Offloads model parameters and optimizer states to CPU memory to enable larger batch sizes and model capacities.
• Mixed Precision Training Utilities - Simulates low-precision arithmetic during training to ensure model convergence with reduced precision.
• Attention Backends - Performs token generation using optimized attention backends to accelerate computation and memory reuse.
• Training Backend Optimizers - Maintains bit-level consistency between rollout and training operations using batch-invariant operators and deterministic kernels.
• Model Parameter Management - Serializes model weights into dictionary structures to facilitate saving, loading, and memory analysis.
• Model Training - Mitigates training collapse by using truncated importance sampling to re-weight policy gradient losses during model training.
• Model Training Interfaces - Standardizes APIs for model state management, output processing, and observation formatting in training loops.
• Model Weight Converters - Exports high-precision model weights into compact formats compatible with optimized inference engines to reduce memory footprint and accelerate execution.
• Multimodal Input Processors - Processes diverse multimodal inputs including images and text into token sequences for model consumption.
• Text-to-Speech - Converts semantic text and acoustic hidden states into audible waveforms using autoregressive token generation and vocoder decoding.
• Training Memory Management - Reduces GPU memory consumption by offloading weights and optimizer states to CPU during training and rollout.
• Batch Matrix Multiplication Utilities - Distributes large matrix multiplications across multiple processing units to fit within memory constraints.
• Shared Memory Data Exchange - Enables zero-copy memory sharing between training and inference engines to minimize data transfer overhead.
• Inference Runtime Integrations - Connects training workflows to standalone inference servers via web endpoints for model weight updates.
• Device Mesh Topologies - Organizes compute resources into logical meshes to coordinate parallel execution and tensor sharding.
• Multi-GPU Load Balancing - Equalizes token counts across GPU ranks to prevent performance bottlenecks caused by uneven sequence lengths.
• Inference Load Balancers - Redistributes generated sequences across GPU ranks using bin-packing algorithms to ensure consistent computational load.
• Response Streaming - Streams generated text incrementally to clients to reduce perceived latency and improve user experience.
• Hardware Topology Optimizers - Automatically detects and selects the most efficient data transfer path between GPUs based on hardware interconnects.
• GPU Memory Lifecycle Managers - Reports reserved and allocated GPU memory statistics to provide a high-level overview of hardware resource consumption.
• Static Graph Execution - Minimizes latency in fixed computation flows by replaying pre-compiled graphs with minimal CPU overhead.
• High-Performance Computing - Executes pre-compiled operation graphs in single calls to bypass dispatcher overhead during inference.
• Security Trust Models - Enforces trust regions by filtering or masking training samples that deviate from the target policy to prevent model divergence.
• Distributed Complexity Abstractions - Exposes a unified single-controller interface while managing complex multi-controller sub-modules for high-performance execution.
• Latency Optimization - Provides techniques for measuring and reducing response times in network and system operations.
• Gradient Path Isolators - Detaches auxiliary model branches from the main model's output layers to ensure only specific parameters are updated during training.
• Training Metrics - Computes reward scores, log probabilities, and advantage estimates to evaluate model performance during training.
• Infrastructure Monitoring - Inspects hardware topology and system resource utilization to identify bottlenecks across GPU infrastructure.
• Processing Engine Metrics - Tracks and reports real-time generation metrics such as tokens per second to diagnose inference engine performance.
• GPU Performance Monitoring - Tracks real-time GPU utilization and metrics to ensure efficient resource usage during distributed computing tasks.
• Execution Profilers - Records timestamps during workflow execution to identify performance bottlenecks across distributed workers.
• Computational Parallelization - Enables parallel processing of recursive calculations by chunking long sequences across hardware accelerators.
• Agent Configuration Tools - Configures tool interactions via schema definitions and manages response truncation for agentic tasks.
• Parallel Tool Execution - Coordinates tool calls within parallel training environments by restricting execution to primary ranks.
• Chat Template Configurations - Selects between training and inference templates to balance consistency with training data against optimized context formatting.
• Gradient Computation - Prevents auxiliary training losses from updating shared model parameters by detaching gradient flows.
• Asynchronous Kernel Launchers - Decouples task scheduling from kernel execution to allow the scheduler to proceed without waiting for hardware.
• Partial Rollout Runners - Trains on completed samples as they arrive to prevent long-tail blocking during reinforcement learning.
• Training and Evaluation Pipelines - Provides automated workflows for filtering and validating training prompts to ensure data quality and prevent reward hacking.
• Lazy Scheduling Pipelines - Pipelines inference tasks using lazy resolution to minimize cross-device synchronization latency.
• Quantization Plugin Interfaces - Allows adding custom quantization methods by implementing configuration and weight processing classes without modifying core framework logic.
• Multimodal Training - Buffers fragmented multimodal tensors across turns and performs single concatenation operations to minimize memory allocation overhead.
• Sequence Alignment Models - Implements attention-based architectures for dynamic alignment in sequence-to-sequence tasks.
• Attention Parallelism Optimizers - Reduces KV cache memory usage in models with few KV heads by using data parallelism for attention layers.
• Length Penalization - Adjusts reward signals based on sequence length to discourage overly verbose reasoning paths.
• Data Exchange Protocols - Implements high-performance interfaces for transferring tensor data between system components with minimal overhead.
• Batched Data Loading - Provides automated collation of data samples into batches to optimize GPU utilization during training.
• Pipeline Customizers - Defines custom generation logic and verification steps within the data rollout phase.
• Sync Parameter Configurations - Provides protocols for resharding and synchronizing parameters between training and rollout engines.
• Shared Memory Transports - Implements shared memory transports to optimize communication efficiency by separating control and data layers.
• Environment Initialization - Configures process groups and communication backends to enable multi-process data exchange.
• Process Grouping Utilities - Organizes processes into custom communication units to enable flexible, isolated data exchange strategies.
• Rollout Context Managers - Dynamically adjusts or reloads inference engines to handle varying sequence lengths without stalling training pipelines.
• Chunked Audio Streaming - Enables low-latency playback by generating and decoding audio frames incrementally as soon as the corresponding tokens are produced.
• High-Performance Data Transfer - Provides high-performance data transfer mechanisms to separate control signaling from large tensor data movement.
• Encoder Memory Reservoirs - Allocates dedicated memory buffers for multi-modal encoders to prevent resource contention during inference.
• Stage-Based Memory Managers - Manages GPU memory budgets across processing stages to prevent resource exhaustion during complex model execution.
• Memory Allocation Tracers - Tracks and visualizes memory usage patterns to identify fragmentation and leaks during complex computational workloads.
• Inference Resource Controllers - Optimizes model execution by configuring tensor parallelism, memory utilization, and caching strategies.
• Policy Divergence Monitors - Adjusts reward signals using divergence metrics to prevent policy drift during reinforcement learning.
• Graph Execution Compilers - Compiles captured graphs into executable objects for optimized GPU execution.
• Tail Latency Measurement - Generates cumulative distribution function graphs to identify long-tail latency issues and optimize response configurations.
• Latency-Based Sampling Filters - Maintains system throughput by discarding requests exceeding latency thresholds during rollout phases.
• Pipeline Optimization Techniques - Reduces idle time in pipeline parallelism using scheduling strategies like 1F1B and interleaved virtual pipelines.
• Process Coordinators - Coordinates distributed processes via remote procedure calls to synchronize state and trigger collective actions.
• Memory Usage Analyzers - Calculates and monitors the memory footprint of running processes to provide a high-level overview of reserved and active memory.
• Server Metrics - Exposes performance data through metrics endpoints to track server health and operational status.
• Performance Profiling Tools - Provides integrated profiling tools to track training metrics and hardware utilization for identifying performance bottlenecks.
• Standardized Interfaces - Provides a consistent set of methods for resetting states, processing model actions, and formatting observations for feedback.
• Worker Lifecycle Management - Instantiates and manages specialized worker roles for actors and critics in distributed systems.
• GPU Command Batchers - Reduces CPU-side scheduling frequency by executing multiple generation steps on the GPU in a single command cycle.
• Scheduling Overlaps - Implements asynchronous scheduling to hide latency by overlapping CPU-bound task preparation with GPU-bound computation.
• Symbolic Scheduling Links - Uses symbolic references to decouple scheduling from synchronization, allowing continuous GPU pipeline execution.

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