Awesome GitHub RepositoriesMemory Profiling

PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui

This project is a machine learning array framework and tensor computation library designed for high-performance numerical computing. It provides a comprehensive suite of tools for constructing and training neural networks, featuring an automatic differentiation engine that facilitates gradient-based optimization and complex mathematical modeling. The library distinguishes itself through a unified memory architecture that allows data to be shared across CPU and GPU devices without explicit copies, significantly reducing data movement overhead. Its execution model relies on a lazy evaluation en

FlameGraph is a performance profiling and visualization toolkit designed to identify bottlenecks in software execution. It functions as a processing engine that transforms raw stack trace samples into interactive, hierarchical diagrams. By representing aggregated execution frequency as nested rectangles, the tool allows developers to visualize hot code paths and analyze system behavior across both kernel and user-space environments. The project distinguishes itself through its ability to perform differential profile analysis, which highlights performance regressions or improvements by compari

Memray is a memory profiler for Python that tracks heap allocations in both Python code and native C or C++ extensions. It captures memory events by hooking into the language runtime and traversing call stacks, providing a comprehensive view of how an application consumes memory. The tool is designed to minimize performance impact on the target application by using thread-local buffering and streaming data to an external process or file. The project distinguishes itself through its ability to monitor complex, multi-threaded systems and child processes in real-time. It provides diagnostic util

stats.js is a JavaScript performance monitor and visual diagnostic tool. It provides a real-time overlay for tracking frame rates, memory allocation, and the rendering efficiency of web graphics and applications. The project includes a visual meter for measuring frames per second and a browser memory profiler that displays allocated memory in megabytes to help detect resource leaks. It is designed as a web graphics debugger to monitor the efficiency of WebGL and Canvas rendering. The tool covers a range of monitoring and observability capabilities, including the creation of custom performanc

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded batch data and unbounded real-time streams. It provides a system for building scalable, data-parallel workflows that operate across compute clusters using a single programming model. The framework utilizes a cross-runner pipeline abstraction that decouples the data processing logic from the underlying execution backend, allowing the same pipeline to run on different distributed compute engines. It supports multi-language pipeline development by translating high-level code fro

This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

gpt-neox is a distributed training system and framework for building large-scale autoregressive language models. It implements the transformer architecture and provides a toolkit for training models with billions of parameters by distributing weights across compute clusters. The framework distinguishes itself through extensive support for distributed model parallelism, including pipeline and sequence parallelism, to overcome single-device memory limits. It further supports sparse model architectures using a mixture of experts system with Sinkhorn-based routing. The project covers a broad ran

This is an interactive Python tutorial delivered as a collection of Jupyter notebooks. It is designed as a structured learning path for beginners, teaching fundamental language concepts through a sequence of lessons that combine explanatory text with runnable code cells and embedded practice exercises. Each notebook is a self-contained unit that introduces a topic, demonstrates it with a minimal code example, and then asks the learner to write code themselves, receiving immediate feedback from the browser-based execution environment. The curriculum is built on a progressive concept-stacking mo

OCaml is a strongly typed functional language featuring a sophisticated type system and a focus on safety and expressiveness. It provides a comprehensive compiling toolchain that transforms source code into either portable bytecode or high-performance native binaries. The project is distinguished by a shared memory parallel runtime that executes computations across multiple processor cores using domains, and an algebraic effect system for managing side effects and control flow through execution context handlers. It also includes a dedicated parser generator to automatically create lexers and

redis-rdb-tools ist eine Sammlung spezialisierter Dienstprogramme zum Parsen, Analysieren und Konvertieren von binären Redis-Datenbank-Dump-Dateien. Es fungiert als Parser und Konverter, der Schlüssel und Werte aus diesen Snapshots extrahiert, um Datenwiederherstellung, Migration und Analyse zu erleichtern. Das Projekt zeichnet sich durch Funktionen für Speicherprofilierung und Snapshot-Manipulation aus. Es enthält einen Speicheranalysator, der verbrauchsorientierte Berichte auf Schlüsselebene generiert, um Speicherineffizienzen zu identifizieren, sowie ein Manipulations-Dienstprogramm, das mehrere Dump-Dateien zusammenführen oder einzelne Snapshots in kleinere Teile aufteilen kann. Das Toolset deckt eine breite Palette an Datenoperationen ab, einschließlich der Konvertierung von binären Dumps in JSON, der Generierung von Protokollbefehlen für den Daten-Reimport und dem Export von Datensätzen in relationale Datenbanken oder Suchmaschinen. Es bietet zudem Dienstprogramme zum Vergleich verschiedener Datenbank-Snapshots, um Änderungen im Zeitverlauf zu identifizieren, sowie zum Filtern von Schlüsseln mittels regulärer Ausdrücke während des Parsing-Prozesses.

Memlab ist ein automatisierter Browser-Speicher-Profiler und JavaScript-Speicherleck-Analysator. Er bietet ein Toolkit zum Erkennen und Analysieren von Speicherlecks durch Inspizieren und Vergleichen von Heap-Snapshots, um ungebundenes Objektwachstum und abgetrennte DOM-Elemente zu identifizieren. Das System zeichnet sich durch ein automatisiertes Leck-Test-Framework aus, das End-to-End-Browser-Interaktionssequenzen ausführt, um Speicherregressionen programmatisch zu isolieren. Es nutzt Heap-Snapshot-Diffing, Retainer-Chain-Tracing und heuristikbasierte Filterung, um zu bestimmen, warum Objekte im Speicher verbleiben, und um den kürzesten Pfad von Garbage-Collection-Roots zu geleakten Objekten abzubilden. Das Projekt deckt breite Funktionsbereiche ab, einschließlich Heap-Inspektion, interaktionsbasierter Wachstumsanalyse und Web-Komponenten-Speicherprofilierung. Es enthält zudem Tools für programmatische Speicher-Assertions, visuelles Leck-Debugging über Browser-Overlays und die Möglichkeit, Analysedaten über das Model Context Protocol für die Exploration in natürlicher Sprache offenzulegen. Das Toolkit kann über eine Befehlszeilenschnittstelle für die Integration in automatisierte Continuous-Integration-Pipelines ausgelöst werden.

Heaptrack ist ein Heap-Memory-Profiler und Diagnosetool für Anwendungen unter Linux. Es fungiert als Speicherleck-Detektor und Performance-Analysesystem, das Heap-Allokationen und Stack-Traces aufzeichnet, um Speicher-Hotspots und Verbrauchsmuster zu identifizieren. Das Projekt bietet einen grafischen Heap-Allokations-Visualisierer zur Untersuchung der Speicherauslastung durch Baumansichten und Peak-Memory-Berichte. Es nutzt Flame-Graphs und Allokationsdiagramme, um Speicher-Hotspots zu visualisieren und bei der Erkennung von Lecks zu unterstützen. Das Toolset enthält Funktionen für das Tracing von Heap-Speicherallokationen und die Generierung von Speicherberichten über Kommandozeilen-Utilities. Diese Utilities erstellen ASCII-Zusammenfassungen der größten Speicherverbraucher und ermöglichen die Konvertierung von Profildaten.