Showing 4 open source projects for "memory usage"

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  • 1
    leak-check

    leak-check

    Personal Information “Leakage ” Detection Interface

    leak-check is a utility designed to help developers detect memory leaks and resource mismanagement in applications. It provides tools to monitor allocations, track usage patterns, and identify potential leaks during runtime. The project focuses on improving application stability by highlighting inefficiencies in memory handling. It can be integrated into development workflows to catch issues early in the debugging process. leak-check is particularly useful in performance-critical applications where memory management is essential. ...
    Downloads: 0 This Week
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  • 2
    claude-code-best-practice

    claude-code-best-practice

    Practice made claude perfect

    ...By combining conceptual guidance with concrete examples and configuration patterns, the project helps teams move from experimental AI usage toward more production-ready agent orchestration.
    Downloads: 6 This Week
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  • 3
    DeepEP

    DeepEP

    DeepEP: an efficient expert-parallel communication library

    ...Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP addresses that by providing optimized GPU kernels and efficient dispatch/combining logic. The library also supports low-precision operations (such as FP8) to reduce memory and bandwidth usage during communication. DeepEP is aimed at large-scale model inference or training systems where expert parallelism is used to scale model capacity without replicating entire networks.
    Downloads: 0 This Week
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  • 4
    cnn-benchmarks

    cnn-benchmarks

    Benchmarks for popular CNN models

    The cnn-benchmarks project is a collection of benchmarking scripts designed to evaluate the performance of convolutional neural networks across different hardware and configurations. It provides standardized implementations of popular CNN architectures, enabling developers to measure training speed, memory usage, and computational efficiency. The project focuses on reproducibility, allowing consistent comparisons between models and environments. It is particularly useful for testing GPUs and optimizing deep learning workloads, as it highlights bottlenecks and performance differences across setups. The repository includes scripts for running benchmarks on various architectures and datasets, making it easy to gather comparative metrics. ...
    Downloads: 0 This Week
    Last Update:
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