Showing 3 open source projects for "train"

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  • Build Data Resilience - Take the Assessment Today Icon
    Build Data Resilience - Take the Assessment Today

    Can you recover when it matters most? Take this quick assessment to identify gaps and build greater recovery confidence.

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  • Paessler: Easy to Use With Enterprise Power. Free Trial Icon
    Paessler: Easy to Use With Enterprise Power. Free Trial

    A low-code dashboard makes monitoring intuitive for any admin, while scripting and custom sensors give experts full control.

    You shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
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    Bean Network Tester

    Bean Network Tester

    Simulate bad network conditions on Windows - latency, packet loss

    Bean Network Tester is a tool for testers and developers: check how your application behaves on a poor connection. Like Clumsy or NetLimiter, it lets you deliberately degrade the network - add ping, drop packets, cap the speed, tear connections down, and more. It works by intercepting traffic with the WinDivert driver (via PyDivert), and offers both a clear windowed interface with tooltips and a command-line mode for CI. https://beannetworktester.donislawdev.com/
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    Downloads: 43 This Week
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  • 2
    Log4JUnit aims to help the testing (using JUnit assertions and test cases) of legacy code that gives a train on the log files it creates, and is, appart from that not very testable (due to missing sources, not testable design, etc).
    Downloads: 0 This Week
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  • 3

    Alchemist plugin

    Alchemist GCC/LLVM plugin for code analysis and tuning

    ...It is intended to to extract program properties for machine learning based optimization (see MILEPOST GCC); optimize programs at fine-grain level (such as unrolling, tiling, prefetching, etc); tune default optimization heuristic; gradually decompose program and detect performance or other anomalies; generate benchmarks particularly useful to train ML-based compilers. GCC plugin is licenced under GPLv3 licensed, while future LLVM plugins will be licensed under BSD license. Alchemist plugin is in a preliminary stage (based on MILEPOST ML plugin), so collaborations are very welcome!
    Downloads: 0 This Week
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