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    PRTG Catches Network Issues Before They Cause Downtime

    Threshold-based alerts flag problems early, so your team can act before users notice, not after.

    Reactive troubleshooting usually means hearing about a problem from frustrated users, not your monitoring tool. PRTG sets threshold-based alerts across devices, servers and applications, notifying your team by email, SMS or push the moment a metric crosses a set limit. That means catching a failing disk or overloaded server before it becomes an outage and getting time back from firefighting. Start a free trial and set your first alerts today.
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    Host LLMs in Production With On-Demand GPUs

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  • 1
    DataFrame

    DataFrame

    C++ DataFrame for statistical, Financial, and ML analysis

    This is a C++ analytical library designed for data analysis similar to libraries in Python and R. For example, you would compare this to Pandas, R data.frame, or Polars. You can slice the data in many different ways. You can join, merge, and group-by the data. You can run various statistical, summarization, financial, and ML algorithms on the data. You can add your custom algorithms easily. You can multi-column sort, custom pick, and delete the data. DataFrame also includes a large collection of analytical algorithms in the form of visitors. ...
    Downloads: 9 This Week
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  • 2
    Deep Lake

    Deep Lake

    Data Lake for Deep Learning. Build, manage, and query datasets

    ...Deeplake automatically decompresses them to raw data only when needed, e.g., when training a model. Treat your cloud datasets as if they are a collection of NumPy arrays in your system's memory. Slice them, index them, or iterate through them.
    Downloads: 6 This Week
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  • 3
    SAHI

    SAHI

    A lightweight vision library for performing large object detection

    A lightweight vision library for performing large-scale object detection & instance segmentation. Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities. Detection of small objects and objects far away in the scene is a major...
    Downloads: 4 This Week
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  • 4
    Compose

    Compose

    A machine learning tool for automated prediction engineering

    ...The process starts by setting the first cutoff time after the minimum amount of data. Then subsequent cutoff times are spaced apart using gaps. Starting from each cutoff time, a window determines the amount of data, also referred to as a data slice, to pass into a labeling function.
    Downloads: 0 This Week
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    General-purpose, compute-optimized, or GPU/TPU-accelerated. Built to your exact specs.

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  • 5
    anno

    anno

    Go package for text annotation

    Go package for text annotation. There are two parts to anno, the first is a series of Finder functions that look for interesting articles (which it calls `Notes`) inside the text, returning a slice of Note structs. The second is the Expander, which replaces the text in each Note with something else, like the HTML for a link or something. It tells you the bytes that it found, the `Start` index and a string describing the kind of `Note`. The kind is useful for when you run pass `Finder` objects to the `FindMany` or `FindManyString` functions. ...
    Downloads: 0 This Week
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  • 6
    PyTorch-BigGraph

    PyTorch-BigGraph

    Generate embeddings from large-scale graph-structured data

    PyTorch-BigGraph (PBG) is a system for learning embeddings on massive graphs—think billions of nodes and edges—using partitioning and distributed training to keep memory and compute tractable. It shards entities into partitions and buckets edges so that each training pass only touches a small slice of parameters, which drastically reduces peak RAM and enables horizontal scaling across machines. PBG supports multi-relation graphs (knowledge graphs) with relation-specific scoring functions, negative sampling strategies, and typed entities, making it suitable for link prediction and retrieval. Its training loop is built for throughput: asynchronous I/O, memory-mapped tensors, and lock-free updates keep GPUs and CPUs fed even at extreme scale. ...
    Downloads: 0 This Week
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