Showing 11 open source projects for "memory"

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    MongoDB Atlas runs apps anywhere

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  • 1
    SDGym

    SDGym

    Benchmarking synthetic data generation methods

    ...Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.
    Downloads: 0 This Week
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  • 2
    Dask

    Dask

    Parallel computing with task scheduling

    ...It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines.
    Downloads: 0 This Week
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  • 3
    Positron

    Positron

    Positron, a next-generation data science IDE

    Positron is a next-generation integrated development environment (IDE) created by Posit PBC (formerly RStudio Inc) specifically tailored for data science workflows in Python, R, and multi-language ecosystems. It aims to unify exploratory data analysis, production code, and data-app authoring in a single environment so that data scientists move from “question → insight → application” without switching tools. Built on the open-source Code-OSS foundation, Positron provides a familiar coding...
    Downloads: 0 This Week
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  • 4
    Pandas Profiling

    Pandas Profiling

    Create HTML profiling reports from pandas DataFrame objects

    ...File sizes, creation dates, dimensions, indication of truncated images and existance of EXIF metadata. Mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint). Comprehensive and automatic list of potential data quality issues (high correlation, skewness, uniformity, zeros, missing values, constant values, between others).
    Downloads: 0 This Week
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  • Build Securely on AWS with Proven Frameworks Icon
    Build Securely on AWS with Proven Frameworks

    Lay a foundation for success with Tested Reference Architectures developed by Fortinet’s experts. Learn more in this white paper.

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  • 5
    NVIDIA Merlin

    NVIDIA Merlin

    Library providing end-to-end GPU-accelerated recommender systems

    ...Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders. Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory. Deploy data transformations and trained models to production with only a few lines of code.
    Downloads: 0 This Week
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  • 6
    text-dedup

    text-dedup

    All-in-one text de-duplication

    ...This is especially useful for NLP tasks where duplicated training data can skew model performance. text-dedup scales to billions of documents and offers tools for chunking, hashing, and comparing text efficiently with low memory usage. It supports Jaccard similarity thresholding, parallel execution, and flexible deduplication strategies, making it ideal for cleaning web-scraped data, language model training datasets, or document archives.
    Downloads: 0 This Week
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  • 7
    Orchest

    Orchest

    Build data pipelines, the easy way

    ...Parameterize your pipelines and run them periodically on a cron schedule. Easily install language or system packages. Built on top of regular Docker container images. Creation of multiple instances with up to 8 vCPU & 32 GiB memory. A free Orchest instance with 2 vCPU & 8 GiB memory. Simple data pipelines with Orchest. Each step runs a file in a container. It's that simple! Spin up services whose lifetime spans across the entire pipeline run. Easily define your dependencies to run on any machine. Run any subset of the pipeline directly or periodically.
    Downloads: 0 This Week
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  • 8
    Wally

    Wally

    Distributed Stream Processing

    ...Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency data processing. Be portable and deploy easily (i.e., run on-prem or any cloud). Manage in-memory state for the application. Allow applications to scale as needed, even when they are live and up-and-running. The primary API for Wally is written in Pony. Wally applications are written using this Pony API.
    Downloads: 2 This Week
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  • 9
    Vaex

    Vaex

    Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python

    ...It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted). Cut development cut development time by 80%. Your prototype is your solution. Create automatic pipelines for any model.
    Downloads: 0 This Week
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  • 10
    gsdview
    Geo-Spatial Data Viewer (GSDView) is a lightweight viewer for geo-spatial data and products. It is written in python and Qt4 and uses the GDAL library. GSDView is modular and has a simple plug-in architecture.
    Downloads: 0 This Week
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  • 11
    QuickFlash is a C++-based analysis library for data files generated by the Flash hydrodynamics code. It provides high-performance access to large datasets in low memory situations and other restrictive environments.
    Downloads: 3 This Week
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