Showing 17 open source projects for "machine learning python"

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  • 1
    Volcano

    Volcano

    A Cloud Native Batch System (Project under CNCF)

    Volcano is a batch system built on Kubernetes. It provides a suite of mechanisms that are commonly required by many classes of batch & elastic workload including machine learning/deep learning, bioinformatics/genomics, and other "big data" applications. These types of applications typically run on generalized domain frameworks like TensorFlow, Spark, Ray, PyTorch, MPI, etc, which Volcano integrates with. Volcano builds upon a decade and a half of experience running a wide variety of high-performance workloads at scale using several systems and platforms, combined with best-of-breed ideas and practices from the open-source community. ...
    Downloads: 36 This Week
    Last Update:
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  • 2
    FSM for Go

    FSM for Go

    Finite State Machine for Go

    FSM is a finite state machine for Go. It is heavily based on two FSM implementations. Javascript Finite State Machine, and Fysom for Python. Visualize outputs a visualization of a FSM in Graphviz format. VisualizeForMermaidWithGraphType outputs a visualization of a FSM in Mermaid format as specified by the graphType. VisualizeWithType outputs a visualization of a FSM in the desired format.
    Downloads: 0 This Week
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  • 3
    Argo Workflows

    Argo Workflows

    Workflow engine for Kubernetes

    ...Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG). Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes. Run CI/CD pipelines natively on Kubernetes without configuring complex software development products. Argo Workflows is the most popular workflow execution engine for Kubernetes. It can run 1000s of workflows a day, each with 1000s of concurrent tasks. ...
    Downloads: 2 This Week
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  • 4
    spaGO

    spaGO

    Self-contained Machine Learning and Natural Language Processing lib

    A Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. Spago is self-contained, in that it uses its own lightweight computational graph both for training and inference, easy to understand from start to finish. The core module of Spago relies only on testify for unit testing.
    Downloads: 0 This Week
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  • 5
    MinIO

    MinIO

    High performance object storage server compatible with Amazon S3 APIs

    MinIO is a high performance object storage server that is API compatible with Amazon S3 cloud storage service. MinIO makes it easy to build high performance, cloud native data infrastructure for machine learning, analytics and application data workloads. It is incredibly fast, enabling object storage to operate as the primary storage tier for a diverse set of workloads. It is also built to be cloud native and enterprise ready. MinIO is being used worldwide in various production deployments, and is leading the way as the most downloaded object storage server in the industry.
    Downloads: 31 This Week
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  • 6
    Netcap

    Netcap

    A framework for secure and scalable network traffic analysis

    The Netcap (NETwork CAPture) framework efficiently converts a stream of network packets into platform-neutral type-safe structured audit records that represent specific protocols or custom abstractions. These audit records can be stored on disk or exchanged over the network, and are well-suited as a data source for machine learning algorithms. Since parsing of untrusted input can be dangerous and network data is potentially malicious, a programming language that provides a garbage-collected memory-safe runtime is used for the implementation.
    Downloads: 3 This Week
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  • 7
    ergo

    ergo

    Framework for creating microservices using technologies of Erlang/OTP

    ...The easiest way to create an OTP-designed application in Golang. The goal of this project is to leverage Erlang/OTP experience with Golang performance. The ideal framework for creating complex and distributed solutions (machine learning, data processing pipeline, etc.) being simple and reliable. You don't have to reinvent the wheel. There are ready-to-use implemented design patterns. Two processes can be linked to each other. Termination one terminates another. Any process can monitor the service node. Receives NODE DOWN if node terminated. Ergo Framework almost 5 times outperforms the original Erlang network messaging. ...
    Downloads: 1 This Week
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  • 8
    KubeEdge

    KubeEdge

    Kubernetes Native Edge Computing Framework (project under CNCF)

    ...It consists of a cloud part and an edge part, and provides core infrastructure support for networking, application deployment, and metadata synchronization between the cloud and edge. It also supports MQTT which enables edge devices to access through edge nodes. With KubeEdge it is easy to get and deploy existing complicated machine learning, image recognition, event processing, and other high-level applications to the Edge. With business logic running at the Edge, much larger volumes of data can be secured & processed locally where the data is produced. With data processed at the Edge, the responsiveness is increased dramatically and data privacy is protected.
    Downloads: 0 This Week
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  • 9
    k3s in docker

    k3s in docker

    Little helper to run CNCF's k3s in Docker

    k3d is a lightweight wrapper to run k3s (Rancher Lab’s minimal Kubernetes distribution) in docker. k3d makes it very easy to create single- and multi-node k3s clusters in docker, e.g. for local development on Kubernetes. Note: k3d is a community-driven project but it’s not an official Rancher (SUSE) product. Sponsoring: To spend any significant amount of time improving k3d, we rely on sponsorships. k3d creates containerized k3s clusters. This means, that you can spin up a multi-node k3s...
    Downloads: 0 This Week
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  • 10
    Whalebrew

    Whalebrew

    Homebrew, but with Docker images

    ...Whalebrew makes those things work with Docker, too. Whalebrew can run almost any CLI tool, but it isn't for everything (e.g. where commands must start instantly). It works particularly well for a Python app that requires C libraries, specific package versions, and other CLI tools that you don't want to clutter up your machine with. Package managers tend to be very closely tied to the system they are running on. Whalebrew packages work on any modern version of macOS, Linux, and Windows.
    Downloads: 0 This Week
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  • 11
    GoNB

    GoNB

    GoNB, a Go Notebook Kernel for Jupyter

    ...It already includes many goodies: cache between cell of results, contextual help and auto-complete (with gopls), compilation error context (by mousing over), bash command execution, images, html, etc. See the tutorial. It's been heavily used by the author (in developing GoMLX, a machine learning framework for Go), but should still be seen as experimental — if we hear success stories from others, we can change this.
    Downloads: 0 This Week
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  • 12
    MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
    Downloads: 0 This Week
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  • 13
    KubeOperator

    KubeOperator

    An open source, lightweight Kubernetes distribution

    ...Support online environment and offline environment deployment. Provides a visual web UI. Supports cluster planning, deployment and operations. Easily run workloads like machine learning, high-performance computing, and more. Quickly deploy and manage applications in K8S. Only two steps to complete the KubeOperator installation and deployment.
    Downloads: 0 This Week
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  • 14
    Amazon SageMaker Operators Kubernetes

    Amazon SageMaker Operators Kubernetes

    Amazon SageMaker operator for Kubernetes

    Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don't have to manage servers.
    Downloads: 0 This Week
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  • 15
    Bazel Container Image Rules

    Bazel Container Image Rules

    Rules for building and handling Docker images with Bazel

    This repository contains a set of rules for pulling down base images, augmenting them with build artifacts and assets, and publishing those images. These rules do not require / use Docker for pulling, building, or pushing images. These rules used to be docker_build, docker_push, etc. and the aliases for these (mostly) legacy names still exist largely for backwards-compatibility. We also have early-stage oci_image, oci_push, etc. aliases for folks that enjoy the consistency of a consistent...
    Downloads: 0 This Week
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  • 16
    The Google Cloud Developer's Cheat Sheet

    The Google Cloud Developer's Cheat Sheet

    Cheat sheet for Google Cloud developers

    Every product in the Google Cloud family described in <=4 words (with liberal use of hyphens and slashes) by the Google Developer Relations Team. This list only includes products that are publicly available. There are several products in pre-release/private-alpha that will not be included until they go public beta or GA. Many of these products have a free tier. There is also a free trial that will enable you try almost everything. API platforms and ecosystems, developer and management tools,...
    Downloads: 0 This Week
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  • 17
    FIND3

    FIND3

    High-precision indoor positioning framework, version 3

    ...(previously just WiFi). Passive scanning built-in (previously required a separate server). Support for Bluetooth scanning in scanning utility (previously just WiFi). Meta-learning with 10 different machine learning classifiers (previously just three). Client uses Websockets+React which reduces bandwidth (and coding complexity). Rolling compression of MAC addresses for much smaller on-disk databases. Data storage in SQLite-database (previously it was BoltDB). Released under MIT license (more commercially compatible than AGPL). ...
    Downloads: 0 This Week
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