Showing 4 open source projects for "usb-1.02-driver-hardlock"

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    Access Google's best plus Claude, Llama, and Gemma. Fine-tune and deploy from one console.

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  • 1
    dm_control

    dm_control

    DeepMind's software stack for physics-based simulation

    ...On Linux these can be installed using your distribution's package manager. "Headless" hardware rendering (i.e. without a windowing system such as X11) requires EXT_platform_device support in the EGL driver. While dm_control has been largely updated to use the pybind11-based bindings provided via the mujoco package, at this time it still relies on some legacy components that are automatically generated.
    Downloads: 1 This Week
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  • 2
    AWS Neuron

    AWS Neuron

    Powering Amazon custom machine learning chips

    ...Neuron is pre-integrated into popular machine learning frameworks like TensorFlow, MXNet and Pytorch to provide a seamless training-to-inference workflow. It includes a compiler, runtime driver, as well as debug and profiling utilities with a TensorBoard plugin for visualization.
    Downloads: 0 This Week
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  • 3
    Ubix Linux

    Ubix Linux

    The Pocket Datalab

    ...Ubix Linux is an open-source, Debian-based Linux distribution geared towards data acquisition, transformation, analysis and presentation. Ubix Linux purpose is to offer a tiny but versatile datalab. Ubix Linux is easily accessible, resource-efficient and completely portable on a simple USB key. Ubix Linux is a perfect toolset for learning data analysis and artificial intelligence basics on small to medium datasets. You can find additional information, technical guidance, and user credentials on the project website https://ubix-linux.sourceforge.io/ or on the project subreddit https://reddit.com/r/UbixLinux.
    Downloads: 1 This Week
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  • 4
    Elephas

    Elephas

    Distributed Deep learning with Keras & Spark

    ...Elephas intends to keep the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models, which can be run on massive data sets. Elephas implements a class of data-parallel algorithms on top of Keras, using Spark's RDDs and data frames. Keras Models are initialized on the driver, then serialized and shipped to workers, alongside with data and broadcasted model parameters. Spark workers deserialize the model, train their chunk of data and send their gradients back to the driver. The "master" model on the driver is updated by an optimizer, which takes gradients either synchronously or asynchronously. Hyper-parameter optimization with elephas is based on hyperas, a convenience wrapper for hyperopt and keras.
    Downloads: 0 This Week
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