Showing 3 open source projects for "requests"

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    Streamlit

    Streamlit

    The fastest way to build data apps in Python

    ...Then see it automatically update as you iteratively save the source file. Adding a widget is the same as declaring a variable. No need to write a backend, define routes, handle HTTP requests, connect a frontend, write HTML, CSS, JavaScript, etc. Effortlessly share, manage and deploy your apps, directly from Streamlit. Streamlit lets you turn data scripts into sharable web apps in minutes, not weeks. It’s all Python, open-source, and free! And once you’ve created an app you can use our cloud platform to deploy, manage, and share your app! ...
    Downloads: 52 This Week
    Last Update:
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  • 2
    SageMaker MXNet Inference Toolkit

    SageMaker MXNet Inference Toolkit

    Toolkit for allowing inference and serving with MXNet in SageMaker

    ...This library provides default pre-processing, predict and postprocessing for certain MXNet model types and utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). ...
    Downloads: 0 This Week
    Last Update:
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  • 3
    SageMaker TensorFlow Serving Container

    SageMaker TensorFlow Serving Container

    A TensorFlow Serving solution for use in SageMaker

    ...But if you want to your updated image in SageMaker, you need to publish it to an ECR repository in your account. You can also run your container locally in Docker to test different models and input inference requests by hand.
    Downloads: 0 This Week
    Last Update:
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