• Grafana: The open and composable observability platform Icon
    Grafana: The open and composable observability platform

    Faster answers, predictable costs, and no lock-in built by the team helping to make observability accessible to anyone.

    Grafana is the open source analytics & monitoring solution for every database.
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    Run applications fast and securely in a fully managed environment

    Cloud Run is a fully-managed compute platform that lets you run your code in a container directly on top of scalable infrastructure.

    Run frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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    AWS Toolkit for Visual Studio Code

    AWS Toolkit for Visual Studio Code

    Local Lambda debug, CodeWhisperer, SAM/CFN syntax, etc.

    ...The AWS CDK Explorer enables you to work with AWS Cloud Development Kit (CDK) applications. It shows a top-level view of your CDK applications that have been synthesized in your workspace. Amazon CodeWhisperer provides inline code suggestions using machine learning and natural language processing on the contents of your current file. Supported languages include Java, Python and Javascript.
    Downloads: 3 This Week
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  • 2
    fastNLP

    fastNLP

    fastNLP: A Modularized and Extensible NLP Framework

    fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models...
    Downloads: 0 This Week
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  • 3
    PyTorch Natural Language Processing

    PyTorch Natural Language Processing

    Basic Utilities for PyTorch Natural Language Processing (NLP)

    ...For example, check out this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. Now you've setup your pipeline, you may want to ensure that some functions run deterministically. Wrap any code that's random, with fork_rng and you'll be good to go. Now that you've computed your vocabulary, you may want to make use of pre-trained word vectors to set your embeddings.
    Downloads: 0 This Week
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