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|\n| [Getting Started](docs/01-start.md) | Getting up and running with AXLearn. |\n| [Concepts](docs/02-concepts.md) | Core concepts and design principles. |\n| [CLI User Guide](docs/03-cli.md) | How to use the CLI. |\n| [Infrastructure](docs/04-infrastructure.md) | Core infrastructure components. |\n\n## Introduction\n\nAXLearn is a library built on top of [JAX](https://jax.readthedocs.io/) and\n[XLA](https://www.tensorflow.org/xla) to support the development of large-scale deep learning models.\n\nAXLearn takes an object-oriented approach to the software engineering challenges that arise from\nbuilding, iterating, and maintaining models.\nThe configuration system of the library lets users compose models from reusable building blocks and\nintegrate with other libraries such as [Flax](https://flax.readthedocs.io/) and\n[Hugging Face transformers](https://github.com/huggingface/transformers).\n\nAXLearn is built to scale.\nIt supports the training of models with up to hundreds of billions of parameters across thousands of\naccelerators at high utilization.\nIt is also designed to run on public clouds and provides tools to deploy and manage jobs and data.\nBuilt on top of [GSPMD](https://arxiv.org/abs/2105.04663), AXLearn adopts a global computation\nparadigm to allow users to describe computation on a virtual global computer rather than on a\nper-accelerator basis.\n\nAXLearn supports a wide range of applications, including natural language processing, computer\nvision, and speech recognition and contains baseline configurations for training state-of-the-art\nmodels.\n\nPlease see [Concepts](docs/02-concepts.md) for more details on the core components and design of AXLearn, or [Getting Started](docs/01-start.md) if you want to get your hands dirty.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapple%2Faxlearn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fapple%2Faxlearn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapple%2Faxlearn/lists"}