| DeepSpeedMicrosoft | ||||||
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| About
            DeepSpeed is an open source deep learning optimization library for PyTorch.  It's designed to reduce computing power and memory use, and to train large distributed models with better parallelism on existing computer hardware.  DeepSpeed is optimized for low latency, high throughput training.
DeepSpeed can train DL models with over a hundred billion parameters on the current generation of GPU clusters.  It can also train up to 13 billion parameters in a single GPU.
DeepSpeed is developed by Microsoft and aims to offer distributed training for large-scale models. It's built on top of PyTorch, which specializes in data parallelism. 
             | About
            JAX is a Python library designed for high-performance numerical computing and machine learning research. It offers a NumPy-like API, facilitating seamless adoption for those familiar with NumPy. Key features of JAX include automatic differentiation, just-in-time compilation, vectorization, and parallelization, all optimized for execution on CPUs, GPUs, and TPUs. These capabilities enable efficient computation for complex mathematical functions and large-scale machine-learning models. JAX also integrates with various libraries within its ecosystem, such as Flax for neural networks and Optax for optimization tasks. Comprehensive documentation, including tutorials and user guides, is available to assist users in leveraging JAX's full potential. 
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| Audience
        Deep learning model developers
         | Audience
        Professional researchers and developers searching for a solution to manage their numerical computing and machine learning operations in Python
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| Company InformationMicrosoft Founded: 1975 United States www.deepspeed.ai/ | Company InformationJAX United States docs.jax.dev/en/latest/ | |||||
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