PlaidML was developed as an open-source project by Vertex.AI in 2017. It was created to provide a portable deep learning engine that could efficiently run machine learning models on a variety of hardware platforms, including those not traditionally supported by mainstream frameworks. In 2018, Intel acquired Vertex.AI, and PlaidML became part of Intel's portfolio to enhance its AI capabilities across diverse hardware architectures.
PlaidML
PlaidML is an open-source, portable deep learning engine designed to enable efficient use of hardware accelerators for machine learning tasks. It allows developers to write machine learning models that can run on a variety of platforms, including GPUs, CPUs, and other accelerators, without being tied to a specific vendor's ecosystem. PlaidML supports multiple machine learning frameworks and provides a backend for executing models efficiently across different hardware architectures.
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About PlaidML
PlaidML's strengths included its portability across various hardware platforms and its ability to support multiple machine learning frameworks, which provided flexibility for developers. Its weaknesses involved limited community support compared to more established frameworks and less optimization for specific hardware compared to vendor-specific solutions. Competitors included TensorFlow, PyTorch, and Apache MXNet, which offered broader ecosystem support and more extensive optimization for specific hardware platforms.
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How to hire a PlaidML expert
A PlaidML expert must have proficiency in Python programming and a strong understanding of machine learning concepts. They should be skilled in working with deep learning frameworks like Keras, as PlaidML often integrates with such frameworks. Familiarity with hardware acceleration and experience in optimizing models for different hardware architectures, including GPUs and CPUs, are crucial. Additionally, they should be adept at troubleshooting and performance tuning within the PlaidML environment.
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