PlaidML

Paid

Accelerate ML model performance, reduce power consumption, and utilize various frameworks like TensorFlow, PyTorch, and Keras.

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About PlaidML

PlaidML is a machine learning framework that allows developers to unlock high-performance hardware for their machine learning models. It is designed to be easy to use and highly efficient, offering developers a simple way to harness the power of GPUs, TPUs, and other specialized devices. PlaidML supports a wide range of popular frameworks, including TensorFlow, PyTorch, and Keras, so developers can quickly and easily develop and deploy their models across different hardware platforms. With PlaidML, developers can achieve higher performance on their machine learning models while also reducing their power consumption and cost. PlaidML also has a comprehensive set of tools and APIs to simplify development and deployment, making it easier for developers to bring their projects to life. With PlaidML, developers can unlock the potential of their hardware and create powerful, high-performance machine learning models.

Key Features

Accelerate ML model performance using the power of GPUs and TPUs.

Reduce power consumption and cost of training.

Utilize a wide range of frameworks, including TensorFlow, PyTorch, and Keras.

Pros & Cons

Pros

Open source and free to use (MIT license implied by GitHub project)
Portable across a wide range of hardware, including non-standard devices
Supports multiple popular deep learning frameworks (Keras, ONNX, nGraph)
Adopts industry-standard MLIR compiler infrastructure for extensibility
Designed for high performance and programmability

Cons

Repository archived on March 29, 2025 and is now read-only; no active maintenance
Development branch (plaidml-v1) is a research project with limited hardware support (only Intel/AMD CPUs with AVX2/AVX512)
Some features, tests, and hardware targets may be broken in the development branch
No longer under development; users should consider alternatives for new projects
Does not support GPU/TPU acceleration in the current development branch (plaidml-v1)

Best For

Accelerate ML model performance using the power of GPUs and TPUs.Reduce power consumption and cost of training.Utilize a wide range of frameworks, including TensorFlow, PyTorch, and Keras.

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FAQ

What is PlaidML?

PlaidML is an open-source portable tensor compiler for deep learning that works on laptops, embedded devices, and other hardware. It sits underneath frameworks like Keras, ONNX, and nGraph to provide hardware access.

What frameworks does PlaidML support?

PlaidML supports Keras, ONNX, and nGraph frameworks.

Is PlaidML still maintained?

No, the PlaidML repository was archived on March 29, 2025 and is now read-only. There is no further development on the master branch or the plaidml-v1 branch.

What hardware does PlaidML support?

The master branch (version 0.7.0) supports various hardware. The development branch plaidml-v1 currently only supports Intel and AMD CPUs with AVX2 and AVX512 instructions. GPU support is not available in plaidml-v1.

How is PlaidML different from other deep learning frameworks?

PlaidML is a tensor compiler that sits underneath common ML frameworks, providing a portable layer to run models on diverse hardware without being tied to a specific vendor's software stack.