science

PlaidML

PlaidML is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand PlaidML rather than just read about it. In short: PlaidML is a portable tensor compiler. Tensor compilers bridge the gap between the universal mathematical descriptions of deep learning operations, such as convolution, and the platform and chip-specific code needed to perform those operations with good performance.

Key takeaways

PlaidML belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect PlaidML to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of PlaidML from memory before moving on to harder problems.

Reference excerpt

PlaidML is a portable tensor compiler. Tensor compilers bridge the gap between the universal mathematical descriptions of deep learning operations, such as convolution, and the platform and chip-specific code needed to perform those operations with good performance. Internally, PlaidML makes use of the Tile eDSL to generate OpenCL, OpenGL, LLVM, or CUDA code. It enables deep learning on devices where the available computing hardware is either not well supported or the available software stack contains only proprietary components. For example, it does not require the usage of CUDA or cuDNN on Nvidia hardware, while achieving comparable performance. PlaidML supports the machine learning libraries Keras, ONNX, and nGraph. However, Keras have dropped support of multiple backends and latest Keras version isn't compatible with PlaidML. An integration with Tensorflow-Keras is planned as a replacement for Keras.

History In August 2018 Intel acquired Vertex.AI, a startup whose mission statement was “deep learning for every platform”. Intel released PlaidML as free software under to the terms of the Apache Licence (version 2.0) to improve compatibility with nGraph, TensorFlow, and other ecosystem software.

References

External links Official website

Worked examples

Example 1 — a first encounter with PlaidML

Start with the simplest possible case. Write down what PlaidML claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to PlaidML before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about PlaidML ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of PlaidML

In research: PlaidML appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses PlaidML in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: PlaidML is common in secondary-school and first-year university syllabi. It links to neighbouring topics Compilers, Tensors, so understanding it makes those chapters shorter.
In everyday life: Look for PlaidML outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study PlaidML in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what PlaidML means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain PlaidML out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is PlaidML in simple terms?

Why does PlaidML matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study PlaidML?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on PlaidML.