What you must know about PlaidML
According to Wikipedia, in computing, a "compiler" is a computer program that translates computer code written in one programming language into another language.
While this process occurs backend within a split second and is not visible on the display interface, compilers are required to convert some set of codes from high-level C++ to machine language that the CPU can comprehend since computer architecture is made up of electrical switches and cables that only function with binary 1s and 0s.
Now that we have that thought in motion, today™s post will identify a type of compiler that is highly sought after for many reasons. Some of the reasons will be highlighted in this read; likewise, will you identify some of its drawbacks and what makes PlaidML unique?
What is PlaidML all about?
PlaidML is an open-source deep learning framework that enables efficient training of neural networks on a wide range of hardware, including GPUs, CPUs, and other accelerators such as FPGAs. PlaidML was developed by the artificial intelligence (AI) startup Vertex.AI, which was later acquired by Intel in 2019.
Does PlaidML have a unique feature?
One of the unique features of PlaidML is its ability to automatically optimize computation for different hardware platforms, including those with different architectures or specifications. This makes it easier for developers to write and deploy deep learning models across a wide range of devices, including laptops, desktops, and embedded systems.
Where can PlaidML be applied?
- Neural network training: PlaidML can significantly reduce the training time of deep learning models by optimizing computations across different hardware platforms. This can be particularly useful for training large models or working with large datasets.
- Inference: PlaidML can also speed up the inference process, which is the process of applying a trained model to new data to make predictions. This is particularly important for real-time applications, such as object detection or natural language processing.
- Edge computing: PlaidML's ability to optimize computations for different hardware platforms makes it a useful tool for edge computing applications. This is because edge devices typically have limited resources, and PlaidML can help developers optimize their models for these devices without sacrificing performance.
- Research: PlaidML is also used by researchers who are exploring new approaches to deep learning and need a flexible, high-performance framework to test their ideas.
How compatible is PlaidML?
PlaidML supports a variety of popular deep-learning frameworks, including TensorFlow, Keras, and PyTorch. This allows developers to continue using the familiar APIs and workflows of their preferred frameworks while benefiting from PlaidML's performance optimizations.
Why is PlaidML sought after compared to others?
- Hardware-agnostic optimization: One of the most significant advantages of PlaidML is its ability to automatically optimize computations for different hardware platforms. This means that developers can write code once and deploy it on a wide range of devices, without having to worry about hardware-specific optimizations. This hardware-agnostic optimization is achieved using PlaidML's innovative tile-based computation engine.
- Cross-platform support: PlaidML is designed to work across a wide range of operating systems and hardware platforms, including Windows, Linux, macOS, and even Raspberry Pi. This makes it a highly versatile and flexible tool for deep learning development.
- Deep learning framework interoperability: PlaidML is compatible with several popular deep learning frameworks, including TensorFlow, Keras, and PyTorch. This means that developers can continue using their preferred framework and programming language while benefiting from PlaidML's performance optimizations.
- Open source: PlaidML is an open-source project, which means that its source code is freely available for developers to modify and contribute to. This encourages collaboration and innovation within the deep learning community.
- Ease of use: PlaidML has a user-friendly interface and a simple installation process, making it accessible to developers of all skill levels.
What is the major drawback of PlaidML?
Please note that PlaidML is an open-source project, therefore it may not have the same level of community support or resources as some other deep learning frameworks. This act could make it more challenging for developers to find the help or documentation they need when working with the tool.
Another potential drawback is that PlaidML may not be the best choice for some specific hardware platforms or architectures. While it is hardware-agnostic to optimize computations for varying hardware, there may be more specialized tools or frameworks available that are better suited for some uses.
Conclusion.
PlaidML is a useful tool for developers who consider accelerating their deep learning workflows and optimizing their models for a wide range of hardware platforms.
It remains a versatile tool that helps developers with its hardware-agnostic optimization, cross-platform support, deep learning framework interoperability, open-source nature, and ease of use, making it a unique and valuable tool for deep learning development.
