Apache TVM

About Apache TVM

The vision of the Apache TVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. TVM provides the following main features:

Compilation of deep learning models into minimum deployable modules. Infrastructure to automatic generate and optimize models on more backend with better performance.

Key Features & Capabilities

Performance

Compilation and minimal runtimes commonly unlock ML workloads on existing hardware.

Run Everywhere

CPUs, GPUs, browsers, microcontrollers, FPGAs and more.

Automatically generate and optimize tensor operators on more backends.

Flexibility

Need support for block sparsity, quantization (1,2,4,8 bit integers, posit), random forests/classical ML, memory planning, MISRA-C compatibility, Python prototyping or all of the above?

TVM’s flexible design enables all of these things and more.

Ease of Use

Compilation of deep learning models in Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet and more. Start using TVM with Python today, build out production stacks using C++, Rust, or Java the next day.

Docs

Written with care & love for you.

Join the TVM community

Blog

Read more about TVM and our thinking

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原文链接: Apache TVM https://www.huajiangbk.com/newsview2251856.html

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