Tvm
Apache TVM is an open-source machine learning compilation framework designed to optimize and compile ML models for deployment across a wide range of hardware platforms, from data center GPUs to edge devices. It uses a Python-first approach that allows users to customize compilation pipelines and produce minimal deployable modules tailored to specific hardware backends. The framework supports multiple hardware backends including CUDA, ROCm, Vulkan, OpenCL, and Metal, enabling efficient execution of ML workloads on diverse environments. TVM is maintained by an active community with nearly a thousand contributors and frequent releases under the Apache-2.0 license, ensuring free and open community ownership.
Apache TVM compiles and optimizes machine learning models for deployment on diverse hardware platforms using a Python-first customizable compiler framework.
Deploy ML models on edge devices
Compile and optimize machine learning models to run efficiently on resource-constrained edge hardware using minimal runtimes.
Optimize ML workloads for data center GPUs
Customize compilation pipelines to maximize performance of ML models on high-performance GPUs in data centers.
Cross-platform ML model deployment
Use universal deployment modules to run the same ML model across different hardware architectures without rewriting code.
git clone --recursive https://github.com/apache/tvm to get the latest source with submodules.cmake .. followed by cmake --build build --config Release adapting commands for your platform.