MCPcopy
hub / github.com/Xilinx/Vitis-AI

github.com/Xilinx/Vitis-AI @v5.0 sqlite

repository ↗ · DeepWiki ↗ · release v5.0 ↗
22,226 symbols 83,309 edges 1,765 files 4,017 documented · 18%
README

Vitis AI

Adaptable & Real-Time AI Inference Acceleration

Release Version License GitHub Pull Requests Documentation Repo Size

AMD Vitis™ AI is an Integrated Development Environment that can be leveraged to accelerate AI inference on AMD adaptable platforms. Vitis AI provides optimized IP, tools, libraries, models, as well as resources, such as example designs and tutorials that aid the user throughout the development process. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on AMD adaptable SoCs and Alveo Data Center accelerator cards.

Getting Started

If your visit here is accidental, but you are enthusiastic to learn more about Vitis AI, please visit the Vitis AI homepage on Xilinx.com.

Otherwise, if your visit is deliberate and you are ready to begin, why not VIEW THE VITIS-AI DOCUMENTATION ON GITHUB.IO?

How to Download the Repository

To get a local copy of Vitis AI, clone this repository to the local system with the following command:

git clone https://github.com/Xilinx/Vitis-AI

This command needs to be executed only once to retrieve the latest version of Vitis AI.

Optionally, configure git-lfs in order to reduce the local storage requirements.

Repository Branching and Tagging Strategy

To understand the branching and tagging strategy leveraged by this repository, please refer to this page

Licenses

Vitis AI License: Apache 2.0

Third party: Components

Core symbols most depended-on inside this repo

make_node
called by 1845
src/vai_quantizer/tensorflow-onnx/tf2onnx/graph.py
append
called by 1315
src/vai_quantizer/vai_q_pytorch/nndct_shared/algorithms/search.py
range
called by 1308
docs/_static/underscore-1.13.1.js
get
called by 1262
src/vai_quantizer/vai_q_pytorch/nndct_shared/utils/registry.py
astype
called by 1117
src/vai_quantizer/xnnc4xir/xnnc/tensor/xtensor.py
append
called by 1072
src/vai_optimizer/nndct_shared/algorithms/search.py
debug
called by 739
src/vai_quantizer/vai_q_pytorch/nndct_shared/utils/log.py
add
called by 643
src/vai_quantizer/tensorflow-onnx/tools/aggregate-patterns.py

Shape

Method 13,105
Function 6,075
Class 2,866
Route 180

Languages

Python98%
TypeScript2%

Modules by API surface

src/vai_quantizer/tensorflow-onnx/tests/test_backend.py508 symbols
src/vai_quantizer/xnnc4xir/xnnc/ir/xnode.py413 symbols
src/vai_quantizer/vai_q_pytorch/pytorch_binding/pytorch_nndct/parse/torch_op_def.py294 symbols
src/vai_optimizer/pytorch_binding/pytorch_nndct/parse/torch_op_def.py294 symbols
src/vai_quantizer/tensorflow-onnx/tf2onnx/onnx_opset/tensor.py230 symbols
src/vai_quantizer/tensorflow-onnx/tests/keras2onnx_unit_tests/test_layers.py186 symbols
src/vai_quantizer/vai_q_pytorch/tensorflow/tf_nndct/graph/op_def.py175 symbols
src/vai_optimizer/tensorflow/tf_nndct/graph/op_def.py175 symbols
src/vai_quantizer/vai_q_pytorch/pytorch_binding/pytorch_nndct/parse/op_dispatcher.py154 symbols
src/vai_optimizer/pytorch_binding/pytorch_nndct/parse/op_dispatcher.py154 symbols
src/vai_quantizer/vai_q_pytorch/nndct_shared/nndct_graph/operator_definition.py150 symbols
src/vai_optimizer/nndct_shared/nndct_graph/operator_definition.py150 symbols

Dependencies from manifests, versioned

PyYAML6.0 · 1×
datasets2.9.0 · 1×
dm-tree0.1.1 · 1×
enum341.1 · 1×
graphviz0.19.1 · 1×
h5py2.10.0 · 1×
keras2.12 · 1×
marshmallow3.0.0rc5 · 1×
networkx2.5.1 · 1×
numpy1.24.2 · 1×
onnx1.14.0 · 1×
onnxruntime1.14.0 · 1×

For agents

$ claude mcp add Vitis-AI \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact