opentensors.dot

 digraph{    
    node[shape=plaintext,color=none]
    //   splines=polyline
    // concentrate=true
    nodesep=0.3
    //   layout=dagre
    edge[penwidth=1,arrowsize=0.6]
  
  
  
    // Hardware
    // {
    //     rank = same
    //     "CPU (Intel)"
    //     "CPU (AMD)"
    //     "CPU (ARM)"
    //     "Nervana NNP"
    //     "GPU (Nvidia)"
    //     "GPU (AMD)"
    //     "Apple SoC"
    //     "GPU (Intel)"
    //     "TPU"
    //     "Accelerator"
    // } 
    
    Keras->"TensorFlow Graph"
    
    NumPy -> CPU
    CPU -> {"CPU (Intel)" "CPU (AMD)" "CPU (ARM)"}
    subgraph cluster_intel {
        label=Intel
        color="#0f7dc2"
        fontcolor="#0f7dc2"
        // https://github.com/intel/ideep
        "iDeep" -> "DNNL"
        // https://github.com/intel/mkl-dnn
        "DNNL" -> {"CPU (Intel)" "GPU (Intel)"}
        // https://github.com/intel-analytics/BigDL
        // https://github.com/intel-analytics/BigDL-core/blob/a1f141cd82b59077f6d5e5234eeb693431a90368/native-dnn/src/main/c/com_intel_analytics_bigdl_mkl_Memory.h
        "BigDL" -> "DNNL"
        "MKL" -> "CPU (Intel)"
        
        // https://github.com/NervanaSystems/ngraph
        "nGraph"
        "Nervana NNP"
        "MLIR: nGraph"
        "PlaidML"
        clDNN->{"CPU (Intel)" "GPU (Intel)"}
        "OpenVino"->clDNN
    }
    
    // https://software.intel.com/en-us/openvino-toolkit/deep-learning-cv
    {Caffe "TensorFlow Graph" MXNet ONNX}->OpenVino
    // https://docs.openvinotoolkit.org/latest/index.html
    OpenVino->{"CPU (Intel)" "GPU (Intel)" "Accelerator"}
    
    // https://github.com/plaidml/plaidml
    {Keras ONNX}->PlaidML
    "PlaidML"->{"GPU (AMD)" "GPU (Intel)" "GPU (Nvidia)"}

    // https://www.ngraph.ai/ecosystem#hardware-&-backend-support
    {"TensorFlow Graph" MXNet}->nGraph->{"CPU (Intel)" clDNN PlaidML "Nervana NNP"}
    
    // https://bigdl-project.github.io/0.9.0/#ProgrammingGuide/tensorflow-support/
    "TensorFlow Graph" -> "BigDL"
    "BigDL" -> "TensorFlow Graph"
    // https://bigdl-project.github.io/0.9.0/#ProgrammingGuide/caffe-support/
    "BigDL" -> "Caffe"
    "Caffe" -> "BigDL"
    // https://bigdl-project.github.io/0.9.0/#ProgrammingGuide/keras-support/
    "Keras" -> "BigDL"

    // https://github.com/intel-analytics/BigDL-core/tree/master/bigquant/src/main/c
    "BigDL" -> "BigQuant"
    // https://github.com/intel-analytics/BigDL-core/blob/master/mkl2017-xeon-blas/src/main/c/mkl.c
    "BigDL" -> "MKL"
    // https://github.com/intel-analytics/BigDL-core/blob/master/opencv/src/main/java/com/intel/analytics/bigdl/opencv/OpenCV.java
    "BigDL" -> "OpenCV"
    "OpenCV" -> "CPU"

    // http://caffe.berkeleyvision.org/installation.html
    "Caffe" -> {"CUDA" "BLAS" "OpenCV" "cuDNN"}
    
    // https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
    "BLAS"->"CPU"
    
    // https://en.wikipedia.org/wiki/Math_Kernel_Library
    "BLAS" -> "MKL"

    // https://en.wikipedia.org/wiki/AMD_Core_Math_Library
    "BLAS" -> "AMD Math Library"
  
    //   https://docs.opencv.org/master/d7/d9f/tutorial_linux_install.html
    "OpenCV"->CUDA
    subgraph cluster_nvidia {
        label=Nvidia
        color="#76B900"
        fontcolor="#76B900"
        // https://en.wikipedia.org/wiki/CUDA
        "CUDA"->"NVVM"-> "GPU (Nvidia)"
        "cuDNN" -> "GPU (Nvidia)"
        "GPU (Nvidia)"
        "TensorRT"->CUDA
    
    }
    subgraph cluster_amd {
        label=AMD
        // https://en.wikipedia.org/wiki/CUDA
        "AMD Math Library"-> "CPU (AMD)"
        "GPU (AMD)"
        // https://github.com/tensorflow/mlir/pull/146
        "ROCDL"->"GPU (AMD)"
        "ROCm"->"GPU (AMD)"
        MIOpen
    }

    subgraph cluster_pn {
        label="Preferred Networks"
        color="#21318F"
        fontcolor="#21318F"
        "Chainer"
        "ChainerX"
        "CuPy"
    }
    // https://github.com/chainer/chainer/blob/master/chainerx_cc/CMakeLists.txt
    "Chainer"->{CuPy iDeep ChainerX "NumPy"}
    
    "NumPy API"->{CuPy ChainerX NumPy}
    
    "CuPy"->{CUDA cuDNN}
    
    
    ChainerX->{CuPy CUDA BLAS}
    
    // https://github.com/NervanaSystems/ngraph/pull/3786
    // https://github.com/NervanaSystems/ngraph/pull/3013
    nGraph -> "MLIR: nGraph" -> {"MLIR: LLVM" "MLIR: Affine"}
    
    
    subgraph cluster_google {
        label=Google
        color="#4285f4"
        fontcolor="#4285f4"
        Keras
        "MLIR: LLVM"
        "MLIR: Affine"
        "TensorFlow Graph"
        // https://github.com/tensorflow/mlir/tree/master/lib/Conversion
        "MLIR: GPU"-> {"MLIR: CUDA" "MLIR: NVVM" "MLIR: SPIR-V" "MLIR: ROCDL"}
        "MLIR: Loop"-> {"MLIR: Standard" "MLIR: GPU"}
        "MLIR: Standard"->{"MLIR: LLVM" "MLIR: SPIR-V"}
        "MLIR: Vector"->"MLIR: LLVM"   
        // https://github.com/tensorflow/community/pull/115
        "TensorFlow Graph" -> "MLIR: TensorFlow"
        
        "TensorFlow Graph"->"XLA"->TPU
    }
    // https://github.com/google/jax
    "NumPy API"->JAX->XLA
    // https://www.tensorflow.org/xla#supported_platforms
    XLA->{"GPU (Nvidia)" "CPU (Intel)"}
    PyTorch->XLA
    
    "MLIR: CUDA"-> CUDA
    "MLIR: LLVM"->LLVM
    "MLIR: NVVM"->NVVM
    "MLIR: ROCDL"->ROCDL
    LLVM->CPU
    "MLIR: SPIR-V"->"SPIR-V"
    
    // https://www.khronos.org/spir/
    "SPIR-V"->{OpenCL Vulkan OpenGL}
    LLVM->"SPIR-V"->LLVM
    
    // https://en.wikipedia.org/wiki/Vulkan_(API)
    Vulkan->{"GPU (AMD)" "GPU (Nvidia)" "CPU (Intel)" "CPU (ARM)"}
    
    subgraph cluster_arm {
        label=ARM
        color="#3d91ba"
        fontcolor="#3d91ba"
        "CPU (ARM)"
    }
    
    // https://en.wikipedia.org/wiki/OpenCL#Devices
    OpenCL->{CPU "GPU (AMD)" "GPU (Nvidia)" "GPU (Intel)"}
    
    // https://rocm-documentation.readthedocs.io/en/latest/ROCm_Libraries/ROCm_Libraries.html
    BLAS->{rocBLAS}
    
    // https://rocm-documentation.readthedocs.io/en/latest/Deep_learning/Deep-learning.html
    "XLA"->ROCm
    "TensorFlow Graph"->ROCm
    MIOpen->ROCm
    PyTorch->ROCm
    Caffe->ROCm
    
    
    subgraph cluster_facebook {
        label=Facebook
        color="#3b5998"
        fontcolor="#3b5998"
        PyTorch->"Glow"
        PyTorch->"Tensor Comprehensions"
    }
    Glow->{LLVM CUDA}
    PyTorch->{CPU CUDA}
    // https://pytorch.org/get-started/locally/
    PyTorch->{CUDA CPU}
    
    // https://research.fb.com/blog/2018/02/announcing-tensor-comprehensions/
    "Tensor Comprehensions"->{CUDA LLVM}
    
    Glow->Accelerator
    
    // 
    "NumPy API"->Numba->{LLVM NVVM ROCm}
    
    Relay->TVM->VTA->Accelerator
    // https://tvm.ai/2017/10/06/nnvm-compiler-announcement
    {ONNX MXNet "Core ML"}->NNVM->TVM->{LLVM CUDA OpenCL}
    

    
    subgraph cluster_apple {
        label=Apple
        color="#a3aaae"
        fontcolor="#a3aaae"
        // https://en.wikipedia.org/wiki/Metal_(API)
        "Core ML"->Metal->"Apple SoC"
    }
    
    Metal->{"GPU (Intel)" "GPU (AMD)" "GPU (Nvidia)"} 
    
    
    // https://github.com/intel/mkl-dnn
    {PyTorch "TensorFlow Graph"}->"DNNL"

    
    // https://github.com/onnx/tutorials
    {Chainer Caffe "Core ML" Keras MXNet PyTorch "TensorFlow Graph"} -> ONNX
    ONNX->{"Core ML" TensorRT "TensorFlow Graph" "ONNX Runtime"}
    // https://github.com/microsoft/onnxruntime
    "ONNX Runtime"->{CUDA "nGraph" TensorRT DNNL}
    
    
    "NumPy API"->"Dask Array"->NumPy

    subgraph cluster_amazon {
        label=Amazon
        color="#ff9900"
        fontcolor="#ff9900"
        MXNet
    }
    
    MXNet->TVM
    // https://mxnet.apache.org/api/faq/caffe
    Caffe->MXNet
    // https://mxnet.apache.org/api/faq/nnpack
    MXNet->NNPACK
    // https://mxnet.apache.org/api/faq/float16
    MXNet->{CUDA cuDNN}
    // https://github.com/apache/incubator-mxnet/blob/master/docs/python_docs/python/tutorials/performance/backend/mkldnn/mkldnn_readme.md
    MXNet->DNNL
    //  https://mxnet.incubator.apache.org/api/python/docs/api/ndarray/index.html
    "NumPy API"->MXNet
    
    // https://github.com/Maratyszcza/NNPACK
    {PyTorch Caffe}->NNPACK->{CPU}
    
    ONNX->TVM
}

trim.py

import pygraphviz as pgv
import itertools

REMOVE_NODES = {
    "MIOpen",
    "AMD Math Library",
    "ROCDL",
    "rocBLAS",
    "ONNX Runtime",
    "clDNN",
    "MKL",
    "OpenVino",
    "BigDL",
    "nGraph",
    "iDeep",
    "MKL",
    "clDNN",
    "Metal",
    "MLIR: nGraph",
    "DNNL",
    "PlaidML",
    "NNVM",
    "OpenCL",
    "Vulkan",
    "SPIR-V",
    "OpenGL",
    "VTA",
    "MLIR: TensorFlow",
    "MLIR: LLVM",
    "MLIR: ROCDL",
    "MLIR: SPIR-V",
    "MLIR: Vector",
    "MLIR: Standard",
    "MLIR: GPU",
    "MLIR: NVVM",
    "MLIR: CUDA",
    "MLIR: Loop",
    "MLIR: Affine",
    "CPU",
    "ROCm",
    "CUDA",
    "LLVM",
    "OpenCV",
    "NNPACK",
    "BLAS",
    "Relay",
    "TensorRT",
    "NVVM",
    "cuDNN",
    "ChainerX",
    "Caffe",
    "BigQuant"
}

G=pgv.AGraph("opentensors.dot")

for n in REMOVE_NODES:
    in_ = G.in_neighbors(n)
    out_ = G.out_neighbors(n)
    for edge in itertools.product(in_, out_):
        if edge not in G.edges():
            G.add_edge(*edge)
    G.delete_node(n)
    
edges = set(G.edges())
G.delete_edges_from(edges)
# Remove self edges
edges = {(l, r) for l, r in edges if l != r}
G.add_edges_from(edges)
G.write("out.dot")