Deploy Deep Learning Models to OpenGL and WebGL

Author: Zhixun Tan

This example shows how to build a neural network with NNVM python frontend and generate runtime library for WebGL running in a browser with TVM. To run this notebook, you need to install tvm and nnvm. Notice that you need to build tvm with OpenGL.


In this tutorial, we will download a pre-trained resnet18 model from Gluon Model Zoo, and run image classification in 3 different ways:

  • Run locally: We will compile the model into a TVM library with OpenGL device code and directly run it locally.
  • Run in a browser through RPC: We will compile the model into a JavaScript TVM library with WebGL device code, and upload it to an RPC server that is hosting JavaScript TVM runtime to run it.
  • Export a JavaScript library and run in a browser: We will compile the model into a JavaScript TVM library with WebGL device code, combine it with JavaScript TVM runtime, and pack everything together. Then we will run it directly in a browser.
from __future__ import print_function

import numpy as np
import tvm
import nnvm.compiler
import nnvm.testing

# This tutorial must be run with OpenGL backend enabled in TVM.
# The NNVM CI does not enable OpenGL yet. But the user can run this script.
opengl_enabled = tvm.module.enabled("opengl")

# To run the local demo, set this flag to True.
run_deploy_local = False

# To run the RPC demo, set this flag to True.
run_deploy_rpc = False

# To run the WebGL deploy demo, set this flag to True.
run_deploy_web = False

Download a Pre-trained Resnet18 Model

Here we define 2 functions:

  • A function that downloads a pre-trained resnet18 model from Gluon Model Zoo. The model that we download is in MXNet format, we then transform it into an NNVM computation graph.
  • A function that downloads a file that contains the name of all the image classes in this model.
def load_mxnet_resnet():
    """Load a pretrained resnet model from MXNet and transform that into NNVM

    net : nnvm.Symbol
        The loaded resnet computation graph.

    params : dict[str -> NDArray]
        The pretrained model parameters.

    data_shape: tuple
        The shape of the input tensor (an image).

    out_shape: tuple
        The shape of the output tensor (probability of all classes).

    print("Loading pretrained resnet model from MXNet...")

    # Download a pre-trained mxnet resnet18_v1 model.
    from import get_model
    block = get_model('resnet18_v1', pretrained=True)

    # Transform the mxnet model into NNVM.
    # We want a probability so add a softmax operator.
    sym, params = nnvm.frontend.from_mxnet(block)
    sym = nnvm.sym.softmax(sym)

    print("- Model loaded!")
    return sym, params, (1, 3, 224, 224), (1, 1000)

def download_synset():
    """Download a dictionary from class index to name.
    This lets us know what our prediction actually is.

    synset : dict[int -> str]
        The loaded synset.

    print("Downloading synset...")

    from mxnet import gluon

    url = "" + \
          "4d0b62f3d01426887599d4f7ede23ee5/raw/" + \
          "596b27d23537e5a1b5751d2b0481ef172f58b539/" + \
    file_name = "synset.txt", file_name)
    with open(file_name) as f:
        synset = eval(

    print("- Synset downloaded!")
    return synset

Download Input Image

Here we define 2 functions that prepare an image that we want to perform classification on.

  • A function that downloads a cat image.
  • A function that performs preprocessing to an image so that it fits the format required by the resnet18 model.
def download_image():
    """Download a cat image and resize it to 224x224 which fits resnet.

    image : PIL.Image.Image
        The loaded and resized image.

    print("Downloading cat image...")

    from matplotlib import pyplot as plt
    from mxnet import gluon
    from PIL import Image

    url = ""
    img_name = "cat.png", img_name)
    image =, 224))

    print("- Cat image downloaded!")


    return image

def transform_image(image):
    """Perform necessary preprocessing to input image.

    image : numpy.ndarray
        The raw image.

    image : numpy.ndarray
        The preprocessed image.

    image = np.array(image) - np.array([123., 117., 104.])
    image /= np.array([58.395, 57.12, 57.375])
    image = image.transpose((2, 0, 1))
    image = image[np.newaxis, :]
    return image

Compile the Model

Here we define a function that invokes the NNVM compiler.

def compile_net(net, target_host, target, data_shape, params):
    """Compiles an NNVM computation graph.

    net : nnvm.Graph
        The NNVM computation graph.

    target_host : str
        The target to compile the host portion of the library.

    target : str
        The target to compile the device portion of the library.

    data_shape : tuple
        The shape of the input data (image).

    params : dict[str -> NDArray]
        Model parameters.

    graph : Graph
        The final execution graph.

    libmod : tvm.Module
        The module that comes with the execution graph

    params : dict[str -> NDArray]
        The updated parameters of graph if params is passed.
        This can be different from the params passed in.

    print("Compiling the neural network...")

    with nnvm.compiler.build_config(opt_level=0):
        deploy_graph, lib, deploy_params =
            shape={"data": data_shape},

    print("- Complilation completed!")
    return deploy_graph, lib, deploy_params

Demo 1: Deploy Locally

In this demo, we will compile the model targetting the local machine.

Then we will demonstrate how to save the compiled model as a shared library and load it back.

Finally, we will run the model.

def deploy_local():
    """Runs the demo that deploys a model locally.

    # Load resnet model.
    net, params, data_shape, out_shape = load_mxnet_resnet()

    # Compile the model.
    # Note that we specify the the host target as "llvm".
    deploy_graph, lib, deploy_params = compile_net(

    # Save the compiled module.
    # Note we need to save all three files returned from the NNVM compiler.
    print("Saving the compiled module...")
    from tvm.contrib import util
    temp = util.tempdir()

    path_lib = temp.relpath("")
    path_graph_json = temp.relpath("deploy_graph.json")
    path_params = temp.relpath("deploy_param.params")

    with open(path_graph_json, "w") as fo:
    with open(path_params, "wb") as fo:

    print("- Saved files:", temp.listdir())

    # Load the module back.
    print("Loading the module back...")
    loaded_lib = tvm.module.load(path_lib)
    with open(path_graph_json) as fi:
        loaded_graph_json =
    with open(path_params, "rb") as fi:
        loaded_params = bytearray(
    print("- Module loaded!")

    # Run the model! We will perform prediction on an image.
    print("Running the graph...")
    from tvm.contrib import graph_runtime

    module = graph_runtime.create(loaded_graph_json, loaded_lib, tvm.opengl(0))

    image = transform_image(download_image())
    input_data = tvm.nd.array(image.astype("float32"), ctx=tvm.opengl(0))

    module.set_input("data", input_data)

    # Retrieve the output.
    out = module.get_output(0, tvm.nd.empty(out_shape, ctx=tvm.opengl(0)))
    top1 = np.argmax(out.asnumpy())
    synset = download_synset()
    print('TVM prediction top-1:', top1, synset[top1])

if run_deploy_local and opengl_enabled:

Demo 2: Deploy the Model to WebGL Remotely with RPC

Following the steps above, we can also compile the model for WebGL. TVM provides rpc module to help with remote deploying.

When we deploy a model locally to OpenGL, the model consists of two parts: the host LLVM part and the device GLSL part. Now that we want to deploy to WebGL, we need to leverage Emscripten to transform LLVM into JavaScript. In order to do that, we will need to specify the host target as ‘llvm -target=asmjs-unknown-emscripten -system-lib`. Then call Emscripten to compile the LLVM binary output into a JavaScript file.

First, we need to manually start an RPC server. Please follow the instructions in tvm/web/ After following the steps, you should have a web page opened in a browser, and a Python script running a proxy.

def deploy_rpc():
    """Runs the demo that deploys a model remotely through RPC.
    from tvm import rpc
    from tvm.contrib import util, emscripten

    # As usual, load the resnet18 model.
    net, params, data_shape, out_shape = load_mxnet_resnet()

    # Compile the model.
    # Note that this time we are changing the target.
    # This is because we want to translate the host library into JavaScript
    # through Emscripten.
    graph, lib, params = compile_net(
        target_host="llvm -target=asmjs-unknown-emscripten -system-lib",

    # Now we want to deploy our model through RPC.
    # First we ned to prepare the module files locally.
    print("Saving the compiled module...")

    temp = util.tempdir()
    path_obj = temp.relpath("deploy.bc") # host LLVM part
    path_dso = temp.relpath("deploy.js") # host JavaScript part
    path_gl = temp.relpath("") # device GLSL part
    path_json = temp.relpath("deploy.tvm_meta.json")
    emscripten.create_js(path_dso, path_obj, side_module=True)

    print("- Saved files:", temp.listdir())

    # Connect to the RPC server.
    print("Connecting to RPC server...")
    proxy_host = 'localhost'
    proxy_port = 9090
    remote = rpc.connect(proxy_host, proxy_port, key="js")
    print("- Connected to RPC server!")

    # Upload module to RPC server.
    print("Uploading module to RPC server...")
    remote.upload(path_dso, "deploy.dso")
    print("- Upload completed!")

    # Load remote library.
    print("Loading remote library...")
    fdev = remote.load_module("")
    fhost = remote.load_module("deploy.dso")
    rlib = fhost
    print("- Remote library loaded!")

    ctx = remote.opengl(0)

    # Upload the parameters.
    print("Uploading parameters...")
    rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
    print("- Parameters uploaded!")

    # Create the remote runtime module.
    print("Running remote module...")
    from tvm.contrib import graph_runtime
    module = graph_runtime.create(graph, rlib, ctx)

    # Set parameter.

    # Set input data.
    input_data = np.random.uniform(size=data_shape)
    module.set_input('data', tvm.nd.array(input_data.astype('float32')))

    # Run.
    print("- Remote module execution completed!")

    out = module.get_output(0, out=tvm.nd.empty(out_shape, ctx=ctx))
    # Print first 10 elements of output.

if run_deploy_rpc and opengl_enabled:

Demo 3: Deploy the Model to WebGL SystemLib

This time we are not using RPC. Instead, we will compile the model and link it with the entire tvm runtime into a single giant JavaScript file. Then we will run the model using JavaScript.

def deploy_web():
    """Runs the demo that deploys to web.

    import base64
    import json
    import os
    import shutil
    import SimpleHTTPServer, SocketServer

    from tvm.contrib import emscripten

    curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(os.getcwd())))
    working_dir = os.getcwd()
    output_dir = os.path.join(working_dir, "resnet")
    if not os.path.exists(output_dir):

    # As usual, load the resnet18 model.
    net, params, data_shape, out_shape = load_mxnet_resnet()

    # As usual, compile the model.
    graph, lib, params = compile_net(
        target_host="llvm -target=asmjs-unknown-emscripten -system-lib",

    # Now we save the model and link it with the TVM web runtime.
    path_lib = os.path.join(output_dir, "resnet.js")
    path_graph = os.path.join(output_dir, "resnet.json")
    path_params = os.path.join(output_dir, "resnet.params")
    path_data_shape = os.path.join(output_dir, "data_shape.json")
    path_out_shape = os.path.join(output_dir, "out_shape.json")

    lib.export_library(path_lib, emscripten.create_js, options=[
        "-s", "USE_GLFW=3",
        "-s", "USE_WEBGL2=1",
        "-s", "TOTAL_MEMORY=1073741824",
    with open(path_graph, "w") as fo:
    with open(path_params, "w") as fo:

    shutil.copyfile(os.path.join(curr_path, "../tvm/web/tvm_runtime.js"),
                    os.path.join(output_dir, "tvm_runtime.js"))
    shutil.copyfile(os.path.join(curr_path, "web/resnet.html"),
                    os.path.join(output_dir, "resnet.html"))

    # Now we want to save some extra files so that we can execute the model from
    # JavaScript.
    # - data shape
    with open(path_data_shape, "w") as fo:
        json.dump(list(data_shape), fo)
    # - out shape
    with open(path_out_shape, "w") as fo:
        json.dump(list(out_shape), fo)
    # - input image
    image = download_image(), "data.png"))
    # - synset
    synset = download_synset()
    with open(os.path.join(output_dir, "synset.json"), "w") as fo:
        json.dump(synset, fo)

    print("Output files are in", output_dir)

    # Finally, we fire up a simple web server to serve all the exported files.
    print("Now running a simple server to serve the files...")
    port = 8080
    handler = SimpleHTTPServer.SimpleHTTPRequestHandler
    httpd = SocketServer.TCPServer(("", port), handler)
    print("Please open http://localhost:" + str(port) + "/resnet.html")

if run_deploy_web and opengl_enabled:

Total running time of the script: ( 0 minutes 0.003 seconds)

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