.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "how_to/deploy_models/deploy_model_on_nano.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_how_to_deploy_models_deploy_model_on_nano.py: .. _tutorial-deploy-model-on-nano: Deploy the Pretrained Model on Jetson Nano =========================================== **Author**: `BBuf `_ This is an example of using Relay to compile a ResNet model and deploy it on Jetson Nano. .. GENERATED FROM PYTHON SOURCE LINES 27-36 .. code-block:: default import tvm from tvm import te import tvm.relay as relay from tvm import rpc from tvm.contrib import utils, graph_executor as runtime from tvm.contrib.download import download_testdata .. GENERATED FROM PYTHON SOURCE LINES 42-84 .. _build-tvm-runtime-on-jetson-nano: Build TVM Runtime on Jetson Nano -------------------------------- The first step is to build the TVM runtime on the remote device. .. note:: All instructions in both this section and next section should be executed on the target device, e.g. Jetson Nano. And we assume it has Linux running. Since we do compilation on local machine, the remote device is only used for running the generated code. We only need to build tvm runtime on the remote device. .. code-block:: bash git clone --recursive https://github.com/apache/tvm tvm cd tvm mkdir build cp cmake/config.cmake build cd build cmake .. make runtime -j4 .. note:: If we want to use Jetson Nano's GPU for inference, we need to enable the CUDA option in `config.cmake`, that is, `set(USE_CUDA ON)` After building runtime successfully, we need to set environment varibles in :code:`~/.bashrc` file. We can edit :code:`~/.bashrc` using :code:`vi ~/.bashrc` and add the line below (Assuming your TVM directory is in :code:`~/tvm`): .. code-block:: bash export PYTHONPATH=$PYTHONPATH:~/tvm/python To update the environment variables, execute :code:`source ~/.bashrc`. .. GENERATED FROM PYTHON SOURCE LINES 86-102 Set Up RPC Server on Device --------------------------- To start an RPC server, run the following command on your remote device (Which is Jetson Nano in our example). .. code-block:: bash python -m tvm.exec.rpc_server --host 0.0.0.0 --port=9091 If you see the line below, it means the RPC server started successfully on your device. .. code-block:: bash INFO:RPCServer:bind to 0.0.0.0:9091 .. GENERATED FROM PYTHON SOURCE LINES 104-111 Prepare the Pre-trained Model ----------------------------- Back to the host machine, which should have a full TVM installed (with LLVM). We will use pre-trained model from `MXNet Gluon model zoo `_. You can found more details about this part at tutorial :ref:`tutorial-from-mxnet`. .. GENERATED FROM PYTHON SOURCE LINES 111-119 .. code-block:: default from mxnet.gluon.model_zoo.vision import get_model from PIL import Image import numpy as np # one line to get the model block = get_model("resnet18_v1", pretrained=True) .. GENERATED FROM PYTHON SOURCE LINES 120-122 In order to test our model, here we download an image of cat and transform its format. .. GENERATED FROM PYTHON SOURCE LINES 122-138 .. code-block:: default img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true" img_name = "cat.png" img_path = download_testdata(img_url, img_name, module="data") image = Image.open(img_path).resize((224, 224)) def transform_image(image): image = np.array(image) - np.array([123.0, 117.0, 104.0]) image /= np.array([58.395, 57.12, 57.375]) image = image.transpose((2, 0, 1)) image = image[np.newaxis, :] return image x = transform_image(image) .. GENERATED FROM PYTHON SOURCE LINES 139-141 synset is used to transform the label from number of ImageNet class to the word human can understand. .. GENERATED FROM PYTHON SOURCE LINES 141-154 .. code-block:: default synset_url = "".join( [ "https://gist.githubusercontent.com/zhreshold/", "4d0b62f3d01426887599d4f7ede23ee5/raw/", "596b27d23537e5a1b5751d2b0481ef172f58b539/", "imagenet1000_clsid_to_human.txt", ] ) synset_name = "imagenet1000_clsid_to_human.txt" synset_path = download_testdata(synset_url, synset_name, module="data") with open(synset_path) as f: synset = eval(f.read()) .. GENERATED FROM PYTHON SOURCE LINES 155-157 Now we would like to port the Gluon model to a portable computational graph. It's as easy as several lines. .. GENERATED FROM PYTHON SOURCE LINES 157-165 .. code-block:: default # We support MXNet static graph(symbol) and HybridBlock in mxnet.gluon shape_dict = {"data": x.shape} mod, params = relay.frontend.from_mxnet(block, shape_dict) # we want a probability so add a softmax operator func = mod["main"] func = relay.Function(func.params, relay.nn.softmax(func.body), None, func.type_params, func.attrs) .. GENERATED FROM PYTHON SOURCE LINES 166-167 Here are some basic data workload configurations. .. GENERATED FROM PYTHON SOURCE LINES 167-172 .. code-block:: default batch_size = 1 num_classes = 1000 image_shape = (3, 224, 224) data_shape = (batch_size,) + image_shape .. GENERATED FROM PYTHON SOURCE LINES 173-182 Compile The Graph ----------------- To compile the graph, we call the :py:func:`relay.build` function with the graph configuration and parameters. However, You cannot to deploy a x86 program on a device with ARM instruction set. It means Relay also needs to know the compilation option of target device, apart from arguments :code:`net` and :code:`params` to specify the deep learning workload. Actually, the option matters, different option will lead to very different performance. .. GENERATED FROM PYTHON SOURCE LINES 184-188 If we run the example on our x86 server for demonstration, we can simply set it as :code:`llvm`. If running it on the Jetson Nano, we need to set it as :code:`nvidia/jetson-nano`. Set :code:`local_demo` to False if you want to run this tutorial with a real device. .. GENERATED FROM PYTHON SOURCE LINES 188-214 .. code-block:: default local_demo = True if local_demo: target = tvm.target.Target("llvm") else: target = tvm.target.Target("nvidia/jetson-nano") assert target.kind.name == "cuda" assert target.attrs["arch"] == "sm_53" assert target.attrs["shared_memory_per_block"] == 49152 assert target.attrs["max_threads_per_block"] == 1024 assert target.attrs["thread_warp_size"] == 32 assert target.attrs["registers_per_block"] == 32768 with tvm.transform.PassContext(opt_level=3): lib = relay.build(func, target, params=params) # After `relay.build`, you will get three return values: graph, # library and the new parameter, since we do some optimization that will # change the parameters but keep the result of model as the same. # Save the library at local temporary directory. tmp = utils.tempdir() lib_fname = tmp.relpath("net.tar") lib.export_library(lib_fname) .. rst-class:: sphx-glr-script-out .. code-block:: none /workspace/python/tvm/relay/build_module.py:348: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function) DeprecationWarning, .. GENERATED FROM PYTHON SOURCE LINES 215-219 Deploy the Model Remotely by RPC -------------------------------- With RPC, you can deploy the model remotely from your host machine to the remote device. .. GENERATED FROM PYTHON SOURCE LINES 219-249 .. code-block:: default # obtain an RPC session from remote device. if local_demo: remote = rpc.LocalSession() else: # The following is my environment, change this to the IP address of your target device host = "192.168.1.11" port = 9091 remote = rpc.connect(host, port) # upload the library to remote device and load it remote.upload(lib_fname) rlib = remote.load_module("net.tar") # create the remote runtime module if local_demo: dev = remote.cpu(0) else: dev = remote.cuda(0) module = runtime.GraphModule(rlib["default"](dev)) # set input data module.set_input("data", tvm.nd.array(x.astype("float32"))) # run module.run() # get output out = module.get_output(0) # get top1 result top1 = np.argmax(out.numpy()) print("TVM prediction top-1: {}".format(synset[top1])) .. rst-class:: sphx-glr-script-out .. code-block:: none TVM prediction top-1: tiger cat .. _sphx_glr_download_how_to_deploy_models_deploy_model_on_nano.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: deploy_model_on_nano.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: deploy_model_on_nano.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_