Add c++ and python local deploy example (#5)
* add deploy example
* fix pylint complain and add more information into readme.
* Test deploy in xilinx FPGA board and udpate ReadMe.md
* Fix python deploy bug.
* add synset support and fix error report
* add bitstream flash logic
* address review comments.
* add bitstream flash file
* change file name
* fix plint complain
* return once no input parameter
diff --git a/apps/deploy/Makefile b/apps/deploy/Makefile
new file mode 100644
index 0000000..777cb89
--- /dev/null
+++ b/apps/deploy/Makefile
@@ -0,0 +1,61 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# Makefile Example to deploy TVM modules.
+TVM_ROOT=${TVM_HOME}
+CUR_DIR=$(shell pwd)
+DMLC_CORE=${TVM_ROOT}/3rdparty/dmlc-core
+
+TARGET := ${shell python3 ../../config/vta_config.py --target}
+
+
+VTA_LIB=vta
+ifeq (${TARGET}, sim)
+ VTA_LIB=vta_fsim
+endif
+
+PKG_CFLAGS = -std=c++11 -O0 -g -fPIC\
+ -I${TVM_ROOT}/include\
+ -I${TVM_ROOT}/vta/include\
+ -I${DMLC_CORE}/include\
+ -I${TVM_ROOT}/3rdparty/dlpack/include\
+ -I${TVM_ROOT}/3rdparty/vta-hw/include\
+ -I${TVM_ROOT}/\
+
+PKG_LDFLAGS = -L${TVM_ROOT}/build -L${CUR_DIR} -ldl -pthread -l${VTA_LIB} -ltvm_runtime
+
+.PHONY: clean all
+
+all:./build/deploy copylib
+
+./build/deploy: ./build/deploy.o ./build/model/lib.so
+ $(CXX) $(PKG_CFLAGS) -o $@ $^ $(PKG_LDFLAGS)
+
+./build/deploy.o: cpp_deploy.cc
+ @mkdir -p $(@D)
+ $(CXX) -c $(PKG_CFLAGS) -o $@ $^
+
+./build/model/lib.so: ./build/model/lib.o
+ $(CXX) $(PKG_CFLAGS) -o $@ $^ $(PKG_LDFLAGS) -shared
+
+copylib: ${TVM_ROOT}/build/libtvm_runtime.so ${TVM_ROOT}/build/lib${VTA_LIB}.so
+ @cp ${TVM_ROOT}/build/libtvm_runtime.so ./build
+ @cp ${TVM_ROOT}/build/lib${VTA_LIB}.so ./build
+
+clean:
+ rm -rf ./build/*.o ./build/deploy
+
diff --git a/apps/deploy/README.md b/apps/deploy/README.md
new file mode 100644
index 0000000..173562f
--- /dev/null
+++ b/apps/deploy/README.md
@@ -0,0 +1,120 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements. See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership. The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License. You may obtain a copy of the License at -->
+
+<!--- http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied. See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+
+How to Deploy TVM-VTA Modules
+=============================
+This folder contains an example on how to deploy TVM-VTA modules.
+It also contains an example code to deploy with C++ and Python.
+
+1. In host machine tvm project enable vta fsim or FPGA and compile tvm successfully.
+
+2. In target FPGA machine, flash bitstream into FPGA, following are example on pynq board
+
+ 'cd' into vta-hw/app/deploy, run following command,
+ "/home/xilinx/vta.bit" is the bitstream file
+
+ ```bash
+ sudo python3 ./bitstream.py /home/xilinx/vta.bit
+ ```
+
+3. Compile and Deploy with C++
+
+ 3.1 Deploy with FPGA
+
+ 3.1.1 in host machine change ./vta-hw/config/vta_config.json TARGET into FPGA type
+ for example "ultra96"
+
+ 3.1.2 in host machine run resnet_export.py, this script would compile mxnet resnet18
+ into vta library, and compute graph, parameter and save into ./build/model folder.
+
+ ```bash
+ python3 ./resnet_export.py
+ ```
+
+ 3.1.3 from host machine, copy './build/' folder(generate by #2) into target FPGA board folder
+ "tvm/3rdparty/vta-hw/apps/deploy/"
+
+ 3.1.4 in target FPGA board, enable FPGA in config file and run following command
+
+ to build libvta.so and libtvm_runtime.so
+ ```bash
+ make runtime vta
+ ```
+
+ 3.1.5 in target FPGA board goto "tvm/3rdparty/vta-hw/apps/deploy/"
+ ```bash
+ cd tvm/3rdparty/vta-hw/apps/deploy/
+ ```
+
+ 3.1.6 int FPGA board Run "make" command, the script would build "lib.so" and cop libtvm_runtime.so
+ and "libvta*.so" into "./build" folder and compile execute file "./deploy"
+ ```bash
+ make
+ ```
+
+ 3.1.7. in FPGA board use following command to convert a image into correct image size that match
+ mxnet resnet18 requirement.
+ ```bash
+ ./img_data_help.py <image path>
+ ```
+ the said command would output a file name 'img_data'
+
+ 3.1.8. in FPGA board run following command to get the image type
+ ```bash
+ ./deploy img_data
+ ```
+
+ 3.2 Deploy with vta simulator(all steps happen in host machine)
+
+ 3.2.1 change ./vta-hw/config/vta_config.json TARGET into "sim"
+
+ 3.2.2 run resnet_export.py, this script would compile mxnet resnet18 into vta library,
+ and compute graph, parameter and save into ./build/model folder.
+
+ ```bash
+ python3 ./resnet_export.py
+ ```
+
+ 3.2.3 Run "make" command, the script would build "lib.so" and copy libtvm_runtime.so
+ and libvta*.so into ./build folder and compile execute file ./deploy
+ ```bash
+ make
+ ```
+
+4. Python deploy
+
+ 4.1 Deploy with FPGA.
+
+ 4.1.1 From host machine Copy "./vta-hw/apps/deploy/build" folder into
+ target FPGA board "vta-hw/apps/deploy/" folder
+
+ 4.1.2 on FPGA board build libtvmruntime.so and libvta.so
+
+ ```bash
+ make runtime vta
+ ```
+
+ 4.1.3 in ./vta-hw/apps/deploy run make to compile ./build/model/lib.so
+ ```
+ make
+ ```
+
+ 4.1.4 run python_deploy.py by "run_python_deploy.sh"
+ ```bash
+ sudo ./run_python_deploy.sh
+ ```
diff --git a/apps/deploy/bitstream.py b/apps/deploy/bitstream.py
new file mode 100644
index 0000000..6f646ef
--- /dev/null
+++ b/apps/deploy/bitstream.py
@@ -0,0 +1,25 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+""" Download bitstream into FPGA """
+
+import sys
+from pynq import Bitstream
+
+assert len(sys.argv) == 2, "usage: <bitstream path>"
+FILE = sys.argv[1]
+BITSTREAM = Bitstream(FILE)
+BITSTREAM.download()
diff --git a/apps/deploy/cpp_deploy.cc b/apps/deploy/cpp_deploy.cc
new file mode 100644
index 0000000..3f21368
--- /dev/null
+++ b/apps/deploy/cpp_deploy.cc
@@ -0,0 +1,122 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+#include <cstdio>
+#include <dlpack/dlpack.h>
+#include <tvm/runtime/module.h>
+#include <tvm/runtime/registry.h>
+#include <tvm/runtime/packed_func.h>
+#include <fstream>
+#include <iterator>
+#include <algorithm>
+#include <vta/runtime/runtime.h>
+
+
+void graph_test(std::string img,
+ std::string model_path,
+ std::string lib,
+ std::string graph,
+ std::string params) {
+ tvm::runtime::Module mod_dylib =
+ tvm::runtime::Module::LoadFromFile((model_path+lib).c_str()) ;
+ std::ifstream json_in((model_path + graph).c_str());
+ if(json_in.fail())
+ {
+ throw std::runtime_error("could not open json file");
+ }
+
+ std::ifstream params_in((model_path + params).c_str(), std::ios::binary);
+ if(params_in.fail())
+ {
+ throw std::runtime_error("could not open json file");
+ }
+
+ const std::string json_data((std::istreambuf_iterator<char>(json_in)),
+ std::istreambuf_iterator<char>());
+ json_in.close();
+ const std::string params_data((std::istreambuf_iterator<char>(params_in)),
+ std::istreambuf_iterator<char>());
+ params_in.close();
+
+ TVMByteArray params_arr;
+ params_arr.data = params_data.c_str();
+ params_arr.size = params_data.length();
+
+ int dtype_code = kDLFloat;
+ int dtype_bits = 32;
+ int dtype_lanes = 1;
+ int device_type = kDLExtDev;
+ int device_id = 0;
+
+ // get global function module for graph runtime
+ tvm::runtime::Module mod =
+ (*tvm::runtime::Registry::Get("tvm.graph_runtime.create"))(json_data,
+ mod_dylib,
+ device_type,
+ device_id);
+ DLTensor* x;
+ tvm::runtime::PackedFunc get_input = mod.GetFunction("get_input");
+ x = get_input(0);
+ VTACommandHandle cmd;
+ char * vta_ptr = (char *)VTABufferCPUPtr(cmd, static_cast<void*>(x->data));
+
+ int in_ndim = 4;
+ int64_t in_shape[4] = {1, 3, 224, 224};
+ // load image data saved in binary
+ std::ifstream data_fin(img.c_str(), std::ios::binary);
+ data_fin.read(static_cast<char*>(vta_ptr), 3 * 224 * 224 * 4);
+ // get the function from the module(load parameters)
+ tvm::runtime::PackedFunc load_params = mod.GetFunction("load_params");
+ load_params(params_arr);
+ tvm::runtime::PackedFunc run = mod.GetFunction("run");
+ run();
+
+ DLTensor* y;
+ int out_ndim = 2;
+ int64_t out_shape[2] = {1, 1000};
+ TVMArrayAlloc(out_shape, out_ndim, dtype_code, dtype_bits, dtype_lanes,
+ kDLCPU, device_id, &y);
+
+ // get the function from the module(get output data)
+ tvm::runtime::PackedFunc get_output = mod.GetFunction("get_output");
+ get_output(0, y);
+
+ // get the maximum position in output vector
+ auto y_iter = static_cast<float*>(y->data);
+ auto max_iter = std::max_element(y_iter, y_iter + 1000);
+ auto max_index = std::distance(y_iter, max_iter);
+ std::cout << "The maximum position in output vector is: " << max_index << std::endl;
+
+ TVMArrayFree(x);
+ TVMArrayFree(y);
+}
+
+int main(int argc, char *argv[]) {
+ if (argc <= 1) {
+ printf("deploy <file name>\n");
+ return 0;
+ }
+ graph_test(argv[1],
+ "./model/",
+ "lib.so",
+ "graph.json",
+ "params.params");
+ return 0;
+
+}
+
diff --git a/apps/deploy/img_data_help.py b/apps/deploy/img_data_help.py
new file mode 100644
index 0000000..61452d6
--- /dev/null
+++ b/apps/deploy/img_data_help.py
@@ -0,0 +1,28 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Resize Image To Match MXNET Model."""
+import sys
+import os
+from PIL import Image
+import numpy as np
+
+if __name__ == "__main__":
+ assert len(sys.argv) == 2, "usage: <image path>"
+ IMG_PATH = sys.argv[1]
+ assert os.path.isfile(IMG_PATH), "file " + IMG_PATH + " not exist"
+ IMAGE = Image.open(IMG_PATH).resize((224, 224))
+ np.array(IMAGE).astype('float32').tofile("./img_data")
diff --git a/apps/deploy/python_deploy.py b/apps/deploy/python_deploy.py
new file mode 100644
index 0000000..dc6c37b
--- /dev/null
+++ b/apps/deploy/python_deploy.py
@@ -0,0 +1,80 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Python VTA Deploy."""
+from __future__ import absolute_import, print_function
+
+import os
+from os.path import join
+from io import BytesIO
+from PIL import Image
+
+import requests
+import numpy as np
+
+import tvm
+from tvm.contrib import graph_runtime, download
+
+
+CTX = tvm.ext_dev(0)
+
+def load_vta_library():
+ """load vta lib"""
+ curr_path = os.path.dirname(
+ os.path.abspath(os.path.expanduser(__file__)))
+ proj_root = os.path.abspath(os.path.join(curr_path, "../../../../"))
+ vtadll = os.path.abspath(os.path.join(proj_root, "build/libvta.so"))
+ return tvm.runtime.load_module(vtadll)
+
+
+def load_model():
+ """ Load VTA Model """
+
+ load_vta_library()
+
+ with open("./build/model/graph.json", "r") as graphfile:
+ graph = graphfile.read()
+
+ lib = tvm.runtime.load_module("./build/model/lib.so")
+
+ model = graph_runtime.create(graph, lib, CTX)
+
+ with open("./build/model/params.params", "rb") as paramfile:
+ param_bytes = paramfile.read()
+
+ categ_url = "https://github.com/uwsaml/web-data/raw/master/vta/models/"
+ categ_fn = "synset.txt"
+ download.download(join(categ_url, categ_fn), categ_fn)
+ synset = eval(open(categ_fn).read())
+
+ return model, param_bytes, synset
+
+if __name__ == "__main__":
+ MOD, PARAMS_BYTES, SYNSET = load_model()
+
+ IMAGE_URL = 'https://homes.cs.washington.edu/~moreau/media/vta/cat.jpg'
+ RESPONSE = requests.get(IMAGE_URL)
+
+ # Prepare test image for inference
+ IMAGE = Image.open(BytesIO(RESPONSE.content)).resize((224, 224))
+
+ MOD.set_input('data', IMAGE)
+ MOD.load_params(PARAMS_BYTES)
+ MOD.run()
+
+ TVM_OUTPUT = MOD.get_output(0, tvm.nd.empty((1, 1000), "float32", CTX))
+ TOP_CATEGORIES = np.argsort(TVM_OUTPUT.asnumpy()[0])
+ print("\t#1:", SYNSET[TOP_CATEGORIES[-1]])
diff --git a/apps/deploy/resnet_export.py b/apps/deploy/resnet_export.py
new file mode 100644
index 0000000..ad248bc
--- /dev/null
+++ b/apps/deploy/resnet_export.py
@@ -0,0 +1,132 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+""" Compile And Export MXNET Resnet18 Model With VTA As Backend """
+from __future__ import absolute_import, print_function
+
+import os
+from os.path import exists
+import numpy as np
+from mxnet.gluon.model_zoo import vision
+
+import tvm
+from tvm import autotvm, relay
+from tvm.relay import op, transform
+
+import vta
+from vta.top import graph_pack
+from vta.top.graphpack import run_opt_pass
+
+# Load VTA parameters from the vta/config/vta_config.json file
+ENV = vta.get_env()
+assert ENV.target.device_name == "vta"
+# Dictionary lookup for when to start/end bit packing
+PACK_DICT = {"resnet18_v1": ["nn.max_pool2d", "nn.global_avg_pool2d", None, None],}
+
+# Name of Gluon model to compile
+MODEL = "resnet18_v1"
+assert MODEL in PACK_DICT
+
+def merge_transform_to_mxnet_model(mod):
+ """ Add Image Transform Logic Into Model """
+ svalue = np.array([123., 117., 104.])
+ sub_data = relay.Constant(tvm.nd.array(svalue)).astype("float32")
+ dvalue = np.array([58.395, 57.12, 57.37])
+ divide_data = relay.Constant(tvm.nd.array(dvalue)).astype("float32")
+
+ data_shape = (224, 224, 3)
+ data = relay.var("data", relay.TensorType(data_shape, "float32"))
+
+ simple_net = relay.expand_dims(data, axis=0, num_newaxis=1)
+ # To do, relay not support dynamic shape now, future need to add resize logic
+ # simple_net = relay.image.resize(simple_net, (224, 224), "NHWC", "bilinear", "align_corners")
+ simple_net = relay.subtract(simple_net, sub_data)
+ simple_net = relay.divide(simple_net, divide_data)
+ simple_net = relay.transpose(simple_net, ((0, 3, 1, 2)))
+
+ #merge tranform into pretrained model network
+ entry = mod["main"]
+ anf = run_opt_pass(entry.body, transform.ToANormalForm())
+ call = anf.value
+ data, weights = call.args
+ first_op = op.nn.conv2d(
+ simple_net,
+ weights,
+ strides=call.attrs.strides,
+ padding=call.attrs.padding,
+ dilation=call.attrs.dilation,
+ groups=call.attrs.groups,
+ channels=call.attrs.channels,
+ kernel_size=call.attrs.kernel_size,
+ out_dtype=call.attrs.out_dtype)
+ net = relay.expr.Let(anf.var, first_op, anf.body)
+ net = run_opt_pass(net, transform.ToGraphNormalForm())
+
+ mod['main'] = net
+ return mod
+
+def compile_mxnet_gulon_resnet(_env, _model):
+ """ Compile Model """
+ # Generate tvm IR from mxnet gluon model
+ # Populate the shape and data type dictionary for ImageNet classifier input
+ dtype_dict = {"data": 'float32'}
+ shape_dict = {"data": (_env.BATCH, 3, 224, 224)}
+ # Get off the shelf gluon model, and convert to relay
+ gluon_model = vision.get_model(_model, pretrained=True)
+ # Start front end compilation
+ mod, params = relay.frontend.from_mxnet(gluon_model, shape_dict)
+ mod = merge_transform_to_mxnet_model(mod)
+ # Update shape and type dictionary
+ shape_dict.update({k: v.shape for k, v in params.items()})
+ dtype_dict.update({k: str(v.dtype) for k, v in params.items()})
+
+ # Load pre-configured AutoTVM schedules
+ with autotvm.tophub.context(_env.target):
+ # Perform quantization in Relay
+ # Note: We set opt_level to 3 in order to fold batch norm
+ with relay.build_config(opt_level=3):
+ with relay.quantize.qconfig(global_scale=8.0, skip_conv_layers=[0]):
+ mod = relay.quantize.quantize(mod, params=params)
+ # Perform graph packing and constant folding for VTA target
+ relay_prog = graph_pack(
+ mod["main"],
+ _env.BATCH,
+ _env.BLOCK_IN,
+ _env.WGT_WIDTH,
+ start_name=PACK_DICT[_model][0],
+ stop_name=PACK_DICT[_model][1])
+
+ # Compile Relay program with AlterOpLayout disabled
+ with relay.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
+ with vta.build_config(debug_flag=0):
+ graph, lib, params = relay.build(
+ relay_prog, target=_env.target,
+ params=params, target_host=_env.target_host)
+
+ return graph, lib, params
+
+def export_tvm_compile(graph, lib, params, path):
+ """ Export Model"""
+ if not exists(path):
+ os.makedirs(path)
+ lib.save(path+"/lib.o")
+ with open(path+"/graph.json", "w") as graphfile:
+ graphfile.write(graph)
+ with open(path+"/params.params", "wb") as paramfile:
+ paramfile.write(relay.save_param_dict(params))
+
+GRAPH, LIB, PARAMS = compile_mxnet_gulon_resnet(ENV, MODEL)
+export_tvm_compile(GRAPH, LIB, PARAMS, "./build/model")
diff --git a/apps/deploy/run_python_deploy.sh b/apps/deploy/run_python_deploy.sh
new file mode 100644
index 0000000..4065d53
--- /dev/null
+++ b/apps/deploy/run_python_deploy.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+PROJROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/../../../../" && pwd )"
+
+export PYTHONPATH=${PYTHONPATH}:${PROJROOT}/python:${PROJROOT}/vta/python:./
+export PYTHONPATH=${PYTHONPATH}:/home/xilinx/pynq
+python3 -m python_deploy
