Jetson/L4T/TRT Customized Example
This page collects information to deploy customized models with TensorRT and some common questions for Jetson.
Contents
TensorRT Python
OpenCV with ONNX model
Below is an example to deploy TensorRT from an ONNX model with OpenCV images.
Verified environment:
- JetPack5.1 + Orin
import cv2
import time
import numpy as np
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
runtime = trt.Runtime(TRT_LOGGER)
batch = 1
host_inputs = []
cuda_inputs = []
host_outputs = []
cuda_outputs = []
bindings = []
def Inference(engine):
image = cv2.imread("/usr/src/tensorrt/data/resnet50/airliner.ppm")
image = (2.0 / 255.0) * image.transpose((2, 0, 1)) - 1.0
np.copyto(host_inputs[0], image.ravel())
stream = cuda.Stream()
context = engine.create_execution_context()
start_time = time.time()
cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)
context.execute_v2(bindings)
cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream)
stream.synchronize()
print("execute times "+str(time.time()-start_time))
output = host_outputs[0]
print(np.argmax(output))
def PrepareEngine():
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
config = builder.create_builder_config()
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 30)
with open('/usr/src/tensorrt/data/resnet50/ResNet50.onnx', 'rb') as model:
if not parser.parse(model.read()):
print ('ERROR: Failed to parse the ONNX file.')
for error in range(parser.num_errors):
print (parser.get_error(error))
serialized_engine = builder.build_serialized_network(network, config)
#with open("sample.engine", "wb") as f:
# f.write(serialized_engine)
#with open('sample.engine', 'rb') as f:
# serialized_engine = f.read()
engine = runtime.deserialize_cuda_engine(serialized_engine)
# create buffer
for binding in engine:
size = trt.volume(engine.get_tensor_shape(binding)) * batch
host_mem = cuda.pagelocked_empty(shape=[size],dtype=np.float32)
cuda_mem = cuda.mem_alloc(host_mem.nbytes)
bindings.append(int(cuda_mem))
if engine.get_tensor_mode(binding)==trt.TensorIOMode.INPUT:
host_inputs.append(host_mem)
cuda_inputs.append(cuda_mem)
else:
host_outputs.append(host_mem)
cuda_outputs.append(cuda_mem)
return engine
if __name__ == "__main__":
engine = PrepareEngine()
Inference(engine)
engine = []
OpenCV with PLAN model
Below is an example to deploy TensorRT from a TensorRT PLAN model with OpenCV images.
Verified environment:
- JetPack5.1 + Orin
$ /usr/src/tensorrt/bin/trtexec --onnx=/usr/src/tensorrt/data/resnet50/ResNet50.onnx --saveEngine=sample.engine
import cv2
import time
import numpy as np
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
batch = 1
host_inputs = []
cuda_inputs = []
host_outputs = []
cuda_outputs = []
bindings = []
def Inference(engine):
image = cv2.imread("/usr/src/tensorrt/data/resnet50/airliner.ppm")
image = (2.0 / 255.0) * image.transpose((2, 0, 1)) - 1.0
np.copyto(host_inputs[0], image.ravel())
stream = cuda.Stream()
context = engine.create_execution_context()
start_time = time.time()
cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)
context.execute_v2(bindings)
cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream)
stream.synchronize()
print("execute times "+str(time.time()-start_time))
output = host_outputs[0]
print(np.argmax(output))
def PrepareEngine():
with open('sample.engine', 'rb') as f:
serialized_engine = f.read()
runtime = trt.Runtime(TRT_LOGGER)
engine = runtime.deserialize_cuda_engine(serialized_engine)
# create buffer
for binding in engine:
size = trt.volume(engine.get_tensor_shape(binding)) * batch
host_mem = cuda.pagelocked_empty(shape=[size],dtype=np.float32)
cuda_mem = cuda.mem_alloc(host_mem.nbytes)
bindings.append(int(cuda_mem))
if engine.get_tensor_mode(binding)==trt.TensorIOMode.INPUT:
host_inputs.append(host_mem)
cuda_inputs.append(cuda_mem)
else:
host_outputs.append(host_mem)
cuda_outputs.append(cuda_mem)
return engine
if __name__ == "__main__":
engine = PrepareEngine()
Inference(engine)
engine = []
Multi-threading
Below is an example to run TensorRT with threads.
Verified environment:
- JetPack4.5.1 + Xavier
$ /usr/src/tensorrt/bin/trtexec --onnx=/usr/src/tensorrt/data/mnist/mnist.onnx --saveEngine=mnist.trt $ cd /usr/src/tensorrt/data/mnist/ $ sudo pip3 install pillow $ python3 download_pgms.py $ wget https://raw.githubusercontent.com/AastaNV/JEP/master/elinux/my_tensorrt_code.py -O my_tensorrt_code.py
import threading
import time
from my_tensorrt_code import TRTInference, trt
exitFlag = 0
class myThread(threading.Thread):
def __init__(self, func, args):
threading.Thread.__init__(self)
self.func = func
self.args = args
def run(self):
print ("Starting " + self.args[0])
self.func(*self.args)
print ("Exiting " + self.args[0])
if __name__ == '__main__':
# Create new threads
'''
format thread:
- func: function names, function that we wished to use
- arguments: arguments that will be used for the func's arguments
'''
trt_engine_path = 'mnist.trt'
max_batch_size = 1
trt_inference_wrapper = TRTInference(trt_engine_path,
trt_engine_datatype=trt.DataType.FLOAT,
batch_size=max_batch_size)
# Get TensorRT SSD model output
input_img_path = '/usr/src/tensorrt/data/mnist/3.pgm'
thread1 = myThread(trt_inference_wrapper.infer, [input_img_path])
# Start new Threads
thread1.start()
thread1.join()
trt_inference_wrapper.destory();
print ("Exiting Main Thread")
Deepstream
YoloV4 Tiny
Verified environment:
- JetPack4.5.1 + Xavier
Deepstream can reach 60fps with 4 video stream on Xavier:
$ cd /opt/nvidia/deepstream/deepstream-5.1/sources/objectDetector_Yolo $ wget https://raw.githubusercontent.com/AastaNV/eLinux_data/main/deepstream/yolov4-tiny/yolov4_tiny.patch $ git apply yolov4_tiny.patch $ export CUDA_VER=10.2 $ make -C nvdsinfer_custom_impl_Yolo
$ wget https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg -q --show-progress $ wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights -q --show-progress $ wget https://raw.githubusercontent.com/AastaNV/eLinux_data/main/deepstream/yolov4-tiny/deepstream_app_config_yoloV4_tiny.txt $ wget https://raw.githubusercontent.com/AastaNV/eLinux_data/main/deepstream/yolov4-tiny/config_infer_primary_yoloV4_tiny.txt
$ deepstream-app -c deepstream_app_config_yoloV4_tiny.txt
Custom Parser for SSD-MobileNet Trained by Jetson-inference
Verified environment:
- JetPack4.5.1 + Xavier
$ cd /opt/nvidia/deepstream/deepstream-5.1/sources/objectDetector_SSD/ $ sudo wget https://raw.githubusercontent.com/AastaNV/eLinux_data/main/deepstream/ssd-jetson_inference/ssd-jetson_inference.patch $ sudo git apply ssd-jetson_inference.patch $ sudo CUDA_VER=10.2 make -C nvdsinfer_custom_impl_ssd/
Update config_infer_primary_ssd.txt:
Ex.
diff --git a/config_infer_primary_ssd.txt b/config_infer_primary_ssd.txt
index e5bf468..81c52fd 100644
--- a/config_infer_primary_ssd.txt
+++ b/config_infer_primary_ssd.txt
@@ -62,15 +62,13 @@ gpu-id=0
net-scale-factor=0.0078431372
offsets=127.5;127.5;127.5
model-color-format=0
-model-engine-file=sample_ssd_relu6.uff_b1_gpu0_fp32.engine
-labelfile-path=ssd_coco_labels.txt
-uff-file=sample_ssd_relu6.uff
+model-engine-file=ssd-mobilenet.uff_b1_gpu0_fp16.engine
+uff-file=ssd.uff
infer-dims=3;300;300
uff-input-order=0
uff-input-blob-name=Input
-batch-size=1
-## 0=FP32, 1=INT8, 2=FP16 mode
-network-mode=0
+labelfile-path=labels.txt
+network-mode=2
num-detected-classes=91
interval=0
gie-unique-id=1
$ deepstream-app -c deepstream_app_config_ssd.txt
Detectron2
Please find the following link for an example from the forum community:
https://forums.developer.nvidia.com/t/passing-gstbuffer-to-tensorrt-for-inferencing/267084/18
VPI
VPI with Jetson-utils
Below is an example to use VPI with jetson-utils
Verified environment:
- JetPack4.6 + XavierNX
import numpy as np
import jetson.utils
import vpi
display = jetson.utils.glDisplay()
camera = jetson.utils.gstCamera(1920, 1280, '0')
camera.Open()
while display.IsOpen():
frame, width, height = camera.CaptureRGBA(zeroCopy=1)
input = vpi.asimage(np.uint8(jetson.utils.cudaToNumpy(frame)))
with vpi.Backend.CUDA:
output = input.convert(vpi.Format.U8)
output = output.box_filter(11, border=vpi.Border.ZERO).convert(vpi.Format.RGB8)
vpi.clear_cache()
display.RenderOnce(jetson.utils.cudaFromNumpy(output.cpu()), width, height)
display.SetTitle("{:s} | {:d}x{:d} | {:.1f} FPS".format("Camera Viewer", width, height, display.GetFPS()))
camera.Close()
VPI with Deepstream
Please find the following link for the example:
https://forums.developer.nvidia.com/t/deepstream-sdk-vpi-on-jetson-tx2/166834/20
VPI with Argus Camera
Please find the following link for the example:
VPI with nvivafilter
Please find the following link for the example:
https://forums.developer.nvidia.com/t/using-vpi-in-gstreamer/223334/21
Stress Test for Orin
We describe the testing tools that can stress the Jetson AGX Orin to the full workload.
The expected power consumption with these steps is listed in the below table:
| Jetson AGX Orin 64GB | Jetson AGX Orin 32GB | |
|---|---|---|
| Maximum Power | 60W | 40W |
Maximize the Device Performance
$ sudo nvpmodel -m 0 $ sudo jetson_clocks
CPU Stress Test
Using Linux stress tool:
$ sudo apt-get install stress $ stress --cpu $(nproc)
GPU Stress Test
Running cuBLAS sample with the half data type:
1. Find matrixMulCUBLAS sample under CUDA sample folder
2. Apply the following change the data type from float to half
diff --git a/Makefile b/Makefile
index 9f5db72..c2a5190 100644
--- a/Makefile
+++ b/Makefile
@@ -274,7 +274,7 @@ ALL_LDFLAGS += $(addprefix -Xlinker ,$(LDFLAGS))
ALL_LDFLAGS += $(addprefix -Xlinker ,$(EXTRA_LDFLAGS))
# Common includes and paths for CUDA
-INCLUDES := -I../../common/inc
+INCLUDES := -I$(CUDA_PATH)/samples/common/inc
LIBRARIES :=
################################################################################
@@ -354,14 +354,11 @@ matrixMulCUBLAS.o:matrixMulCUBLAS.cpp
matrixMulCUBLAS: matrixMulCUBLAS.o
$(EXEC) $(NVCC) $(ALL_LDFLAGS) $(GENCODE_FLAGS) -o $@ $+ $(LIBRARIES)
- $(EXEC) mkdir -p ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
- $(EXEC) cp $@ ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
run: build
$(EXEC) ./matrixMulCUBLAS
clean:
rm -f matrixMulCUBLAS matrixMulCUBLAS.o
- rm -rf ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)/matrixMulCUBLAS
clobber: clean
diff --git a/matrixMulCUBLAS.cpp b/matrixMulCUBLAS.cpp
index 132afd4..8e96a43 100644
--- a/matrixMulCUBLAS.cpp
+++ b/matrixMulCUBLAS.cpp
@@ -72,6 +72,52 @@ typedef struct _matrixSize // Optional Command-line multiplier for matrix s
unsigned int uiWA, uiHA, uiWB, uiHB, uiWC, uiHC;
} sMatrixSize;
+// Modified from helper_image.h to support half precision arguments
+inline bool sdkCompareL2fe(const __half *reference, const __half *data,
+ const unsigned int len, const float epsilon) {
+ assert(epsilon >= 0);
+
+ float error = 0;
+ float ref = 0;
+
+ for (unsigned int i = 0; i < len; ++i) {
+ float diff = (float)reference[i] - (float)data[i];
+ error += diff * diff;
+ ref += (float)reference[i] * (float)reference[i];
+ }
+
+ float normRef = sqrtf(ref);
+
+ if (fabs(ref) < 1e-7) {
+#ifdef _DEBUG
+ std::cerr << "ERROR, reference l2-norm is 0\n";
+#endif
+ return false;
+ }
+
+ float normError = sqrtf(error);
+ error = normError / normRef;
+ bool result = error < epsilon;
+#ifdef _DEBUG
+
+ if (!result) {
+ std::cerr << "ERROR, l2-norm error " << error << " is greater than epsilon "
+ << epsilon << "\n";
+ }
+
+#endif
+
+ return result;
+}
+
+inline int idx_n(int row, int col, int height, int width) {
+ return row * width + col;
+}
+
+inline int idx_t(int row, int col, int height, int width) {
+ return col * height + row;
+}
+
////////////////////////////////////////////////////////////////////////////////
//! Compute reference data set matrix multiply on CPU
//! C = A * B
@@ -82,32 +128,34 @@ typedef struct _matrixSize // Optional Command-line multiplier for matrix s
//! @param wB width of matrix B
////////////////////////////////////////////////////////////////////////////////
void
-matrixMulCPU(float *C, const float *A, const float *B, unsigned int hA, unsigned int wA, unsigned int wB)
+matrixMulCPU(__half *C, const __half *A, const __half *B, unsigned int hA, unsigned int wA, unsigned int wB, const bool ta, const bool tb)
{
+ auto idx_a = (ta) ? idx_t : idx_n;
+ auto idx_b = (tb) ? idx_t : idx_n;
for (unsigned int i = 0; i < hA; ++i)
for (unsigned int j = 0; j < wB; ++j)
{
- double sum = 0;
+ float sum = 0;
for (unsigned int k = 0; k < wA; ++k)
{
- double a = A[i * wA + k];
- double b = B[k * wB + j];
+ __half a = A[idx_a(i, k, hA, wA)];
+ __half b = B[idx_b(k, j, wA, wB)];
sum += a * b;
}
- C[i * wB + j] = (float)sum;
+ C[i * wB + j] = sum;
}
}
-// Allocates a matrix with random float entries.
-void randomInit(float *data, int size)
+// Allocates a matrix with random __half entries.
+void randomInit(__half *data, int size)
{
for (int i = 0; i < size; ++i)
- data[i] = rand() / (float)RAND_MAX;
+ data[i] = (__half)(rand() / (float)(RAND_MAX));
}
-void printDiff(float *data1, float *data2, int width, int height, int iListLength, float fListTol)
+void printDiff(__half *data1, __half *data2, int width, int height, int iListLength, float fListTol)
{
printf("Listing first %d Differences > %.6f...\n", iListLength, fListTol);
int i,j,k;
@@ -129,7 +177,7 @@ void printDiff(float *data1, float *data2, int width, int height, int iListLengt
{
if (error_count < iListLength)
{
- printf(" Loc(%d,%d)\tCPU=%.5f\tGPU=%.5f\tDiff=%.6f\n", i, j, data1[k], data2[k], fDiff);
+ printf(" Loc(%d,%d)\tCPU=%.5f\tGPU=%.5f\tDiff=%.6f\n", i, j, (float)data1[k], (float)data2[k], fDiff);
}
error_count++;
@@ -170,12 +218,13 @@ void initializeCUDA(int argc, char **argv, int &devID, int &iSizeMultiple, sMatr
int block_size = 32;
- matrix_size.uiWA = 3 * block_size * iSizeMultiple;
- matrix_size.uiHA = 4 * block_size * iSizeMultiple;
- matrix_size.uiWB = 2 * block_size * iSizeMultiple;
- matrix_size.uiHB = 3 * block_size * iSizeMultiple;
- matrix_size.uiWC = 2 * block_size * iSizeMultiple;
- matrix_size.uiHC = 4 * block_size * iSizeMultiple;
+ const int N = 2048;
+ matrix_size.uiWA = N;//3 * block_size * iSizeMultiple;
+ matrix_size.uiHA = N;//4 * block_size * iSizeMultiple;
+ matrix_size.uiWB = N;//2 * block_size * iSizeMultiple;
+ matrix_size.uiHB = N;//3 * block_size * iSizeMultiple;
+ matrix_size.uiWC = N;//2 * block_size * iSizeMultiple;
+ matrix_size.uiHC = N;//4 * block_size * iSizeMultiple;
printf("MatrixA(%u,%u), MatrixB(%u,%u), MatrixC(%u,%u)\n",
matrix_size.uiHA, matrix_size.uiWA,
@@ -194,7 +243,7 @@ void initializeCUDA(int argc, char **argv, int &devID, int &iSizeMultiple, sMatr
////////////////////////////////////////////////////////////////////////////////
//! Run a simple test matrix multiply using CUBLAS
////////////////////////////////////////////////////////////////////////////////
-int matrixMultiply(int argc, char **argv, int devID, sMatrixSize &matrix_size)
+int matrixMultiply(int argc, char **argv, int devID, sMatrixSize &matrix_size, bool ta, bool tb)
{
cudaDeviceProp deviceProp;
@@ -207,11 +256,11 @@ int matrixMultiply(int argc, char **argv, int devID, sMatrixSize &matrix_size)
// allocate host memory for matrices A and B
unsigned int size_A = matrix_size.uiWA * matrix_size.uiHA;
- unsigned int mem_size_A = sizeof(float) * size_A;
- float *h_A = (float *)malloc(mem_size_A);
+ unsigned int mem_size_A = sizeof(__half) * size_A;
+ __half *h_A = (__half *)malloc(mem_size_A);
unsigned int size_B = matrix_size.uiWB * matrix_size.uiHB;
- unsigned int mem_size_B = sizeof(float) * size_B;
- float *h_B = (float *)malloc(mem_size_B);
+ unsigned int mem_size_B = sizeof(__half) * size_B;
+ __half *h_B = (__half *)malloc(mem_size_B);
// set seed for rand()
srand(2006);
@@ -221,13 +270,13 @@ int matrixMultiply(int argc, char **argv, int devID, sMatrixSize &matrix_size)
randomInit(h_B, size_B);
// allocate device memory
- float *d_A, *d_B, *d_C;
+ __half *d_A, *d_B, *d_C;
unsigned int size_C = matrix_size.uiWC * matrix_size.uiHC;
- unsigned int mem_size_C = sizeof(float) * size_C;
+ unsigned int mem_size_C = sizeof(__half) * size_C;
// allocate host memory for the result
- float *h_C = (float *) malloc(mem_size_C);
- float *h_CUBLAS = (float *) malloc(mem_size_C);
+ __half *h_C = (__half *) malloc(mem_size_C);
+ __half *h_CUBLAS = (__half *) malloc(mem_size_C);
checkCudaErrors(cudaMalloc((void **) &d_A, mem_size_A));
checkCudaErrors(cudaMalloc((void **) &d_B, mem_size_B));
@@ -243,19 +292,30 @@ int matrixMultiply(int argc, char **argv, int devID, sMatrixSize &matrix_size)
printf("Computing result using CUBLAS...");
// execute the kernel
- int nIter = 30;
+ int nIter = 9999999;
// CUBLAS version 2.0
{
- const float alpha = 1.0f;
- const float beta = 0.0f;
+ cublasOperation_t trans_A = (ta) ? CUBLAS_OP_T : CUBLAS_OP_N;
+ cublasOperation_t trans_B = (tb) ? CUBLAS_OP_T : CUBLAS_OP_N;
+ int m = matrix_size.uiWC;
+ int n = matrix_size.uiHC;
+ int k = matrix_size.uiWA;
+ int lda = (trans_A == CUBLAS_OP_N) ? k : n;
+ int ldb = (trans_B == CUBLAS_OP_N) ? m : k;
+ int ldc = m;
+ const __half alpha = 1.0f;
+ const __half beta = 0.0f;
cublasHandle_t handle;
cudaEvent_t start, stop;
checkCudaErrors(cublasCreate(&handle));
//Perform warmup operation with cublas
- checkCudaErrors(cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, matrix_size.uiWB, matrix_size.uiHA, matrix_size.uiWA, &alpha, d_B, matrix_size.uiWB, d_A, matrix_size.uiWA, &beta, d_C, matrix_size.uiWB));
+ checkCudaErrors(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
+ cublasGemmAlgo_t algo = cublasGemmAlgo_t::CUBLAS_GEMM_ALGO4_TENSOR_OP;
+ checkCudaErrors(cublasGemmEx(handle, trans_B, trans_A, m, n, k, &alpha, d_B, CUDA_R_16F,
+ ldb, d_A, CUDA_R_16F, lda, &beta, d_C, CUDA_R_16F, ldc, CUBLAS_COMPUTE_16F, algo));
// Allocate CUDA events that we'll use for timing
checkCudaErrors(cudaEventCreate(&start));
@@ -268,8 +328,8 @@ int matrixMultiply(int argc, char **argv, int devID, sMatrixSize &matrix_size)
{
//note cublas is column primary!
//need to transpose the order
- checkCudaErrors(cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, matrix_size.uiWB, matrix_size.uiHA, matrix_size.uiWA, &alpha, d_B, matrix_size.uiWB, d_A, matrix_size.uiWA, &beta, d_C, matrix_size.uiWB));
-
+ checkCudaErrors(cublasGemmEx(handle, trans_B, trans_A, m, n, k, &alpha, d_B, CUDA_R_16F,
+ ldb, d_A, CUDA_R_16F, lda, &beta, d_C, CUDA_R_16F, ldc, CUBLAS_COMPUTE_16F, algo));
}
printf("done.\n");
@@ -302,16 +362,16 @@ int matrixMultiply(int argc, char **argv, int devID, sMatrixSize &matrix_size)
// compute reference solution
printf("Computing result using host CPU...");
- float *reference = (float *)malloc(mem_size_C);
- matrixMulCPU(reference, h_A, h_B, matrix_size.uiHA, matrix_size.uiWA, matrix_size.uiWB);
+ __half *reference = (__half *)malloc(mem_size_C);
+ matrixMulCPU(reference, h_A, h_B, matrix_size.uiHA, matrix_size.uiWA, matrix_size.uiWB, ta, tb);
printf("done.\n");
// check result (CUBLAS)
- bool resCUBLAS = sdkCompareL2fe(reference, h_CUBLAS, size_C, 1.0e-6f);
+ bool resCUBLAS = sdkCompareL2fe(reference, h_CUBLAS, size_C, 1.0e-2f);
if (resCUBLAS != true)
{
- printDiff(reference, h_CUBLAS, matrix_size.uiWC, matrix_size.uiHC, 100, 1.0e-5f);
+ printDiff(reference, h_CUBLAS, matrix_size.uiWC, matrix_size.uiHC, 100, 1.0e-2f);
}
printf("Comparing CUBLAS Matrix Multiply with CPU results: %s\n", (true == resCUBLAS) ? "PASS" : "FAIL");
@@ -349,7 +409,7 @@ int main(int argc, char **argv)
initializeCUDA(argc, argv, devID, sizeMult, matrix_size);
- int matrix_result = matrixMultiply(argc, argv, devID, matrix_size);
+ int matrix_result = matrixMultiply(argc, argv, devID, matrix_size, true, false);
return matrix_result;
}
3. Run the sample
$ ./matrixMulCUBLAS
Installation Steps
Darknet with cuDNN-8 Support
Below are the steps to build darknet with cuDNN-8 support.
Verified environment:
- JetPack4.5.1 + Xavier
1. Get source
$ git clone https://github.com/pjreddie/darknet.git $ cd darknet/ $ wget https://raw.githubusercontent.com/AastaNV/JEP/master/script/topics/0001-fix-for-cudnn_v8-limited-memory-to-default-darknet-s.patch $ wget https://raw.githubusercontent.com/AastaNV/JEP/master/elinux/opencv-darknet.patch -O opencv-darknet.patch $ git am 0001-fix-for-cudnn_v8-limited-memory-to-default-darknet-s.patch $ git am opencv-darknet.patch
2. Update Makefile based on your device
GPU=1
CUDNN=1
OPENCV=1
- Xavier & XavierNX:
ARCH= -gencode arch=compute_72,code=sm_72 \
-gencode arch=compute_72,code=[sm_72,compute_72]
- TX2:
ARCH= -gencode arch=compute_62,code=sm_62 \
-gencode arch=compute_62,code=[sm_62,compute_62]
- Nano:
ARCH= -gencode arch=compute_53,code=sm_53 \
-gencode arch=compute_53,code=[sm_53,compute_53]
3. Build and Test
$ make -j8 $ wget https://pjreddie.com/media/files/yolov3-tiny.weights $ ./darknet detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights [video]
TensorRT Python Bindings
Below are the steps to build TensorRT Python 3.9 bindings.
Verified environment:
- JetPack4.6 + Xavier
1. Building python3.9
$ sudo apt install zlib1g-dev libncurses5-dev libgdbm-dev libnss3-dev libssl-dev libreadline-dev libffi-dev libsqlite3-dev libbz2-dev $ wget https://www.python.org/ftp/python/3.9.1/Python-3.9.1.tar.xz $ tar xvf Python-3.9.1.tar.xz Python-3.9.1/
$ mkdir build-python-3.9.1 $ cd build-python-3.9.1/ $ ../Python-3.9.1/configure --enable-optimizations $ make -j $(nproc) $ sudo -H make altinstall $ cd ../
2. Build cmake 3.13.5
$ sudo apt-get install -y protobuf-compiler libprotobuf-dev openssl libssl-dev libcurl4-openssl-dev $ wget https://github.com/Kitware/CMake/releases/download/v3.13.5/cmake-3.13.5.tar.gz $ tar xvf cmake-3.13.5.tar.gz $ rm cmake-3.13.5.tar.gz
$ cd cmake-3.13.5/ $ ./bootstrap --system-curl $ make -j$(nproc)
$ echo 'export PATH='${PWD}'/bin/:$PATH' >> ~/.bashrc
$ source ~/.bashrc
$ cd ../
3. Prepare header
$ mkdir python3.9 $ mkdir python3.9/include $ wget http://ftp.us.debian.org/debian/pool/main/p/python3.9/libpython3.9-dev_3.9.9-2_arm64.deb $ ar x libpython3.9-dev_3.9.9-2_arm64.deb $ tar -xvf data.tar.xz $ cp ./usr/include/aarch64-linux-gnu/python3.9/pyconfig.h python3.9/include/ $ cp -r Python-3.9.1/Include/* python3.9/include/
4. Build TensorRT pybinding
$ git clone https://github.com/pybind/pybind11.git $ git clone -b release/8.0 https://github.com/NVIDIA/TensorRT.git $ cd TensorRT $ git submodule update --init --recursive
$ cd python/
$ TRT_OSSPATH=${PWD}/.. EXT_PATH=${PWD}/../.. TARGET=aarch64 PYTHON_MINOR_VERSION=9 ./build.sh
$ python3.9 -m pip install build/dist/tensorrt-8.0.1.6-cp39-none-linux_aarch64.whl
Caffe
Below are the steps to build the Caffe library.
Verified environment:
- JetPack4.6 + Xavier
$ wget https://raw.githubusercontent.com/AastaNV/JEP/master/elinux/install_caffe_jp46.sh -O install_caffe_jp46.sh $ wget https://raw.githubusercontent.com/AastaNV/JEP/master/elinux/0001-patch-for-jp4.6.patch -O 0001-patch-for-jp4.6.patch $ ./install_caffe_jp46.sh $ source ~/.bashrc
MXNet
Below are the steps to build the MXNet 1.8.0 library.
Verified environment:
- JetPack4.5.1 + Xavier
$ wget https://raw.githubusercontent.com/AastaNV/JEP/master/elinux/mxnet_v1.8.x.patch -O mxnet_v1.8.x.patch $ wget https://raw.githubusercontent.com/AastaNV/JEP/master/elinux/autobuild_mxnet.sh -O autobuild_mxnet.sh $ sudo chmod +x autobuild_mxnet.sh $ ./autobuild_mxnet.sh Xavier $ cd mxnet/build/ $ pip3 install mxnet-1.8.0-py3-none-any.whl
PyInstaller with OpenCV
Currently, the OpenCV version between JetPack default and Pyinstaller is not consistent.
To solve this issue, you can either upgrade the python-opencv version or downgrade the PyInstaller version.
- Upgrade python-opencv
$ pip3 install opencv-python
- Downgrade pyinstaller and pyinstaller-hooks-contrib
$ sudo pip3 install pyinstaller==4.2 $ sudo pip3 install pyinstaller-hooks-contrib==2021.2
$ pyinstaller --onefile --paths="/usr/lib/python3.6/dist-packages/cv2/python-3.6" myfile.py
Cross-compile OpenCV with jetpack-linux-aarch64-crosscompile-x86 container
Please find the following link for the example:
https://forums.developer.nvidia.com/t/cannot-build-opencv-4-8-0-with-nvidia-docker/268951/13
Common Issues
"Unsupported ONNX data type: UINT8 (2)"
This error is from TensorRT. The root cause is that ONNX expects the input image to be INT8 but TensorRT uses Float32.
To solve this issue, you can modify the input data format of ONNX with our graphsurgeon API.
$ sudo apt-get install python3-pip libprotobuf-dev protobuf-compiler $ git clone https://github.com/NVIDIA/TensorRT.git $ cd TensorRT/tools/onnx-graphsurgeon/ $ make install
import onnx_graphsurgeon as gs
import onnx
import numpy as np
graph = gs.import_onnx(onnx.load("model.onnx"))
for inp in graph.inputs:
inp.dtype = np.float32
onnx.save(gs.export_onnx(graph), "updated_model.onnx")
"Illegal instruction (core dumped)"
This is a known issue in NumPy v1.19.5.
To solve this issue, you can either downgrade your NumPy into 1.19.4 or manually update an environment variable.
- Downgrade NumPy
$ sudo apt-get install python3-pip $ pip3 install Cython $ pip3 install numpy==1.19.4
- Update environment variable
$ export OPENBLAS_CORETYPE=ARMV8
Long delays when submitting several cudaMemcpy
Please try to increase the computing channel
$ export CUDA_DEVICE_MAX_CONNECTIONS=32
A document can be found here:
https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars
h5py error when installing TensorFlow on the r32 branch
Compatibility Issue. Please try the below command to install:
$ sudo apt-get install libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran $ sudo apt-get install python3-pip $ sudo pip3 install -U pip testresources setuptools $ sudo ln -s /usr/include/locale.h /usr/include/xlocale.h $ pip3 install Cython==0.29.36 $ pip3 install pkgconfig
$ git clone https://github.com/h5py/h5py.git $ git checkout 3.1.0 $ git cherry-pick 3bf862daa4ebeb2eeaf3a0491e05f5415c1818e4 $ H5PY_SETUP_REQUIRES=0 pip3 install . --no-deps --no-build-isolation
$ sudo pip3 install -U numpy==1.19.4 future mock keras_preprocessing keras_applications gast==0.2.1 protobuf pybind11 packaging $ sudo pip3 install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v461 tensorflow
More info can be found here:
https://forums.developer.nvidia.com/t/failed-building-wheel-of-h5py/263322/5
