export PYTHONPATH=~/caffe/python:$PYTHONPATH sudo pip install lmdb opencv-python其中 ~/caffe 是當初 download and build caffe source 的位置。
real 1m25.823s user 1m10.579s sys 0m19.228sCPU:
real 11m25.795s user 11m28.551s sys 0m0.444s
./data/mnist/get_mnist.sh ./examples/mnist/create_mnist.sh開始 run train in..
./examples/mnist/train_lenet.shrun 完，結果會是在 example/mnist 下..lenet_iter_*
./examples/mnist/train_lenet.sh 2>&1 | tee -a lenet然後用 parse_log.py 整理一下...
./tools/extra/parse_log.py lenet .會產生 lenet.test, lenet.train
set datafile separator ','
set term x11 0
plot './lenet.train' using 1:4 with line,\
'./lenet.test' using 1:5 with line
用 gnuplot 畫出來..
gnuplot -persist plotcmd
real 2m32.821s user 1m53.398s sys 0m43.228s
sudo apt-get install caffe-cuda
sudo apt build-dep caffe-cuda然後就可以 git clone https://github.com/BVLC/caffe.git
cp Makefile.config.example Makefile.config # Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired) make all make test make runtest但是依照這一篇，要un comment USE_PKG_CONFIG := 1。
After more research I found that the newest cuda version (9.0) doesn't support compute_20 anymore. This means that you have two options, disable the compute_20 target or install cuda version 8.0. If your GPU supports newer compute architectures you should use the newest cuda version and disable compute_20.果然，在 Makefile.config 中有...
# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
# For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
-gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_52,code=sm_52 \
-gencode arch=compute_60,code=sm_60 \
-gencode arch=compute_61,code=sm_61 \
-gencode arch=compute_61,code=compute_61
接著是 hdf5.h : No such file or directory Error。--- INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include +++ INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
--- LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5 +++ LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serialOK
.build_release/lib/libcaffe.so: undefined reference to `cv::imread(cv::String const&, int)但是又真的有裝 libopencv 的話。
# Uncomment to use `pkg-config` to specify OpenCV library paths. # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.) -# USE_PKG_CONFIG := 1 +USE_PKG_CONFIG := 1
sudo pip install -r requirement.txt
Check failed: error == cudaSuccess (35 vs. 0) CUDA driver version is insufficient for CUDA runtime version發現是因為 /usr/share/cuda link 到 cuda-10.1，但是 nvidia-smi 顯示的 cuda 版本卻是 10.0
>>from tensorflow.python.client import device_lib
>>device_lib.list_local_devices()
2018-06-25 10:45:58.955248: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:45:59.256407: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-06-25 10:45:59.257329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.62
pciBusID: 0000:01:00.0
totalMemory: 3.95GiB freeMemory: 3.56GiB
2018-06-25 10:45:59.257385: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:46:04.408233: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:46:04.408308: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929] 0
2018-06-25 10:46:04.408330: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0: N
2018-06-25 10:46:04.425190: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/device:GPU:0 with 3290 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 1869392221051952077
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 3450404864
locality {
bus_id: 1
links {
}
}
incarnation: 8818617857580197841
physical_device_desc: "device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1"
]
sudo ln -s /usr/bin/gcc-6 /usr/local/cuda/bin/gcc sudo ln -s /usr/bin/g++-6 /usr/local/cuda/bin/g++就可以 make OK 了。
libgflags-dev libgoogle-glog-dev liblmdb-dev libboost-all-dev libprotobuf-dev protobuf-compiler libhdf5-dev libleveldb-dev libsnappy-dev libopencv-dev libatlas-base-dev python-numpycmake 3.10 一樣會在link 的時候找不到 cublas.要用 3.14 以上版本。可以自己build一個版本
mkdir build && cd build cmake .. make make install這樣就可以了。
:~$ sudo locale-gen zh_TW.UTF-8然後再選 ＋ 才會出現。
sudo apt install nvidia-cuda-toolkit sudo apt-add-repository ppa:graphics-drivers/ppa sudo apt update sudo apt install nvidia-driver-396重新開機。用 command 看..
~$ nvidia-smi
Fri Jun 22 16:40:36 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 396.24.02 Driver Version: 396.24.02 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 105... Off | 00000000:01:00.0 Off | N/A |
| N/A 36C P8 N/A / N/A | 515MiB / 4042MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 8154 G /usr/lib/xorg/Xorg 28MiB |
| 0 8245 G /usr/bin/gnome-shell 58MiB |
| 0 15542 G /usr/lib/xorg/Xorg 203MiB |
| 0 15711 G /usr/bin/gnome-shell 91MiB |
| 0 17581 G ...-token=B5E610D3E9F21DF705985515A610A2E7 132MiB |
+-----------------------------------------------------------------------------+
~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Nov__3_21:07:56_CDT_2017
Cuda compilation tools, release 9.1, V9.1.85
~$ sudo apt-get install clinfo
~$ clinfo
Number of platforms 1
Platform Name NVIDIA CUDA
Platform Vendor NVIDIA Corporation
Platform Version OpenCL 1.2 CUDA 9.2.127
Platform Profile FULL_PROFILE
Platform Extensions cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_copy_opts cl_nv_create_buffer
Platform Extensions function suffix NV
Platform Name NVIDIA CUDA
Number of devices 1
Device Name GeForce GTX 1050 Ti
Device Vendor NVIDIA Corporation
Device Vendor ID 0x10de
Device Version OpenCL 1.2 CUDA
Driver Version 396.24.02
Device OpenCL C Version OpenCL C 1.2
Device Type GPU
Device Topology (NV) PCI-E, 01:00.0
Device Profile FULL_PROFILE
Device Available Yes
Compiler Available Yes
Linker Available Yes
Max compute units 6
Max clock frequency 1620MHz
Compute Capability (NV) 6.1
Device Partition (core)
Max number of sub-devices 1
Supported partition types None
Max work item dimensions 3
Max work item sizes 1024x1024x64
Max work group size 1024
Preferred work group size multiple 32
Warp size (NV) 32
Preferred / native vector sizes
char 1 / 1
short 1 / 1
int 1 / 1
long 1 / 1
half 0 / 0 (n/a)
float 1 / 1
double 1 / 1 (cl_khr_fp64)
Half-precision Floating-point support (n/a)
Single-precision Floating-point support (core)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations Yes
Double-precision Floating-point support (cl_khr_fp64)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Address bits 64, Little-Endian
Global memory size 4238737408 (3.948GiB)
Error Correction support No
Max memory allocation 1059684352 (1011MiB)
Unified memory for Host and Device No
Integrated memory (NV) No
Minimum alignment for any data type 128 bytes
Alignment of base address 4096 bits (512 bytes)
Global Memory cache type Read/Write
Global Memory cache size 98304 (96KiB)
Global Memory cache line size 128 bytes
Image support Yes
Max number of samplers per kernel 32
Max size for 1D images from buffer 134217728 pixels
Max 1D or 2D image array size 2048 images
Max 2D image size 16384x32768 pixels
Max 3D image size 16384x16384x16384 pixels
Max number of read image args 256
Max number of write image args 16
Local memory type Local
Local memory size 49152 (48KiB)
Registers per block (NV) 65536
Max number of constant args 9
Max constant buffer size 65536 (64KiB)
Max size of kernel argument 4352 (4.25KiB)
Queue properties
Out-of-order execution Yes
Profiling Yes
Prefer user sync for interop No
Profiling timer resolution 1000ns
Execution capabilities
Run OpenCL kernels Yes
Run native kernels No
Kernel execution timeout (NV) Yes
Concurrent copy and kernel execution (NV) Yes
Number of async copy engines 2
printf() buffer size 1048576 (1024KiB)
Built-in kernels
Device Extensions cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_copy_opts cl_nv_create_buffer
NULL platform behavior
clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...) NVIDIA CUDA
clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...) Success [NV]
clCreateContext(NULL, ...) [default] Success [NV]
clCreateContextFromType(NULL, CL_DEVICE_TYPE_DEFAULT) No platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU) No platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM) Invalid device type for platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL) No platform
ICD loader properties
ICD loader Name OpenCL ICD Loader
ICD loader Vendor OCL Icd free software
ICD loader Version 2.2.11
ICD loader Profile OpenCL 2.1
import gobject
import dbus
import dbus.service
from dbus.mainloop.glib import DBusGMainLoop
DBusGMainLoop(set_as_default=True)
OPATH = "/com/example/HelloHell"
IFACE = "com.example.HelloHell"
BUS_NAME = "com.example.HelloHell"
class Example(dbus.service.Object):
def __init__(self):
bus = dbus.SessionBus()
bus.request_name(BUS_NAME)
bus_name = dbus.service.BusName(BUS_NAME, bus=bus)
dbus.service.Object.__init__(self, bus_name, OPATH)
@dbus.service.method(dbus_interface=IFACE + ".SayHello",
in_signature="", out_signature="")
def SayHello(self):
print "hello, world"
if __name__ == "__main__":
a = Example()
loop = gobject.MainLoop()
loop.run()
用 python run 起來後，用 dbus-send 送message 給'他'，就會print Hello 出來..
dbus-send --session --print-reply --type=method_call --dest=com.example.HelloHell /com/example/HelloHell com.example.HelloHell.SayHello.SayHello
GNU_INSTALL_ROOT ?= /usr/bin/ GNU_VERSION ?= 5.4.1 GNU_PREFIX ?= arm-none-eabi然後就可以到 examples/peripheral/<board name>/blank/armgcc/ 下 make 了。
diff --git a/Application/src/main/AndroidManifest.xml b/Application/src/main/AndroidManifest.xml
index d3cf257..7979018 100644
--- a/Application/src/main/AndroidManifest.xml
+++ b/Application/src/main/AndroidManifest.xml
@@ -32,6 +32,7 @@
<uses-permission android:name="android.permission.BLUETOOTH"/>
<uses-permission android:name="android.permission.BLUETOOTH_ADMIN"/>
+ <uses-permission android:name="android.permission.ACCESS_COARSE_LOCATION" />
<application android:label="@string/app_name"
android:icon="@drawable/ic_launcher"
diff --git a/Application/src/main/java/com/example/android/bluetoothlegatt/DeviceScanActivity.java b/Application/src/main/java/com/example/android/bluetoothlegatt/DeviceScanActivity.java
index 9b86f7a..7c654dc 100644
--- a/Application/src/main/java/com/example/android/bluetoothlegatt/DeviceScanActivity.java
+++ b/Application/src/main/java/com/example/android/bluetoothlegatt/DeviceScanActivity.java
@@ -16,6 +16,7 @@
package com.example.android.bluetoothlegatt;
+import android.Manifest;
import android.app.Activity;
import android.app.ListActivity;
import android.bluetooth.BluetoothAdapter;
@@ -46,6 +47,7 @@ public class DeviceScanActivity extends ListActivity {
private BluetoothAdapter mBluetoothAdapter;
private boolean mScanning;
private Handler mHandler;
+ private static final int PERMISSION_REQUEST_CORASE_LOCATION = 7788;
private static final int REQUEST_ENABLE_BT = 1;
// Stops scanning after 10 seconds.
@@ -64,6 +66,8 @@ public class DeviceScanActivity extends ListActivity {
finish();
}
+ requestPermissions(new String[]{Manifest.permission.ACCESS_COARSE_LOCATION},PERMISSION_REQUEST_CORASE_LOCATION);
+
// Initializes a Bluetooth adapter. For API level 18 and above, get a reference to
// BluetoothAdapter through BluetoothManager.
final BluetoothManager bluetoothManager =
project(':ble').projectDir = file('../Android-BLE-Library/ble')
$ git clone https://github.com/checko/Android-BLE-Library.git $ git clone https://github.com/checko/Android-nRF-Toolbox.git $ cd Android-nRF-Toolox $ git checkout fixRequstClassNotFound我是先 import Android-BLE-Library, build 好，再 import Android-nRF-Toolbox, build apk