NVIDIA CUDA 2017 示例指南：从入门到实战

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CUDA Samples TRM-06704-001_v9.0|8

Chapter2.

GETTING STARTED

The CUDA Samples are an educational resource provided to teach CUDA programming

concepts. The CUDA Samples are not meant to be used for performance measurements.

For system requirements and installation instructions, please refer to the Linux

Installation Guide, the Windows Installation Guide, and the Mac Installation Guide.

2.1.Getting CUDA Samples

Windows

On Windows, the CUDA Samples are installed using the CUDA Toolkit Windows

Installer. By default, the CUDA Samples are installed in:

C:\ProgramData\NVIDIA Corporation\CUDA Samples\v9.0\

The installation location can be changed at installation time.

Linux

On Linux, to install the CUDA Samples, the CUDA toolkit must first be installed. See the

Linux Installation Guide for more information on how to install the CUDA Toolkit.

Then the CUDA Samples can be installed by running the following command, where

<target_path> is the location where to install the samples:

$ cuda-install-samples-9.0.sh <target_path>

Mac OSX

On Mac OSX, to install the CUDA Samples, the CUDA toolkit must first be installed. See

the Mac Installation Guide for more information on how to install the CUDA Toolkit.

Then the CUDA Samples can be installed by running the following command, where

<target_path> is the location where to install the samples:

$ cuda-install-samples-9.0.sh <target_path>

Getting Started

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CUDA Samples TRM-06704-001_v9.0|11

TARGET_ARCH

The target architecture must be specified when cross-compiling applications. If not

specified, it defaults to the host architecture. Allowed architectures are:

‣

x86_64 - 64-bit x86 CPU architecture

‣

armv7l - 32-bit ARM CPU architecture, like that found on Jetson TK1

‣

aarch64 - 64-bit ARM CPU architecture, found on certain Android systems

‣

ppc64le - 64-bit little-endian IBM POWER8 architecture

TARGET_OS

The target OS must be specified when cross-compiling applications. If not specified, it

defaults to the host OS. Allowed OSes are:

‣

linux - for any Linux distributions

‣

darwin - for Mac OS X

‣

android - for any supported device running Android

‣

qnx - for any supported device running QNX

TARGET_FS

The most reliable method to cross-compile the CUDA Samples is to use the TARGET_FS

variable. To do so, mount the target's filesystem on the host, say at /mnt/target. This is

typically done using exportfs. In cases where exportfs is unavailable, it is sufficient

to copy the target's filesystem to /mnt/target. To cross-compile a sample, execute:

$ make TARGET_ARCH=<arch> TARGET_OS=<os> TARGET_FS=/mnt/target

Copying Libraries

If the TARGET_FS option is not available, the libraries used should be copied from

the target system to the host system, say at /opt/target/libs. If the sample uses

GL, the GL headers must also be copied, say at /opt/target/include. The linker

must then be told where the libraries are with the -rpath-link and/or -L options. To

ignore unresolved symbols from some libraries, use the --unresolved-symbols option

as shown below. SAMPLE_ENABLED should be used to force the sample to build. For

example, to cross-compile a sample which uses such libraries, execute:

$ make TARGET_ARCH=<arch> TARGET_OS=<os> \

EXTRA_LDFLAGS="-rpath-link=/opt/target/libs -L/opt/target/libs --

unresolved-symbols=ignore-in-shared-libs" \

EXTRA_CCFLAGS="-I /opt/target/include" \

SAMPLE_ENABLED=1

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NVIDIA CUDA 2017 示例指南：从入门到实战

CUDA-9.0－sample文件

nvidia cuda-samples

CUDA中文手册.pdf+技术文档+不带标签

编写python代码，通过complete_train_samples.csv搭建，，训练线性回归模型，KNN模型，利用XA，XB预测Y

df_train = pd.DataFrame(x_train.reshape(x_train.shape[0]*x_train.shape[1], x_train.shape[2]))

while len(self.PER_memory) <= train_params.BATCH_SIZE: sys.stdout.write('\rPopulating replay memory up to batch_size samples...') sys.stdout.flush()

speech_level = loudness_meter.integrated_loudness(s1_samples) noise_level = loudness_meter.integrated_loudness(noise_samples) gain_db = speaker1_target_snr_db + noise_level - speech_level

解释X_train = X_train.reshape((train_samples, train_nx * train_ny))，preprocessor = prep.StandardScaler().fit(X_train)，X_train = preprocessor.transform(X_train)，X_train = X_train.reshape((train_samples, train_nx, train_ny))为什么要改变两次形状

如何在上述matlab代码里插入肤色样本库load('skin_samples.mat');和人脸检测算法 vision.CascadeObjectDetector;

X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]什么意思

plt.contour(X_3,X_4,classifier2.predict(np.array([X_3.ravel(),X_4.ravel()]).T).reshape(X_3.shape),1,colors="b")

from sklearn.tree import DecisionTreeClassifier from sklearn import tree best_dt =DecisionTreeClassifier(max_depth=2,criterion='entropy',min_samples_split= 2) best_dt.fit(X_train, y_train) print (best_dt.score(X_train, y_train)) print (best_dt.score(X_test, y_test))各行代码解释

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