Ubuntu16.04下安装tensorflow并配置GPU

Ubuntu 16.04下安装Tensorflow(GPU)

安装之前参考安装英伟达驱动的文章安装相关的显卡驱动。

1.首先安装nvidia显卡驱动:

系统设置->软件更新->附加驱动->选择nvidia最新驱动（第一项）->应用更改

在ubuntu16.04中，更换驱动非常方便，去
系统设置->软件更新->附加驱动->切换到最新的NVIDIA驱动即可。应用更改->重启

​2.下载CUDA8.0地址https://developer.nvidia.com/cuda-release-candidate-download(需要登陆)

请先确定显卡型号和是否支持GPU加速，查询网址：https://developer.nvidia.com/cuda-gpus

下载.run文件，进入文件目录，执行安装命令：

sudo ./cuda_8.0.61_375.26_linux.run  (目录和文件名由你下载的文件进行更改)

根据提示输入y或回车等操作：此安装过程可选择不安装显卡驱动。

Do you accept the previously read EULA?

accept/decline/quit: accept

 

Install NVIDIA Accelerated Graphics DriverforLinux-x86_64367.48?

(y)es/(n)o/(q)uit: n

 

Install the CUDA8.0Toolkit?

(y)es/(n)o/(q)uit: y

 

Enter Toolkit Location

[defaultis /usr/local/cuda-8.0]:

 

Do you want to install a symbolic link at /usr/local/cuda?

(y)es/(n)o/(q)uit: y

 

Install the CUDA8.0Samples?

(y)es/(n)o/(q)uit: y

安装完毕后，再声明一下环境变量，并将其写入到 ~/.bashrc 的尾部:

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda

保存退出，运行source ~/.bashrc，生效配置。

3.测试是否安装成功(可选)

cd /usr/local/cuda/samples/1_Utilities/deviceQuery

sudo make

./deviceQuery

显示结果：

4.安装CuDNN

如果要使用gpu来对tensorflow进行加速，除了安装CUDA以外，cuDNN也是必须要安装的。跟cuda一样，去nvidia的官网下载cuDNN的安装包。不过这次没法直接下载，需要先注册，然后还要做个调查问卷什么的，稍微有点麻烦。我下的是cuDNN v5.1 Library for Linux这个版本。不要下cuDNN v5.1 Developer Library for Ubuntu16.04 Power8 (Deb)这个版本,因为是给powe8处理器用的，不是amd64.

下载地址：https://developer.nvidia.com/cudnn（需要登录）

下载完成后复制文件到cuda目录/usr/local/cuda/，解压下载文件：

tar xvzf cudnn-8.0-linux-x64-v5.1-ga.tgz                   ###（解压这个文件）

sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* (root用户可以忽略)

1.4 Tensorflow 0.11

tensorflow github上面提到 4 种安装方式，本教程使用 第四种 源码安装：

参看https://github.com/tensorflow/tensorflow/blob/master/README.md

https://github.com/tensorflow/tensorflow（下载地址）

说明：
（1）打开README.md页面，往下翻，直到下图这个位置：

Installation

See Installing TensorFlow for instructions on how to install our release binaries or how to build from source.

People who are a little more adventurous can also try our nightly binaries:

• Linux CPU-only: Python 2 (build history) /Python 3.4 (build history) /Python 3.5 (build history)
• Linux GPU: Python 2 (build history) /Python 3.4 (build history) /Python 3.5 (build history)
• Mac CPU-only: Python 2 (build history) /Python 3 (build history)
• Mac GPU: Python 2 (build history) /Python 3 (build history)
• Windows CPU-only: Python 3.5 64-bit (build history) /Python 3.6 64-bit (build history)
• Windows GPU: Python 3.5 64-bit (build history) /Python 3.6 64-bit (build history)
• Android: demo APK, native libs(build history)

（2） 点击Python 2开始下载。

最后，将1.2-1.4中下载文件全部存放至相应文件夹内，等待安装时候使用。

1.5 gcc降版本

ubuntu的gcc编译器是5.4.0，然而cuda8.0不支持5.0以上的编译器，因此需要降级，把编译器版本降到4.9：
在terminal中执行：

sudo apt-get install g++-4.9sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 20sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 10sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.9 20sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 10sudo update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30sudo update-alternatives --set cc /usr/bin/gccsudo update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30sudo update-alternatives --set c++ /usr/bin/g++输入gcc -v查看版本是否是4.9.3

4.2 安装其他库

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md
我们是在github的Tensorflow官方网页上，根据提示安装，地址如上。

在terminal中输入以下命令：

sudo apt-get install python-pip python-dev 

4. 安装Bazel

4.1 安装Bazel依赖

由于本教程使用tensorflow源码编译/安装，所以需要使用 bazel build。
链接：https://www.bazel.io/versions/master/docs/install.html

Installing Bazel

See the instructions for installing Bazel on:

• Ubuntu Linux (16.04, 15.10, and 14.04)
• Mac OS X
• Windows (experimental) 
  

参考地址：
https://docs.bazel.build/versions/master/install-ubuntu.html

在terminal中依次输入以下1-7的命令

Install Bazel on Ubuntu

Supported Ubuntu Linux platforms:

• 16.04 (LTS)
• 14.04 (LTS)

Install Bazel on Ubuntu using one of the following methods:

• Use our custom APT repository (recommended)
• Use the binary installer
• Compile Bazel from source

Bazel comes with two completion scripts. After installing Bazel, you can:

• access the bash completion script
• install the zsh completion script

Using Bazel custom APT repository (recommended)

1. Install JDK 8

Install JDK 8 by using:

sudo apt-get install openjdk-8-jdk

On Ubuntu 14.04 LTS you'll have to use a PPA:

sudo add-apt-repository ppa:webupd8team/javasudo apt-get update && sudo apt-get install oracle-java8-installer

2. Add Bazel distribution URI as a package source (one time setup)

echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.listcurl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -

If you want to install the testing version of Bazel, replace stable withtesting.

3. Install and update Bazel

sudo apt-get update && sudo apt-get install bazel

Once installed, you can upgrade to a newer version of Bazel with:

sudo apt-get upgrade bazel

4. Set up your environment

If you ran the Bazel installer with the --user flag as above, the Bazel executable is installed in your$HOME/bin directory. It's a good idea to add this directory to your default paths, as follows:

export PATH="$PATH:$HOME/bin"

You can also add this command to your ~/.bashrc file.

Once installed, you can upgrade to a newer version of Bazel with:

sudo apt-get upgrade bazel

4.3 安装第三方库

在terminal中输入以下命令

sudo apt-get install python-numpy swig python-dev python-wheel #安装第三方库sudo apt-get install gitgit clone git://github.com/numpy/numpy.git numpy   #也可以直接在输入网址打包ZIP下载

5. 安装tensorflow

5.1 下载tensorflow

在terminal中输入以下命令

git clone https://github.com/tensorflow/tensorflow

特别注意，我使用的是tensorflow 0.11版本，该版本要求cuda 7.5 以上，cuDNN v5。
默认下载目录是在/home下

5.2 配置tensorflow

还是刚刚的网址
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md

3） 编译安装TensorFlow:

首先从github上克隆TensorFlow最新的代码：

git clone https://github.com/tensorflow/tensorflow

代码下载完毕之后，进入tensorflow主目录，执行：

./configure

会有一系列提示：
Please specify the location of python. [Default is /usr/bin/python]:
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] y
Google Cloud Platform support will be enabled for TensorFlow

ERROR: It appears that the development version of libcurl is not available. Please install the libcurl3-dev package.

第二项"是否选择Google云平台的支持"选择y之后出现了一个错误，需要libcurl，用apt-get安装，当然，基于国内的网络现状，这一项也可以选择no:

sudo apt-get install libcurl3 libcurl3-dev

安装完毕之后重新执行

./configure

除了两处选择yes or no 的地方外，其他地方一路回车:

Please specify the location of python. [Default is /usr/bin/python]:
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] y
Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with GPU support? [y/N] y
GPU support will be enabled for TensorFlow
Please specify which gcc nvcc should use as the host compiler. [Default is /usr/bin/gcc]:
Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]:
Please specify the location where CUDA toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify the Cudnn version you want to use. [Leave empty to use system default]:
Please specify the location where cuDNN library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size.
[Default is: "3.5,5.2"]:
Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Setting up CUPTI include
Setting up CUPTI lib64
Configuration finished

最后就是通过Bazel进行编译安装了:

bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer

这个过程中需要通过git下载和编译google protobuf 和 boringssl:

INFO: Cloning https://github.com/google/protobuf: Receiving objects
INFO: Cloning https://github.com/google/boringssl.git: Receiving objects
....

不过第一次安装的时候遇到报错：

configure: error: zlib not installed
Target //tensorflow/cc:tutorials_example_trainer failed to build

google了一下，需要安装zlib1g-dev:

sudo apt-get install zlib1g-dev

之后重新编译安装TensorFlow就没有问题了，不过需要等待一段时间：

编译TensorFlow成功结束的时候，提示如下：

......
Target //tensorflow/cc:tutorials_example_trainer up-to-date:
bazel-bin/tensorflow/cc/tutorials_example_trainer
INFO: Elapsed time: 897.845s, Critical Path: 533.72s

执行一下TensorFlow官方文档里的例子，看看能否成功调用GTX 1080：

bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu

I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.835
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.65GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
000003/000006 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
000006/000007 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
000009/000006 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
000009/000004 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
000000/000005 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
000000/000004 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
......

没有问题，说明这种通过源代码编译TensorFlow使其支持GPU的方式已经成功了。再在Python中调用一下TensorFlow:

import tensorflow as tf

提示错误：

ImportError: cannot import name pywrap_tensorflow

虽然我们通过源代码安装编译的TensorFlow可用，但是Python版本并没有ready，所以继续：

bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
sudo pip install /tmp/tensorflow_pkg/tensorflow-0.9.0-py2-none-any.whl

Requirement already satisfied (use --upgrade to upgrade): setuptools in /usr/lib/python2.7/dist-packages (from protobuf==3.0.0b2->tensorflow==0.9.0)
Installing collected packages: six, funcsigs, pbr, mock, protobuf, tensorflow
Successfully installed funcsigs-1.0.2 mock-2.0.0 pbr-1.10.0 protobuf-3.0.0b2 six-1.10.0 tensorflow-0.9.0

我们再次打开ipython，试一下tensorflow官方样例:

Python 2.7.12 (default, Jul  1 2016, 15:12:24)
Type "copyright", "credits" or "license" for more information.

IPython 2.4.1 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally

In [2]: import numpy as np

In [3]: x_data = np.random.rand(100).astype(np.float32)

In [4]: y_data = x_data * 0.1 + 0.3

In [5]: W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))

In [6]: b = tf.Variable(tf.zeros([1]))

In [7]: y = W * x_data + b

In [8]: loss = tf.reduce_mean(tf.square(y - y_data))

In [9]: optimizer = tf.train.GradientDescentOptimizer(0.5)

In [10]: train = optimizer.minimize(loss)

In [11]: init = tf.initialize_all_variables()

In [12]: sess = tf.Session()
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.835
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.65GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)

In [13]: sess.run(init)

In [14]: for step in range(201):
   ....:     sess.run(train)
   ....:     if step % 20 == 0:
   ....:         print(step, sess.run(W), sess.run(b))
   ....:        
(0, array([-0.10331395], dtype=float32), array([ 0.62236434], dtype=float32))
(20, array([ 0.03067014], dtype=float32), array([ 0.3403711], dtype=float32))
(40, array([ 0.08353967], dtype=float32), array([ 0.30958495], dtype=float32))
(60, array([ 0.09609199], dtype=float32), array([ 0.30227566], dtype=float32))
(80, array([ 0.09907217], dtype=float32), array([ 0.3005403], dtype=float32))
(100, array([ 0.09977971], dtype=float32), array([ 0.30012828], dtype=float32))
(120, array([ 0.0999477], dtype=float32), array([ 0.30003047], dtype=float32))
(140, array([ 0.0999876], dtype=float32), array([ 0.30000722], dtype=float32))
(160, array([ 0.09999706], dtype=float32), array([ 0.30000171], dtype=float32))
(180, array([ 0.09999929], dtype=float32), array([ 0.30000043], dtype=float32))
(200, array([ 0.09999985], dtype=float32), array([ 0.3000001], dtype=float32))

终于OK了，之后就可以尽情享用基于GTX 1080 GPU版的TensorFlow了。

6. 测试tensorflow

这里进行测试，如果你能跟我看到同样的画面，那恭喜你成功配置GPU版的tensorflow啦！


跑这个例子，会出现很多提示，如果你在运行过程中发现自己的显卡型号，并提示成功调用cuda库，并每次step小于100ms，说明成功，否则就检查下哪里出现问题吧~
下面就尽情调戏tensorflow啦！
这里给出很有意思的教程链接：http://m.blog.csdn.net/article/details?hmsr=toutiao.io&id=52658965&utm_medium=toutiao.io&utm_source=toutiao.io
用tensorflow实现梵高作画。

7. 常见问题

7.1循环登录

在ubuntu14.04安装N卡驱动后，会出现无法显示登录界面或者循环登录的问题。这主要是显卡不兼容，具体解决思路可以参考google上的解决方案，关键词 ubuntu login loop。
经过测试，网上的教程对我都不适用，无奈转向ubuntu16.04

7.2 缺少第三方库

因为这个教程是我安装成功之后写的，其中难免遗忘某些库的安装，例如Git、pip这些库，安装过程很简单，具体可以google。

7.3 tensorflow配置问题

在执行./configure 或者设置tensorflow环境时，如果出现无法找到某个库的路径，那么检查是否正确的设置了cuda的环境变量，具体参考 4.1节。

7.4 cuda8.0不支持gcc 5.3以上版本

这个问题可以通过对gcc降版本解决。相关连接 http://m.blog.csdn.net/article/details?id=51999566

7.5 测试tensorflow时出现IOError

在测试tensorflow中，执行

python convolutional.py
1
1

出现 IOError错误，这是由于convolutional.py中需要从网上下载MNIST数据库。如果出现错误，那么重新执行Python convolutional.py命令，或者手动从网站下载数据库并放在相应文件夹就好啦。

8. 经验与总结

1. google是最好的老师！
2. 感谢七月在线团队的无私帮助： qq群：472899334
3. 失败是成功之母，经过这么多次尝试，以后的配置应该都不是问题啦
4. 欢迎联系我的QQ： 3062984605
5. 欢迎留言补充或讨论

9. 参考文献

[1] http://blog.csdn.net/u010789558/article/details/51867648
[2] http://textminingonline.com/dive-into-tensorflow-part-iii-gtx-1080-ubuntu16-04-cuda8-0-cudnn5-0-tensorflow
[3] http://m.blog.csdn.net/article/details?id=52658965
[4] https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md#installing-from-sources
[5] http://www.tensorfly.cn/tfdoc/get_started/os_setup.html
[6] http://ramhiser.com/2016/01/05/installing-tensorflow-on-an-aws-ec2-instance-with-gpu-support/
[7]http://blog.csdn.net/u012436149/article/details/52554176
[8] http://m.blog.csdn.net/article/details?id=51999566

---

利用pip安装方法 ##(版权归属： QQ 1395569872)

Ubuntu16.04从U盘安装纯净单系统

Ubuntu16.04安装NVIDIA显卡官方驱动
1.点桌面左上角搜索本机程序的图标，找到“附加驱动”
2.在“附加驱动”里，系统会自动搜索N卡驱动，列表里会提供对应你显卡的最新版官方驱动。例如我的显卡是GT730，选择第一项361.42就可以了。
3.最后点“应用更改”，等待安装完毕即可。

安装CUDA【Debian安装】
1、下载安装
进入下载文件所在目录，执行下列命令：

$ sudo dpkg --install cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64.deb$ sudo apt-get update$ sudo apt-get install cuda
1
2
3
1
2
3

安装cuDNN
1、下载安装Cudnn v5.1（https://developer.nvidia.com/cudnn）
进入下载文件所在目录，执行下列命令：

$ tar xvzf cudnn-8.0-linux-x64-v5.1.tgz$ sudo cp cuda/include/cudnn.h /usr/local/cuda/include$ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
1
2
3
1
2
3

退到根目录，运行下面语句：

$ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
1
1

2、配置环境变量：
在terminal根目录中输入以下命令：

$ sudo gedit ~/.bash_profile
1
1

然后在打开的文本末尾加入：

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"export CUDA_HOME=/usr/local/cuda
1
2
1
2

继续在terminal中输入:

$ source ~/.bash_profile 
1
1

安装pip

$ sudo apt-get install python-pip python-dev$ sudo apt-get install python-numpy swig python-dev python-wheel
1
2
1
2

安装TensorFlow

# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Install from sources" below.$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0-cp27-none-linux_x86_64.whl$ sudo -H pip install --upgrade $TF_BINARY_URL