Showing posts with label GPU. Show all posts

Sunday, 12 November 2017

How to install JetPack 3.1 on Nvidia Jetson TX2

Earlier this year I was writing about How to install JetPack on Jetson TX1. Soon after (in March) NVIDIA released a new version of the Tegra processor, TX2, and also its development board, Jetson TX2. Consequently, a new version of JetPack development toolset was released as well, with the major version increased to 3. See here the full spec of the Jetson TX2 Module and the Development Kit.

Jetson TX2 with connected peripherals on the right side: Ethernet, HDMI, USB (4-way USB hub for connecting keyboard, mouse and memory stick), micro USB (connected to host PC), 2 Wi-Fi antennas, power plug

I got hold of Jetson TX2 and the first thing I did was installing the latest version of JetPack on my host machine and also on the Jetson board.

Jetson TX2

One significant change I noticed was that it is not necessary to switch back to Ubuntu 14.04 on a host machine anymore - entire installation goes well with running Ubuntu 16.04 (16.04.3 in my case) on the host. This is still not official recommendation though - official JetPack installation guide states that Ubuntu 14.04 is required.

Tegra TX2 module is under this heat sink and fan

Entire installation process is very similar to installing JetPack 2 on TX1. Nevertheless, I'll show you here all necessary steps.

Download first JetPack installer from Jetson Development Pack download page. At the time of writing, the latest was version 3.1: JetPack-L4T-3.1-linux-x64.run. Place it in the folder on the host machine where you intend to install JetPack. I chose /opt/JetPack. Make the file executable, either in Terminal or by right-clicking the file and opening Properties dialog:

Running the installer from the terminal:

...opens an Installation Wizard:

We'll confirm the installation directory and Jetson module:

Installation requires elevation of privileges:

Component Manager opens where we have to select JetPack version we want to install and also Jetson module:

If we have previous version of the JetPack installed, we can uninstall it by selecting that version and set the Action to uninstall:

Component Manager shows suggested actions for each component for the selected JetPack and Jetson module. Components are grouped into two groups: Host (Ubuntu) and Target (Jetson).

We have to accept legal stuff:

...after which downloading of the installers for each component starts:

After all installers are downloaded and just before the installation starts, Component Manager will warn you to stay at your keyboard as your input might be required. When we hit Next button, installation starts.

Installation on the host will eventually be completed:

Now select your network configuration. In my case both host (PC with Ubuntu) and target (Jetson TX2) were connected to the local router (both via WiFi):

In the next step we have to select network interface through which is the host machine connected to the same network as the Jetson. Run ifconfig to get the names of all interfaces:

...then select in the Wizard the one connected to the local router:

In the next step Jetpack installer lists all actions that will be performed on the Jetson:

JetPack installer opens a separate Terminal window (on the host) with instructions on how to manually prepare Jetson for flashing:

Once Jetson is in recovery mode, host sees it as the USB device.

Flashing then starts:

Once flashing of the Linux for Tegra on the Jetson is done (via USB cable), installation of all Nvidia JetPack modules selected earlier starts. This happens via SSH connection so installer is trying to determine target's IP address:

Here I rebooted my Jetson after a while as thought Installation was stuck in this state forever...and later re-ran Jetson Installer. This was probably not necessary as next time I got the same message but waited a bit more after which I was prompted for entering the IP manually:

JetPack installer will now push all modules listed below via SSH tunnel:

Installation of the modules eventually completes:

After closing the Terminal window, JetPack Installer Wizard shows the last page where we can opt for removing all module installers from the host machine:

In order to verify that installation was indeed successful, we can run the OceanFFT application - a demo suggested by Nvidia:

The duration of the entire installation process depends on the host's processing power and internet speed. With fast hosts and broadband connections it could last about 15 minutes.

What are your experiences with installing JetPack on TX2? Did you also have success with running Ubuntu 16.04 on the host PC?

Sunday, 26 March 2017

Installing TensorFlow on Ubuntu 16.04

As per TensorFlow installation manual, TensorFlow can be installed with either CPU or GPU support.

In order to check if we can install TensorFlow with GPU support we have to check if we have NVIDIA graphics card:

$ lspci

...
01:00.0 VGA compatible controller: NVIDIA Corporation GK107 [GeForce GT 640 OEM] (rev a1)
...

We do have it so we can go forward with installation of GPU-accelerated version of TensorFlow.

Installing TensorFlow Dependencies

1) CUDA Toolkit 8.0

If CUDA is installed on local machine, we should be able to find the location of NVIDIA CUDA compiler (nvcc) and check its version (which matches the version of CUDA package):

$ which nvcc

/usr/local/cuda-8.0/bin/nvcc

$ nvcc --version

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Sun_Sep__4_22:14:01_CDT_2016
Cuda compilation tools, release 8.0, V8.0.44

If CUDA is not installed, download Ubuntu installer from CUDA Downloads page and follow Installation instructions in CUDA Quick start Guide.

In our case, CUDA 8.0 was installed so no upgrade was necessary.

2) NVIDIA drivers associated with CUDA Toolkit 8.0

They get installed within CUDA Installation and get loaded upon the next system boot.

Installed NVIDIA driver files have names with pattern nvidia-XXX where XXX is a number so we can use:

$ sudo apt list | grep -P 'nvidia-[0-9]+'

nvidia-304/xenial-updates,xenial-security 304.134-0ubuntu0.16.04.1 amd64
nvidia-304-dev/xenial-updates,xenial-security 304.134-0ubuntu0.16.04.1 amd64
nvidia-304-updates/xenial-updates,xenial-security 304.134-0ubuntu0.16.04.1 amd64
nvidia-304-updates-dev/xenial-updates,xenial-security 304.134-0ubuntu0.16.04.1 amd64
nvidia-331/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-331-dev/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-331-updates/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-331-updates-dev/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-331-updates-uvm/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-331-uvm/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-340/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-340-dev/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-340-updates/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-340-updates-dev/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-340-updates-uvm/xenial 340.96-0ubuntu2 amd64
nvidia-340-uvm/xenial-updates,xenial-security 340.101-0ubuntu0.16.04.1 amd64
nvidia-346/xenial 352.63-0ubuntu3 amd64
nvidia-346-dev/xenial 352.63-0ubuntu3 amd64
nvidia-346-updates/xenial 352.63-0ubuntu3 amd64
nvidia-346-updates-dev/xenial 352.63-0ubuntu3 amd64
nvidia-352/xenial 361.42-0ubuntu2 i386
nvidia-352-dev/xenial 361.42-0ubuntu2 i386
nvidia-352-updates/xenial 361.42-0ubuntu2 i386
nvidia-352-updates-dev/xenial 361.42-0ubuntu2 i386
nvidia-361/xenial-updates,xenial-security 367.57-0ubuntu0.16.04.1 amd64
nvidia-361-dev/xenial-updates,xenial-security 367.57-0ubuntu0.16.04.1 amd64
nvidia-361-updates/xenial 361.42-0ubuntu2 i386
nvidia-361-updates-dev/xenial 361.42-0ubuntu2 i386
nvidia-367/xenial-updates,xenial-security,now 367.57-0ubuntu0.16.04.1 amd64 [installed,automatic]
nvidia-367-dev/xenial-updates,xenial-security,now 367.57-0ubuntu0.16.04.1 amd64 [installed,automatic]

Driver in use is the latest one, with version 367.57.

We can also run NVIDIA System Management Interface:

$ nvidia-smi

...or query NVIDIA settings for NvidiaDriverVersion:

$ nvidia-settings -q NvidiaDriverVersion

Attribute 'NvidiaDriverVersion' (my-PC:0.0): 367.57
Attribute 'NvidiaDriverVersion' (my-PC:0[gpu:0]): 367.57

...or open NVIDIA settings GUI (we have to open it from Terminal) and check driver version there:

$ nvidia-settings

3) cuDNN v5.1

Installation of cuDNN is a matter of downloading its package, uncompressing it and copying header file and a library into CUDA installation directories.

We detected earlier that CUDA root directory is /usr/local/cuda-8.0/. We have to check that /usr/local/cuda-8.0/include and /usr/local/cuda-8.0/lib64 directories contain cuDNN. In our case they do as I already had cuDNN installed:

/usr/local/cuda-8.0/include$ ls cudnn*
cudnn.h

/usr/local/cuda-8.0/lib64$ ls libcudnn*
libcudnn.so libcudnn.so.5 libcudnn.so.5.1.5 libcudnn_static.a

Header file contains version information:

/usr/local/cuda-8.0/include$ cat cudnn.h | grep CUDNN_MAJOR -A 2

#define CUDNN_MAJOR 5
#define CUDNN_MINOR 1
#define CUDNN_PATCHLEVEL 5

We verified that have cuDNN v5.1 installed.

4) GPU card with CUDA Compute Capability 3.0 or higher

Compute Capability is a version of GPU's architecture generation.

NVIDIA's CUDA GPUs page lists two versions of GeForce GT 640:
GeForce GT 640 (GDDR5) 3.5
GeForce GT 640 (GDDR3) 2.1

We have to find out if GeForce GT 640 OEM has GDDR5 or GDDR3.

GeForce GT 640 OEM Specification does not mention Computing Capability.

I tried various tools with no success:

$ nvidia-smi

$ nvidia-smi --query

==============NVSMI LOG==============

Timestamp : Sun Mar 19 22:50:19 2017
Driver Version : 367.57

Attached GPUs : 1
GPU 0000:01:00.0
Product Name : GeForce GT 640
Product Brand : GeForce
Display Mode : N/A
Display Active : N/A
Persistence Mode : Disabled
Accounting Mode : N/A
Accounting Mode Buffer Size : N/A
Driver Model
Current : N/A
Pending : N/A
Serial Number : N/A
GPU UUID : GPU-f2583df9-404d-2564-d332-e7878a94d087
Minor Number : 0
VBIOS Version : 80.07.53.00.21
MultiGPU Board : N/A
Board ID : N/A
GPU Part Number : N/A
Inforom Version
Image Version : N/A
OEM Object : N/A
ECC Object : N/A
Power Management Object : N/A
GPU Operation Mode
Current : N/A
Pending : N/A
GPU Virtualization Mode
Virtualization mode : N/A
PCI
Bus : 0x01
Device : 0x00
Domain : 0x0000
Device Id : 0x0FC010DE
Bus Id : 0000:01:00.0
Sub System Id : 0x3B861642
GPU Link Info
PCIe Generation
Max : N/A
Current : N/A
Link Width
Max : N/A
Current : N/A
Bridge Chip
Type : N/A
Firmware : N/A
Replays since reset : 0
Tx Throughput : N/A
Rx Throughput : N/A
Fan Speed : 40 %
Performance State : P0
Clocks Throttle Reasons : N/A
FB Memory Usage
Total : 1990 MiB
Used : 733 MiB
Free : 1257 MiB
BAR1 Memory Usage
Total : N/A
Used : N/A
Free : N/A
Compute Mode : Default
Utilization
Gpu : N/A
Memory : N/A
Encoder : N/A
Decoder : N/A
Ecc Mode
Current : N/A
Pending : N/A
ECC Errors
Volatile
Single Bit
Device Memory : N/A
Register File : N/A
L1 Cache : N/A
L2 Cache : N/A
Texture Memory : N/A
Texture Shared : N/A
Total : N/A
Double Bit
Device Memory : N/A
Register File : N/A
L1 Cache : N/A
L2 Cache : N/A
Texture Memory : N/A
Texture Shared : N/A
Total : N/A
Aggregate
Single Bit
Device Memory : N/A
Register File : N/A
L1 Cache : N/A
L2 Cache : N/A
Texture Memory : N/A
Texture Shared : N/A
Total : N/A
Double Bit
Device Memory : N/A
Register File : N/A
L1 Cache : N/A
L2 Cache : N/A
Texture Memory : N/A
Texture Shared : N/A
Total : N/A
Retired Pages
Single Bit ECC : N/A
Double Bit ECC : N/A
Pending : N/A
Temperature
GPU Current Temp : 38 C
GPU Shutdown Temp : N/A
GPU Slowdown Temp : N/A
Power Readings
Power Management : N/A
Power Draw : N/A
Power Limit : N/A
Default Power Limit : N/A
Enforced Power Limit : N/A
Min Power Limit : N/A
Max Power Limit : N/A
Clocks
Graphics : N/A
SM : N/A
Memory : N/A
Video : N/A
Applications Clocks
Graphics : N/A
Memory : N/A
Default Applications Clocks
Graphics : N/A
Memory : N/A
Max Clocks
Graphics : N/A
SM : N/A
Memory : N/A
Video : N/A
Clock Policy
Auto Boost : N/A
Auto Boost Default : N/A
Processes : N/A

$ sudo dmidecode -t memory

# dmidecode 3.0
Getting SMBIOS data from sysfs.
SMBIOS 2.7 present.

Handle 0x000F, DMI type 16, 23 bytes
Physical Memory Array
Location: System Board Or Motherboard
Use: System Memory
Error Correction Type: None
Maximum Capacity: 32 GB
Error Information Handle: Not Provided
Number Of Devices: 4

Handle 0x0011, DMI type 17, 34 bytes
Memory Device
Array Handle: 0x000F
Error Information Handle: Not Provided
Total Width: 64 bits
Data Width: 64 bits
Size: 4096 MB
Form Factor: DIMM
Set: None
Locator: ChannelA-DIMM0
Bank Locator: BANK 0
Type: DDR3
Type Detail: Synchronous
Speed: 1600 MHz
Manufacturer: 0443
Serial Number: 42718838
Asset Tag: 9876543210
Part Number: RMR5040ED58E9W1600
Rank: 2
Configured Clock Speed: 1600 MHz

Handle 0x0013, DMI type 17, 34 bytes
Memory Device
Array Handle: 0x000F
Error Information Handle: Not Provided
Total Width: 64 bits
Data Width: 64 bits
Size: 4096 MB
Form Factor: DIMM
Set: None
Locator: ChannelA-DIMM1
Bank Locator: BANK 1
Type: DDR3
Type Detail: Synchronous
Speed: 1600 MHz
Manufacturer: 0443
Serial Number: 42218738
Asset Tag: 9876543210
Part Number: RMR5040ED58E9W1600
Rank: 2
Configured Clock Speed: 1600 MHz

Handle 0x0015, DMI type 17, 34 bytes
Memory Device
Array Handle: 0x000F
Error Information Handle: Not Provided
Total Width: 64 bits
Data Width: 64 bits
Size: 4096 MB
Form Factor: DIMM
Set: None
Locator: ChannelB-DIMM0
Bank Locator: BANK 2
Type: DDR3
Type Detail: Synchronous
Speed: 1600 MHz
Manufacturer: 0443
Serial Number: 42508938
Asset Tag: 9876543210
Part Number: RMR5040ED58E9W1600
Rank: 2
Configured Clock Speed: 1600 MHz

Handle 0x0017, DMI type 17, 34 bytes
Memory Device
Array Handle: 0x000F
Error Information Handle: Not Provided
Total Width: 64 bits
Data Width: 64 bits
Size: 4096 MB
Form Factor: DIMM
Set: None
Locator: ChannelB-DIMM1
Bank Locator: BANK 3
Type: DDR3
Type Detail: Synchronous
Speed: 1600 MHz
Manufacturer: 0443
Serial Number: 42B3C138
Asset Tag: 9876543210
Part Number: RMR5040ED58E9W1600
Rank: 2
Configured Clock Speed: 1600 MHz

$ sudo lshw -short -C memory

H/W path Device Class Description
====================================================================
/0/0 memory 64KiB BIOS
/0/c memory 1MiB L2 cache
/0/d memory 256KiB L1 cache
/0/e memory 8MiB L3 cache
/0/f memory 16GiB System Memory
/0/f/0 memory 4GiB DIMM DDR3 Synchron
/0/f/1 memory 4GiB DIMM DDR3 Synchron
/0/f/2 memory 4GiB DIMM DDR3 Synchron
/0/f/3 memory 4GiB DIMM DDR3 Synchron

$ sudo lshw -C video

*-display
description: VGA compatible controller
product: GK107 [GeForce GT 640 OEM]
vendor: NVIDIA Corporation
physical id: 0
bus info: pci@0000:01:00.0
version: a1
width: 64 bits
clock: 33MHz
capabilities: pm msi pciexpress vga_controller bus_master cap_list rom
configuration: driver=nvidia latency=0
resources: irq:31 memory:f6000000-f6ffffff memory:e0000000-efffffff memory:f0000000-f1ffffff ioport:e000(size=128) memory:f7000000-f707ffff

$ lspci

...
01:00.0 VGA compatible controller: NVIDIA Corporation GK107 [GeForce GT 640 OEM] (rev a1)
...

$ sudo lspci -v -s 01:00.0
01:00.0 VGA compatible controller: NVIDIA Corporation GK107 [GeForce GT 640 OEM] (rev a1) (prog-if 00 [VGA controller])
Subsystem: Bitland(ShenZhen) Information Technology Co., Ltd. GK107 [GeForce GT 640 OEM]
Flags: bus master, fast devsel, latency 0, IRQ 31
Memory at f6000000 (32-bit, non-prefetchable) [size=16M]
Memory at e0000000 (64-bit, prefetchable) [size=256M]
Memory at f0000000 (64-bit, prefetchable) [size=32M]
I/O ports at e000 [size=128]
[virtual] Expansion ROM at f7000000 [disabled] [size=512K]
Capabilities: [60] Power Management version 3
Capabilities: [68] MSI: Enable+ Count=1/1 Maskable- 64bit+
Capabilities: [78] Express Endpoint, MSI 00
Capabilities: [b4] Vendor Specific Information: Len=14
Capabilities: [100] Virtual Channel
Capabilities: [128] Power Budgeting
Capabilities: [600] Vendor Specific Information: ID=0001 Rev=1 Len=024
Capabilities: [900] #19
Kernel driver in use: nvidia
Kernel modules: nvidiafb, nouveau, nvidia_367, nvidia_367_drm

None of these tools gave me the answer. I found on Wikipedia's page List of Nvidia graphics processing units that "The GeForce GT 640 (OEM) card is a rebranded GeForce GT 545 (DDR3)." That would mean that Compute Capability is 2.1.

Nevertheless, I decided to try to use CUDA's API cudaGetDeviceProperties which populates structure cudaDeviceProp. This structure has fields major and minor which actually denote Compute Capability. Example's code is here and after I compiled it and ran, I got:

$ ./CudaDeviceInfo.out

Device Number: 0
Device name: GeForce GT 640
Memory Clock Rate (KHz): 891000
Memory Bus Width (bits): 128
Peak Memory Bandwidth (GB/s): 28.512000
Compute Capability: 3.0

Hooray! Compute Capability is 3.0, we can use this GPU!

5) libcupti-dev library

This library is NVIDIA CUDA Profile Tools Interface. It can be installed like here:

$ sudo apt-get install libcupti-dev

Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages were automatically installed and are no longer required:
linux-headers-4.4.0-31 linux-headers-4.4.0-31-generic linux-headers-4.4.0-62 linux-headers-4.4.0-62-generic linux-image-4.4.0-31-generic
linux-image-4.4.0-62-generic linux-image-extra-4.4.0-31-generic linux-image-extra-4.4.0-62-generic
Use 'sudo apt autoremove' to remove them.
The following additional packages will be installed:
libcupti-doc libcupti7.5
The following NEW packages will be installed
libcupti-dev libcupti-doc libcupti7.5
0 to upgrade, 3 to newly install, 0 to remove and 131 not to upgrade.
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TensorFlow Installation

There are four options how to install TensorFlow: using virtualenv, native pip, Docker or Anaconda. Official documentation recommends virtualenv and we'll go this way.

I haven't used virtualenv before so let's install it (with verifying if other required packages are present):

$ sudo apt-get install python-pip python-dev python-virtualenv

Reading package lists... Done
Building dependency tree
Reading state information... Done
python-dev is already the newest version (2.7.11-1).
python-dev set to manually installed.
The following additional packages will be installed:
libpython-all-dev python-all python-all-dev python-setuptools python-wheel
python3-virtualenv virtualenv
Suggested packages:
python-setuptools-doc
The following NEW packages will be installed
libpython-all-dev python-all python-all-dev python-pip python-setuptools
python-virtualenv python-wheel python3-virtualenv virtualenv
0 to upgrade, 9 to newly install, 0 to remove and 0 not to upgrade.
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Setting up python-all-dev (2.7.11-1) ...
Setting up python-pip (8.1.1-2ubuntu0.4) ...
Setting up python-setuptools (20.7.0-1) ...
Setting up python-virtualenv (15.0.1+ds-3ubuntu1) ...
Setting up python-wheel (0.29.0-1) ...
Setting up python3-virtualenv (15.0.1+ds-3ubuntu1) ...
Setting up virtualenv (15.0.1+ds-3ubuntu1) ...

We can verify that virtualenv has been properly installed by querying its version:

$ virtualenv --version

15.0.1

or simply call it to get usage help:

$ virtualenv

Running virtualenv with interpreter /usr/bin/python2
You must provide a DEST_DIR
Usage: virtualenv.py [OPTIONS] DEST_DIR

Options:
--version show program's version number and exit
-h, --help show this help message and exit
-v, --verbose Increase verbosity.
-q, --quiet Decrease verbosity.
-p PYTHON_EXE, --python=PYTHON_EXE
The Python interpreter to use, e.g.,
--python=python2.5 will use the python2.5 interpreter
to create the new environment. The default is the
python2 interpreter on your path (e.g.
/usr/bin/python2)
--clear Clear out the non-root install and start from scratch.
--no-site-packages DEPRECATED. Retained only for backward compatibility.
Not having access to global site-packages is now the
default behavior.
--system-site-packages
Give the virtual environment access to the global
site-packages.
--always-copy Always copy files rather than symlinking.
--unzip-setuptools Unzip Setuptools when installing it.
--relocatable Make an EXISTING virtualenv environment relocatable.
This fixes up scripts and makes all .pth files
relative.
--no-setuptools Do not install setuptools in the new virtualenv.
--no-pip Do not install pip in the new virtualenv.
--no-wheel Do not install wheel in the new virtualenv.
--extra-search-dir=DIR
Directory to look for setuptools/pip distributions in.
This option can be used multiple times.
--download Download preinstalled packages from PyPI.
--no-download, --never-download
Do not download preinstalled packages from PyPI.
--prompt=PROMPT Provides an alternative prompt prefix for this
environment.
--setuptools DEPRECATED. Retained only for backward compatibility.
This option has no effect.
--distribute DEPRECATED. Retained only for backward compatibility.
This option has no effect.

We can now create virtualenv for Tensorflow, in directory "tensorflow":

$ virtualenv --python=python3 --system-site-packages tensorflow

Already using interpreter /usr/bin/python3
Using base prefix '/usr'
New python executable in /home/user_name/dev/tensorflow/bin/python3
Also creating executable in /home/user_name/dev/tensorflow/bin/python
Installing setuptools, pkg_resources, pip, wheel...done.

After activating virtual environment, prompt will be prepended with the name of the folder where we created it (e.g. (tensorflow)):

tensorflow/bin$ source activate

(tensorflow) user@user-machine:~/tensorflow/bin$

We want to install GPU-enabled TensorFlow for Python3:

$ pip3 install --upgrade tensorflow-gpu

Collecting tensorflow-gpu
Downloading tensorflow_gpu-1.0.1-cp35-cp35m-manylinux1_x86_64.whl (94.8MB)
100% |████████████████████████████████| 94.8MB 18kB/s
Requirement already up-to-date: wheel>=0.26 in /home/bojan/dev/tensorflow/lib/python3.5/site-packages (from tensorflow-gpu)
Requirement already up-to-date: protobuf>=3.1.0 in /usr/local/lib/python3.5/dist-packages (from tensorflow-gpu)
Collecting numpy>=1.11.0 (from tensorflow-gpu)
Downloading numpy-1.12.1-cp35-cp35m-manylinux1_x86_64.whl (16.8MB)
100% |████████████████████████████████| 16.8MB 87kB/s
Requirement already up-to-date: six>=1.10.0 in /home/bojan/dev/tensorflow/lib/python3.5/site-packages (from tensorflow-gpu)
Requirement already up-to-date: setuptools in /home/bojan/dev/tensorflow/lib/python3.5/site-packages (from protobuf>=3.1.0->tensorflow-gpu)
Requirement already up-to-date: appdirs>=1.4.0 in /home/bojan/dev/tensorflow/lib/python3.5/site-packages (from setuptools->protobuf>=3.1.0->tensorflow-gpu)
Requirement already up-to-date: packaging>=16.8 in /home/bojan/dev/tensorflow/lib/python3.5/site-packages (from setuptools->protobuf>=3.1.0->tensorflow-gpu)
Requirement already up-to-date: pyparsing in /home/bojan/dev/tensorflow/lib/python3.5/site-packages (from packaging>=16.8->setuptools->protobuf>=3.1.0->tensorflow-gpu)
Installing collected packages: numpy, tensorflow-gpu
Found existing installation: numpy 1.12.0
Not uninstalling numpy at /usr/local/lib/python3.5/dist-packages, outside environment /home/bojan/dev/tensorflow
Successfully installed numpy-1.12.1 tensorflow-gpu-1.0.1

TensorFlow Installation Verification

We can run a short program within Python interactive shell:

$ python
Python 3.5.2 (default, Nov 17 2016, 17:05:23)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: GeForce GT 640
major: 3 minor: 0 memoryClockRate (GHz) 0.797
pciBusID 0000:01:00.0
Total memory: 1.94GiB
Free memory: 1.46GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 640, pci bus id: 0000:01:00.0)
>>> print(sess.run(hello))
b'Hello, TensorFlow!'
>>>

Yay! We're ready to use TensorFlow!

Once we're done working with TensorFlow, we should deactivate its virtualenv:

$ deactivate

To remove virtualenv completely, simply delete its directory:

$ rm -rf tensorflow