IT Systemelektroniker & Master of Science, IT Security, Networks, Embedded Systems, Docker Campus Ambassador and Raspberry Pi Geek
If you happen to write a lot of python scripts and just want to check which of the added "imports" are actually needed to function - and do not want to use an IDE - just check out https://pypi.org/project/importchecker/ - it comes down to a quick
easy_install importchecker
and afterwards you can check your scripts by using
importchecker myScript.py
It will only output the imports NOT needed, which you can then remove by hand
In this howto we will get CUDA working in Docker. And - as bonus - add Tensorflow on top! However, please note that you'll need following prereqs:
GNU/Linux x86_64 with kernel version > 3.10 Docker >= 1.9 (official docker-engine, docker-ce or docker-ee only) NVIDIA GPU with Architecture > Fermi (2.1) NVIDIA drivers >= 340.29 with binary nvidia-modprobe
We will install the NVIDIA drivers in this tutorial, so you should only have the right kernel and docker version already installed, we're using a Ubuntu 15.05 x64 machine here. For CUDA, you'll need a Fermi 2.1 CUDA card (or better), for tensorflow a >= 3.0 CUDA card...
lspci | grep VGA sudo lshw -C video
Output i.e.:
product: GF108 [GeForce GT 430] vendor: NVIDIA Corporation
You should lookup on google if it works with cuda / Fermi 2.1, i.e. on https://developer.nvidia.com/cuda-gpus
GeForce GT 430 - Compute: 2.1
Ok, that one works!
I got additional infos from: https://www.geforce.com/hardware/desktop-gpus/geforce-gt-430/specifications
You can find out more about that topic on https://github.com/NVIDIA/nvidia-docker
Getting it to work will be the next step:
from http://www.nvidia.com/object/unix.html
I choose Linux x86_64/AMD64/EM64T, Latest Long Lived Branch version: 375.66, but please check in the description of the file, if your graphics card is supported!
chmod +x NVIDIA-Linux-x86_64-375.66.run sudo ./NVIDIA-Linux-x86_64-375.66.run
It will ask for permission, accept it. If it gives info that the nouveau driver needs to be disabled, just accept that, in the next step, it will generate a blacklist file and exit the setup. Afterwards, run
sudo update-initramfs -u
and reboot your server. Then, rerun the setup with
sudo ./NVIDIA-Linux-x86_64-375.66.run
You can check the installation with
nvidia-smi
and get an output similar to this one:
Mon Jul 24 09:03:47 2017 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 375.66 Driver Version: 375.66 | |-------------------------------+----------------------+----------------------+ | 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 GT 430 Off | 0000:01:00.0 N/A | N/A | | N/A 40C P0 N/A / N/A | 0MiB / 963MiB | N/A Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 Not Supported | +-----------------------------------------------------------------------------+
which means that it worked!
wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb
nvidia-docker run --rm nvidia/cuda nvidia-smi
should output:
Using default tag: latest latest: Pulling from nvidia/cuda e0a742c2abfd: Pull complete 486cb8339a27: Pull complete dc6f0d824617: Pull complete 4f7a5649a30e: Pull complete 672363445ad2: Pull complete ba1240a1e18b: Pull complete e875cd2ab63c: Pull complete e87b2e3b4b38: Pull complete 17f7df84dc83: Pull complete 6c05bfef6324: Pull complete Digest: sha256:c8c492ec656ecd4472891cd01d61ed3628d195459d967f833d83ffc3770a9d80 Status: Downloaded newer image for nvidia/cuda:latest Mon Jul 24 07:07:12 2017 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 375.66 Driver Version: 375.66 | |-------------------------------+----------------------+----------------------+ | 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 GT 430 Off | 0000:01:00.0 N/A | N/A | | N/A 40C P8 N/A / N/A | 0MiB / 963MiB | N/A Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 Not Supported | +-----------------------------------------------------------------------------+
Yep, you got it working in Docker!
NV_GPU=0 nvidia-docker run -ti --rm nvidia/cuda
echo '#include <stdio.h>
// Kernel-execution with __global__: empty function at this point
__global__ void kernel(void) {
// printf("Hello, Cuda!\n");
}
int main(void) {
// Kernel execution with <<<1,1>>>
kernel<<<1,1>>>();
printf("Hello, World!\n");
return 0;
}' > helloWorld.cu
nvcc helloWorld.cu -o helloWorld
./helloWorld
Hello, World!
Congrats, you got it working!
nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu
It will output something like:
Copy/paste this URL into your browser when you connect for the first time, to login with a token: http://localhost:8888/?token=d747247b33023883c1a929bc97d9a115e8b2dd0db9437620
you should do that 🙂
Then enter the 1_hello_tensorflow notebook and run the first sample:
from __future__ import print_function
import tensorflow as tf
with tf.Session():
input1 = tf.constant([1.0, 1.0, 1.0, 1.0])
input2 = tf.constant([2.0, 2.0, 2.0, 2.0])
output = tf.add(input1, input2)
result = output.eval()
print("result: ", result)
by selecting it and clicking on the >| (run cell, select below) Button.
This worked for me:
result: [ 3. 3. 3. 3.]
however... sadly not the GPU was calculating the results as shown by the Docker CLI:
Kernel started: 2bc4c3b0-61f3-4ec8-b95b-88ed06379d85 [I 07:31:45.544 NotebookApp] Adapting to protocol v5.1 for kernel 2bc4c3b0-61f3-4ec8-b95b-88ed06379d85 2017-07-24 07:32:17.780122: 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. 2017-07-24 07:32:17.837112: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2017-07-24 07:32:17.837440: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties: name: GeForce GT 430 major: 2 minor: 1 memoryClockRate (GHz) 1.4 pciBusID 0000:01:00.0 Total memory: 963.19MiB Free memory: 954.56MiB 2017-07-24 07:32:17.837498: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 2017-07-24 07:32:17.837522: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y 2017-07-24 07:32:17.837549: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] Ignoring visible gpu device (device: 0, name: GeForce GT 430, pci bus id: 0000:01:00.0) with Cuda compute capability 2.1. The minimum required Cuda capability is 3.0.
So, CUDA >= 3.0 devices only for tensorflow 🙁 - but, it still works, as it is using the CPU (however, not as fast as it could :/)
https://github.com/NVIDIA/nvidia-docker
https://developer.nvidia.com/cuda-gpus
https://hub.docker.com/r/tensorflow/tensorflow/
I have been building an complete Invoice / Cashier system for Cafés in my spare time for quite some time. It consists of an server, running the web app, an laptop to use as the real cashier system, multiple Android Phones as a way for the waiters to enter their orders and two Raspberry Pis with Brother QL-570s as Invoice Printers. One of those is set at the counter, printing the "normal invoice", the second one is located at the kitchen and got some special routing logic, so that it only recieves the items actually produced in the kitchen. However, as I was using these special kind of printers, I was bound to use the printer-ptouch drivers... and yeah, they are *kind of* buggy. Buggy in that way, that, after installing them with cups and hacking your paper media in there - they will just stop working from time to time. Without further warning. They will stop printing just somewhere in the middle of the job. I really tried all options available to me now - and was now keen on finding *other* kinds of solutions. And after two days of searching, I stumpled upon this master piece: https://github.com/pklaus/brother_ql. Mr. Philipp Klaus did actually dump the whole cups and printer server stuff and directly converted png files into the needed binary code for the printer. And not only the QL-570, but other types as well. I really recommend checking out his code and trying his way on printing - its a huge timesaver, especially on embedded devices like the Raspberry Pi :)!
ØMQ (zeroMQ) is one of these sexy "new" Message Queue Systems. I wanted to dive into it right away, starting with Java. However, building zeroMQ with Java Binding on Windows 7, x64 is a pain (read: Did not work out fast enough - as I wanted to get my feet wet fast!) - so I switched over to Python. Well. Getting that to work is quite easy.
1.) Download and install latest zeroMQ
You can get the Windows Downloads on http://zeromq.org/distro:microsoft-windows, i choose the Stable Release 4.0.4 for Windows, x64 Release for Vista and newer. Just download and install it.
2.) Download and install Python
As I really like the 2.7 branch, I went for Python 2.7.9 which can be downloaded at https://www.python.org/downloads/.
You should install it with all default settings, but enable the "Add Python to System Path".
After installation you should check that the Path Variables for your Python Install are added correctly, i.e. "C:\Python27;C:\Python27\Scripts"
( More information on how to add path variables on http://www.computerhope.com/issues/ch000549.htm)
3.) Download and install the Python Binding pyzmq
Open a cmd / command prompt and enter pip install pyzmq. That should install pyzmq.
4.) Final words
After that you can open your Python IDLE and start with some demo code, which can be found on the Github of pyzmq: https://github.com/zeromq/pyzmq/tree/master/examples
The getting started guide is also an awesome source of information:
http://zguide.zeromq.org/py:all
Common Problems:
If you get an "pip not found", you most probably have not set the Path / Enviromental Variables correctly.
As fidepus learned the "Python HTTP Server One Liner" from anyk, I needed to duplicate this wisdom myself - because I'll forget it as well ;)!
These commands will start an HTTP server on the current directory, listening on Port 8080.
Python 2.X:
python -m SimpleHTTPServer
Python 3.X:
python -m http.server
Original Post and Kudos to: http://www.fidepus.de/2014/05/python-server-one-liner/