Installing TensorFlow on Pop!_OS using Tensorman

Pop!_OS allows easy installation and management of Tensorflow using 'tensorman'.

Installing TensorFlow on Pop!_OS using Tensorman

First, make sure you have all the updates installed:

sudo apt update
sudo apt full-upgrade

Then, install the tensorman package:

sudo apt install tensorman

 In order to get Nvidia CUDA support, install the nvidia docker package:

sudo apt install nvidia-docker2

Installing the New Anaconda Native TensorFlow Package

For a while now, the most reliable two ways to get TensorFlow installed is to either use the pip package, or compile from source.

Compiling TensorFlow from source takes hours, and still prone to errors (see "Failed Attempts at Building TensorFlow GPU from Source"). While the pip package is relatively easier, getting the GPU version of TensorFlow installed using pip was a hassle.

But not anymore. Because the conda native TensorFlow packages are here now.

Installing is quite easy.

Note: Don't install the pip and conda versions of TensorFlow on the same conda environment. If you already have the pip version installed uninstall it using,

pip uninstall tensorflow


To install the CPU version of TensorFlow, just run,

conda install tensorflow


To install the GPU version,

conda install tensorflow-gpu

Failed Attempts at Building TensorFlow GPU from Source

For the last 3 weeks, I've been trying to build TensorFlow from source. I wanted to get TensorFlow GPU version working on Windows with CUDA 9.2 and cuDNN 7.1. Since the pre-built wheels only work with CUDA 9.0, the only way we can get it working with 9.2 is to build it ourselves from source.

The Windows build of TensorFlow is done using CMake. The official instructions are here: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/cmake

Unfortunately, as I found out after multiple attempts, the build process is not as simple as it sounds.

Every attempt I have made to build it failed so far.

But, I decided to post the steps I took - which didn't work - so that you all may be able use it as a reference if you decided to try it out yourselves. Again, note that these steps did not work.

First, I started with gathering all the dependencies to build on Windows 10:

• Visual Studio 2015 Community Edition With Update 3 (14.0.25431.01) with C++
• Anaconda Python 3.6.5
• Git for Windows 2.18.0
• Swigwin 3.0.12
• CUDA Toolkit 9.2
• cuDNN 7.1
• CMake 3.11.3

Setting up TensorFlow with CUDA on Windows

I did the post about How to setup TensorFlow on Windows about a month back. I only covered setting up the CPU version of TensorFlow there, and promised that I'll do the guide for the GPU version soon. But I haven't had the change to come round to write the guide until now.

I guess better late than never. So, here's how to setup the GPU version of TensorFlow on Windows.

So, what do you need to get TensorFlow working on GPU?
You need to setup the following prerequisites, in that order.

1. Microsoft Visual Studio 2015 (The free community edition of VS 2015 will work)
2. CUDA 8.0
3. cuDNN 5.1 for CUDA 8.0

Start by installing Visual Studio 2015. You can get the free community edition from here. When you install, make sure to select ‘Custom Installation’, and select ‘Visual C++’ in the programming language selection (by default, C++ is not selected). Once installed, check whether you have C++ capability by checking the ‘New Project’ options.

Visual Studio 2015 installed with C++

How to solve CNMEM_STATUS_OUT_OF_MEMORY error with Theano on CUDA

Have yo come across the CNMEM_STATUS_OUT_OF_MEMORY error when using Theano with CUDA, with Keras? You might have been trying to train a slightly larger model, and just when the training starts it throws this error and fails.

The CNMEM_STATUS_OUT_OF_MEMORY thrown in Theano with CUDA

The full error stack looks something like this,

Can the LeNet model handle Face Recognition?

I recently followed a blog post - at PyImageSearch by Adrian Rosebrock - on using the LeNet Convolutional Neural Network model on the MNIST dataset - i.e. use for handwritten digit recognition - using Keras with Theano backend. I was able to easily try it out thanks to the very detailed and well thought out guide.

The LeNet model itself is quite simple, just 5 layers. Yet it performs impressively well on the MNIST dataset. We can get around 98% accuracy with just 20 iterations of training with ease.

The training time for the model is also quite low. I tested on my MSI GE60 2PF Apache Pro laptop with CUDA enabled, and the training time was just 2 minutes 20 seconds on average. On CPU only (with CUDA disabled) it took around 30 minutes.

LeNet giving 98% accuracy on MNIST data

As you can see, we got 98.11% accuracy, and it has correctly classified a digit that has been cut-off.

It even classifies a quite deformed '2' correctly.

LeNet correctly classifying a deformed digit

Working Theano configs

Here are the Theano configurations that I have tested and worked.
These were tested on Windows 10 64-Bit, and Windows 7 64-Bit.
(I will update when I test on other OS's and setups)

With GPU support, on CUDA and cuDNN

In order to allow Theano to use the GPU, you need to be on a machine with a supported Nvidia GPU, and have the CUDA toolkit and cuDNN setup. I will cover how to setup CUDA on a different post.

 [global]  
 floatX = float32  
 device = gpu  
   
 [nvcc]  
 flags=-LC:\Users\Thimira\Anaconda3  
 compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin  
   
 [dnn]  
 enabled = True  
   
 [lib]  
 cnmem=0.75  
   
 [blas]   
 ldflags=-LC:\Dev_Tools\openblas\bin -lopenblas  

device = gpu tells Theano to use the GPU instead of the CPU.
flags=-LC:\Users\Thimira\Anaconda3 point this to your Python installation (I'm using Anaconda Python)
compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin point this to the bin dir of your Visual Studio installation (Note: CUDA only worked with Visual Studio 2013 for me)
[dnn] enabled = True this enables cuDNN
cnmem=0.75 set the memory limit Theano can use of the GPU. Here it's set to 75% of the GPU memory
ldflags=-LC:\Dev_Tools\openblas\bin -lopenblas point to your OpenBLAS installation. Refer to my earlier post Getting Theano working with OpenBLAS on Windows

With only CPU support

Since not everyone have a compatible Nvidia GPU to have CUDA.

 [global]  
 floatX = float32  
 device = cpu  
   
 [blas]  
 ldflags=-LC:\Dev_Tools\openblas\bin -lopenblas  

device = cpu tells Theano to use the CPU.
ldflags=-LC:\Dev_Tools\openblas\bin -lopenblas point to your OpenBLAS installation. Refer to my earlier post Getting Theano working with OpenBLAS on Windows

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