Checking out TSLearn

I’m toying around with my new dashcam videos and thought I would try to build a neural network. I found Adam Geitgey’s article really interesting.

My setup

  • Surface Book
  • Graphics Card GeForce 900M Series (Notebooks)
    • GeForce 940M (1 GB)
    • 5.0 Compute Capability
  • Windows 10 x86_64
  • Python 2.7.14 Anaconda 5.1
  • CUDA 9.0
  • cuDNN v7.1.2 (Mar 21, 2018), for CUDA 9.0

TFLearn requires TensorFlow

Installing TensorFlow - Prerequisites

  • CUDA® Toolkit 9.0.
    • Base Installer cuda_9.0.176_win10_network.exe
    • Patch 1 (Released Jan 25, 2018)
    • Patch 3 (Released Mar 5, 2018)
  • cuDNN v7.0
  • TensorFlow
  • Python 3


Install Cudo Driver

Check for valid installation

nvcc --version

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:32_Central_Daylight_Time_2017
Cuda compilation tools, release 9.0, V9.0.176

Build Projects and run device query


deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GPU"
  CUDA Driver Version / Runtime Version          9.0 / 9.0
  CUDA Capability Major/Minor version number:    5.0
  Total amount of global memory:                 1024 MBytes (1073741824 bytes)
  ( 3) Multiprocessors, (128) CUDA Cores/MP:     384 CUDA Cores
  GPU Max Clock rate:                            993 MHz (0.99 GHz)
  Memory Clock rate:                             2505 Mhz
  Memory Bus Width:                              64-bit
  L2 Cache Size:                                 1048576 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  CUDA Device Driver Mode (TCC or WDDM):         WDDM (Windows Display Driver Model)
  Device supports Unified Addressing (UVA):      Yes
  Supports Cooperative Kernel Launch:            No
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.0, CUDA Runtime Version = 9.0, NumDevs = 1
Result = PASS

Install cuDNN

Installed Download cuDNN v7.1.2 (Mar 21, 2018), for CUDA 9.0

Copied files from zip to

\cuda\bin\cudnn64_7.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin. 
\cuda\include\cudnn.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\include. 
\cuda\lib\x64\cudnn.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\lib\x64.

Install Python 3

Tensor Flow requires python three. Use conda to create a new environment

conda create -n py36 python=3.6 anaconda

# To activate this environment, use:
# > activate py36
# To deactivate an active environment, use:
# > deactivate

activate py36

conda install pip

Install TSLearn

pip install tflearn


Must use CUDA 9.0 NOT 9.1

ImportError: Could not find ‘cudart64_90.dll’. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable. Download and install CUDA 9.0 from this URL: