How to make Nvidia Digits work with Caffe and Tensorflow installed with Python 3?

54 views

Skip to first unread message

shlok Purani

unread,

Feb 12, 2020, 4:21:44 AM2/12/20

to DIGITS Users

After installing digits setting it to root and running ./digits-devserver gave me this error. Numpy is installed but still it is not detecting numpy. Can anyone please guide me through this as it would be really helpful for me to learn this exciting concept? Thanks and cheers. Though I will try my best to figure out this issue and will keep posted.



Traceback (most recent call last):
  File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
    exec code in run_globals
  File "/home/sp/digits/digits/__main__.py", line 70, in <module>
    main()
  File "/home/sp/digits/digits/__main__.py", line 53, in main
    import digits.config
  File "digits/config/__init__.py", line 7, in <module>
    from . import (  # noqa
  File "digits/config/caffe.py", line 13, in <module>
    from digits.utils import parse_version
  File "digits/utils/__init__.py", line 167, in <module>
    from . import constants, image, time_filters, errors, forms, routing, auth  # noqa
  File "digits/utils/image.py", line 14, in <module>
    import numpy as np
ImportError: No module named numpy

I have build and installed caffe and tensorflow with python 3.5. I made sure my installation is successful so I ran some test I hope this test considered valid.

python
Python 3.5.2 (default, Oct  8 2019, 13:06:37) 
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import caffe
>>> import tensorflow as tf; print(tf.__version__)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
1.12.3
>>> import tensorflow as tf; print(tf.contrib.eager.num_gpus())
2020-02-06 19:56:45.632580: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-06 19:56:45.632820: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties: 
name: GeForce GTX 1050 major: 6 minor: 1 memoryClockRate(GHz): 1.455
pciBusID: 0000:01:00.0
totalMemory: 1.95GiB freeMemory: 1.71GiB
2020-02-06 19:56:45.632844: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2020-02-06 19:56:47.796481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-02-06 19:56:47.796562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 
2020-02-06 19:56:47.796587: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N 
2020-02-06 19:56:47.796908: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1462 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050, pci bus id: 0000:01:00.0, compute capability: 6.1)
1

nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176

+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1094 G /usr/lib/xorg/Xorg 101MiB |
| 0 2105 G compiz 90MiB |
+-----------------------------------------------------------------------------+

./deviceQuery Starting...

CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX 1050"
CUDA Driver Version / Runtime Version 10.2 / 9.0
CUDA Capability Major/Minor version number: 6.1
Total amount of global memory: 1998 MBytes (2094989312 bytes)
( 5) Multiprocessors, (128) CUDA Cores/MP: 640 CUDA Cores
GPU Max Clock rate: 1455 MHz (1.46 GHz)
Memory Clock rate: 3504 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 1048576 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 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 2 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
Device supports Unified Addressing (UVA): Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: Yes
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 = 10.2, CUDA Runtime Version = 9.0, NumDevs = 1
Result = PASS

./mnistCUDNN

cudnnGetVersion() : 7005 , CUDNN_VERSION from cudnn.h : 7005 (7.0.5)
 Host compiler version : GCC 5.4.0
 There are 1 CUDA capable devices on your machine :
 device 0 : sms 5 Capabilities 6.1, SmClock 1455.0 Mhz, MemSize (Mb) 1997, MemClock 3504.0 Mhz, Ecc=0, boardGroupID=0
 Using device 0 



Testing single precision
 Loading image data/one_28x28.pgm
 Performing forward propagation ...
 Testing cudnnGetConvolutionForwardAlgorithm ...
 Fastest algorithm is Algo 1
 Testing cudnnFindConvolutionForwardAlgorithm ...
 ^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.061440 time requiring 0 memory
 ^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.072704 time requiring 3464 memory
 ^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.122880 time requiring 57600 memory
 ^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.235520 time requiring 2057744 memory
 ^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.395264 time requiring 203008 memory
 Resulting weights from Softmax:
 0.0000000 0.9999399 0.0000000 0.0000000 0.0000561 0.0000000 0.0000012 0.0000017 0.0000010 0.0000000
 Loading image data/three_28x28.pgm
 Performing forward propagation ...
 Resulting weights from Softmax:
 0.0000000 0.0000000 0.0000000 0.9999288 0.0000000 0.0000711 0.0000000 0.0000000 0.0000000 0.0000000
 Loading image data/five_28x28.pgm
 Performing forward propagation ...
 Resulting weights from Softmax:
 0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 0.9999820 0.0000154 0.0000000 0.0000012 0.0000006 

Result of classification: 1 3 5 

Test passed! 

Testing half precision (math in single precision)
 Loading image data/one_28x28.pgm
 Performing forward propagation ...
 Testing cudnnGetConvolutionForwardAlgorithm ...
 Fastest algorithm is Algo 1
 Testing cudnnFindConvolutionForwardAlgorithm ...
 ^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.057344 time requiring 0 memory
 ^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.065536 time requiring 3464 memory
 ^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.090112 time requiring 28800 memory
 ^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.215040 time requiring 2057744 memory
 ^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.365568 time requiring 203008 memory
 Resulting weights from Softmax:
 0.0000001 1.0000000 0.0000001 0.0000000 0.0000563 0.0000001 0.0000012 0.0000017 0.0000010 0.0000001
 Loading image data/three_28x28.pgm
 Performing forward propagation ...
 Resulting weights from Softmax:
 0.0000000 0.0000000 0.0000000 1.0000000 0.0000000 0.0000714 0.0000000 0.0000000 0.0000000 0.0000000
 Loading image data/five_28x28.pgm
 Performing forward propagation ...
 Resulting weights from Softmax:
 0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 1.0000000 0.0000154 0.0000000 0.0000012 0.0000006 

Result of classification: 1 3 5 

Test passed!