Problem with setting a Python Deep Learning Environment

aworker · November 13, 2019, 3:40pm

Dear all

I’m facing a problem when trying to create a new Python Deep Learning Environment with Knime version 4.0.2 in a new laptop. The error message in red showed at the “Python Deep Learning Environment” setting window is :

“Python Installation could not be determined.2019-11-13 16:02:06: F tensorflow/python/lib/core/bfloat16.cc:675] Check failed: PyBfloat16_Type.tp_base!= nullptr”

I followed the instructions given on the Knime web site to install from scratch the last version of Anaconda and then to create the Conda environment using Knime. I also did from scratch a fresh installation of Knime 4.0.2. to make sure everything was ok with both.

The problem arrives in both cases, CPU and GPU Python Deep Learning options. If a create the environment directly from Conda following again the recommendations in the Knime web site, the problem is still the same. If I try directly from Python the manually created environment as recommended by Knime and I do Import tensorflow, it crashes arguing that numpy version is not compatible, so I believe the underlying problem is with tensorflow version 1.12.0 and numpy. I tried to update both with more recent versions (i.e. 1.13.x) and it works in python but then it is not anymore compatible with the required versions by the DL Knime nodes which crash when I try to run them. To be sure that this is not a problem related with Knime version 4.0.2, I did the same tests with previous versions and the problem is the same.

Just for information, I have done this same installation in another computer exactly in the same way as initially mentioned and everything worked perfectly. Please find attached the Knime logs file for information.

Has anybody faced this problem before ? I would be grateful if I could have help to solve this issue. Many thanks in advance.

Best regards

Ael

knime.log (315.6 KB)

mlauber71 · November 13, 2019, 5:33pm

A few more general remarks:

with Python and KNIME it is all about compatibility and consistency of the packages. That is where Anaconda tries to help
you should not update individual packages but let Anaconda manage that
concerning Tensorflow and Keras note which versions are supported by the various KNIME nodes (typically that is not the most recent version)
these versions must be compatible with your Python version (it might be that it is to large)

I am working on a text about KNIME and Python. But in general your path could look something like this. Please check hints that would come from conda and adapt accordingly.

# create KNIME’s Conda environment
# KNIME Python Integration Guide
# https://docs.knime.com/latest/python_installation_guide/img/py36_knime.yml
conda env create -f=/Users/test/py36_knime.yml

# check the environments if py36_knime is there
conda info --envs

# active KNIME Python environment
source activate py36_knime

# set anaconda as top priority channel
# my impression is that anaconda as priority is more stable
conda config --prepend channels anaconda

# add conda-forge at the end of channels
conda config --append channels conda-forge

# check channel priority
conda config --get channels

# should look something like this:
# --add channels ‘conda-forge’ # lowest priority
# --add channels ‘defaults’
# --add channels ‘anaconda’ # highest priority

# set channel priority to flexible
conda config --set channel_priority flexible

# update anaconda
conda update conda
conda update anaconda

# install tensorflow (you might try to set a different version, you might have to try)
conda install -c conda-forge tensorflow=1.8.0
a
# install Keras (you might try to set a different version, you might have to try)
conda install -c conda-forge keras=2.1.6

# repeat update anaconda
conda update conda
conda update anaconda

aworker · November 13, 2019, 8:54pm

Thanks @mlauber71 for your prompt reply and guidelines. I followed your set of instructions but unfortunately this didn’t work neither. Normally, the creation of the environment based on py36_knime yml file + instructions in https://docs.knime.com/2019-06/deep_learning_installation_guide/index.htm should be enough to set up the right environment for Keras & tensorflow in Knime but it doesn’t work in this case. As an extra information, I get the following message error when I try to import tensorflow after activating the newly created environment and executing “import tensorflow” in python :

"
python
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 14:00:49) [MSC v.1915 64 bit (AMD64)] on win32
Type “help”, “copyright”, “credits” or “license” for more information.

I>import tensorflow
ModuleNotFoundError: No module named ‘numpy.core._multiarray_umath’
ImportError: numpy.core.multiarray failed to import

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File “”, line 968, in _find_and_load
SystemError: <class ‘_frozen_importlib._ModuleLockManager’> returned a result with an error set
ImportError: numpy.core._multiarray_umath failed to import
ImportError: numpy.core.umath failed to import
2019-11-13 19:43:47.086015: F tensorflow/python/lib/core/bfloat16.cc:675] Check failed: PyBfloat16_Type.tp_base != nullptr

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File “”, line 968, in _find_and_load
SystemError: <class ‘_frozen_importlib._ModuleLockManager’> returned a result with an error set
ImportError: numpy.core._multiarray_umath failed to import
ImportError: numpy.core.umath failed to import
2019-11-13 19:43:47.086015: F tensorflow/python/lib/core/bfloat16.cc:675] Check failed: PyBfloat16_Type.tp_base != nullptr
"

Any attempt to fix the problem by doing updates seems to be unsuccesful so far.

Any help would be much appreciated.

Ael

MarcelW · November 13, 2019, 9:57pm

Hi Ael,

Sorry for the trouble and thanks for your detailed problem description! According to this GitHub issue, the problem can be solved by upgrading numpy to version 1.16.2. In some other issue, it was suggested to also upgrade TensorFlow. Could you try to do that (preferably only numpy, for starters - since you also mentioned issues after upgrading TensorFlow) and let us know if it helped? Then we can adapt our documentation accordingly. Thanks!

Also, does the machine on which you experience the issue differ in any significant way from the one where this setup works (e.g., operating system)?

Marcel

aworker · November 13, 2019, 11:07pm

Hi Marcel,

Thanks a lot for your prompt reply and help. The one were everything works fine is a workstation under Windows 8.1 and is 5 years old. The new machine is a laptop under WIndows 10 with the following computer configuration (extracted from “System Information”, sorry for the French :

begin -------------------------------------------------------------------------------------------------
Nom du système d’exploitation Microsoft Windows 10 Professionnel pour les Stations de travail
Version 10.0.17763 Numéro 17763
Autre description du système d’exploitation Non disponible
Fabricant du système d’exploitation Microsoft Corporation
Ordinateur LAPTOT-THINKPAD
Fabricant LENOVO
Modèle 20M9CTO1WW
Type PC à base de x64
Référence (SKU) du système LENOVO_MT_20M9_BU_Think_FM_ThinkPad P52
Processeur Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz, 2712 MHz, 6 cœur(s), 12 processeur(s) logique(s)
Version du BIOS/Date LENOVO N2CET42W (1.25 ), 15/04/2019
Version SMBIOS 3.1
Version du contrôleur embarqué 1.13
Mode BIOS UEFI
Fabricant de la carte de base LENOVO
Produit de la carte de base 20M9CTO1WW
Version de la carte de base SDK0Q40104 WIN
Rôle de la plateforme Mobile
État du démarrage sécurisé Désactivé
Configuration de PCR 7 Élévation requise à afficher
Répertoire Windows C:\Windows
Répertoire système C:\Windows\system32
Périphérique de démarrage \Device\HarddiskVolume3
Option régionale France
Couche d’abstraction matérielle Version = “10.0.17763.831”
Utilisateur LAPTOT-THINKPAD\Worker
Fuseaux horaires Paris, Madrid
Mémoire physique (RAM) installée 64,0 Go
Mémoire physique totale 63,7 Go
Mémoire physique disponible 51,6 Go
Mémoire virtuelle totale 73,2 Go
Mémoire virtuelle disponible 59,2 Go
Espace pour le fichier d’échange 9,50 Go
Fichier d’échange C:\pagefile.sys
Protection DMA du noyau Désactivé
Sécurité basée sur la virtualisation Désactivé
Prise en charge du chiffrement d’appareil%s Élévation requise à afficher
Hyper-V - Extensions du mode de moniteur des ordinateurs virtuels Oui
Hyper-V - Extensions de la conversion des adresses de second niveau Oui
Hyper-V - Virtualisation activée dans le microprogramme Non
Hyper-V - Protection de l’exécution des données Oui
end -------------------------------------------------------------------------------------------------

The graphics card is an NVIDIA QUADRO P2000 with driver n. 431.94

A copy of the Keras environment installation steps I followed using Cuda :

begin ------------------------------------------------------------------------------------------

(base) PS C:\Users\Worker> conda env create --file C:\Users\Worker\py36_knime.yml
Collecting package metadata (repodata.json): done
Solving environment: done

Downloading and Extracting Packages
colorama-0.4.1 | 25 KB mkl-2019.4 | 99.2 MB pixman-0.38.0 | 395 KB setuptools-41.6.0 | 677 KB parso-0.5.1 | 68 KB traitlets-4.3.3 | 138 KB snappy-1.1.7 | 73 KB thrift-cpp-0.11.0 | 1.9 MB certifi-2019.9.11 | 155 KB freetype-2.9.1 | 450 KB jpeg-9b | 245 KB lz4-c-1.8.1.2 | 176 KB tk-8.6.8 | 3.1 MB pickleshare-0.7.5 | 13 KB zstd-1.3.7 | 337 KB prompt_toolkit-2.0.1 | 227 KB decorator-4.4.1 | 13 KB wheel-0.33.6 | 58 KB pip-19.3.1 | 1.9 MB icc_rt-2019.0.0 | 6.0 MB attrs-19.3.0 | 39 KB ca-certificates-2019 | 163 KB wcwidth-0.1.7 | 24 KB libtiff-4.0.10 | 730 KB zlib-1.2.11 | 110 KB olefile-0.46 | 49 KB blas-1.0 | 6 KB pywin32-223 | 5.4 MB python-3.6.9 | 15.9 MB jupyter_core-4.6.1 | 97 KB intel-openmp-2019.4 | 1.4 MB cycler-0.10.0 | 13 KB gflags-2.2.2 | 233 KB tornado-6.0.3 | 593 KB zipp-0.6.0 | 9 KB qt-5.9.7 | 72.5 MB libboost-1.67.0 | 18.6 MB kiwisolver-1.1.0 | 53 KB pygments-2.4.2 | 664 KB ipython_genutils-0.2 | 39 KB backcall-0.1.0 | 21 KB libpng-1.6.37 | 333 KB icu-58.2 | 9.4 MB pytz-2019.3 | 231 KB wincertstore-0.2 | 14 KB xz-5.2.4 | 458 KB Preparing transaction: done
Verifying transaction: done
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To activate this environment, use

$ conda activate py36_knime

To deactivate an active environment, use

$ conda deactivate

(base) PS C:\Users\Worker> conda activate py36_knime
(py36_knime) PS C:\Users\Worker> conda list | Select-String -Pattern ‘numpy’

numpy 1.15.4 py36h19fb1c0_0
numpy-base 1.15.4 py36hc3f5095_0

(py36_knime) PS C:\Users\Worker> conda install numpy=1.16.2
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: done

Package Plan

environment location: C:\Program_Others\Anaconda3\envs\py36_knime

added / updated specs:
- numpy=1.16.2

The following packages will be downloaded:

package                    |            build
---------------------------|-----------------
ca-certificates-2019.10.16 |                0         163 KB  anaconda
certifi-2019.9.11          |           py36_0         155 KB  anaconda
numpy-1.16.2               |   py36h19fb1c0_0          49 KB  anaconda
numpy-base-1.16.2          |   py36hc3f5095_0         4.1 MB  anaconda
------------------------------------------------------------
                                       Total:         4.5 MB

The following packages will be UPDATED:

numpy pkgs/main::numpy-1.15.4-py36h19fb1c0_0 → anaconda::numpy-1.16.2-py36h19fb1c0_0
numpy-base pkgs/main::numpy-base-1.15.4-py36hc3f~ → anaconda::numpy-base-1.16.2-py36hc3f5095_0
openssl pkgs/main::openssl-1.1.1c-he774522_1 → anaconda::openssl-1.1.1-he774522_0

The following packages will be SUPERSEDED by a higher-priority channel:

ca-certificates pkgs/main → anaconda
certifi pkgs/main → anaconda

Proceed ([y]/n)? y

Downloading and Extracting Packages
ca-certificates-2019 | 163 KB | ######################################################################################################### | 100%
certifi-2019.9.11 | 155 KB | ######################################################################################################### | 100%
numpy-base-1.16.2 | 4.1 MB | ######################################################################################################### | 100%
numpy-1.16.2 | 49 KB | ######################################################################################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(py36_knime) PS C:\Users\Worker> python
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 14:00:49) [MSC v.1915 64 bit (AMD64)] on win32
Type “help”, “copyright”, “credits” or “license” for more information.

import numpy
quit()
(py36_knime) PS C:\Users\Worker> conda install h5py=2.8 tensorflow-mkl=1.12 keras=2.2.4
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: done

Package Plan

environment location: C:\Program_Others\Anaconda3\envs\py36_knime

added / updated specs:
- h5py=2.8
- keras=2.2.4
- tensorflow-mkl=1.12

The following packages will be downloaded:

package                    |            build
---------------------------|-----------------
h5py-2.8.0                 |   py36h3bdd7fb_2         835 KB  anaconda
hdf5-1.10.2                |       hac2f561_1        34.8 MB  anaconda
libprotobuf-3.8.0          |       h7bd577a_0         2.2 MB  anaconda
protobuf-3.8.0             |   py36h33f27b4_0         582 KB  anaconda
tensorflow-mkl-1.12.0      |       h4fcabd2_0           3 KB  anaconda
------------------------------------------------------------
                                       Total:        38.4 MB

The following NEW packages will be INSTALLED:

_tflow_select anaconda/win-64::_tflow_select-2.3.0-mkl
absl-py anaconda/win-64::absl-py-0.8.1-py36_0
astor anaconda/win-64::astor-0.8.0-py36_0
gast anaconda/noarch::gast-0.3.2-py_0
grpcio anaconda/win-64::grpcio-1.16.1-py36h351948d_1
h5py anaconda/win-64::h5py-2.8.0-py36h3bdd7fb_2
hdf5 anaconda/win-64::hdf5-1.10.2-hac2f561_1
keras anaconda/win-64::keras-2.2.4-0
keras-applications anaconda/noarch::keras-applications-1.0.8-py_0
keras-base anaconda/win-64::keras-base-2.2.4-py36_0
keras-preprocessi~ anaconda/noarch::keras-preprocessing-1.1.0-py_1
libmklml anaconda/win-64::libmklml-2019.0.5-0
libprotobuf anaconda/win-64::libprotobuf-3.8.0-h7bd577a_0
markdown anaconda/win-64::markdown-3.1.1-py36_0
protobuf anaconda/win-64::protobuf-3.8.0-py36h33f27b4_0
pyyaml anaconda/win-64::pyyaml-5.1.2-py36he774522_0
tensorboard anaconda/win-64::tensorboard-1.12.2-py36h33f27b4_0
tensorflow anaconda/win-64::tensorflow-1.12.0-mkl_py36h4f00353_0
tensorflow-base anaconda/win-64::tensorflow-base-1.12.0-mkl_py36h81393da_0
tensorflow-mkl anaconda/win-64::tensorflow-mkl-1.12.0-h4fcabd2_0
termcolor anaconda/win-64::termcolor-1.1.0-py36_1
werkzeug anaconda/noarch::werkzeug-0.16.0-py_0
yaml anaconda/win-64::yaml-0.1.7-hc54c509_2

Proceed ([y]/n)? y

Downloading and Extracting Packages
hdf5-1.10.2 | 34.8 MB | ################################################################################################################################################ | 100%
h5py-2.8.0 | 835 KB | ################################################################################################################################################ | 100%
libprotobuf-3.8.0 | 2.2 MB | ################################################################################################################################################ | 100%
protobuf-3.8.0 | 582 KB | ################################################################################################################################################ | 100%
tensorflow-mkl-1.12. | 3 KB | ################################################################################################################################################ | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(py36_knime) PS C:\Users\Worker> python
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 14:00:49) [MSC v.1915 64 bit (AMD64)] on win32
Type “help”, “copyright”, “credits” or “license” for more information.

import tensorflow
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
2019-11-13 23:25:59.031708: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
[name: “/device:CPU:0”
device_type: “CPU”
memory_limit: 268435456
locality {
}
incarnation: 14330428152300301188
]
quit()

end -------------------------------------------------------------------------------

So tensorflow can be imported now and KNIME accepts the activation of this environment at the “Python Deep Learning” preferences setting. The problem now is somewhere else as shown when running the “03_Train_MNST_classifier” workflow that I use as exemple for testing on KNIME :

begin -------------------------------------------------------------------------------

WARN DL Python Network Creator 0:88 Using TensorFlow backend.
ERROR DL Python Network Creator 0:88 Execute failed: module ‘tensorflow’ has no attribute ‘get_default_graph’
Traceback (most recent call last):
File “”, line 19, in
File “C:\Program_Others\Anaconda3\envs\py36_knime\lib\site-packages\keras\engine\sequential.py”, line 87, in init
super(Sequential, self).init(name=name)
File “C:\Program_Others\Anaconda3\envs\py36_knime\lib\site-packages\keras\legacy\interfaces.py”, line 91, in wrapper
return func(*args, **kwargs)
File “C:\Program_Others\Anaconda3\envs\py36_knime\lib\site-packages\keras\engine\network.py”, line 96, in init
self._init_subclassed_network(**kwargs)
File “C:\Program_Others\Anaconda3\envs\py36_knime\lib\site-packages\keras\engine\network.py”, line 294, in _init_subclassed_network
self._base_init(name=name)
File “C:\Program_Others\Anaconda3\envs\py36_knime\lib\site-packages\keras\engine\network.py”, line 109, in base_init
name = prefix + '’ + str(K.get_uid(prefix))
File “C:\Program_Others\Anaconda3\envs\py36_knime\lib\site-packages\keras\backend\tensorflow_backend.py”, line 74, in get_uid
graph = tf.get_default_graph()
AttributeError: module ‘tensorflow’ has no attribute ‘get_default_graph’

end -------------------------------------------------------------------------------

If I come back to running tensorflow in python, I can confirm this problem :

begin -------------------------------------------------------------------------------

(py36_knime) PS C:\Users\Worker> python
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 14:00:49) [MSC v.1915 64 bit (AMD64)] on win32
Type “help”, “copyright”, “credits” or “license” for more information.

import tensorflow as tf
graph = tf.get_default_graph()
Traceback (most recent call last):
File “”, line 1, in
AttributeError: module ‘tensorflow’ has no attribute ‘get_default_graph’

end -------------------------------------------------------------------------------

I’m a bit confused because this error should not happen with version 1.12.0 of tensorflow (I know this is deprecated in last tensorflow release but should not be in version 1.12.0).

Hope this extra information can shed some light to clarify the problem. Please let me know whether you need any extra information. Many thanks in advance for your help.

Best regards

Ael

mlauber71 · November 14, 2019, 7:17am

I played around with KNIME and Tensorflow and got the same error. I have no solution yet.

aworker · November 14, 2019, 4:56pm

Many thanks @mlauber71 for your time and for sharing this information. It is interesting to see that the error can be reproduced.

Best regards

Ael

aworker · November 15, 2019, 4:57pm

Dear Knimers

I eventually found a workaround to solve this Enigma =>

On a Conda command-line window :

delete all directories related to tensorflow => C:\Users\Worker\AppData\Roaming\Python\Python36\site-packages\tensorflow\
Execute the following Conda commands :

134 conda env create --file C:\Users\Worker\py36_knime.yml --name py36_knime_tf_cpu
135 conda activate py36_knime_tf_cpu
136 python
137 > import numpy
138 quit() # No complain

139 conda install tensorflow-base=1.12 -c anaconda --freeze-installed
140 python
141 > import tensorflow
142 quit() # No complain

143 conda install keras=2.2.4 -c anaconda --freeze-installed
144 python
145 > import keras
146 quit() # No complain

147 conda install h5py=2.8 -c anaconda --freeze-installed

148 python
# same import tests as before to check again everything works as before
149 >>> import tensorflow as tf
150 >>> from tensorflow.python.client import device_lib
151 >>> device_lib.list_local_devices()
2019-11-15 13:39:40.964411: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
[name: “/device:CPU:0”
device_type: “CPU”
memory_limit: 268435456
locality {
}
incarnation: 16165568108189707881
]
152 >>> import keras
153 Using TensorFlow backend.
153 >>> import pandas
153 >>> import matplotlib
153 >>> quit() # No complain

149 => run KNIME & activate py36_knime_tf_cpu environment. it worked at this point !

======================================

IF GPU installation then =>

134 conda env create --file C:\Users\Worker\py36_knime.yml --name py36_knime_tf_gpu
135 conda activate py36_knime_tf_gpu
136 python
137 > import numpy
138 quit() # No complain

139 conda install tensorflow-gpu=1.12 -c anaconda --freeze-installed
140 python
141 > import tensorflow
142 quit() # No complain

143 conda install keras=2.2.4 -c anaconda --freeze-installed
144 python
145 > import keras
146 conda install h5py=2.8 -c anaconda --freeze-installed
147 quit() # No complain

148 python
# same import tests as before to check again everything works fine
149 >>> import tensorflow as tf
150 >>> from tensorflow.python.client import device_lib
151 >>> device_lib.list_local_devices()
2019-11-15 17:37:49.714012: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2019-11-15 17:37:49.894883: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: Quadro P2000 major: 6 minor: 1 memoryClockRate(GHz): 1.607
pciBusID: 0000:01:00.0
totalMemory: 4.00GiB freeMemory: 1.71GiB
2019-11-15 17:37:49.902258: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-11-15 17:37:50.408704: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-15 17:37:50.412570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-11-15 17:37:50.415250: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-11-15 17:37:50.417994: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:0 with 1425 MB memory) -> physical GPU (device: 0, name: Quadro P2000, pci bus id: 0000:01:00.0, compute capability: 6.1)
[name: “/device:CPU:0”
device_type: “CPU”
memory_limit: 268435456
locality {
}
incarnation: 1313116399824212057
, name: “/device:GPU:0”
device_type: “GPU”
memory_limit: 1495046552
locality {
bus_id: 1
links {
}
}
incarnation: 8001539658469800458
physical_device_desc: “device: 0, name: Quadro P2000, pci bus id: 0000:01:00.0, compute capability: 6.1”
]

152 >>> import keras
153 Using TensorFlow backend.
153 >>> import pandas
153 >>> import matplotlib
153 >>> quit() # No complains

149 => run KNIME & activate py36_knime_tf_gpu environment. it worked at this point !

==========================================

My goal was to do DL + cheminformatics on these environments so just for information I installed scikit-learn & RDKIT too as follows :

conda install rdkit -c conda-forge --freeze-installed
conda install scikit-learn -c anaconda --freeze-installed

After trying on different workflows and jupyter notebooks, making use of all these packages, everything worked fine, so I think the problem is solved unless proven otherwise.

Thanks @mlauber71 & @MarcelW for your help and thanks to KNIME !

Ael

system · November 22, 2019, 4:57pm

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