source /cvmfs/sft.cern.ch/lcg/views/LCG_98python3/x86_64-centos7-gcc8-opt/setup.sh
[mtesta@lxplus759 ML_setup]$ python
Python 3.7.6 (default, Aug 12 2020, 09:46:40)
[GCC 8.3.0] on linux
Type “help”, “copyright”, “credits” or “license” for more information.
from ROOT import TMVA, TFile, TTree, TCut
It’s stuck here. The culprit seems “from ROOT import TFile”
Hi Marianna,
on lxplus it can take a while to read the files from CMVS that are required to import ROOT – a while as in, 30 seconds max, and typically only the first time you run that script.
I just tried myself:
[eguiraud@lxplus727 ~]$ source /cvmfs/sft.cern.ch/lcg/views/LCG_98python3/x86_64-centos7-gcc8-opt/setup.sh
[eguiraud@lxplus727 ~]$ python
Python 3.7.6 (default, Aug 12 2020, 09:46:40)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from ROOT import TMVA
>>> from ROOT import TFile
Error in <RootX11ErrorHandler>: BadAccess (attempt to access private resource denied) (XID: 418, XREQ: 18)
>>>
That last error message should not be there, there is something wrong with this LCG view.
To work around the issue you can use a different LCG view, e.g. /cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc8-opt/setup.sh works.
I’ll report the problem with LCG_98python3 upstream.
Hi Enrico,
I find out that using the -Y option when connecting: ssh -Y lxplus.cern.ch
makes the `“from ROOT import TFile” command stucking, also with LCG_99
Can this be fixed?
Best, Marianna.
~ ssh -Y eguiraud@lxplus.cern.ch
Warning: Permanently added the ECDSA host key for IP address '188.185.91.62' to the list of known hosts.
Password:
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[eguiraud@lxplus7117 ~]$ source /cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc8-opt/setup.sh
[eguiraud@lxplus7117 ~]$ python
Python 3.8.6 (default, Dec 11 2020, 21:39:59)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from ROOT import TFile
>>>
>>>
[eguiraud@lxplus700 ~]$ source /cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc8-opt/setup.sh
[eguiraud@lxplus700 ~]$ python $(root-config --tutdir)/tmva/keras/RegressionKeras.py
Using TensorFlow backend.
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 110k 100 110k 0 0 14.2M 0 --:--:-- --:--:-- --:--:-- 15.4M
DataSetInfo : [dataset] : Added class "Regression"
: Add Tree TreeR of type Regression with 10000 events
: Dataset[dataset] : Class index : 0 name : Regression
Traceback (most recent call last):
File "/cvmfs/sft.cern.ch/lcg/releases/ROOT/v6.22.06-1d426/x86_64-centos7-gcc8-opt/tutorials/tmva/keras/RegressionKeras.py", line 51, in <module>
model = Sequential()
File "/cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc8-opt/lib/python3.8/site-packages/keras/engine/sequential.py", line 87, in __init__
super(Sequential, self).__init__(name=name)
File "/cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc8-opt/lib/python3.8/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc8-opt/lib/python3.8/site-packages/keras/engine/network.py", line 96, in __init__
self._init_subclassed_network(**kwargs)
File "/cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc8-opt/lib/python3.8/site-packages/keras/engine/network.py", line 294, in _init_subclassed_network
self._base_init(name=name)
File "/cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc8-opt/lib/python3.8/site-packages/keras/engine/network.py", line 109, in _base_init
name = prefix + '_' + str(K.get_uid(prefix))
File "/cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc8-opt/lib/python3.8/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'
I’m not sure whether it’s a problem with the tutorial code or with the LCG release, we need @moneta .
This is cause by an incompatibility between keras and tensorflow in LCG 99. With tensorflow 2 you need either to use keras 2.3 or tensorflow.keras (recommended solution).
We need to fix the tutorials in ROOT for tf.keras.