n01r
commented
6 years ago The xstal_diffraction.ipynb notebook works on Maxwell @ DESY
Open n01r opened 6 years ago
n01r
commented
6 years ago The xstal_diffraction.ipynb notebook works on Maxwell @ DESY
n01r
commented
6 years ago Running the start_to_end_demo.ipynb yields errors.
The photon source file is not available but a test file can be downloaded from https://zenodo.org/record/888853/files/s2e_tutorial_example_source_input.h5
Then the notebook runs fine until the photon matter interaction
photon_matter_interactor.backengine(); photon_matter_interactor.saveH5()
Previous module: simex_notebooks
NOT: data
NOT: history
info
misc
params
version
['arrEhor', 'arrEver']
[('arrEhor', <HDF5 dataset "arrEhor": shape (42, 84, 258, 2), type "<f4">), ('arrEver', <HDF5 dataset "arrEver": shape (42, 84, 258, 2), type "<f4">)]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-27-37c843a80bc5> in <module>()
----> 1 photon_matter_interactor.backengine(); photon_matter_interactor.saveH5()
/data/netapp/s2e/simex/lib/python3.4/SimEx/Calculators/XMDYNDemoPhotonMatterInteractor.py in backengine(self)
187 pmi_demo.f_init_random()
188 pmi_demo.f_save_info()
--> 189 pmi_demo.f_load_pulse( pmi_demo.g_s2e['prop_out'] )
190
191 # Get file extension.
/data/netapp/s2e/simex/lib/python3.4/SimEx/Calculators/XMDYNDemoPhotonMatterInteractor.py in f_load_pulse(self, a_prop_out)
501 sel_x = self.g_s2e['pulse']['nx'] // 2 ;
502 sel_y = self.g_s2e['pulse']['ny'] // 2 ;
--> 503 sel_pixV = self.g_s2e['pulse']['arrEver'] [sel_x,sel_y,:,:]
504 sel_pixH = self.g_s2e['pulse']['arrEhor'] [sel_x,sel_y,:,:]
505 dt = ( self.g_s2e['pulse']['sliceMax'] - self.g_s2e['pulse']['sliceMin'] ) / ( self.g_s2e['pulse']['nSlices'] * 1.0 )
IndexError: index 42 is out of bounds for axis 0 with size 42Seems to be that there is something wrong with the slicing order.
ping @CFGrote
n01r
commented
6 years ago After #166 is merged, we can upload a new example notebook that uses the new example FEL source file.
rshopa
commented
5 years ago Multiple errors on running start_to_end_demo.ipynb. I am not sure whither it is the consequence of bad install (see an issue related to Polish XFEL users) or not.
If run manually line by line and initially it looks ok, at least by the line
In [4]: source_analysis.plotTotalPower()
Plotting total power.
Pulse energy 0.00017 J
# image here is okNext line yields segmentation fault (core dumped) error on bash and a message The kernel appears to have died. It will restart automatically. in the notebook:
source_analysis.plotTotalPower(spectrum=True)The same error appears for prop_analysis.plotTotalPower(spectrum=True) later.
Next, an error if trying to plot
In [5]: source_analysis.plotIntensityMap()
Plotting intensity map.
R-space
Error in callback <function install_repl_displayhook.<locals>.post_execute at 0x7fd772f9cbf8> (for post_execute):
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/pyplot.py in post_execute()
107 def post_execute():
108 if matplotlib.is_interactive():
--> 109 draw_all()
110
111 # IPython >= 2
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/_pylab_helpers.py in draw_all(cls, force)
130 for f_mgr in cls.get_all_fig_managers():
131 if force or f_mgr.canvas.figure.stale:
--> 132 f_mgr.canvas.draw_idle()
133
134 atexit.register(Gcf.destroy_all)
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/backend_bases.py in draw_idle(self, *args, **kwargs)
1897 if not self._is_idle_drawing:
1898 with self._idle_draw_cntx():
-> 1899 self.draw(*args, **kwargs)
1900
1901 def draw_cursor(self, event):
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py in draw(self)
400 toolbar = self.toolbar
401 try:
--> 402 self.figure.draw(self.renderer)
403 # A GUI class may be need to update a window using this draw, so
404 # don't forget to call the superclass.
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
48 renderer.start_filter()
49
---> 50 return draw(artist, renderer, *args, **kwargs)
51 finally:
52 if artist.get_agg_filter() is not None:
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/figure.py in draw(self, renderer)
1647
1648 mimage._draw_list_compositing_images(
-> 1649 renderer, self, artists, self.suppressComposite)
1650
1651 renderer.close_group('figure')
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
136 if not_composite or not has_images:
137 for a in artists:
--> 138 a.draw(renderer)
139 else:
140 # Composite any adjacent images together
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
48 renderer.start_filter()
49
---> 50 return draw(artist, renderer, *args, **kwargs)
51 finally:
52 if artist.get_agg_filter() is not None:
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/axes/_base.py in draw(self, renderer, inframe)
2573 self.apply_aspect(pos)
2574 else:
-> 2575 self.apply_aspect()
2576
2577 artists = self.get_children()
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/axes/_base.py in apply_aspect(self, position)
1561 # Not sure whether we need this check:
1562 if shared_x and shared_y:
-> 1563 raise RuntimeError("adjustable='datalim' is not allowed when both"
1564 " axes are shared.")
1565
RuntimeError: adjustable='datalim' is not allowed when both axes are shared.
<Figure size 432x288 with 0 Axes>
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
~/miniconda3/envs/xfel/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
339 pass
340 else:
--> 341 return printer(obj)
342 # Finally look for special method names
343 method = get_real_method(obj, self.print_method)
~/miniconda3/envs/xfel/lib/python3.6/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
242
243 if 'png' in formats:
--> 244 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
245 if 'retina' in formats or 'png2x' in formats:
246 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))
~/miniconda3/envs/xfel/lib/python3.6/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
126
127 bytes_io = BytesIO()
--> 128 fig.canvas.print_figure(bytes_io, **kw)
129 data = bytes_io.getvalue()
130 if fmt == 'svg':
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)
2047 orientation=orientation,
2048 dryrun=True,
-> 2049 **kwargs)
2050 renderer = self.figure._cachedRenderer
2051 bbox_artists = kwargs.pop("bbox_extra_artists", None)
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs)
508
509 """
--> 510 FigureCanvasAgg.draw(self)
511 renderer = self.get_renderer()
512
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py in draw(self)
400 toolbar = self.toolbar
401 try:
--> 402 self.figure.draw(self.renderer)
403 # A GUI class may be need to update a window using this draw, so
404 # don't forget to call the superclass.
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
48 renderer.start_filter()
49
---> 50 return draw(artist, renderer, *args, **kwargs)
51 finally:
52 if artist.get_agg_filter() is not None:
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/figure.py in draw(self, renderer)
1647
1648 mimage._draw_list_compositing_images(
-> 1649 renderer, self, artists, self.suppressComposite)
1650
1651 renderer.close_group('figure')
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
136 if not_composite or not has_images:
137 for a in artists:
--> 138 a.draw(renderer)
139 else:
140 # Composite any adjacent images together
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
48 renderer.start_filter()
49
---> 50 return draw(artist, renderer, *args, **kwargs)
51 finally:
52 if artist.get_agg_filter() is not None:
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/axes/_base.py in draw(self, renderer, inframe)
2573 self.apply_aspect(pos)
2574 else:
-> 2575 self.apply_aspect()
2576
2577 artists = self.get_children()
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/axes/_base.py in apply_aspect(self, position)
1561 # Not sure whether we need this check:
1562 if shared_x and shared_y:
-> 1563 raise RuntimeError("adjustable='datalim' is not allowed when both"
1564 " axes are shared.")
1565
RuntimeError: adjustable='datalim' is not allowed when both axes are shared.
<Figure size 1000x1000 with 3 Axes>Propagator apparently does not create .h5 file:
In [6]: propagator = XFELPhotonPropagator(parameters=propagation_parameters,
input_path='FELsource_out_0000001.h5',
output_path='prop_out.h5')
propagator.backengine()
In [7]: prop_analysis=XFELPhotonAnalysis(input_path='prop_out.h5')
Start initialization.
Loading wavefront from /mnt/home/rshopa/SIMEX/simex_platform/Playground/prop_out.h5.
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-26-878ea2f958c6> in <module>
----> 1 prop_analysis=XFELPhotonAnalysis(input_path='/mnt/home/rshopa/SIMEX/simex_platform/Playground/prop_out.h5')
~/SIMEX/simex_platform/lib/python3.6/SimEx/Analysis/XFELPhotonAnalysis.py in __init__(self, input_path)
46
47 print("\n Loading wavefront from %s." % (self.input_path))
---> 48 wavefront.load_hdf5(self.input_path)
49 print(" ... done.")
50
~/SIMEX/simex_platform/lib/python3.6/wpg/wavefront.py in load_hdf5(self, file_name)
182 :type file_name: string
183 """
--> 184 self._update_from_dict(utils.load_dict_slash_hdf5(file_name))
185
186 def get_intensity(self, slice_number=None, polarization=None):
~/SIMEX/simex_platform/lib/python3.6/wpg/utils.py in load_dict_slash_hdf5(hdf5_file_name)
91 out_dict.update({name.encode(): obj.value})
92
---> 93 with h5py.File(hdf5_file_name, 'r') as h5_file:
94 h5_file.visititems(add_item)
95
~/miniconda3/envs/xfel/lib/python3.6/site-packages/h5py/_hl/files.py in __init__(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, **kwds)
392 fid = make_fid(name, mode, userblock_size,
393 fapl, fcpl=make_fcpl(track_order=track_order),
--> 394 swmr=swmr)
395
396 if swmr_support:
~/miniconda3/envs/xfel/lib/python3.6/site-packages/h5py/_hl/files.py in make_fid(name, mode, userblock_size, fapl, fcpl, swmr)
168 if swmr and swmr_support:
169 flags |= h5f.ACC_SWMR_READ
--> 170 fid = h5f.open(name, flags, fapl=fapl)
171 elif mode == 'r+':
172 fid = h5f.open(name, h5f.ACC_RDWR, fapl=fapl)
h5py/_objects.pyx in h5py._objects.with_phil.wrapper()
h5py/_objects.pyx in h5py._objects.with_phil.wrapper()
h5py/h5f.pyx in h5py.h5f.open()
OSError: Unable to open file (unable to open file: name = '/mnt/home/rshopa/SIMEX/simex_platform/Playground/prop_out.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)As an alternative, I loaded it from tests and tried to visualise:
In [11]: prop_analysis=XFELPhotonAnalysis(input_path='***/simex_platform/Tests/python/unittest/TestFiles/prop_out/prop_out_0000001.h5')
Start initialization.
Loading wavefront from ***/simex_platform/Tests/python/unittest/TestFiles/prop_out/prop_out_0000001.h5.
... done.
Getting intensities.
... done.
Data dimensions = (16, 16, 550)
Masking NANs.
... done.
In [12]: prop_analysis.plotTotalPower()
Plotting total power.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-28-32cf5cebdb4d> in <module>
----> 1 prop_analysis.plotTotalPower()
~/SIMEX/simex_platform/lib/python3.6/SimEx/Analysis/XFELPhotonAnalysis.py in plotTotalPower(self, spectrum)
246 # Get meaningful slices.
247 aw = [a[0] for a in numpy.argwhere(int0 > int0max*0.01)]
--> 248 int0_mean = int0[min(aw):max(aw)] # meaningful range of pulse
249 dSlice = (mesh.sliceMax - mesh.sliceMin)/(mesh.nSlices - 1)
250 xs = numpy.arange(mesh.nSlices)*dSlice+ mesh.sliceMin
ValueError: min() arg is an empty sequence
<Figure size 432x288 with 0 Axes>So, it looks like there is a major problem with dependencies, paths to libraries or regarding the installation itself.
CFGrote
commented
5 years ago your propout*.h5 file has suspiciously few transverse grid points. how did you generate it? most probably, the propagation failed and all entries are 0 or NaN, hence min() and max() fail as well.
rshopa
commented
5 years ago I installed git-lfs and loaded .h5 file by git checkout (apparently no more errors while testing). Yet the problems remain. Maybe it's better to solve them one by one.
I follow the example from the notebook, omitting # Cleanup previous run. Why the notebook breaks on the line source_analysis.plotTotalPower(spectrum=True)?
In [1]: # Import all SimEx modules
import SimEx
from SimEx import *
source_analysis = XFELPhotonAnalysis(input_path="FELsource_out_0000001.h5")
Start initialization.
Loading wavefront from FELsource_out_0000001.h5.
... done.
Getting intensities.
... done.
Data dimensions = (104, 104, 651)
Masking NANs.
... done.
In [2]: source_analysis.plotTotalPower()
Plotting total power.
Pulse energy 0.00017 J
# image here is ok just as in the example!
In [3]: source_analysis.plotTotalPower(spectrum=True)
...Here, a message appears and notebook is down: The kernel appears to have died. It will restart automatically. Without jupyter, in mere bash, it's segmentation fault (core dumped) error. The same happens for prop_analysis.plotTotalPower(spectrum=True)
CFGrote
commented
5 years ago one more thought: are you using anaconda? if yes, this will very likely be the cause for your issue with wpg/srw.
either use native python3 or follow https://github.com/samoylv/WPG/issues/117#issuecomment-336665382
Regards, Carsten
On 4/11/19 2:02 PM, Roman Shopa wrote:
I installed git-lfs and loaded .h5 file by git checkout (apparently no more errors while testing). Yet the problems remain. Maybe it's better to solve them one by one.
I follow the example from the notebook https://github.com/eucall-software/simex_notebooks/blob/master/start_to_end_demo.ipynb, omitting |# Cleanup previous run|. Why the notebook breaks on the line |source_analysis.plotTotalPower(spectrum=True)|?
|In [1]: # Import all SimEx modules import SimEx from SimEx import * source_analysis = XFELPhotonAnalysis(input_path="FELsource_out_0000001.h5") Start initialization. Loading wavefront from FELsource_out_0000001.h5. ... done. Getting intensities. ... done. Data dimensions = (104, 104, 651) Masking NANs. ... done. In [2]: source_analysis.plotTotalPower() Plotting total power. Pulse energy 0.00017 J # image here is ok just as in the example! In [3]: source_analysis.plotTotalPower(spectrum=True) ... |
Here, a message appears and notebook is down: |The kernel appears to have died. It will restart automatically|. Without jupyter, in mere bash, it's |segmentation fault (core dumped)| error. The same happens for |prop_analysis.plotTotalPower(spectrum=True)|
— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/eucall-software/simex_platform/issues/163#issuecomment-482085891, or mute the thread https://github.com/notifications/unsubscribe-auth/AOwoR_hPybJlF6SuNac6i4PUS-1ruvWwks5vfyRGgaJpZM4TaACr.
rshopa
commented
5 years ago [UPD]
Thank you, Carsten, I'll try: have no options for python3 on cluster except anaconda. Apparently, the solution you mentioned would remove MKL. Apparently, it AFFECTS the rest of the framework.
$ conda install nomkl numpy scipy scikit-learn numexpr
$ conda remove mkl mkl-service
$ python Test.py -v 2>&1 | tee Test.log
...
testBackengine (SimExTest.Calculators.FEFFPhotonMatterInteractorTest.FEFFPhotonMatterInteractorTest)
Test the backengine execution. ... feff85L: error while loading shared libraries: libmkl_gf_lp64.so: cannot open shared object file: No such file or directory
ERROR
... # and so on...The error in the notebook for source_analysis.plotTotalPower(spectrum=True) disappeared, but the very next line returns an error:
In [7]: source_analysis.plotIntensityMap()
Plotting intensity map.
R-space
Error in callback <function install_repl_displayhook.<locals>.post_execute at 0x7f685894abf8> (for post_execute):
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/pyplot.py in post_execute()
107 def post_execute():
108 if matplotlib.is_interactive():
--> 109 draw_all()
110
111 # IPython >= 2
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/_pylab_helpers.py in draw_all(cls, force)
130 for f_mgr in cls.get_all_fig_managers():
131 if force or f_mgr.canvas.figure.stale:
--> 132 f_mgr.canvas.draw_idle()
133
134 atexit.register(Gcf.destroy_all)
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/backend_bases.py in draw_idle(self, *args, **kwargs)
1897 if not self._is_idle_drawing:
1898 with self._idle_draw_cntx():
-> 1899 self.draw(*args, **kwargs)
1900
1901 def draw_cursor(self, event):
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py in draw(self)
400 toolbar = self.toolbar
401 try:
--> 402 self.figure.draw(self.renderer)
403 # A GUI class may be need to update a window using this draw, so
404 # don't forget to call the superclass.
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
48 renderer.start_filter()
49
---> 50 return draw(artist, renderer, *args, **kwargs)
51 finally:
52 if artist.get_agg_filter() is not None:
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/figure.py in draw(self, renderer)
1647
1648 mimage._draw_list_compositing_images(
-> 1649 renderer, self, artists, self.suppressComposite)
1650
1651 renderer.close_group('figure')
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
136 if not_composite or not has_images:
137 for a in artists:
--> 138 a.draw(renderer)
139 else:
140 # Composite any adjacent images together
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
48 renderer.start_filter()
49
---> 50 return draw(artist, renderer, *args, **kwargs)
51 finally:
52 if artist.get_agg_filter() is not None:
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/axes/_base.py in draw(self, renderer, inframe)
2573 self.apply_aspect(pos)
2574 else:
-> 2575 self.apply_aspect()
2576
2577 artists = self.get_children()
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/axes/_base.py in apply_aspect(self, position)
1561 # Not sure whether we need this check:
1562 if shared_x and shared_y:
-> 1563 raise RuntimeError("adjustable='datalim' is not allowed when both"
1564 " axes are shared.")
1565
RuntimeError: adjustable='datalim' is not allowed when both axes are shared.
<Figure size 432x288 with 0 Axes>
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
~/miniconda3/envs/xfel/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
339 pass
340 else:
--> 341 return printer(obj)
342 # Finally look for special method names
343 method = get_real_method(obj, self.print_method)
~/miniconda3/envs/xfel/lib/python3.6/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
242
243 if 'png' in formats:
--> 244 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
245 if 'retina' in formats or 'png2x' in formats:
246 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))
~/miniconda3/envs/xfel/lib/python3.6/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
126
127 bytes_io = BytesIO()
--> 128 fig.canvas.print_figure(bytes_io, **kw)
129 data = bytes_io.getvalue()
130 if fmt == 'svg':
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)
2047 orientation=orientation,
2048 dryrun=True,
-> 2049 **kwargs)
2050 renderer = self.figure._cachedRenderer
2051 bbox_artists = kwargs.pop("bbox_extra_artists", None)
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs)
508
509 """
--> 510 FigureCanvasAgg.draw(self)
511 renderer = self.get_renderer()
512
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py in draw(self)
400 toolbar = self.toolbar
401 try:
--> 402 self.figure.draw(self.renderer)
403 # A GUI class may be need to update a window using this draw, so
404 # don't forget to call the superclass.
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
48 renderer.start_filter()
49
---> 50 return draw(artist, renderer, *args, **kwargs)
51 finally:
52 if artist.get_agg_filter() is not None:
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/figure.py in draw(self, renderer)
1647
1648 mimage._draw_list_compositing_images(
-> 1649 renderer, self, artists, self.suppressComposite)
1650
1651 renderer.close_group('figure')
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
136 if not_composite or not has_images:
137 for a in artists:
--> 138 a.draw(renderer)
139 else:
140 # Composite any adjacent images together
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
48 renderer.start_filter()
49
---> 50 return draw(artist, renderer, *args, **kwargs)
51 finally:
52 if artist.get_agg_filter() is not None:
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/axes/_base.py in draw(self, renderer, inframe)
2573 self.apply_aspect(pos)
2574 else:
-> 2575 self.apply_aspect()
2576
2577 artists = self.get_children()
~/miniconda3/envs/xfel/lib/python3.6/site-packages/matplotlib/axes/_base.py in apply_aspect(self, position)
1561 # Not sure whether we need this check:
1562 if shared_x and shared_y:
-> 1563 raise RuntimeError("adjustable='datalim' is not allowed when both"
1564 " axes are shared.")
1565
RuntimeError: adjustable='datalim' is not allowed when both axes are shared.
<Figure size 1000x1000 with 3 Axes>
I ran the example several times both on cluster and on laptop (docker container) and noticed that the impulse /Tests/python/unittest/TestFiles/FELsource_out/FELsource_out_0000001.h5 differs from the one presented here. For instance, Data dimensions = (21, 21, 550) is much smaller (as was for prop_out_0000001.h5 mentioned above).
Could it be the cause of the error on source_analysis.plotIntensityMap()? Apparently .h5 files in /Tests/python/unittest/TestFiles/ are somewhat deprecated.
CFGrote
commented
5 years ago hi roman, thanks for reporting.
i'll look into this later today.
-- Regards, Carsten
On 5/7/19 12:12 PM, Roman Shopa wrote:
I ran the example several times both on cluster and on laptop (docker container) and noticed that the impulse |/Tests/python/unittest/TestFiles/FELsource_out/FELsource_out_0000001.h5| differs from the one presented here. For instance, |Data dimensions = (21, 21, 550)| is much smaller (as was for |prop_out_0000001.h5| mentioned above).
Could it be the cause of the error on |source_analysis.plotIntensityMap()|? Apparently |.h5| files in |/Tests/python/unittest/TestFiles/| are somewhat deprecated.
image https://user-images.githubusercontent.com/15617645/57291791-6c07e800-70c0-11e9-9cac-937c5b6bcb05.png image https://user-images.githubusercontent.com/15617645/57291815-7b873100-70c0-11e9-889e-8cde567a3117.png
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CFGrote
commented
5 years ago hi roman, looks like i will have to recreate my simex environment on the desy cluster, it was removed during my absence from XFEL. that will take bit of time, hope that is not too big a setback for you.
best regards, carsten
rshopa
commented
5 years ago Hi Carsten,
Did you mean that .h5 files from Tests are corrupted (by "simex environment on the desy cluster")? Or it concerns the whole repository?
I ask because there are errors in multiple examples from wiki, e.g. the very first example from a year-old workshop:
CFGrote
commented
5 years ago yep, i'm on it.
carsten
On 5/10/19 12:16 PM, Roman Shopa wrote:
Hi Carsten,
Yeah, it would be great, as I encounter errors in multiple examples from wiki. E.g. the very first example from a year-old workshop https://github.com/eucall-software/simex_platform/wiki/Tutorial-on-nano-crystal-diffraction: image https://user-images.githubusercontent.com/15617645/57520127-3449ac00-731d-11e9-8f87-b92d1883236e.png
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CFGrote
commented
5 years ago hi roman, it took me a while, but now i have an up-to-date simex installation on the desy cluster, again. the notebook https://github.com/eucall-software/simex_notebooks/blob/master/start_to_end_demo.ipynb goes through without problems and produces reasonable results (diffraction patterns). i used this file as FELsource output: https://desycloud.desy.de/index.php/s/oq76Zfs9P6KiqFd
can you try again?
rshopa
commented
5 years ago Hi. Carsten. Thanks a lot. I'll check this as soon as possible.
rshopa
commented
5 years ago Hi, Carsten. Apparently there's a major problem with the installation - none of the reported problems disappeared.
The line from the notebook In [9]: source_analysis.plotIntensityMap() still doesn't work. An error occurs which ends as RuntimeError: adjustable='datalim' is not allowed when both axes are shared (I mentioned this in the very first comment).
The further error is as previously: propagator.backengine() returns 255 and no prop_out.h5 file is created:
In [11]: propagation_parameters = WavePropagatorParameters(beamline=exfel_spb_kb_beamline)
In [12]: propagator = XFELPhotonPropagator(parameters=propagation_parameters,
input_path='5keV_9fs_2015_slice12_fromYoon2016.h5',
output_path='prop_out.h5')
In [13]: propagator.backengine()
Out [13]: 255
...roman, the first one is an issue in XFELPhotonAnalysis, due to a change in matplotlib. i fixed it in the develop branch but need to push and run through CI before pushing it to master. i'll let you know. in the meantime, you could just comment the two lines where "datalim" appears.
the second: could you tell me as much as possible about your environment, both hardware (in particular RAM), and software (python version, c/c++ compiler, fortran compiler, version of libraries fftw, numpy, blas).
we have to narrow it down this way.
Regards, Carsten
On 6/18/19 3:01 PM, Roman Shopa wrote:
Hi, Carsten. Apparently there's a major problem with the installation - none of the reported problems disappeared.
The line from the notebook |In [9]: source_analysis.plotIntensityMap()| still doesn't work. An error occurs which ends as |RuntimeError: adjustable='datalim' is not allowed when both axes are shared| (I mentioned this in the very first comment https://github.com/eucall-software/simex_platform/issues/163#issuecomment-480191396).
The further error is as previously: |propagator.backengine()| returns |255| and no |prop_out.h5| file is created:
|In [11]: propagation_parameters = WavePropagatorParameters(beamline=exfel_spb_kb_beamline) In [12]: propagator = XFELPhotonPropagator(parameters=propagation_parameters, input_path='5keV_9fs_2015_slice12_fromYoon2016.h5', output_path='prop_out.h5') In [13]: propagator.backengine() Out [13]: 255 ... |
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rshopa
commented
5 years ago Hi Carsten.
Here is the description of the environment I use on CIŚ cluster. :
System: Scientific Linux CERN SLC release 6.8 (Carbon)
Memory (free).......: 46.18GB / 47.10GB (98 %)$ lscpu
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 24
On-line CPU(s) list: 0-23
Thread(s) per core: 1
Core(s) per socket: 12
Socket(s): 2
NUMA node(s): 4
Vendor ID: AuthenticAMD
CPU family: 16
Model: 9
Model name: AMD Opteron(tm) Processor 6174
Stepping: 1
CPU MHz: 2199.930
BogoMIPS: 4400.08
Virtualization: AMD-V
L1d cache: 64K
L1i cache: 64K
L2 cache: 512K
L3 cache: 5118K
NUMA node0 CPU(s): 0,2,4,6,8,10
NUMA node1 CPU(s): 12,14,16,18,20,22
NUMA node2 CPU(s): 13,15,17,19,21,23
NUMA node3 CPU(s): 1,3,5,7,9,11Python:
Python 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34)
[GCC 7.3.0] on linuxSince I use anaconda, I removed mkl, as you advised (unit tests would fail):
$ conda install nomkl numpy scipy scikit-learn numexpr
$ conda remove mkl mkl-servicegcc (I might have used higher version for the compilation):
$ gcc -v
Using built-in specs.
COLLECT_GCC=gcc
COLLECT_LTO_WRAPPER=/mnt/opt/tools/slc6/gcc/4.6.3/libexec/gcc/x86_64-unknown-linux-gnu/4.6.3/lto-wrapper
Target: x86_64-unknown-linux-gnu
Configured with: ../gcc-4.6.3/configure --enable-languages=c,c++,fortran --prefix=/mnt/opt/tools/slc6/gcc/4.6.3
Thread model: posix
gcc version 4.6.3 (GCC)Here is the list of other modules loaded on cluster:
hdf5/1.8.17.patch1
blas/3.5.0-x86_64-gcc46
bzip2/1.0.6
cuda/4.2.9
lapack/3.5.0-x86_64-gcc46
boost/1.64.0-x86_64-gcc71
binutils/2.22
fftw/3.3.4-x86_64-gcc482
intel-composer/2016.2.181 # for mkl, but I turned it offFull list of conda modules:
$ conda list
# packages in environment at /mnt/home/rshopa/miniconda3/envs/xfel:
#
# Name Version Build Channel
alabaster 0.7.12 py36_0
asn1crypto 0.24.0 py36_0
attrs 19.1.0 py36_1
babel 2.6.0 py36_0
backcall 0.1.0 py36_0
biopython 1.72 py36h04863e7_0
blas 2.4 openblas conda-forge
bleach 3.1.0 py36_0
breathe 4.12.0 py_0 conda-forge
ca-certificates 2019.5.15 0
certifi 2019.3.9 py36_0
cffi 1.12.2 py36h2e261b9_1
chardet 3.0.4 py36_1
cryptography 2.6.1 py36h1ba5d50_0
cycler 0.10.0 py36_0
cython 0.29.6 py36he6710b0_0
dbus 1.13.6 h746ee38_0
decorator 4.4.0 py_0
defusedxml 0.5.0 py36_1
dill 0.2.9 py36_0
docutils 0.14 py36_0
entrypoints 0.3 py36_0
expat 2.2.6 he6710b0_0
fabio 0.8.0 py36h3010b51_1000 conda-forge
fontconfig 2.13.0 h9420a91_0
freetype 2.9.1 h8a8886c_1
glib 2.56.2 hd408876_0
gmp 6.1.2 h6c8ec71_1
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
h5py 2.9.0 py36h7918eee_0
hdf5 1.10.4 hb1b8bf9_0
icu 58.2 h9c2bf20_1
idna 2.8 py36_0
imagesize 1.1.0 py36_0
intel-openmp 2019.1 144
ipykernel 5.1.0 py36h39e3cac_0
ipython 7.3.0 py36h39e3cac_0
ipython_genutils 0.2.0 py36_0
ipywidgets 7.4.2 py36_0
jedi 0.13.3 py36_0
jinja2 2.10 py36_0
joblib 0.13.2 py36_0
jpeg 9b h024ee3a_2
jsonschema 3.0.1 py36_0
jupyter 1.0.0 py36_7
jupyter_client 5.2.4 py36_0
jupyter_console 6.0.0 py36_0
jupyter_core 4.4.0 py36_0
kiwisolver 1.0.1 py36hf484d3e_0
libblas 3.8.0 4_openblas conda-forge
libcblas 3.8.0 4_openblas conda-forge
libedit 3.1.20181209 hc058e9b_0
libffi 3.2.1 hd88cf55_4
libgcc-ng 8.2.0 hdf63c60_1
libgfortran-ng 7.3.0 hdf63c60_0
liblapack 3.8.0 4_openblas conda-forge
liblapacke 3.8.0 4_openblas conda-forge
libopenblas 0.3.3 h5a2b251_3
libpng 1.6.36 hbc83047_0
libsodium 1.0.16 h1bed415_0
libstdcxx-ng 8.2.0 hdf63c60_1
libtiff 4.0.10 h2733197_2
libuuid 1.0.3 h1bed415_2
libxcb 1.13 h1bed415_1
libxml2 2.9.9 he19cac6_0
libxslt 1.1.33 h7d1a2b0_0
llvmlite 0.28.0 py36hd408876_0
lxml 4.3.2 py36hefd8a0e_0
mako 1.0.7 py36_0
markupsafe 1.1.1 py36h7b6447c_0
matplotlib 3.0.3 py36h5429711_0
mistune 0.8.4 py36h7b6447c_0
mpi4py 2.0.0 py36_2
mpich2 1.4.1p1 0
nbconvert 5.4.1 py36_3
nbformat 4.4.0 py36_0
ncurses 6.1 he6710b0_1
nomkl 3.0 0
notebook 5.7.8 py36_0
numba 0.43.0 py36h962f231_0
numexpr 2.6.9 py36h2ffa06c_0
numpy 1.16.4 py36h99e49ec_0
numpy-base 1.16.4 py36h2f8d375_0
nvidia-ml-py3 7.352.0 py_0 fastai
olefile 0.46 py36_0
openblas 0.3.5 h9ac9557_1001 conda-forge
openssl 1.1.1c h7b6447c_1
packaging 19.0 py36_0
pandoc 2.2.3.2 0
pandocfilters 1.4.2 py36_1
parso 0.3.4 py36_0
pcre 8.43 he6710b0_0
periodictable 1.5.0 py_1 conda-forge
pexpect 4.6.0 py36_0
pickleshare 0.7.5 py36_0
pillow 5.4.1 py36h34e0f95_0
pint 0.9 py36_2 conda-forge
pip 19.0.3 py36_0
prometheus_client 0.6.0 py36_0
prompt_toolkit 2.0.9 py36_0
ptyprocess 0.6.0 py36_0
py3nvml 0.2.3 <pip>
pycparser 2.19 py36_0
pyfai 0.17.0 py36hf2d7682_1001 conda-forge
pyfftw 0.11.1 py36h3010b51_1001 conda-forge
pygments 2.3.1 py36_0
pyopenssl 19.0.0 py36_0
pyparsing 2.3.1 py36_0
pyqt 5.9.2 py36h05f1152_2
pyrsistent 0.14.11 py36h7b6447c_0
pysocks 1.6.8 py36_0
python 3.6.8 h0371630_0
python-dateutil 2.8.0 py36_0
pytz 2018.9 py36_0
pyzmq 18.0.0 py36he6710b0_0
qt 5.9.7 h5867ecd_1
qtconsole 4.4.3 py36_0
readline 7.0 h7b6447c_5
requests 2.21.0 py36_0
scikit-learn 0.21.2 py36h22eb022_0
scipy 1.2.1 py36he2b7bc3_0
send2trash 1.5.0 py36_0
setuptools 40.8.0 py36_0
silx 0.10.0 py36h637b7d7_0 conda-forge
sip 4.19.8 py36hf484d3e_0
six 1.12.0 py36_0
snowballstemmer 1.2.1 py36_0
sphinx 1.8.5 py36_0
sphinxcontrib 1.0 py36_1
sphinxcontrib-websupport 1.1.0 py36_1
sqlalchemy 1.3.1 py36h7b6447c_0
sqlite 3.27.2 h7b6447c_0
terminado 0.8.1 py36_1
testpath 0.4.2 py36_0
tk 8.6.8 hbc83047_0
tornado 6.0.1 py36h7b6447c_0
traitlets 4.3.2 py36_0
urllib3 1.24.1 py36_0
wcwidth 0.1.7 py36_0
webencodings 0.5.1 py36_1
wheel 0.33.1 py36_0
widgetsnbextension 3.4.2 py36_0
xmltodict 0.12.0 <pip>
xz 5.2.4 h14c3975_4
zeromq 4.3.1 he6710b0_3
zlib 1.2.11 h7b6447c_3
zstd 1.3.7 h0b5b093_0 my suspicion is that your RAM is insufficient. i typically run these simulations on a 500GB shared memory node. right now, i'm running the notebook under limited memory conditions.
can you trace your memory consumption over time while the notebook executes?
Regards, Carsten
On 6/19/19 1:15 PM, Roman Shopa wrote:
Hi Carsten.
Here is the description of the environment I use on CIŚ cluster. :
|System: Scientific Linux CERN SLC release 6.8 (Carbon) Memory (free).......: 46.18GB / 47.10GB (98 %) |
|$ lscpu Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 24 On-line CPU(s) list: 0-23 Thread(s) per core: 1 Core(s) per socket: 12 Socket(s): 2 NUMA node(s): 4 Vendor ID: AuthenticAMD CPU family: 16 Model: 9 Model name: AMD Opteron(tm) Processor 6174 Stepping: 1 CPU MHz: 2199.930 BogoMIPS: 4400.08 Virtualization: AMD-V L1d cache: 64K L1i cache: 64K L2 cache: 512K L3 cache: 5118K NUMA node0 CPU(s): 0,2,4,6,8,10 NUMA node1 CPU(s): 12,14,16,18,20,22 NUMA node2 CPU(s): 13,15,17,19,21,23 NUMA node3 CPU(s): 1,3,5,7,9,11 |
Python:
|Python 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34) [GCC 7.3.0] on linux |
Since I use anaconda, I removed mkl, as you advised (unit tests would fail):
|$ conda install nomkl numpy scipy scikit-learn numexpr $ conda remove mkl mkl-service |
gcc (I might have used higher version for the compilation):
|$ gcc -v Using built-in specs. COLLECT_GCC=gcc COLLECT_LTO_WRAPPER=/mnt/opt/tools/slc6/gcc/4.6.3/libexec/gcc/x86_64-unknown-linux-gnu/4.6.3/lto-wrapper Target: x86_64-unknown-linux-gnu Configured with: ../gcc-4.6.3/configure --enable-languages=c,c++,fortran --prefix=/mnt/opt/tools/slc6/gcc/4.6.3 Thread model: posix gcc version 4.6.3 (GCC) |
Here is the list of other modules loaded on cluster:
|hdf5/1.8.17.patch1 blas/3.5.0-x86_64-gcc46 bzip2/1.0.6 cuda/4.2.9 lapack/3.5.0-x86_64-gcc46 boost/1.64.0-x86_64-gcc71 binutils/2.22 fftw/3.3.4-x86_64-gcc482 intel-composer/2016.2.181 # for mkl, but I turned it off |
Full list of conda modules:
|$ conda list # packages in environment at /mnt/home/rshopa/miniconda3/envs/xfel: # # Name Version Build Channel alabaster 0.7.12 py36_0 asn1crypto 0.24.0 py36_0 attrs 19.1.0 py36_1 babel 2.6.0 py36_0 backcall 0.1.0 py36_0 biopython 1.72 py36h04863e7_0 blas 2.4 openblas conda-forge bleach 3.1.0 py36_0 breathe 4.12.0 py_0 conda-forge ca-certificates 2019.5.15 0 certifi 2019.3.9 py36_0 cffi 1.12.2 py36h2e261b9_1 chardet 3.0.4 py36_1 cryptography 2.6.1 py36h1ba5d50_0 cycler 0.10.0 py36_0 cython 0.29.6 py36he6710b0_0 dbus 1.13.6 h746ee38_0 decorator 4.4.0 py_0 defusedxml 0.5.0 py36_1 dill 0.2.9 py36_0 docutils 0.14 py36_0 entrypoints 0.3 py36_0 expat 2.2.6 he6710b0_0 fabio 0.8.0 py36h3010b51_1000 conda-forge fontconfig 2.13.0 h9420a91_0 freetype 2.9.1 h8a8886c_1 glib 2.56.2 hd408876_0 gmp 6.1.2 h6c8ec71_1 gst-plugins-base 1.14.0 hbbd80ab_1 gstreamer 1.14.0 hb453b48_1 h5py 2.9.0 py36h7918eee_0 hdf5 1.10.4 hb1b8bf9_0 icu 58.2 h9c2bf20_1 idna 2.8 py36_0 imagesize 1.1.0 py36_0 intel-openmp 2019.1 144 ipykernel 5.1.0 py36h39e3cac_0 ipython 7.3.0 py36h39e3cac_0 ipython_genutils 0.2.0 py36_0 ipywidgets 7.4.2 py36_0 jedi 0.13.3 py36_0 jinja2 2.10 py36_0 joblib 0.13.2 py36_0 jpeg 9b h024ee3a_2 jsonschema 3.0.1 py36_0 jupyter 1.0.0 py36_7 jupyter_client 5.2.4 py36_0 jupyter_console 6.0.0 py36_0 jupyter_core 4.4.0 py36_0 kiwisolver 1.0.1 py36hf484d3e_0 libblas 3.8.0 4_openblas conda-forge libcblas 3.8.0 4_openblas conda-forge libedit 3.1.20181209 hc058e9b_0 libffi 3.2.1 hd88cf55_4 libgcc-ng 8.2.0 hdf63c60_1 libgfortran-ng 7.3.0 hdf63c60_0 liblapack 3.8.0 4_openblas conda-forge liblapacke 3.8.0 4_openblas conda-forge libopenblas 0.3.3 h5a2b251_3 libpng 1.6.36 hbc83047_0 libsodium 1.0.16 h1bed415_0 libstdcxx-ng 8.2.0 hdf63c60_1 libtiff 4.0.10 h2733197_2 libuuid 1.0.3 h1bed415_2 libxcb 1.13 h1bed415_1 libxml2 2.9.9 he19cac6_0 libxslt 1.1.33 h7d1a2b0_0 llvmlite 0.28.0 py36hd408876_0 lxml 4.3.2 py36hefd8a0e_0 mako 1.0.7 py36_0 markupsafe 1.1.1 py36h7b6447c_0 matplotlib 3.0.3 py36h5429711_0 mistune 0.8.4 py36h7b6447c_0 mpi4py 2.0.0 py36_2 mpich2 1.4.1p1 0 nbconvert 5.4.1 py36_3 nbformat 4.4.0 py36_0 ncurses 6.1 he6710b0_1 nomkl 3.0 0 notebook 5.7.8 py36_0 numba 0.43.0 py36h962f231_0 numexpr 2.6.9 py36h2ffa06c_0 numpy 1.16.4 py36h99e49ec_0 numpy-base 1.16.4 py36h2f8d375_0 nvidia-ml-py3 7.352.0 py_0 fastai olefile 0.46 py36_0 openblas 0.3.5 h9ac9557_1001 conda-forge openssl 1.1.1c h7b6447c_1 packaging 19.0 py36_0 pandoc 2.2.3.2 0 pandocfilters 1.4.2 py36_1 parso 0.3.4 py36_0 pcre 8.43 he6710b0_0 periodictable 1.5.0 py_1 conda-forge pexpect 4.6.0 py36_0 pickleshare 0.7.5 py36_0 pillow 5.4.1 py36h34e0f95_0 pint 0.9 py36_2 conda-forge pip 19.0.3 py36_0 prometheus_client 0.6.0 py36_0 prompt_toolkit 2.0.9 py36_0 ptyprocess 0.6.0 py36_0 py3nvml 0.2.3
pycparser 2.19 py36_0 pyfai 0.17.0 py36hf2d7682_1001 conda-forge pyfftw 0.11.1 py36h3010b51_1001 conda-forge pygments 2.3.1 py36_0 pyopenssl 19.0.0 py36_0 pyparsing 2.3.1 py36_0 pyqt 5.9.2 py36h05f1152_2 pyrsistent 0.14.11 py36h7b6447c_0 pysocks 1.6.8 py36_0 python 3.6.8 h0371630_0 python-dateutil 2.8.0 py36_0 pytz 2018.9 py36_0 pyzmq 18.0.0 py36he6710b0_0 qt 5.9.7 h5867ecd_1 qtconsole 4.4.3 py36_0 readline 7.0 h7b6447c_5 requests 2.21.0 py36_0 scikit-learn 0.21.2 py36h22eb022_0 scipy 1.2.1 py36he2b7bc3_0 send2trash 1.5.0 py36_0 setuptools 40.8.0 py36_0 silx 0.10.0 py36h637b7d7_0 conda-forge sip 4.19.8 py36hf484d3e_0 six 1.12.0 py36_0 snowballstemmer 1.2.1 py36_0 sphinx 1.8.5 py36_0 sphinxcontrib 1.0 py36_1 sphinxcontrib-websupport 1.1.0 py36_1 sqlalchemy 1.3.1 py36h7b6447c_0 sqlite 3.27.2 h7b6447c_0 terminado 0.8.1 py36_1 testpath 0.4.2 py36_0 tk 8.6.8 hbc83047_0 tornado 6.0.1 py36h7b6447c_0 traitlets 4.3.2 py36_0 urllib3 1.24.1 py36_0 wcwidth 0.1.7 py36_0 webencodings 0.5.1 py36_1 wheel 0.33.1 py36_0 widgetsnbextension 3.4.2 py36_0 xmltodict 0.12.0 xz 5.2.4 h14c3975_4 zeromq 4.3.1 he6710b0_3 zlib 1.2.11 h7b6447c_3 zstd 1.3.7 h0b5b093_0 | — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/eucall-software/simex_platform/issues/163?email_source=notifications&email_token=ADWCQR37YEUEDGF4UBQX46TP3IIOPA5CNFSM4E3IACV2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODYBRCHQ#issuecomment-503517470, or mute the thread https://github.com/notifications/unsubscribe-auth/ADWCQRY7TFKGZXLUZJMHKIDP3IIOPANCNFSM4E3IACVQ.
CFGrote
commented
5 years ago my runs consumes approx. 34 GB RAM (virtual memory). do you have anything else running on the system? e.g. on my node, there are root processes summing up to ~30GB.
Regards, Carsten
On 6/19/19 5:36 PM, Carsten Fortmann-Grote wrote:
my suspicion is that your RAM is insufficient. i typically run these simulations on a 500GB shared memory node. right now, i'm running the notebook under limited memory conditions.
can you trace your memory consumption over time while the notebook executes?
Regards, Carsten
On 6/19/19 1:15 PM, Roman Shopa wrote:
Hi Carsten.
Here is the description of the environment I use on CIŚ cluster. :
|System: Scientific Linux CERN SLC release 6.8 (Carbon) Memory (free).......: 46.18GB / 47.10GB (98 %) |
|$ lscpu Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 24 On-line CPU(s) list: 0-23 Thread(s) per core: 1 Core(s) per socket: 12 Socket(s): 2 NUMA node(s): 4 Vendor ID: AuthenticAMD CPU family: 16 Model: 9 Model name: AMD Opteron(tm) Processor 6174 Stepping: 1 CPU MHz: 2199.930 BogoMIPS: 4400.08 Virtualization: AMD-V L1d cache: 64K L1i cache: 64K L2 cache: 512K L3 cache: 5118K NUMA node0 CPU(s): 0,2,4,6,8,10 NUMA node1 CPU(s): 12,14,16,18,20,22 NUMA node2 CPU(s): 13,15,17,19,21,23 NUMA node3 CPU(s): 1,3,5,7,9,11 |
Python:
|Python 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34) [GCC 7.3.0] on linux |
Since I use anaconda, I removed mkl, as you advised (unit tests would fail):
|$ conda install nomkl numpy scipy scikit-learn numexpr $ conda remove mkl mkl-service |
gcc (I might have used higher version for the compilation):
|$ gcc -v Using built-in specs. COLLECT_GCC=gcc COLLECT_LTO_WRAPPER=/mnt/opt/tools/slc6/gcc/4.6.3/libexec/gcc/x86_64-unknown-linux-gnu/4.6.3/lto-wrapper Target: x86_64-unknown-linux-gnu Configured with: ../gcc-4.6.3/configure --enable-languages=c,c++,fortran --prefix=/mnt/opt/tools/slc6/gcc/4.6.3 Thread model: posix gcc version 4.6.3 (GCC) |
Here is the list of other modules loaded on cluster:
|hdf5/1.8.17.patch1 blas/3.5.0-x86_64-gcc46 bzip2/1.0.6 cuda/4.2.9 lapack/3.5.0-x86_64-gcc46 boost/1.64.0-x86_64-gcc71 binutils/2.22 fftw/3.3.4-x86_64-gcc482 intel-composer/2016.2.181 # for mkl, but I turned it off |
Full list of conda modules:
|$ conda list # packages in environment at /mnt/home/rshopa/miniconda3/envs/xfel: # # Name Version Build Channel alabaster 0.7.12 py36_0 asn1crypto 0.24.0 py36_0 attrs 19.1.0 py36_1 babel 2.6.0 py36_0 backcall 0.1.0 py36_0 biopython 1.72 py36h04863e7_0 blas 2.4 openblas conda-forge bleach 3.1.0 py36_0 breathe 4.12.0 py_0 conda-forge ca-certificates 2019.5.15 0 certifi 2019.3.9 py36_0 cffi 1.12.2 py36h2e261b9_1 chardet 3.0.4 py36_1 cryptography 2.6.1 py36h1ba5d50_0 cycler 0.10.0 py36_0 cython 0.29.6 py36he6710b0_0 dbus 1.13.6 h746ee38_0 decorator 4.4.0 py_0 defusedxml 0.5.0 py36_1 dill 0.2.9 py36_0 docutils 0.14 py36_0 entrypoints 0.3 py36_0 expat 2.2.6 he6710b0_0 fabio 0.8.0 py36h3010b51_1000 conda-forge fontconfig 2.13.0 h9420a91_0 freetype 2.9.1 h8a8886c_1 glib 2.56.2 hd408876_0 gmp 6.1.2 h6c8ec71_1 gst-plugins-base 1.14.0 hbbd80ab_1 gstreamer 1.14.0 hb453b48_1 h5py 2.9.0 py36h7918eee_0 hdf5 1.10.4 hb1b8bf9_0 icu 58.2 h9c2bf20_1 idna 2.8 py36_0 imagesize 1.1.0 py36_0 intel-openmp 2019.1 144 ipykernel 5.1.0 py36h39e3cac_0 ipython 7.3.0 py36h39e3cac_0 ipython_genutils 0.2.0 py36_0 ipywidgets 7.4.2 py36_0 jedi 0.13.3 py36_0 jinja2 2.10 py36_0 joblib 0.13.2 py36_0 jpeg 9b h024ee3a_2 jsonschema 3.0.1 py36_0 jupyter 1.0.0 py36_7 jupyter_client 5.2.4 py36_0 jupyter_console 6.0.0 py36_0 jupyter_core 4.4.0 py36_0 kiwisolver 1.0.1 py36hf484d3e_0 libblas 3.8.0 4_openblas conda-forge libcblas 3.8.0 4_openblas conda-forge libedit 3.1.20181209 hc058e9b_0 libffi 3.2.1 hd88cf55_4 libgcc-ng 8.2.0 hdf63c60_1 libgfortran-ng 7.3.0 hdf63c60_0 liblapack 3.8.0 4_openblas conda-forge liblapacke 3.8.0 4_openblas conda-forge libopenblas 0.3.3 h5a2b251_3 libpng 1.6.36 hbc83047_0 libsodium 1.0.16 h1bed415_0 libstdcxx-ng 8.2.0 hdf63c60_1 libtiff 4.0.10 h2733197_2 libuuid 1.0.3 h1bed415_2 libxcb 1.13 h1bed415_1 libxml2 2.9.9 he19cac6_0 libxslt 1.1.33 h7d1a2b0_0 llvmlite 0.28.0 py36hd408876_0 lxml 4.3.2 py36hefd8a0e_0 mako 1.0.7 py36_0 markupsafe 1.1.1 py36h7b6447c_0 matplotlib 3.0.3 py36h5429711_0 mistune 0.8.4 py36h7b6447c_0 mpi4py 2.0.0 py36_2 mpich2 1.4.1p1 0 nbconvert 5.4.1 py36_3 nbformat 4.4.0 py36_0 ncurses 6.1 he6710b0_1 nomkl 3.0 0 notebook 5.7.8 py36_0 numba 0.43.0 py36h962f231_0 numexpr 2.6.9 py36h2ffa06c_0 numpy 1.16.4 py36h99e49ec_0 numpy-base 1.16.4 py36h2f8d375_0 nvidia-ml-py3 7.352.0 py_0 fastai olefile 0.46 py36_0 openblas 0.3.5 h9ac9557_1001 conda-forge openssl 1.1.1c h7b6447c_1 packaging 19.0 py36_0 pandoc 2.2.3.2 0 pandocfilters 1.4.2 py36_1 parso 0.3.4 py36_0 pcre 8.43 he6710b0_0 periodictable 1.5.0 py_1 conda-forge pexpect 4.6.0 py36_0 pickleshare 0.7.5 py36_0 pillow 5.4.1 py36h34e0f95_0 pint 0.9 py36_2 conda-forge pip 19.0.3 py36_0 prometheus_client 0.6.0 py36_0 prompt_toolkit 2.0.9 py36_0 ptyprocess 0.6.0 py36_0 py3nvml 0.2.3
pycparser 2.19 py36_0 pyfai 0.17.0 py36hf2d7682_1001 conda-forge pyfftw 0.11.1 py36h3010b51_1001 conda-forge pygments 2.3.1 py36_0 pyopenssl 19.0.0 py36_0 pyparsing 2.3.1 py36_0 pyqt 5.9.2 py36h05f1152_2 pyrsistent 0.14.11 py36h7b6447c_0 pysocks 1.6.8 py36_0 python 3.6.8 h0371630_0 python-dateutil 2.8.0 py36_0 pytz 2018.9 py36_0 pyzmq 18.0.0 py36he6710b0_0 qt 5.9.7 h5867ecd_1 qtconsole 4.4.3 py36_0 readline 7.0 h7b6447c_5 requests 2.21.0 py36_0 scikit-learn 0.21.2 py36h22eb022_0 scipy 1.2.1 py36he2b7bc3_0 send2trash 1.5.0 py36_0 setuptools 40.8.0 py36_0 silx 0.10.0 py36h637b7d7_0 conda-forge sip 4.19.8 py36hf484d3e_0 six 1.12.0 py36_0 snowballstemmer 1.2.1 py36_0 sphinx 1.8.5 py36_0 sphinxcontrib 1.0 py36_1 sphinxcontrib-websupport 1.1.0 py36_1 sqlalchemy 1.3.1 py36h7b6447c_0 sqlite 3.27.2 h7b6447c_0 terminado 0.8.1 py36_1 testpath 0.4.2 py36_0 tk 8.6.8 hbc83047_0 tornado 6.0.1 py36h7b6447c_0 traitlets 4.3.2 py36_0 urllib3 1.24.1 py36_0 wcwidth 0.1.7 py36_0 webencodings 0.5.1 py36_1 wheel 0.33.1 py36_0 widgetsnbextension 3.4.2 py36_0 xmltodict 0.12.0 xz 5.2.4 h14c3975_4 zeromq 4.3.1 he6710b0_3 zlib 1.2.11 h7b6447c_3 zstd 1.3.7 h0b5b093_0 | — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/eucall-software/simex_platform/issues/163?email_source=notifications&email_token=ADWCQR37YEUEDGF4UBQX46TP3IIOPA5CNFSM4E3IACV2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODYBRCHQ#issuecomment-503517470, or mute the thread https://github.com/notifications/unsubscribe-auth/ADWCQRY7TFKGZXLUZJMHKIDP3IIOPANCNFSM4E3IACVQ.
CFGrote
commented
5 years ago and on another note, i just pushed v0.4.1 which fixes the matplotlib issue you reported.
Regards, Carsten
On 6/19/19 5:51 PM, Carsten Fortmann-Grote wrote:
my runs consumes approx. 34 GB RAM (virtual memory). do you have anything else running on the system? e.g. on my node, there are root processes summing up to ~30GB.
Regards, Carsten
On 6/19/19 5:36 PM, Carsten Fortmann-Grote wrote:
my suspicion is that your RAM is insufficient. i typically run these simulations on a 500GB shared memory node. right now, i'm running the notebook under limited memory conditions.
can you trace your memory consumption over time while the notebook executes?
Regards, Carsten
On 6/19/19 1:15 PM, Roman Shopa wrote:
Hi Carsten.
Here is the description of the environment I use on CIŚ cluster. :
|System: Scientific Linux CERN SLC release 6.8 (Carbon) Memory (free).......: 46.18GB / 47.10GB (98 %) |
|$ lscpu Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 24 On-line CPU(s) list: 0-23 Thread(s) per core: 1 Core(s) per socket: 12 Socket(s): 2 NUMA node(s): 4 Vendor ID: AuthenticAMD CPU family: 16 Model: 9 Model name: AMD Opteron(tm) Processor 6174 Stepping: 1 CPU MHz: 2199.930 BogoMIPS: 4400.08 Virtualization: AMD-V L1d cache: 64K L1i cache: 64K L2 cache: 512K L3 cache: 5118K NUMA node0 CPU(s): 0,2,4,6,8,10 NUMA node1 CPU(s): 12,14,16,18,20,22 NUMA node2 CPU(s): 13,15,17,19,21,23 NUMA node3 CPU(s): 1,3,5,7,9,11 |
Python:
|Python 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34) [GCC 7.3.0] on linux |
Since I use anaconda, I removed mkl, as you advised (unit tests would fail):
|$ conda install nomkl numpy scipy scikit-learn numexpr $ conda remove mkl mkl-service |
gcc (I might have used higher version for the compilation):
|$ gcc -v Using built-in specs. COLLECT_GCC=gcc COLLECT_LTO_WRAPPER=/mnt/opt/tools/slc6/gcc/4.6.3/libexec/gcc/x86_64-unknown-linux-gnu/4.6.3/lto-wrapper Target: x86_64-unknown-linux-gnu Configured with: ../gcc-4.6.3/configure --enable-languages=c,c++,fortran --prefix=/mnt/opt/tools/slc6/gcc/4.6.3 Thread model: posix gcc version 4.6.3 (GCC) |
Here is the list of other modules loaded on cluster:
|hdf5/1.8.17.patch1 blas/3.5.0-x86_64-gcc46 bzip2/1.0.6 cuda/4.2.9 lapack/3.5.0-x86_64-gcc46 boost/1.64.0-x86_64-gcc71 binutils/2.22 fftw/3.3.4-x86_64-gcc482 intel-composer/2016.2.181 # for mkl, but I turned it off |
Full list of conda modules:
|$ conda list # packages in environment at /mnt/home/rshopa/miniconda3/envs/xfel: # # Name Version Build Channel alabaster 0.7.12 py36_0 asn1crypto 0.24.0 py36_0 attrs 19.1.0 py36_1 babel 2.6.0 py36_0 backcall 0.1.0 py36_0 biopython 1.72 py36h04863e7_0 blas 2.4 openblas conda-forge bleach 3.1.0 py36_0 breathe 4.12.0 py_0 conda-forge ca-certificates 2019.5.15 0 certifi 2019.3.9 py36_0 cffi 1.12.2 py36h2e261b9_1 chardet 3.0.4 py36_1 cryptography 2.6.1 py36h1ba5d50_0 cycler 0.10.0 py36_0 cython 0.29.6 py36he6710b0_0 dbus 1.13.6 h746ee38_0 decorator 4.4.0 py_0 defusedxml 0.5.0 py36_1 dill 0.2.9 py36_0 docutils 0.14 py36_0 entrypoints 0.3 py36_0 expat 2.2.6 he6710b0_0 fabio 0.8.0 py36h3010b51_1000 conda-forge fontconfig 2.13.0 h9420a91_0 freetype 2.9.1 h8a8886c_1 glib 2.56.2 hd408876_0 gmp 6.1.2 h6c8ec71_1 gst-plugins-base 1.14.0 hbbd80ab_1 gstreamer 1.14.0 hb453b48_1 h5py 2.9.0 py36h7918eee_0 hdf5 1.10.4 hb1b8bf9_0 icu 58.2 h9c2bf20_1 idna 2.8 py36_0 imagesize 1.1.0 py36_0 intel-openmp 2019.1 144 ipykernel 5.1.0 py36h39e3cac_0 ipython 7.3.0 py36h39e3cac_0 ipython_genutils 0.2.0 py36_0 ipywidgets 7.4.2 py36_0 jedi 0.13.3 py36_0 jinja2 2.10 py36_0 joblib 0.13.2 py36_0 jpeg 9b h024ee3a_2 jsonschema 3.0.1 py36_0 jupyter 1.0.0 py36_7 jupyter_client 5.2.4 py36_0 jupyter_console 6.0.0 py36_0 jupyter_core 4.4.0 py36_0 kiwisolver 1.0.1 py36hf484d3e_0 libblas 3.8.0 4_openblas conda-forge libcblas 3.8.0 4_openblas conda-forge libedit 3.1.20181209 hc058e9b_0 libffi 3.2.1 hd88cf55_4 libgcc-ng 8.2.0 hdf63c60_1 libgfortran-ng 7.3.0 hdf63c60_0 liblapack 3.8.0 4_openblas conda-forge liblapacke 3.8.0 4_openblas conda-forge libopenblas 0.3.3 h5a2b251_3 libpng 1.6.36 hbc83047_0 libsodium 1.0.16 h1bed415_0 libstdcxx-ng 8.2.0 hdf63c60_1 libtiff 4.0.10 h2733197_2 libuuid 1.0.3 h1bed415_2 libxcb 1.13 h1bed415_1 libxml2 2.9.9 he19cac6_0 libxslt 1.1.33 h7d1a2b0_0 llvmlite 0.28.0 py36hd408876_0 lxml 4.3.2 py36hefd8a0e_0 mako 1.0.7 py36_0 markupsafe 1.1.1 py36h7b6447c_0 matplotlib 3.0.3 py36h5429711_0 mistune 0.8.4 py36h7b6447c_0 mpi4py 2.0.0 py36_2 mpich2 1.4.1p1 0 nbconvert 5.4.1 py36_3 nbformat 4.4.0 py36_0 ncurses 6.1 he6710b0_1 nomkl 3.0 0 notebook 5.7.8 py36_0 numba 0.43.0 py36h962f231_0 numexpr 2.6.9 py36h2ffa06c_0 numpy 1.16.4 py36h99e49ec_0 numpy-base 1.16.4 py36h2f8d375_0 nvidia-ml-py3 7.352.0 py_0 fastai olefile 0.46 py36_0 openblas 0.3.5 h9ac9557_1001 conda-forge openssl 1.1.1c h7b6447c_1 packaging 19.0 py36_0 pandoc 2.2.3.2 0 pandocfilters 1.4.2 py36_1 parso 0.3.4 py36_0 pcre 8.43 he6710b0_0 periodictable 1.5.0 py_1 conda-forge pexpect 4.6.0 py36_0 pickleshare 0.7.5 py36_0 pillow 5.4.1 py36h34e0f95_0 pint 0.9 py36_2 conda-forge pip 19.0.3 py36_0 prometheus_client 0.6.0 py36_0 prompt_toolkit 2.0.9 py36_0 ptyprocess 0.6.0 py36_0 py3nvml 0.2.3
pycparser 2.19 py36_0 pyfai 0.17.0 py36hf2d7682_1001 conda-forge pyfftw 0.11.1 py36h3010b51_1001 conda-forge pygments 2.3.1 py36_0 pyopenssl 19.0.0 py36_0 pyparsing 2.3.1 py36_0 pyqt 5.9.2 py36h05f1152_2 pyrsistent 0.14.11 py36h7b6447c_0 pysocks 1.6.8 py36_0 python 3.6.8 h0371630_0 python-dateutil 2.8.0 py36_0 pytz 2018.9 py36_0 pyzmq 18.0.0 py36he6710b0_0 qt 5.9.7 h5867ecd_1 qtconsole 4.4.3 py36_0 readline 7.0 h7b6447c_5 requests 2.21.0 py36_0 scikit-learn 0.21.2 py36h22eb022_0 scipy 1.2.1 py36he2b7bc3_0 send2trash 1.5.0 py36_0 setuptools 40.8.0 py36_0 silx 0.10.0 py36h637b7d7_0 conda-forge sip 4.19.8 py36hf484d3e_0 six 1.12.0 py36_0 snowballstemmer 1.2.1 py36_0 sphinx 1.8.5 py36_0 sphinxcontrib 1.0 py36_1 sphinxcontrib-websupport 1.1.0 py36_1 sqlalchemy 1.3.1 py36h7b6447c_0 sqlite 3.27.2 h7b6447c_0 terminado 0.8.1 py36_1 testpath 0.4.2 py36_0 tk 8.6.8 hbc83047_0 tornado 6.0.1 py36h7b6447c_0 traitlets 4.3.2 py36_0 urllib3 1.24.1 py36_0 wcwidth 0.1.7 py36_0 webencodings 0.5.1 py36_1 wheel 0.33.1 py36_0 widgetsnbextension 3.4.2 py36_0 xmltodict 0.12.0 xz 5.2.4 h14c3975_4 zeromq 4.3.1 he6710b0_3 zlib 1.2.11 h7b6447c_3 zstd 1.3.7 h0b5b093_0 | — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/eucall-software/simex_platform/issues/163?email_source=notifications&email_token=ADWCQR37YEUEDGF4UBQX46TP3IIOPA5CNFSM4E3IACV2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODYBRCHQ#issuecomment-503517470, or mute the thread https://github.com/notifications/unsubscribe-auth/ADWCQRY7TFKGZXLUZJMHKIDP3IIOPANCNFSM4E3IACVQ.
rshopa
commented
5 years ago Hi Carsten, Thank you for the hints. Probably I have to discuss the latest issue with our admins.
It still seems odd for me that memory issues cause error at propagator.backengine() stage.
CFGrote
commented
5 years ago the propagation is certainly among the most RAM consuming parts. consider the data volume for this raster in t,x, and y, where each voxel has to store two complex fields in double precision.
Regards, Carsten
On 6/20/19 3:48 PM, Roman Shopa wrote:
Hi Carsten, Thank you for the hints. Probably I have to discuss the latest issue with our admins.
It still seems odd for me that memory issues cause error at |propagator.backengine()| stage.
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rshopa
commented
5 years ago Hi Carsten,
The problem with .backengine() functions was related to open MPI (apparently I forgot to add the library to conda environment). It disappears after I loaded the module.
The example appears to run ok (haven't updated to v0.4.1 to test the issue with matplotlib), but with little difference in images. Since I yet have to dive into the physics, I don't know whether it is consistent or not. Here's some screenshots:
Thank you again for the help and for the enormous patience!
I hope that the examples from wiki would work, as well.
CFGrote
commented
5 years ago looks great to me!
Regards, Carsten
On 7/8/19 2:28 PM, Roman Shopa wrote:
Hi Carsten, The problem with .backengine() functions was related to open MPI. It disappears after I loaded the module. The example appears to run ok (haven't updated to v0.4.1 to test the issue with matplotlib), but with little difference in images. Since I yet have to dive into the physics, I don't know whether it is ok. Here's some screenshots: image https://user-images.githubusercontent.com/15617645/60810024-6b590400-a18c-11e9-9b77-33df68fdc9b0.png image https://user-images.githubusercontent.com/15617645/60809988-51b7bc80-a18c-11e9-9259-dc20af9dec2b.png
Thank you again for the help and for the enormous patience!
I hope that the examples from wiki would work, as well.
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