Measuring and logging the CPU usage at the hpc05

Takes a measuring point every 15 minutes and then updates this website. Originally developed by Bas Nijholt, now maintained by the Quantum tinkererers.

Found a mistake or want to know something? Open an issue in the project repository.

You can also find this ipynb here and the data of the last 60 days here.

Last time this script ran is at 2024-06-16 22:50:33.611187+02:00, on qt2.tudelft.net

Current usage at the hpc05

Total in cores in use: 223, free: 697

mates uses 140 cores
mboky uses 83 cores

Data of the last 60 days

CPU time (days) Reserved time (days) IDLE time (days) Activity (%)
Username
mates 437.204931 1535.537720 1098.332790 28.472432
jasanders 253.393310 879.498996 626.105686 28.811097
lprielinger 799.043831 1362.096601 563.052770 58.662787
jdtorreslun 1057.984572 1389.768310 331.783738 76.126687
rikbroekhov 308.686701 620.541943 311.855242 49.744696
kostasvilke 754.738183 928.710720 173.972537 81.267306
pkeekstra 6.914444 120.297509 113.383065 5.747787
ckaraca1 1577.419144 1667.624507 90.205363 94.590787
arigottiman 90.524352 137.935442 47.411090 65.628058
smiles2 31.959097 74.813105 42.854008 42.718581
tbeauchamp 219.783600 230.414581 10.630981 95.386151
rzijderveld 0.000000 0.000000 0.000000 NaN
mboky 14829.111539 11322.276082 -3506.835457 130.972884
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Data of the last week

/tmp/ipykernel_3173906/4256890020.py:39: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
  week_data = xr.DataArray(week_data, dims = ['Weekday', 'Hours'],
/tmp/ipykernel_3173906/4256890020.py:42: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  cmap = matplotlib.cm.get_cmap("viridis", 10)
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Only today

CPU time (days) Reserved time (days) IDLE time (days) Activity (%)
Username
mates 22.747442 109.477171 86.729729 20.778252
mboky 783.024144 661.695226 -121.328918 118.336073
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Ideas?

  • Showing usage per department
  • Average number of cores used per day