%matplotlib inline
import pandas as pd
import socket
host = socket.getfqdn()
from core import load, zoom, calc, save,plots,monitor
#reload funcs after updating ./core/*.py
import importlib
importlib.reload(load)
importlib.reload(zoom)
importlib.reload(calc)
importlib.reload(save)
importlib.reload(plots)
importlib.reload(monitor)
<module 'core.monitor' from '/ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/monitor.py'>
# 'month': = 'JOBID' almost month but not really,
# If you submit the job with job scheduler, above
#below are list of enviroment variable one can pass
#%env local='2"
# local : if True run dask local cluster, if not true, put number of workers
# setted in the 'local'
# if no 'local ' given, local will be setted automatically to 'True'
#%env ychunk='2'
#%env tchunk='2'
# controls chunk. 'False' sets no modification from original netcdf file's chunk.
# ychunk=10 will group the original netcdf file to 10 by 10
# tchunk=1 will chunk the time coordinate one by one
#%env control=FWC_SSH
# name of control file to be used for computation/plots/save/
#%env file_exp=
# 'file_exp': Which 'experiment' name is it?
#. this corresopnds to intake catalog name without path and .yaml
#%env year=
# for Validation, this correspoinds to path/year/month 's year
# for monitoring, this corresponids to 'date' having * means do all files in the monitoring directory
# setting it as *0[0-9] &*1[0-9]& *[2-3][0-9], the job can be separated in three lots.
#%env month=
# for monitoring this corresponds to file path path-XIOS.{month}/
#
#%env save= proceed saving? True or False , Default is setted as True
#%env plot= proceed plotting? True or False , Default is setted as True
#%env calc= proceed computation? or just load computed result? True or False , Default is setted as True
#%env save=False
#%env lazy=False
%%time
# 'savefig': Do we save output in html? or not. keep it true.
savefig=True
client,cluster,control,catalog_url,month,year,daskreport,outputpath = load.set_control(host)
!mkdir -p $outputpath
!mkdir -p $daskreport
client
local True using host= irene4142.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True workers 16 10000000000 False rome local cluster starting This code is running on irene4142.c-irene.mg1.tgcc.ccc.cea.fr using SEDNA_DELTA_MONITOR file experiment, read from ../lib/SEDNA_DELTA_MONITOR.yaml on year= 2012 on month= 01 outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6414347irene4142.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_01M_AWTMD/_lazyFalse CPU times: user 547 ms, sys: 131 ms, total: 678 ms Wall time: 20.8 s
Client-6c2003bd-134c-11ed-9226-080038b9322d
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
4b258f93
Dashboard: http://127.0.0.1:8787/status | Workers: 16 |
Total threads: 128 | Total memory: 251.06 GiB |
Status: running | Using processes: True |
Scheduler-d4d4301a-60d5-48e2-a560-2867d12bbf7f
Comm: tcp://127.0.0.1:41591 | Workers: 16 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 128 |
Started: Just now | Total memory: 251.06 GiB |
Comm: tcp://127.0.0.1:44687 | Total threads: 8 |
Dashboard: http://127.0.0.1:45338/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:32965 | |
Local directory: /tmp/dask-worker-space/worker-oyji_us0 |
Comm: tcp://127.0.0.1:40653 | Total threads: 8 |
Dashboard: http://127.0.0.1:36892/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34277 | |
Local directory: /tmp/dask-worker-space/worker-kf_gjjid |
Comm: tcp://127.0.0.1:43814 | Total threads: 8 |
Dashboard: http://127.0.0.1:46689/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:41237 | |
Local directory: /tmp/dask-worker-space/worker-5ejsgszx |
Comm: tcp://127.0.0.1:38091 | Total threads: 8 |
Dashboard: http://127.0.0.1:45179/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:41381 | |
Local directory: /tmp/dask-worker-space/worker-8_q_zsh5 |
Comm: tcp://127.0.0.1:37452 | Total threads: 8 |
Dashboard: http://127.0.0.1:34851/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:41442 | |
Local directory: /tmp/dask-worker-space/worker-5t_kcs9k |
Comm: tcp://127.0.0.1:40079 | Total threads: 8 |
Dashboard: http://127.0.0.1:45951/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:35055 | |
Local directory: /tmp/dask-worker-space/worker-h9y5raac |
Comm: tcp://127.0.0.1:46358 | Total threads: 8 |
Dashboard: http://127.0.0.1:36867/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:43702 | |
Local directory: /tmp/dask-worker-space/worker-s4u6gxpz |
Comm: tcp://127.0.0.1:45578 | Total threads: 8 |
Dashboard: http://127.0.0.1:39488/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:41239 | |
Local directory: /tmp/dask-worker-space/worker-53uj66vk |
Comm: tcp://127.0.0.1:38246 | Total threads: 8 |
Dashboard: http://127.0.0.1:40295/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:42627 | |
Local directory: /tmp/dask-worker-space/worker-x3ssz726 |
Comm: tcp://127.0.0.1:38662 | Total threads: 8 |
Dashboard: http://127.0.0.1:45889/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:40175 | |
Local directory: /tmp/dask-worker-space/worker-q3a3lzvx |
Comm: tcp://127.0.0.1:45336 | Total threads: 8 |
Dashboard: http://127.0.0.1:46087/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34900 | |
Local directory: /tmp/dask-worker-space/worker-eiyghpnu |
Comm: tcp://127.0.0.1:35645 | Total threads: 8 |
Dashboard: http://127.0.0.1:34270/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:40556 | |
Local directory: /tmp/dask-worker-space/worker-ij7e18ol |
Comm: tcp://127.0.0.1:41643 | Total threads: 8 |
Dashboard: http://127.0.0.1:44149/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:32874 | |
Local directory: /tmp/dask-worker-space/worker-hymn_r2k |
Comm: tcp://127.0.0.1:39067 | Total threads: 8 |
Dashboard: http://127.0.0.1:45612/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:44259 | |
Local directory: /tmp/dask-worker-space/worker-5w5d_iwd |
Comm: tcp://127.0.0.1:46104 | Total threads: 8 |
Dashboard: http://127.0.0.1:43568/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:36179 | |
Local directory: /tmp/dask-worker-space/worker-u0c58_ga |
Comm: tcp://127.0.0.1:43598 | Total threads: 8 |
Dashboard: http://127.0.0.1:40746/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:39161 | |
Local directory: /tmp/dask-worker-space/worker-au5hao06 |
df=load.controlfile(control)
#Take out 'later' tagged computations
#df=df[~df['Value'].str.contains('later')]
df
Value | Inputs | Equation | Zone | Plot | Colourmap | MinMax | Unit | Oldname | Unnamed: 10 | |
---|---|---|---|---|---|---|---|---|---|---|
AW_maxtemp_depth | gridT.votemper,gridS.vosaline,param.mask,param... | calc.AWTD4(data) | ALL | AWTD_map | jet | (0,800) | m | M-5 |
Each computation consists of
%%time
import os
calcswitch=os.environ.get('calc', 'True')
lazy=os.environ.get('lazy','False' )
loaddata=((df.Inputs != '').any())
print('calcswitch=',calcswitch,'df.Inputs != nothing',loaddata, 'lazy=',lazy)
data = load.datas(catalog_url,df.Inputs,month,year,daskreport,lazy=lazy) if ((calcswitch=='True' )*loaddata) else 0
data
calcswitch= True df.Inputs != nothing True lazy= False ../lib/SEDNA_DELTA_MONITOR.yaml using param_xios reading ../lib/SEDNA_DELTA_MONITOR.yaml using param_xios reading <bound method DataSourceBase.describe of sources: param_xios: args: combine: nested concat_dim: y urlpath: /ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/SEDNA-I/SEDNA_Domain_cfg_Tgt_20210423_tsh10m_L1/param_f32/x_*.nc xarray_kwargs: compat: override coords: minimal data_vars: minimal parallel: true description: SEDNA NEMO parameters from MPI output nav_lon lat fails driver: intake_xarray.netcdf.NetCDFSource metadata: catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/ > {'name': 'param_xios', 'container': 'xarray', 'plugin': ['netcdf'], 'driver': ['netcdf'], 'description': 'SEDNA NEMO parameters from MPI output nav_lon lat fails', 'direct_access': 'forbid', 'user_parameters': [{'name': 'path', 'description': 'file coordinate', 'type': 'str', 'default': '/ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/MESH/SEDNA_mesh_mask_Tgt_20210423_tsh10m_L1/param'}], 'metadata': {}, 'args': {'urlpath': '/ccc/work/cont003/gen7420/odakatin/CONFIGS/SEDNA/SEDNA-I/SEDNA_Domain_cfg_Tgt_20210423_tsh10m_L1/param_f32/x_*.nc', 'combine': 'nested', 'concat_dim': 'y'}} 0 read gridS ['vosaline'] using load_data_xios_kerchunk reading gridS using load_data_xios_kerchunk reading <bound method DataSourceBase.describe of sources: data_xios_kerchunk: args: consolidated: false storage_options: fo: file:////ccc/cont003/home/ra5563/ra5563/catalogue/DELTA/201201/gridS_0[0-5][0-9][0-9].json target_protocol: file urlpath: reference:// description: CREG025 NEMO outputs from different xios server in kerchunk format driver: intake_xarray.xzarr.ZarrSource metadata: catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/ > took 39.4211642742157 seconds 0 merging gridS ['vosaline'] 1 read gridT ['votemper'] using load_data_xios_kerchunk reading gridT using load_data_xios_kerchunk reading <bound method DataSourceBase.describe of sources: data_xios_kerchunk: args: consolidated: false storage_options: fo: file:////ccc/cont003/home/ra5563/ra5563/catalogue/DELTA/201201/gridT_0[0-5][0-9][0-9].json target_protocol: file urlpath: reference:// description: CREG025 NEMO outputs from different xios server in kerchunk format driver: intake_xarray.xzarr.ZarrSource metadata: catalog_dir: /ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/../lib/ > took 38.375192165374756 seconds 1 merging gridT ['votemper'] took 0.7663354873657227 seconds param nav_lon will be included in data param mask will be included in data param nav_lat will be included in data param mask2d will be included in data param depth will be included in data CPU times: user 37.3 s, sys: 5.66 s, total: 42.9 s Wall time: 1min 41s
<xarray.Dataset> Dimensions: (t: 31, z: 150, y: 6540, x: 6560) Coordinates: time_centered (t) object dask.array<chunksize=(1,), meta=np.ndarray> * t (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:00:00 * y (y) int64 1 2 3 4 5 6 7 ... 6535 6536 6537 6538 6539 6540 * x (x) int64 1 2 3 4 5 6 7 ... 6555 6556 6557 6558 6559 6560 * z (z) int64 1 2 3 4 5 6 7 8 ... 143 144 145 146 147 148 149 150 nav_lon (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> mask (z, y, x) bool dask.array<chunksize=(150, 13, 6560), meta=np.ndarray> nav_lat (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(13, 6560), meta=np.ndarray> depth (z, y, x) float32 dask.array<chunksize=(150, 13, 6560), meta=np.ndarray> Data variables: vosaline (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray> votemper (t, z, y, x) float32 dask.array<chunksize=(1, 150, 13, 6560), meta=np.ndarray> Attributes: (12/26) CASE: DELTA CONFIG: SEDNA Conventions: CF-1.6 DOMAIN_dimensions_ids: [2, 3] DOMAIN_halo_size_end: [0, 0] DOMAIN_halo_size_start: [0, 0] ... ... nj: 13 output_frequency: 1d start_date: 20090101 timeStamp: 2022-Jan-17 19:00:16 GMT title: ocean T grid variables uuid: d8db76f6-a436-451a-9ab1-72dc892753af
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
)
#calc= True #save= True #plot= False Value='AW_maxtemp_depth' Zone='ALL' Plot='AWTD_map' cmap='jet' clabel='m' clim= (0, 800) outputpath='../results/SEDNA_DELTA_MONITOR/' nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/' filename='SEDNA_AWTD_map_ALL_AW_maxtemp_depth' monitor.optimize_dataset(data) #3 Start computing dtaa= calc.AWTD4(data) monitor.optimize_dataset(dtaa)
<xarray.Dataset> Dimensions: (t: 31, y: 6540, x: 6560) Coordinates: time_centered (t) object dask.array<chunksize=(1,), meta=np.ndarray> * t (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:00:00 * y (y) int64 1 2 3 4 5 6 7 ... 6535 6536 6537 6538 6539 6540 * x (x) int64 1 2 3 4 5 6 7 ... 6555 6556 6557 6558 6559 6560 nav_lon (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> nav_lat (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> mask2d (y, x) bool dask.array<chunksize=(13, 6560), meta=np.ndarray> Data variables: AWT (t, y, x) float32 dask.array<chunksize=(1, 13, 6560), meta=np.ndarray> AWD (t, y, x) float32 dask.array<chunksize=(1, 13, 6560), meta=np.ndarray>
#4 Saving SEDNA_AWTD_map_ALL_AW_maxtemp_depth dtaa=save.datas(data,plot=Plot,path=nc_outputpath,filename=filename) start saving data saving data in a file t (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 slice(0, 1, None)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
slice(1, 2, None)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
slice(2, 3, None)
/ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims) /ccc/cont003/home/ra5563/ra5563/monitor/lib/python3.10/site-packages/dask/array/reductions.py:608: RuntimeWarning: All-NaN slice encountered return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
slice(3, 4, None) slice(4, 5, None) slice(5, 6, None) slice(6, 7, None) slice(7, 8, None) slice(8, 9, None) slice(9, 10, None) slice(10, 11, None) slice(11, 12, None)
2022-08-03 19:00:43,046 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(12, 13, None)
2022-08-03 19:01:06,359 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-03 19:01:09,835 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(13, 14, None)
2022-08-03 19:01:40,955 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:01:44,057 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:01:45,778 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(14, 15, None)
2022-08-03 19:02:17,364 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:02:20,215 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:02:43,465 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(15, 16, None)
2022-08-03 19:02:52,875 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:02:55,559 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(16, 17, None)
2022-08-03 19:03:25,847 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:03:29,064 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:03:41,030 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(17, 18, None)
2022-08-03 19:04:01,404 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:04:05,173 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(18, 19, None)
2022-08-03 19:04:37,482 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:04:40,523 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-03 19:04:44,535 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(19, 20, None)
2022-08-03 19:05:07,202 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:05:10,223 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(20, 21, None)
2022-08-03 19:05:42,751 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-03 19:05:52,142 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-03 19:05:54,264 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(21, 22, None)
2022-08-03 19:06:24,693 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-03 19:06:27,520 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-03 19:06:50,570 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(22, 23, None)
2022-08-03 19:07:00,318 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:07:03,030 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(23, 24, None)
2022-08-03 19:07:42,426 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:07:45,429 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-03 19:08:07,976 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(24, 25, None)
2022-08-03 19:08:18,125 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:08:20,824 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(25, 26, None)
2022-08-03 19:08:51,703 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:08:54,554 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:09:24,037 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(26, 27, None)
2022-08-03 19:09:34,716 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-03 19:09:37,640 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(27, 28, None)
2022-08-03 19:10:09,628 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-03 19:10:12,535 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(28, 29, None)
2022-08-03 19:10:36,512 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-03 19:10:45,696 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-03 19:10:48,243 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(29, 30, None)
2022-08-03 19:11:19,000 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(30, 31, None) CPU times: user 11min 50s, sys: 1min 24s, total: 13min 15s Wall time: 20min 13s