%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'>
below are list of enviroment variable one can pass
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'
#
AWTD.sh M_AWTMD
Ice_quant_flux.sh M_Fluxnet M_Ice_quantities
FWC_SSH.sh M_FWC_2D M_FWC_integrals M_FWC_SSH M_SSH_anomaly
IceClim.sh M_IceClim M_IceConce M_IceThick
M_Mean_temp_velo M_MLD_2D M_Mooring M_Sectionx M_Sectiony
%%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= irene5759.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 irene5759.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/6417344irene5759.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_01M_AWTMD/ CPU times: user 588 ms, sys: 126 ms, total: 714 ms Wall time: 20.9 s
Client-e9037650-13c4-11ed-af8a-080038b9381b
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: http://127.0.0.1:8787/status |
423ca5f0
Dashboard: http://127.0.0.1:8787/status | Workers: 16 |
Total threads: 128 | Total memory: 251.06 GiB |
Status: running | Using processes: True |
Scheduler-4cb8b057-16e5-4f1b-abfd-68a29295fec5
Comm: tcp://127.0.0.1:41958 | Workers: 16 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 128 |
Started: Just now | Total memory: 251.06 GiB |
Comm: tcp://127.0.0.1:38564 | Total threads: 8 |
Dashboard: http://127.0.0.1:38681/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34992 | |
Local directory: /tmp/dask-worker-space/worker-dznsw8gw |
Comm: tcp://127.0.0.1:37225 | Total threads: 8 |
Dashboard: http://127.0.0.1:44907/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:39385 | |
Local directory: /tmp/dask-worker-space/worker-q1gh80g2 |
Comm: tcp://127.0.0.1:36648 | Total threads: 8 |
Dashboard: http://127.0.0.1:33956/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:46230 | |
Local directory: /tmp/dask-worker-space/worker-__y3uys6 |
Comm: tcp://127.0.0.1:37412 | Total threads: 8 |
Dashboard: http://127.0.0.1:41333/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:38838 | |
Local directory: /tmp/dask-worker-space/worker-h7dr8viv |
Comm: tcp://127.0.0.1:40026 | Total threads: 8 |
Dashboard: http://127.0.0.1:41628/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:36916 | |
Local directory: /tmp/dask-worker-space/worker-bo0b2f9t |
Comm: tcp://127.0.0.1:38649 | Total threads: 8 |
Dashboard: http://127.0.0.1:37373/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:35516 | |
Local directory: /tmp/dask-worker-space/worker-em1jv_x2 |
Comm: tcp://127.0.0.1:33190 | Total threads: 8 |
Dashboard: http://127.0.0.1:39578/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:45552 | |
Local directory: /tmp/dask-worker-space/worker-nl84rbtx |
Comm: tcp://127.0.0.1:37475 | Total threads: 8 |
Dashboard: http://127.0.0.1:42821/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:35888 | |
Local directory: /tmp/dask-worker-space/worker-nwuv9tnk |
Comm: tcp://127.0.0.1:45140 | Total threads: 8 |
Dashboard: http://127.0.0.1:43877/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:40939 | |
Local directory: /tmp/dask-worker-space/worker-pu4vlye9 |
Comm: tcp://127.0.0.1:39965 | Total threads: 8 |
Dashboard: http://127.0.0.1:34529/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34453 | |
Local directory: /tmp/dask-worker-space/worker-3mo_w1vo |
Comm: tcp://127.0.0.1:45865 | Total threads: 8 |
Dashboard: http://127.0.0.1:39635/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:32963 | |
Local directory: /tmp/dask-worker-space/worker-vxpaikzg |
Comm: tcp://127.0.0.1:46343 | Total threads: 8 |
Dashboard: http://127.0.0.1:35420/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:45369 | |
Local directory: /tmp/dask-worker-space/worker-27kqo9ei |
Comm: tcp://127.0.0.1:35583 | Total threads: 8 |
Dashboard: http://127.0.0.1:36659/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:39486 | |
Local directory: /tmp/dask-worker-space/worker-ai25vuse |
Comm: tcp://127.0.0.1:44201 | Total threads: 8 |
Dashboard: http://127.0.0.1:35465/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:41509 | |
Local directory: /tmp/dask-worker-space/worker-4b7jcvf_ |
Comm: tcp://127.0.0.1:43874 | Total threads: 8 |
Dashboard: http://127.0.0.1:45713/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:34538 | |
Local directory: /tmp/dask-worker-space/worker-2c6s_ox7 |
Comm: tcp://127.0.0.1:41853 | Total threads: 8 |
Dashboard: http://127.0.0.1:35091/status | Memory: 15.69 GiB |
Nanny: tcp://127.0.0.1:38416 | |
Local directory: /tmp/dask-worker-space/worker-cbfpih0m |
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'] lazy= False 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 40.45571279525757 seconds 0 merging gridS ['vosaline'] 1 read gridT ['votemper'] lazy= False 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 38.848931550979614 seconds 1 merging gridT ['votemper'] took 0.7499361038208008 seconds param mask2d will be included in data param nav_lat will be included in data param depth will be included in data param nav_lon will be included in data param mask will be included in data CPU times: user 37.3 s, sys: 5.6 s, total: 42.9 s Wall time: 1min 43s
<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 mask2d (y, x) bool dask.array<chunksize=(13, 6560), meta=np.ndarray> nav_lat (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> depth (z, y, x) float32 dask.array<chunksize=(150, 13, 6560), meta=np.ndarray> 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> 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' data=monitor.optimize_dataset(data) #3 Start computing data= calc.AWTD4(data) monitor.optimize_dataset(data) add optimise here once otimise can recognise
<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 mask2d (y, x) bool dask.array<chunksize=(13, 6560), meta=np.ndarray> nav_lat (y, x) float32 dask.array<chunksize=(13, 6560), meta=np.ndarray> nav_lon (y, x) float32 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 data=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)
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)
/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(4, 5, 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)
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) slice(12, 13, None)
2022-08-04 09:22:53,510 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(13, 14, None)
2022-08-04 09:23:20,571 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:23:34,761 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(14, 15, None)
2022-08-04 09:23:53,066 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:23:55,697 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(15, 16, None)
2022-08-04 09:24:26,292 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:24:33,339 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:24:35,799 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%)
slice(16, 17, None)
2022-08-04 09:25:07,384 - distributed.utils_perf - WARNING - full garbage collections took 11% CPU time recently (threshold: 10%) 2022-08-04 09:25:14,507 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:25:16,885 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(17, 18, None)
2022-08-04 09:25:53,762 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:26:00,573 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:26:03,083 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(18, 19, None)
2022-08-04 09:26:31,568 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:26:34,466 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:26:35,793 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(19, 20, None)
2022-08-04 09:27:03,006 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:27:05,788 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:27:08,877 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(20, 21, None)
2022-08-04 09:27:34,666 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:27:37,418 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:27:53,476 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(21, 22, None)
2022-08-04 09:28:07,978 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:28:10,544 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(22, 23, None)
2022-08-04 09:28:39,545 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(23, 24, None) slice(24, 25, None) slice(25, 26, None)
2022-08-04 09:30:39,724 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(26, 27, None)
2022-08-04 09:31:02,568 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:31:05,713 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(27, 28, None)
2022-08-04 09:31:36,324 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:31:38,750 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(28, 29, None)
2022-08-04 09:32:02,390 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:32:09,819 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:32:11,885 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(29, 30, None)
2022-08-04 09:32:49,601 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:32:54,046 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
slice(30, 31, None)
2022-08-04 09:33:21,780 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%) 2022-08-04 09:33:24,557 - distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)
CPU times: user 10min 50s, sys: 1min 23s, total: 12min 13s Wall time: 18min 49s