Investigate performance degradation with latest dependencies #986
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Contributor
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@tomvothecoder thanks for working on this. For reference, my current run with pinning python and several packages following e3sm_unified seems to be promissing, here is the basic packages in my test env (changes in bold): |
Contributor
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I spoke too soon. The run with the new env as commended #986 (comment) still exceed 4 hour time limit. I haven't pinned numpy, maybe that is the source if the slowness, but in any case, I will try to work on a smaller example today. |
Collaborator
Author
Thanks Jill. Here is the diff between E3SM Unified and the latest dev environment. Maybe we can rule out Python version differences being an issue. I also recall that the dev environment used to be Python >= 3.9 (now 3.10), which meant our dev environments would often build with Python 3.12 and potentially 3.13. Based on #911, we should focus on
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Contributor
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@tomvothecoder I added a minimum example. To run on chrysalis:
in e3sm-unified, the run finished in 2.5 mins, it hangs in the new e3sm_diags env. |
Collaborator
Author
Thanks @chengzhuzhang. I will investigate today. |
Collaborator
Author
Note: The LCRC filesystem is experiencing issues this morning, which is affecting how efficient I can debug (dropped SSH connections, really laggy). This script runs successfully on the login node. On the compute node, the run stalls and I noticed I will pin xesmf/esmf/esmpy to the E3SM Unified version and try running again on the compute node. [UPDATE: Same results as the latest versions] 1. Login Node (latest dependencies)Result -- SuccessThis script successfully completed in ~2 minutes with the latest There is no Log file: https://web.lcrc.anl.gov/public/e3sm/cdat-migration-fy24/985-perf-degrade/prov/e3sm_diags_run.log python 3.13.3 hf636f53_101_cp313 conda-forge
dask 2025.5.1 pyhd8ed1ab_0 conda-forge
numpy 2.2.6 py313h17eae1a_0 conda-forge
pandas 2.3.0 py313ha87cce1_0 conda-forge
xesmf 0.8.10 pyhd8ed1ab_0 conda-forge
esmf 8.8.1 mpi_mpich_h10ba997_101 conda-forge
esmpy 8.8.1 pyhec2. Compute Node (latest dependencies)Result -- Stalls with Slurm/MPI errorThe run stalls and there is an Full console log: ================================================================================
E3SM Diagnostics Run
--------------------
Timestamp: 2025-06-12 12:52:15
Version Info: version v3.0.0
Results Path: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node
Log Path: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/prov/e3sm_diags_run.log
Parameter Files Path: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/prov/core_set.cfg
Python Script Path: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/prov/e3sm_diags_extended.py
Environment YML Path: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/prov/environment.yml
Provenance Index HTML Path: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/prov/index.html
================================================================================
2025-06-12 12:52:20,660 [INFO]: lat_lon_driver.py(run_diag:69) >> Variable: PRECT
2025-06-12 12:52:20,661 [INFO]: zonal_mean_2d_driver.py(run_diag:42) >> Variable: T
2025-06-12 12:52:21,478 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the test climatology variable using the source variables: ('T',)
2025-06-12 12:52:21,519 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the test climatology variable using the source variables: ('PRECC', 'PRECL')
2025-06-12 12:52:21,719 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the ref climatology variable using the source variables: ('sat_gauge_precip',)
2025-06-12 12:52:21,847 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the ref climatology variable using the source variables: ('ta',)
2025-06-12 12:52:22,053 [INFO]: zonal_mean_2d_driver.py(_run_diags_3d:87) >> Selected pressure level: [50.0, 100.0, 150.0, 200.0, 250.0, 300.0, 350.0, 400.0, 450.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0]
2025-06-12 12:52:22,143 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
warnings.warn(
2025-06-12 12:52:22,414 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
warnings.warn(
2025-06-12 12:52:22,733 [INFO]: regrid.py(subset_and_align_datasets:69) >> Selected region: global
slurmstepd: error: mpi/pmi2: invalid pmi1 command received: 'init'
2025-06-12 12:52:30,931 [INFO]: io.py(_save_data_metrics_and_plots:85) >> Metrics saved in /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/zonal_mean_2d/MERRA2/MERRA2-T-SON-global.json
2025-06-12 12:52:31,717 [INFO]: utils.py(_save_plot:92) >> Plot saved in: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/zonal_mean_2d/MERRA2/MERRA2-T-SON-global.png
2025-06-12 12:52:31,718 [INFO]: zonal_mean_2d_driver.py(run_diag:42) >> Variable: T
2025-06-12 12:52:32,320 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the test climatology variable using the source variables: ('T',)
2025-06-12 12:52:32,551 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the ref climatology variable using the source variables: ('ta',)
2025-06-12 12:52:32,760 [INFO]: zonal_mean_2d_driver.py(_run_diags_3d:87) >> Selected pressure level: [50.0, 100.0, 150.0, 200.0, 250.0, 300.0, 350.0, 400.0, 450.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0]
2025-06-12 12:52:32,847 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
warnings.warn(
2025-06-12 12:52:33,114 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
warnings.warn(
2025-06-12 12:52:40,788 [INFO]: io.py(_save_data_metrics_and_plots:85) >> Metrics saved in /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/zonal_mean_2d/MERRA2/MERRA2-T-JJA-global.json
2025-06-12 12:52:41,252 [INFO]: utils.py(_save_plot:92) >> Plot saved in: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/zonal_mean_2d/MERRA2/MERRA2-T-JJA-global.png
2025-06-12 12:52:41,252 [INFO]: zonal_mean_2d_driver.py(run_diag:42) >> Variable: T
2025-06-12 12:52:41,902 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the test climatology variable using the source variables: ('T',)
2025-06-12 12:52:42,230 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the ref climatology variable using the source variables: ('ta',)
2025-06-12 12:52:42,442 [INFO]: zonal_mean_2d_driver.py(_run_diags_3d:87) >> Selected pressure level: [50.0, 100.0, 150.0, 200.0, 250.0, 300.0, 350.0, 400.0, 450.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0]
2025-06-12 12:52:42,529 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
warnings.warn(
2025-06-12 12:52:42,798 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
warnings.warn(
2025-06-12 12:52:50,189 [INFO]: io.py(_save_data_metrics_and_plots:85) >> Metrics saved in /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/zonal_mean_2d/MERRA2/MERRA2-T-MAM-global.json
2025-06-12 12:52:50,652 [INFO]: utils.py(_save_plot:92) >> Plot saved in: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/zonal_mean_2d/MERRA2/MERRA2-T-MAM-global.png
2025-06-12 12:52:50,653 [INFO]: zonal_mean_2d_driver.py(run_diag:42) >> Variable: T
2025-06-12 12:52:51,478 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the test climatology variable using the source variables: ('T',)
2025-06-12 12:52:51,789 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the ref climatology variable using the source variables: ('ta',)
2025-06-12 12:52:51,998 [INFO]: zonal_mean_2d_driver.py(_run_diags_3d:87) >> Selected pressure level: [50.0, 100.0, 150.0, 200.0, 250.0, 300.0, 350.0, 400.0, 450.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0]
2025-06-12 12:52:52,085 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
warnings.warn(
2025-06-12 12:52:52,346 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
warnings.warn(
2025-06-12 12:52:59,732 [INFO]: io.py(_save_data_metrics_and_plots:85) >> Metrics saved in /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/zonal_mean_2d/MERRA2/MERRA2-T-DJF-global.json
2025-06-12 12:53:00,194 [INFO]: utils.py(_save_plot:92) >> Plot saved in: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/zonal_mean_2d/MERRA2/MERRA2-T-DJF-global.png
2025-06-12 12:53:00,195 [INFO]: zonal_mean_2d_driver.py(run_diag:42) >> Variable: T
2025-06-12 12:53:00,862 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the test climatology variable using the source variables: ('T',)
2025-06-12 12:53:01,239 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the ref climatology variable using the source variables: ('ta',)
2025-06-12 12:53:01,655 [INFO]: zonal_mean_2d_driver.py(_run_diags_3d:87) >> Selected pressure level: [50.0, 100.0, 150.0, 200.0, 250.0, 300.0, 350.0, 400.0, 450.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0]
2025-06-12 12:53:01,739 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
warnings.warn(
2025-06-12 12:53:02,007 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
warnings.warn(
2025-06-12 12:53:09,569 [INFO]: io.py(_save_data_metrics_and_plots:85) >> Metrics saved in /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/zonal_mean_2d/MERRA2/MERRA2-T-ANN-global.json
2025-06-12 12:53:10,030 [INFO]: utils.py(_save_plot:92) >> Plot saved in: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/zonal_mean_2d/MERRA2/MERRA2-T-ANN-global.png
2025-06-12 12:53:10,032 [INFO]: lat_lon_driver.py(run_diag:69) >> Variable: PRECT
2025-06-12 12:53:10,576 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the test climatology variable using the source variables: ('PRECC', 'PRECL')
2025-06-12 12:53:10,691 [INFO]: dataset_xr.py(_get_dataset_with_derived_climo_var:714) >> Deriving the ref climatology variable using the source variables: ('sat_gauge_precip',)
2025-06-12 12:53:11,746 [INFO]: regrid.py(subset_and_align_datasets:69) >> Selected region: global
2025-06-12 12:53:20,061 [INFO]: io.py(_save_data_metrics_and_plots:85) >> Metrics saved in /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/lat_lon/GPCP_v3.2/GPCP_v3.2-PRECT-SON-global.json
2025-06-12 12:53:20,092 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/cartopy/mpl/geoaxes.py:856: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.
self.set_xlim([x1, x2])
2025-06-12 12:53:21,145 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/cartopy/mpl/geoaxes.py:856: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.
self.set_xlim([x1, x2])
2025-06-12 12:53:25,282 [WARNING]: warnings.py(_showwarnmsg:110) >> /home/ac.tvo/miniforge3/envs/e3sm_diags_dev_985/lib/python3.13/site-packages/cartopy/mpl/geoaxes.py:856: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.
self.set_xlim([x1, x2])
2025-06-12 12:53:28,662 [INFO]: utils.py(_save_plot:92) >> Plot saved in: /lcrc/group/e3sm/public_html/cdat-migration-fy24/985-perf-degrade-compute-node/lat_lon/GPCP_v3.2/GPCP_v3.2-PRECT-SON-global.pngLog file: https://web.lcrc.anl.gov/public/e3sm/cdat-migration-fy24/985-perf-degrade-compute-node/prov/e3sm_diags_run.log python 3.13.3 hf636f53_101_cp313 conda-forge
dask 2025.5.1 pyhd8ed1ab_0 conda-forge
numpy 2.2.6 py313h17eae1a_0 conda-forge
pandas 2.3.0 py313ha87cce1_0 conda-forge
xesmf 0.8.10 pyhd8ed1ab_0 conda-forge
esmf 8.8.1 mpi_mpich_h10ba997_101 conda-forge
esmpy 8.8.1 pyhec3. Compute Node (pinned xESMF/esmpy/ESMF to E3SM Unified versions)Results -- Same result as 2, stalls with MPI init errorCore dependencies: python 3.13.5 hf636f53_100_cp313 conda-forge
dask 2025.5.1 pyhd8ed1ab_0 conda-forge
numpy 2.2.6 py313h17eae1a_0 conda-forge
pandas 2.3.0 py313ha87cce1_0 conda-forge
xesmf 0.8.8 pyhd8ed1ab_1 conda-forge
esmf 8.8.0 mpi_mpich_h7cf99a1_100 conda-forge
esmpy 8.8.0 pyhecae5ae_1 conda-forge4. Compute Node (latest dependencies, pinned
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Contributor
|
[UPDATE], I did see |
Collaborator
Author
Updated comment above. Pinning xESMF/ESMF/esmpy to E3SM Unified versions still stalled with MPI init init error on compute node. I'm going to try to pin dask and python <3.13.0 (separately) to see which one it might be. |
Contributor
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@tomvothecoder could you also list the version and build for these packages in e3sm-unified on compute node? |
Collaborator
Author
Ahh, looks like the Maybe we need to constrain e3sm_diags conda envs with the same thing? The dev environment currently installs the MPI version: Core dependencies: python 3.10.16 habfa6aa_3_cpython conda-forge
dask 2024.11.2 pyhff2d567_1 conda-forge
numpy 2.2.4 py310hefbff90_0 conda-forge
pandas 2.2.3 py310h5eaa309_1 conda-forge
xesmf 0.8.8 pyhd8ed1ab_1 conda-forge
esmf 8.8.0 nompi_h4441c20_0 conda-forge
esmpy 8.8.0 pyhecae5ae_1 conda-forgeFull dependencies: (e3sm_diags_dev_985_dask_esmf_pinned) [ac.tvo@chr-0165 2-reproduce]$ source /lcrc/soft/climate/e3sm-unified/load_latest_e3sm_unified_chrysalis.sh
(e3sm_unified_1.11.1_chrysalis) [ac.tvo@chr-0165 2-reproduce]$ conda list
# packages in environment at /lcrc/soft/climate/e3sm-unified/base/envs/e3sm_unified_1.11.1_chrysalis:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
_r-mutex 1.0.1 anacondar_1 conda-forge
adwaita-icon-theme 48.0 unix_0 conda-forge
affine 2.4.0 pyhd8ed1ab_1 conda-forge
algopy 0.7.2 pyhd8ed1ab_1 conda-forge
alsa-lib 1.2.13 hb9d3cd8_0 conda-forge
antimeridian 0.4.1 pyhd8ed1ab_0 conda-forge
anyio 4.9.0 pyh29332c3_0 conda-forge
aom 3.9.1 hac33072_0 conda-forge
argon2-cffi 23.1.0 pyhd8ed1ab_1 conda-forge
argon2-cffi-bindings 21.2.0 py310ha75aee5_5 conda-forge
arpack 3.9.1 nompi_hf03ea27_102 conda-forge
arrow 1.3.0 pyhd8ed1ab_1 conda-forge
asttokens 3.0.0 pyhd8ed1ab_1 conda-forge
async-lru 2.0.5 pyh29332c3_0 conda-forge
at-spi2-atk 2.38.0 h0630a04_3 conda-forge
at-spi2-core 2.40.3 h0630a04_0 conda-forge
atk-1.0 2.38.0 h04ea711_2 conda-forge
attr 2.5.1 h166bdaf_1 conda-forge
attrs 25.3.0 pyh71513ae_0 conda-forge
aws-c-auth 0.8.7 h7743f02_1 conda-forge
aws-c-cal 0.8.7 h7d555fd_1 conda-forge
aws-c-common 0.12.1 hb9d3cd8_0 conda-forge
aws-c-compression 0.3.1 hcbd9e4e_3 conda-forge
aws-c-event-stream 0.5.4 h286e7e7_3 conda-forge
aws-c-http 0.9.5 hbca0721_0 conda-forge
aws-c-io 0.17.0 ha855f32_8 conda-forge
aws-c-mqtt 0.12.2 hffac463_3 conda-forge
aws-c-s3 0.7.13 h4c9fe3b_3 conda-forge
aws-c-sdkutils 0.2.3 hcbd9e4e_3 conda-forge
aws-checksums 0.2.3 hcbd9e4e_3 conda-forge
aws-crt-cpp 0.31.1 h46b750d_1 conda-forge
aws-sdk-cpp 1.11.510 h1fa5cb7_4 conda-forge
azure-core-cpp 1.14.0 h5cfcd09_0 conda-forge
azure-identity-cpp 1.10.0 h113e628_0 conda-forge
azure-storage-blobs-cpp 12.13.0 h3cf044e_1 conda-forge
azure-storage-common-cpp 12.8.0 h736e048_1 conda-forge
azure-storage-files-datalake-cpp 12.12.0 ha633028_1 conda-forge
babel 2.17.0 pyhd8ed1ab_0 conda-forge
backrefs 5.8 pyhd8ed1ab_0 conda-forge
beautifulsoup4 4.13.3 pyha770c72_0 conda-forge
binutils_impl_linux-64 2.43 h4bf12b8_4 conda-forge
blas 2.131 openblas conda-forge
blas-devel 3.9.0 31_h1ea3ea9_openblas conda-forge
bleach 6.2.0 pyh29332c3_4 conda-forge
bleach-with-css 6.2.0 h82add2a_4 conda-forge
blosc 1.21.6 he440d0b_1 conda-forge
bokeh 3.7.2 pyhd8ed1ab_1 conda-forge
bottleneck 1.4.2 py310hf462985_0 conda-forge
branca 0.8.1 pyhd8ed1ab_0 conda-forge
brotli 1.1.0 hb9d3cd8_2 conda-forge
brotli-bin 1.1.0 hb9d3cd8_2 conda-forge
brotli-python 1.1.0 py310hf71b8c6_2 conda-forge
brunsli 0.1 h9c3ff4c_0 conda-forge
bwidget 1.10.1 ha770c72_0 conda-forge
bzip2 1.0.8 h4bc722e_7 conda-forge
c-ares 1.34.5 hb9d3cd8_0 conda-forge
c-blosc2 2.15.2 h3122c55_1 conda-forge
ca-certificates 2025.1.31 hbcca054_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cairo 1.18.4 h3394656_0 conda-forge
cartopy 0.24.0 py310h5eaa309_0 conda-forge
cartopy_offlinedata 0.24.0 pyhd8ed1ab_1 conda-forge
cdat_info 8.2.1 pyhd8ed1ab_3 conda-forge
cdms2 3.1.5 py310h4f3777b_25 conda-forge
cdo 2.4.4 h0e6046d_2 conda-forge
cdp 1.7.0 pyhd8ed1ab_2 conda-forge
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zstd 1.5.7 hb8e6e7a_2 conda-forgeE3SM Unified v1.11.1 dependencies (compute node): |
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Author
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I can confirm this is related to the I think we need to constrain our conda environments to use the |
Contributor
|
@tomvothecoder , @chengzhuzhang , it's up to you but any such constraint you use adds that restriction all packages in the environment, of course. So it would remove MPI parallelism from laptop/desktop users. Probably not a major user base for us. |
Contributor
|
Is this related to xESFM? If so, I really think you might consider calling ESMF directly. I've found that extra level of control is critical. And there's the option to swap out for MOAB, which is, even more performant with MPI parallelism. |
Collaborator
Author
Thanks @xylar. @chengzhuzhang — I’ve updated my investigation to confirm that Since most I’ve pushed commit Can you confirm the latest |
Collaborator
Author
I think this is related to how Not 100% sure. |
Contributor
Okay, I see. But it indicates that we should basically seek to keep MPI and python separate from one another, which still speaks to using tools like ESMF and MOAB with |
Collaborator
Author
I see what you're saying. I recall you suggested using ESMF's regridding weight generator as a separate process call, which could workaround the MPI incompatibilities. Are there any other packages in E3SM Unified that use xESMF for regridding (including weight generation), similar to I'll open up a new issue for the team to re-think how we implement regridding in
import subprocess
subprocess.run([
"ESMF_RegridWeightGen",
"--source", "source_grid.nc",
"--destination", "target_grid.nc",
"--weight", "weights_file.nc",
"--method", "bilinear",
"--ignore_unmapped"
])
regridder = xesmf.Regridder(ds, ds_out, weights="weights_file.nc", reuse_weights=True)As a near-term solution, constraining |
Contributor
No, I think e3sm_diags and MPAS-Analysis are the main codes that do regridding outside of NCO. And NCO definitely doesn't use xESMF. MPAS-Analysis uses pyremap, which makes subprocess calls to ESMF or MOAB. |
Contributor
|
I am definitely in favor of the long-term solution you proposed -- ESMF (or MOAB) for making mapping files, xESMF if you like for applying the weights (ncremap works fine as well). |
Contributor
@tomvothecoder I can confirm that with |
- Ensures compatibility with compute node using `srun` - Same version used in E3SM Unified
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Contributor
|
I just completed a full zppy run with the new dependencies for code on latest main. The run finished in reasonable time (66mins) |
Collaborator
Author
Nice! I will go ahead and merge this PR. We can tackle #988 later. |
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