Pangeo zarr benchmarking package
Benchmarking & Scaling Studies of the Pangeo Platform
Creating an Environment
To run the benchmarks, it's recommended to create a dedicated conda environment by running:
conda env create -f ./binder/environment.yml
This will create a conda environment named
pangeo-bench with all of the required packages.
You can activate the environment with:
conda activate pangeo-bench
and then run the post build script:
benchmark-configs directory contains YAML files that are used to run benchmarks on different machines. So far, the following HPC systems' configs are provided:
$ tree ./benchmark-configs/ benchmark-configs/ ├── cheyenne.yaml └── hal.yaml └── wrangler.yaml
In case you are interested in running the benchmarks on another system, you will need to create a new YAML file for your system with the right configurations. See the existing config files for reference.
Running the Benchmarks
from command line
To run the benchmarks, a command utility
pangeobench is provided in this repository.
To use it, you simply need to specify the location of the benchmark configuration file. For example:
$ ./pangeobench --help Usage: pangeobench [OPTIONS] CONFIG_FILE Options: --help Show this message and exit.
Running the Benchmarks
from jupyter notebook.
To run the benchmarks from jupyter notebook, install 'pangeo-bench' kernel to your jupyter notebook enviroment, then start run.ipynb notebook. You will need to specify the configuration file as described above in your notebook.
To install your 'pangeo-bench' kernel to your jupyter notebook enviroment you'll need to connect a terminal of your HPC enviroment and run following command.
source activate pangeo-bench ipython kernel install --user --name pangeo-bench
Before starting your jupyternotebook, you can verify that if your kernel is well installed or not by follwing command
jupyter kernelspec list
Benchmark results are persisted in the
results directory by default. The exact location of the benchmark results depends on the machine name (specified in the config file) and the date on which the benchmarks were run. For instance, if the benchmarks were run on Cheyenne supercomputer on 2019-09-07, the results would be saved in:
results/cheyenne/2019-09-07/ directory. The file name follows this template:
Visualisation can be done using jupyter notebooks placed in analysis directories.
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