Run parameter sweeps easily, in parallel, with JSON parameters, logs, diverse language support, and parameter data frames.
Python 3.7 or above is required for running sweeps. Versions 3.6 and below will encounter error.
To install: Download sweeps package, navigate to its directory (cd sweeps) and execute the following:
python setup.py install --userAfter installation, sweeps may now be invoked from the command line anywhere on your system.
This guide assumes you are working in the top-level of your parameter sweeps directory. For an example, see the directory tree below.
- Initialize this directory by creating
binandrfsdirectories. - Add a JSON file to the top-level containing parameter sweep information.
⋅⋅* An example parameter sweep file, such as
sweep_config.json, may be seen in the test folder. - Add a script file to the
binfolder. - Run the script using
sweeps run(below)
Run folders (rfs) represent individual runs of a script file for a particular parameter. The folder name is a hash depending on the parameter value and script file.
sweeps . create sweep_config.jsonRequirement: A script file, such as script.py, must be located inside a bin folder on your top-level directory. (See example directory tree below:)
sweeps . run python script_file.pyQuerying shows the status of your run, including the number of rfs completed, queued, running, and failed.
Requirement: A script file, such as script.py, must be located inside a bin folder on your top-level directory. (This is already satisfied if sweeps run was used.)
sweeps . query script_file.pyClosing produces finalizes a run by designating a directory within the data directory which
includes all parameter, log, and status information of the combined run. Additionally, it includes
a copy of the script and produces a pandas dataframe containing any data produced by your script organized by the parameters used for the data.
sweeps . close sweep_config.jsonThe following data have support for sweeps . close.
- HDF5 (.hdf5)
- Matlab (.mat)
- JSON (.json) Note: Ensure that saved data file is not named params.json
- Binary JSON (.bson)
- Numpy array (.npz)
- Python Pickel file (.pklz or .pkl)
- Julia, using HDF5 encoding (.jld or .jld2) (returns Numpy array if it is the only object stored in file, otherwise returns HDF5 keys)
Closing the sweep will find all data files from the above list produced by all completed runs of your script, and aggregate them within the dataframe.
Here is a possible example of a dataframe produced by a script which, depending on the input parameters, produces one or two datafiles whos results are aggregated in a tuple:
>>> import os
>>> import sweeps as sw
>>> cwd = os.getcwd()
>>> dataframes = sw.get_dataframe(cwd) # generator of dataframes for finished runs
>>> df = next(dataframes)
>>> df
a b c results
e9b0f2081a509199 1 0.5 0.0 4
e9b0f2858a7b70b1 1 0.5 20.0 (4, 20.0) # run produced two data files
a49e0b9b22f135a7 1 0.5 40.0 4
6648175135df5aec 1 0.5 60.0 4
f0a0ae3eaf59f41c 4 0.5 0.0 0
42aced5e6c241677 4 0.5 20.0 (0, 20.0) # run produced two data files
c920026405b40f2d 4 0.5 40.0 0
a503c32641b59f8b 4 0.5 60.0 0.
├── bin
│ └── script_file.jl
├── history
│ ├── 2019-12-10_16-34-13.create.json
│ ├── 2019-12-10_16-34-40.run
│ └── 2019-12-10_16-34-40.script
├── rfs
│ ├── 0e37e95b8301883e
│ │ ├── log.txt
│ │ ├── params.json
│ │ ├── result1.pklz
│ │ ├── result2.pklz
│ │ └── status.txt
│ ├── 6e733249c3ae5dd1
│ │ ├── log.txt
│ │ ├── params.json
│ │ └── status.txt
│ ├── 7bfacd4db6a44d40
│ │ ├── log.txt
│ │ ├── params.json
│ │ ├── result1.pklz
│ │ └── status.txt
│ ├── 9ac81a2c5029aa08
│ │ ├── log.txt
│ │ ├── params.json
│ │ ├── result1.pklz
│ │ ├── result2.pklz
│ │ └── status.txt
│ └── d73ece6dc1a2f5e8
│ ├── log.txt
│ ├── params.json
│ ├── result1.pklz
│ └── status.txt
├── data
│ ├── eeab1386ca484886
│ ├── result.pkl
│ ├── sweep_config_copy.json
│ ├── script_file_copy.jl
└── sweep_config.json