scikit-learn/asv_benchmarks/benchmarks/config.json

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{
// "regular": Bencharks are run on small to medium datasets. Each benchmark
// is run multiple times and averaged.
// "fast": Benchmarks are run on small to medium datasets. Each benchmark
// is run only once. May provide unstable benchmarks.
// "large_scale": Benchmarks are run on large datasets. Each benchmark is
// run multiple times and averaged. This profile is meant to
// benchmark scalability and will take hours on single core.
// Can be overridden by environment variable SKLBENCH_PROFILE.
"profile": "regular",
// List of values of n_jobs to use for estimators which accept this
// parameter (-1 means all cores). An empty list means all values from 1 to
// the maximum number of available cores.
// Can be overridden by environment variable SKLBENCH_NJOBS.
"n_jobs_vals": [1],
// If true, fitted estimators are saved in ./cache/estimators/<commit hash>
// Can be overridden by environment variable SKLBENCH_SAVE_ESTIMATORS.
"save_estimators": false,
// Commit hash to compare estimator predictions with.
// If null, predictions are not compared.
// Can be overridden by environment variable SKLBENCH_BASE_COMMIT.
"base_commit": null,
// If false, the predict (resp. transform) method of the estimators won't
// be benchmarked.
// Can be overridden by environment variables SKLBENCH_PREDICT and
// SKLBENCH_TRANSFORM.
"bench_predict": true,
"bench_transform": true
}