ENH add batching benchmarks #3
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tomMoral
reviewed
Jul 1, 2019
| # defines a variety of task duration profiles. | ||
| random_state = np.random.RandomState(42) | ||
| high_variance = np.abs( | ||
| random_state.normal(loc=0.000001, scale=0.01, size=5000) |
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For float like this, I'd rather use 1e-6 as it is easier to read in my opinion
| params = ([10000, 100000, 1000000], [1, 2, 4]) | ||
| parallel_parameters = dict( | ||
| verbose=10, backend="loky", pre_dispatch="2*n_jobs" | ||
| ) |
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Maybe you could also run the benchmark with auto-memmap disabled max_nbytes=None?
|
Actually, we also need to wait before |
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This PR adds a few benchmarks to assess the performance of joblib's batching strategy.