mirror of
https://github.com/wassname/ray.git
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krfricke
06af62ba91
* Added basic functionality and tests * Feature parity with old tune search space config * Convert Optuna search spaces * Introduced quantized values * Updated Optuna resolving * Added HyperOpt search space conversion * Convert search spaces to AxSearch * Convert search spaces to BayesOpt * Added basic functionality and tests * Feature parity with old tune search space config * Convert Optuna search spaces * Introduced quantized values * Updated Optuna resolving * Added HyperOpt search space conversion * Convert search spaces to AxSearch * Convert search spaces to BayesOpt * Re-factored samplers into domain classes * Re-added base classes * Re-factored into list comprehensions * Added `from_config` classmethod for config conversion * Applied suggestions from code review * Removed truncated normal distribution * Set search properties in tune.run * Added test for tune.run search properties * Move sampler initializers to base classes * Add tune API sampling test, fixed includes, fixed resampling bug * Add to API docs * Fix docs * Update metric and mode only when set. Set default metric and mode to experiment analysis object. * Fix experiment analysis tests * Raise error when delimiter is used in the config keys * Added randint/qrandint to API docs, added additional check in tune.run * Fix tests * Fix linting error * Applied suggestions from code review. Re-aded tune.function for the time being * Fix sampling tests * Fix experiment analysis tests * Fix tests and linting error * Removed unnecessary default_config attribute from OptunaSearch * Revert to set AxSearch default metric * fix-min-max * fix * nits * Added function check, enhanced loguniform error message * fix-print * fix * fix * Raise if unresolved values are in config and search space is already set Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
44 lines
1.1 KiB
Python
44 lines
1.1 KiB
Python
# flake8: noqa
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# This is an example quickstart for Tune.
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# To connect to a cluster, uncomment below:
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# import ray
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# import argparse
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# parser = argparse.ArgumentParser()
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# parser.add_argument("--address")
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# args = parser.parse_args()
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# ray.init(address=args.address)
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# __quick_start_begin__
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from ray import tune
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def objective(step, alpha, beta):
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return (0.1 + alpha * step / 100)**(-1) + beta * 0.1
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def training_function(config):
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# Hyperparameters
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alpha, beta = config["alpha"], config["beta"]
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for step in range(10):
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# Iterative training function - can be any arbitrary training procedure.
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intermediate_score = objective(step, alpha, beta)
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# Feed the score back back to Tune.
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tune.report(mean_loss=intermediate_score)
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analysis = tune.run(
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training_function,
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config={
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"alpha": tune.grid_search([0.001, 0.01, 0.1]),
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"beta": tune.choice([1, 2, 3])
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})
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print("Best config: ", analysis.get_best_config(
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metric="mean_loss", mode="min"))
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# Get a dataframe for analyzing trial results.
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df = analysis.dataframe()
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# __quick_start_end__
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