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https://github.com/vale981/ray
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8fe439998e
Signed-off-by: Kai Fricke coding@kaifricke.com Why are these changes needed? Splitting up #26884: This PR includes changes to use Tuner() instead of tune.run() for most docs files (rst and py), and a change to move reuse_actors to the TuneConfig
163 lines
4.7 KiB
Python
163 lines
4.7 KiB
Python
# flake8: noqa
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# __function_api_start__
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from ray.air import session
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def objective(x, a, b): # Define an objective function.
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return a * (x ** 0.5) + b
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def trainable(config): # Pass a "config" dictionary into your trainable.
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for x in range(20): # "Train" for 20 iterations and compute intermediate scores.
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score = objective(x, config["a"], config["b"])
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session.report({"score": score}) # Send the score to Tune.
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# __function_api_end__
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# __class_api_start__
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from ray import tune
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def objective(x, a, b):
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return a * (x ** 2) + b
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class Trainable(tune.Trainable):
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def setup(self, config):
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# config (dict): A dict of hyperparameters
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self.x = 0
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self.a = config["a"]
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self.b = config["b"]
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def step(self): # This is called iteratively.
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score = objective(self.x, self.a, self.b)
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self.x += 1
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return {"score": score}
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# __class_api_end__
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# TODO: this example does not work as advertised. Errors out.
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def save_checkpoint(self, checkpoint_dir):
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pass
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def load_checkpoint(self, checkpoint_dir):
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pass
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# __run_tunable_start__
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# Pass in a Trainable class or function, along with a search space "config".
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tuner = tune.Tuner(trainable, param_space={"a": 2, "b": 4})
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tuner.fit()
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# __run_tunable_end__
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# __run_tunable_samples_start__
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tuner = tune.Tuner(
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trainable, param_space={"a": 2, "b": 4}, tune_config=tune.TuneConfig(num_samples=10)
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)
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tuner.fit()
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# __run_tunable_samples_end__
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# __search_space_start__
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space = {"a": tune.uniform(0, 1), "b": tune.uniform(0, 1)}
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tuner = tune.Tuner(
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trainable, param_space=space, tune_config=tune.TuneConfig(num_samples=10)
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)
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tuner.fit()
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# __search_space_end__
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# __config_start__
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config = {
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"uniform": tune.uniform(-5, -1), # Uniform float between -5 and -1
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"quniform": tune.quniform(3.2, 5.4, 0.2), # Round to increments of 0.2
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"loguniform": tune.loguniform(1e-4, 1e-1), # Uniform float in log space
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"qloguniform": tune.qloguniform(1e-4, 1e-1, 5e-5), # Round to increments of 0.00005
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"randn": tune.randn(10, 2), # Normal distribution with mean 10 and sd 2
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"qrandn": tune.qrandn(10, 2, 0.2), # Round to increments of 0.2
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"randint": tune.randint(-9, 15), # Random integer between -9 and 15
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"qrandint": tune.qrandint(-21, 12, 3), # Round to increments of 3 (includes 12)
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"lograndint": tune.lograndint(1, 10), # Random integer in log space
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"qlograndint": tune.qlograndint(1, 10, 2), # Round to increments of 2
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"choice": tune.choice(["a", "b", "c"]), # Choose one of these options uniformly
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"func": tune.sample_from(
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lambda spec: spec.config.uniform * 0.01
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), # Depends on other value
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"grid": tune.grid_search([32, 64, 128]), # Search over all these values
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}
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# __config_end__
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# __bayes_start__
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from ray.tune.search.bayesopt import BayesOptSearch
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from ray import air
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# Define the search space
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search_space = {"a": tune.uniform(0, 1), "b": tune.uniform(0, 20)}
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algo = BayesOptSearch(random_search_steps=4)
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tuner = tune.Tuner(
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trainable,
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tune_config=tune.TuneConfig(
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metric="score",
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mode="min",
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search_alg=algo,
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),
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run_config=air.RunConfig(stop={"training_iteration": 20}),
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param_space=search_space,
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)
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tuner.fit()
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# __bayes_end__
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# __hyperband_start__
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from ray.tune.schedulers import HyperBandScheduler
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# Create HyperBand scheduler and minimize the score
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hyperband = HyperBandScheduler(metric="score", mode="max")
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config = {"a": tune.uniform(0, 1), "b": tune.uniform(0, 1)}
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tuner = tune.Tuner(
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trainable,
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tune_config=tune.TuneConfig(
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num_samples=20,
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scheduler=hyperband,
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),
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param_space=config,
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)
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tuner.fit()
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# __hyperband_end__
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# __analysis_start__
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tuner = tune.Tuner(
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trainable,
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tune_config=tune.TuneConfig(
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metric="score",
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mode="min",
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search_alg=BayesOptSearch(random_search_steps=4),
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),
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run_config=air.RunConfig(
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stop={"training_iteration": 20},
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),
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param_space=config,
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)
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results = tuner.fit()
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best_result = results.get_best_result() # Get best result object
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best_config = best_result.config # Get best trial's hyperparameters
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best_logdir = best_result.log_dir # Get best trial's logdir
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best_checkpoint = best_result.checkpoint # Get best trial's best checkpoint
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best_metrics = best_result.metrics # Get best trial's last results
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best_result_df = best_result.metrics_dataframe # Get best result as pandas dataframe
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# __analysis_end__
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# __results_start__
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# Get a dataframe with the last results for each trial
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df_results = results.get_dataframe()
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# Get a dataframe of results for a specific score or mode
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df = results.get_dataframe(filter_metric="score", filter_mode="max")
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# __results_end__
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