wehub-resource-sync
61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
# flake8: noqa
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# fmt: off
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# __step1_begin__
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from ray import tune
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import ray
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import os
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NUM_MODELS = 100
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def train_model(config):
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score = config["model_id"]
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# Import model libraries, etc...
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# Load data and train model code here...
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# Return final stats. You can also return intermediate progress
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# using ray.tune.report() if needed.
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# To return your model, you could write it to storage and return its
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# URI in this dict, or return it as a Tune Checkpoint:
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# https://docs.ray.io/en/latest/tune/tutorials/tune-checkpoints.html
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return {"score": score, "other_data": ...}
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# __step1_end__
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# __step2_begin__
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# Define trial parameters as a single grid sweep.
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trial_space = {
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# This is an example parameter. You could replace it with filesystem paths,
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# model types, or even full nested Python dicts of model configurations, etc.,
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# that enumerate the set of trials to run.
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"model_id": tune.grid_search([
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"model_{}".format(i)
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for i in range(NUM_MODELS)
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])
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}
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# __step2_end__
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# __step3_begin__
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# Can customize resources per trial, here we set 1 CPU each.
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train_model = tune.with_resources(train_model, {"cpu": 1})
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# __step3_end__
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# __step4_begin__
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# Start a Tune run and print the best result.
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tuner = tune.Tuner(train_model, param_space=trial_space)
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results = tuner.fit()
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# Access individual results.
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print(results[0])
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print(results[1])
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print(results[2])
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# __step4_end__
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# __tasks_begin__
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remote_train = ray.remote(train_model)
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futures = [remote_train.remote({"model_id": i}) for i in range(NUM_MODELS)]
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print("Submitting tasks...")
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results = ray.get(futures)
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print("Trial results", results)
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# __tasks_end__
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