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tune.py
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tune.py
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import datetime
import json
import os
import ray
import typer
from ray import tune
from ray.air.config import (
CheckpointConfig,
DatasetConfig,
RunConfig,
ScalingConfig,
)
from ray.air.integrations.mlflow import MLflowLoggerCallback
from ray.train.torch import TorchTrainer
from ray.tune import Tuner
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.search import ConcurrencyLimiter
from ray.tune.search.hyperopt import HyperOptSearch
from typing_extensions import Annotated
from madewithml import data, train, utils
from madewithml.config import EFS_DIR, MLFLOW_TRACKING_URI, logger
# Initialize Typer CLI app
app = typer.Typer()
@app.command()
def tune_models(
experiment_name: Annotated[str, typer.Option(help="name of the experiment for this training workload.")] = None,
dataset_loc: Annotated[str, typer.Option(help="location of the dataset.")] = None,
initial_params: Annotated[str, typer.Option(help="initial config for the tuning workload.")] = None,
num_workers: Annotated[int, typer.Option(help="number of workers to use for training.")] = 1,
cpu_per_worker: Annotated[int, typer.Option(help="number of CPUs to use per worker.")] = 1,
gpu_per_worker: Annotated[int, typer.Option(help="number of GPUs to use per worker.")] = 0,
num_runs: Annotated[int, typer.Option(help="number of runs in this tuning experiment.")] = 1,
num_samples: Annotated[int, typer.Option(help="number of samples to use from dataset.")] = None,
num_epochs: Annotated[int, typer.Option(help="number of epochs to train for.")] = 1,
batch_size: Annotated[int, typer.Option(help="number of samples per batch.")] = 256,
results_fp: Annotated[str, typer.Option(help="filepath to save results to.")] = None,
) -> ray.tune.result_grid.ResultGrid:
"""Hyperparameter tuning experiment.
Args:
experiment_name (str): name of the experiment for this training workload.
dataset_loc (str): location of the dataset.
initial_params (str): initial config for the tuning workload.
num_workers (int, optional): number of workers to use for training. Defaults to 1.
cpu_per_worker (int, optional): number of CPUs to use per worker. Defaults to 1.
gpu_per_worker (int, optional): number of GPUs to use per worker. Defaults to 0.
num_runs (int, optional): number of runs in this tuning experiment. Defaults to 1.
num_samples (int, optional): number of samples to use from dataset.
If this is passed in, it will override the config. Defaults to None.
num_epochs (int, optional): number of epochs to train for.
If this is passed in, it will override the config. Defaults to None.
batch_size (int, optional): number of samples per batch.
If this is passed in, it will override the config. Defaults to None.
results_fp (str, optional): filepath to save the tuning results. Defaults to None.
Returns:
ray.tune.result_grid.ResultGrid: results of the tuning experiment.
"""
# Set up
utils.set_seeds()
train_loop_config = {}
train_loop_config["num_samples"] = num_samples
train_loop_config["num_epochs"] = num_epochs
train_loop_config["batch_size"] = batch_size
# Scaling config
scaling_config = ScalingConfig(
num_workers=num_workers,
use_gpu=bool(gpu_per_worker),
resources_per_worker={"CPU": cpu_per_worker, "GPU": gpu_per_worker},
)
# Dataset
ds = data.load_data(dataset_loc=dataset_loc, num_samples=train_loop_config.get("num_samples", None))
train_ds, val_ds = data.stratify_split(ds, stratify="tag", test_size=0.2)
tags = train_ds.unique(column="tag")
train_loop_config["num_classes"] = len(tags)
# Dataset config
dataset_config = {
"train": DatasetConfig(fit=False, transform=False, randomize_block_order=False),
"val": DatasetConfig(fit=False, transform=False, randomize_block_order=False),
}
# Preprocess
preprocessor = data.CustomPreprocessor()
preprocessor = preprocessor.fit(train_ds)
train_ds = preprocessor.transform(train_ds)
val_ds = preprocessor.transform(val_ds)
train_ds = train_ds.materialize()
val_ds = val_ds.materialize()
# Trainer
trainer = TorchTrainer(
train_loop_per_worker=train.train_loop_per_worker,
train_loop_config=train_loop_config,
scaling_config=scaling_config,
datasets={"train": train_ds, "val": val_ds},
dataset_config=dataset_config,
metadata={"class_to_index": preprocessor.class_to_index},
)
# Checkpoint configuration
checkpoint_config = CheckpointConfig(
num_to_keep=1,
checkpoint_score_attribute="val_loss",
checkpoint_score_order="min",
)
# Run configuration
mlflow_callback = MLflowLoggerCallback(
tracking_uri=MLFLOW_TRACKING_URI,
experiment_name=experiment_name,
save_artifact=True,
)
run_config = RunConfig(callbacks=[mlflow_callback], checkpoint_config=checkpoint_config, storage_path=EFS_DIR, local_dir=EFS_DIR)
# Hyperparameters to start with
initial_params = json.loads(initial_params)
search_alg = HyperOptSearch(points_to_evaluate=initial_params)
search_alg = ConcurrencyLimiter(search_alg, max_concurrent=2) # trade off b/w optimization and search space
# Parameter space
param_space = {
"train_loop_config": {
"dropout_p": tune.uniform(0.3, 0.9),
"lr": tune.loguniform(1e-5, 5e-4),
"lr_factor": tune.uniform(0.1, 0.9),
"lr_patience": tune.uniform(1, 10),
}
}
# Scheduler
scheduler = AsyncHyperBandScheduler(
max_t=train_loop_config["num_epochs"], # max epoch (<time_attr>) per trial
grace_period=1, # min epoch (<time_attr>) per trial
)
# Tune config
tune_config = tune.TuneConfig(
metric="val_loss",
mode="min",
search_alg=search_alg,
scheduler=scheduler,
num_samples=num_runs,
)
# Tuner
tuner = Tuner(
trainable=trainer,
run_config=run_config,
param_space=param_space,
tune_config=tune_config,
)
# Tune
results = tuner.fit()
best_trial = results.get_best_result(metric="val_loss", mode="min")
d = {
"timestamp": datetime.datetime.now().strftime("%B %d, %Y %I:%M:%S %p"),
"run_id": utils.get_run_id(experiment_name=experiment_name, trial_id=best_trial.metrics["trial_id"]),
"params": best_trial.config["train_loop_config"],
"metrics": utils.dict_to_list(best_trial.metrics_dataframe.to_dict(), keys=["epoch", "train_loss", "val_loss"]),
}
logger.info(json.dumps(d, indent=2))
if results_fp: # pragma: no cover, saving results
utils.save_dict(d, results_fp)
return results
if __name__ == "__main__": # pragma: no cover, application
if ray.is_initialized():
ray.shutdown()
ray.init(runtime_env={"env_vars": {"GITHUB_USERNAME": os.environ["GITHUB_USERNAME"]}})
app()