import numpy as np from tensorflow.keras.datasets import mnist from ray import tune from ray.tune.schedulers import ASHAScheduler from ray.tune.integration.keras import TuneReportCallback from ray.train import Trainer # Load the MNIST dataset (x_train, y_train), (x_test, y_test) = mnist.load_data() # Define the neural network model using Keras model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(10, activation='softmax') ]) # Define the callback for reporting metrics to Tune tune_callback = TuneReportCallback( { "loss": "val_loss" }, on="validation_end" ) # Define the Trainer object for training the model trainer = Trainer( model=model, config={ "optimizer": keras.optimizers.Adam(lr=tune.choice([1e-2, 1e-3, 1e-4])), "loss": "sparse_categorical_crossentropy", "metrics": ["accuracy"], "batch_size": tune.choice([32, 64, 128]) }, train_data=np.expand_dims(x_train, axis=-1), train_label=y_train, validation_data=(np.expand_dims(x_test, axis=-1), y_test), batch_size=trainer_config["batch_size"], callbacks=[tune_callback], scheduler=ASHAScheduler( max_t=max_epochs, grace_period=2, reduction_factor=2 ), num_workers=2, use_gpu=True ) # Train the model trainer.fit()
import ray import ray.rllib.agents.ppo as ppo from ray.tune.registry import register_env from ray.train import Trainer # Define the function for creating the Gym environment def create_env(env_config): import gym return gym.make(env_config["env_id"]) # Register the Gym environment with Ray register_env("CustomEnv-v0", create_env) # Define the configuration for the PPO agent config = { "env": "CustomEnv-v0", "lr": 1e-4, "num_workers": 2, } # Define the Trainer object for training the agent trainer = ppo.PPOTrainer(config=config) # Train the agent for i in range(1000): result = trainer.train()This example uses the `ray.train.Trainer` class to train a reinforcement learning agent using the PPO algorithm and the OpenAI Gym environment. It uses the `ray.rllib` package to define and configure the PPO agent, and the `Ray.tune.registry` package to register the Gym environment with Ray. Package Library: Ray