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Home Page: https://nextgrid.ai/dll
Deep Learning Labs by Nextgrid
Home Page: https://nextgrid.ai/dll
Create setup that allows running a fleet of docker containers in Kubernetes
trial.report(intermediate_value, step)
import sklearn.datasets
import sklearn.linear_model
import sklearn.model_selection
import optuna
def objective(trial):
iris = sklearn.datasets.load_iris()
classes = list(set(iris.target))
train_x, valid_x, train_y, valid_y = \
sklearn.model_selection.train_test_split(iris.data, iris.target, test_size=0.25, random_state=0)
alpha = trial.suggest_loguniform('alpha', 1e-5, 1e-1)
clf = sklearn.linear_model.SGDClassifier(alpha=alpha)
for step in range(100):
clf.partial_fit(train_x, train_y, classes=classes)
# Report intermediate objective value.
intermediate_value = 1.0 - clf.score(valid_x, valid_y)
trial.report(intermediate_value, step)
# Handle pruning based on the intermediate value.
if trial.should_prune():
raise optuna.TrialPruned()
return 1.0 - clf.score(valid_x, valid_y)
study = optuna.create_study(pruner=optuna.pruners.MedianPruner())
study.optimize(objective, n_trials=20)
Solve Pendulum-v0
Solve & take the top position
Implement an evolutionary strategy
class SaveOnBestTrainingRewardCallback(BaseCallback):
"""
Callback for saving a model (the check is done every ``check_freq`` steps)
based on the training reward (in practice, we recommend using ``EvalCallback``).
:param check_freq: (int)
:param log_dir: (str) Path to the folder where the model will be saved.
It must contains the file created by the ``Monitor`` wrapper.
:param verbose: (int)
"""
def __init__(self, check_freq: int, log_dir: str, verbose=1):
super(SaveOnBestTrainingRewardCallback, self).__init__(verbose)
self.check_freq = check_freq
self.log_dir = log_dir
self.save_path = os.path.join(log_dir, 'best_model')
self.best_mean_reward = -np.inf
def _init_callback(self) -> None:
# Create folder if needed
if self.save_path is not None:
os.makedirs(self.save_path, exist_ok=True)
def _on_step(self) -> bool:
if self.n_calls % self.check_freq == 0:
# Retrieve training reward
x, y = ts2xy(load_results(self.log_dir), 'timesteps')
if len(x) > 0:
# Mean training reward over the last 100 episodes
mean_reward = np.mean(y[-100:])
if self.verbose > 0:
print(f"Num timesteps: {self.num_timesteps}")
print(f"Best mean reward: {self.best_mean_reward:.2f} - Last mean reward per episode: {mean_reward:.2f}")
# New best model, you could save the agent here
if mean_reward > self.best_mean_reward:
self.best_mean_reward = mean_reward
# Example for saving best model
if self.verbose > 0:
print(f"Saving new best model to {self.save_path}.zip")
self.model.save(self.save_path)
return True
In the code it appears be 10, which surely can't be correct.
Take leaderboard positioning
Solve & take position
To improve performance implement TD3 and run tests with that.
line 29 is lunar-lander and not cartpole
Add TB integration to notebook
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