def main():
args = parse_args()
experiment = Run()
params = load_values(args.param_file)
if params:
experiment.log_inputs(**params)
metrics = load_values(args.metric_file)
if metrics:
experiment.log_metrics(**metrics)
if args.tag:
experiment.log_tags(args.tag)
for dataset in load_datasets(args.data_file):
experiment.log_data_ref(**dataset)
if args.capture_png:
imgs = discover_png(experiment.get_outputs_path())
for img in imgs:
if isinstance(img, str):
experiment.log_image(img)
elif isinstance(img, SerialImages):
for idx, path in enumerate(img.paths):
experiment.log_image(path, name=img.name, step=idx)
else:
raise NotImplementedError('We should never get here.')
return accuracy_score(pred, y_test)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--max_depth', type=int, default=3)
parser.add_argument('--num_rounds', type=int, default=10)
parser.add_argument('--min_child_weight', type=int, default=5)
args = parser.parse_args()
# Polyaxon
experiment = Run(project='iris')
experiment.create(tags=['examples', 'xgboost'])
experiment.log_inputs(log_learning_rate=args.log_learning_rate,
max_depth=args.max_depth,
num_rounds=args.num_rounds,
min_child_weight=args.min_child_weight)
iris = load_iris()
X = iris.data
Y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
# Polyaxon
experiment.log_data_ref(content=X_train, name='x_train')
experiment.log_data_ref(content=y_train, name='y_train')
experiment.log_data_ref(content=X_test, name='X_test')
experiment.log_data_ref(content=y_test, name='y_train')
logger.info('Train model...')
'--max_iter',
type=int,
default=1000)
parser.add_argument(
'--tol',
type=float,
default=0.001
)
args = parser.parse_args()
# Polyaxon
experiment = Run(project='sgd-classifier')
experiment.create(tags=['examples', 'scikit-learn'])
experiment.log_inputs(loss=args.loss,
penalty=args.penalty,
l1_ratio=args.l1_ratio,
max_iter=args.max_iter,
tol=args.tol)
(X, y) = load_data()
# Polyaxon
experiment.log_data_ref(content=X, name='dataset_X')
experiment.log_data_ref(content=y, name='dataset_y')
accuracies = model(X=X,
y=y,
loss=args.loss,
penalty=args.penalty,
l1_ratio=args.l1_ratio,
max_iter=args.max_iter,
parser.add_argument('--conv1_size', type=int, default=32)
parser.add_argument('--conv2_size', type=int, default=64)
parser.add_argument('--dropout', type=float, default=0.8)
parser.add_argument('--hidden1_size', type=int, default=500)
parser.add_argument('--optimizer', type=str, default='adam')
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--epochs', type=int, default=1)
args = parser.parse_args()
# Polyaxon
experiment = Run(project='mnist')
experiment.create(tags=['keras'])
experiment.log_inputs(conv1_size=args.conv1_size,
conv2_size=args.conv2_size,
dropout=args.dropout,
hidden1_size=args.hidden1_size,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate,
epochs=args.epochs)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Polyaxon
experiment.log_data_ref(content=x_train, name='x_train')
experiment.log_data_ref(content=y_train, name='y_train')
experiment.log_data_ref(content=x_test, name='x_test')
experiment.log_data_ref(content=y_test, name='y_test')
x_train, y_train, x_test, y_test = transform_data(x_train, y_train, x_test,
y_test)
accuracy = train(conv1_size=args.conv1_size,
parser.add_argument('--fc1_hidden', type=int, default=10)
parser.add_argument('--fc1_activation', type=str, default='relu')
parser.add_argument('--optimizer', type=str, default='adam')
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--epochs', type=int, default=1)
args = parser.parse_args()
experiment = Run(project='mnist')
experiment.create(tags=['examples', 'mxnet'])
experiment.log_inputs(conv1_kernel=args.conv1_kernel,
conv1_filters=args.conv1_filters,
conv1_activation=args.conv1_activation,
conv2_kernel=args.conv1_kernel,
conv2_filters=args.conv1_filters,
conv2_activation=args.conv1_activation,
fc1_hidden=args.fc1_hidden,
fc1_activation=args.fc1_activation,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate,
epochs=args.epochs)
logger.info('Downloading data ...')
mnist = mx.test_utils.get_mnist()
train_iter = mx.io.NDArrayIter(mnist['train_data'],
mnist['train_label'],
args.batch_size,
shuffle=True)
val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'],
args.batch_size)
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--epochs', type=int, default=1)
args = parser.parse_args()
# Polyaxon
experiment = Run(project='mnist', artifacts_path='/tmp/mnist')
experiment.create(tags=['examples', 'tensorflow'])
experiment.log_inputs(conv1_size=args.conv1_size,
conv1_out=args.conv1_out,
conv1_activation=args.conv1_activation,
pool1_size=args.pool1_size,
conv2_size=args.conv2_size,
conv2_out=args.conv2_out,
conv2_activation=args.conv2_activation,
pool2_size=args.pool2_size,
fc1_activation=args.fc1_activation,
fc1_size=args.fc1_size,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate,
batch_size=args.batch_size,
dropout=args.dropout,
epochs=args.epochs)
(x_train, y_train), (x_test, y_test) = load_mnist_data()
# Polyaxon
experiment.log_data_ref(content=x_train, name='x_train')
experiment.log_data_ref(content=y_train, name='y_train')
experiment.log_data_ref(content=x_test, name='x_test')
experiment.log_data_ref(content=y_test, name='y_test')
# Polyaxon
if hvd.rank() == 0:
experiment = Run()
# Horovod: pin GPU to be used to process local rank (one GPU per process)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = str(hvd.local_rank())
K.set_session(tf.Session(config=config))
batch_size = 128
num_classes = 10
# Polyaxon
if hvd.rank() == 0:
experiment.log_inputs(batch_size=128, num_classes=10)
# Horovod: adjust number of epochs based on number of GPUs.
epochs = int(math.ceil(12.0 / hvd.size()))
# Input image dimensions
img_rows, img_cols = 28, 28
# The data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Polyaxon
if hvd.rank() == 0:
experiment.log_data_ref(content=x_train, name='x_train')
experiment.log_data_ref(content=y_train, name='y_train')
experiment.log_data_ref(content=x_test, name='x_test')
)
return cross_val_score(classifier, X, y, cv=5)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--n_estimators', type=int, default=3)
parser.add_argument('--max_features', type=int, default=3)
parser.add_argument('--min_samples_leaf', type=int, default=80)
args = parser.parse_args()
# Polyaxon
experiment = Run(project='random-forest')
experiment.create(tags=['examples', 'scikit-learn'])
experiment.log_inputs(n_estimators=args.n_estimators,
max_features=args.max_features,
min_samples_leaf=args.min_samples_leaf)
(X, y) = load_data()
# Polyaxon
experiment.log_data_ref(content=X, name='dataset_X')
experiment.log_data_ref(content=y, name='dataset_y')
accuracies = model(X=X,
y=y,
n_estimators=args.n_estimators,
max_features=args.max_features,
min_samples_leaf=args.min_samples_leaf)
accuracy_mean, accuracy_std = (np.mean(accuracies), np.std(accuracies))
print('Accuracy: {} +/- {}'.format(accuracy_mean, accuracy_std))
parser.add_argument('--num_nodes', type=int, default=8)
parser.add_argument('--optimizer', type=str, default='adam')
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--dropout', type=float, default=0.8)
parser.add_argument('--epochs', type=int, default=1)
parser.add_argument('--seed', type=int, default=234)
args = parser.parse_args()
# Polyaxon
experiment = Run(project='bidirectional-lstm')
experiment.create(tags=['examples', 'keras'])
experiment.log_inputs(max_features=args.max_features,
skip_top=args.skip_top,
maxlen=args.maxlen,
batch_size=args.batch_size,
num_nodes=args.num_nodes,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate,
dropout=args.dropout,
epochs=args.epochs,
seed=args.seed)
logger.info('Loading data...')
(x_train, y_train), (x_test,
y_test) = imdb.load_data(num_words=args.max_features,
skip_top=args.skip_top,
seed=args.seed)
logger.info('train sequences %s', len(x_train))
logger.info('test sequences %s', len(x_test))
# Polyaxon
experiment.log_data_ref(content=x_train, name='x_train')
parser.add_argument('--lstm_output_size', type=int, default=70)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--optimizer', type=str, default='adam')
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--epochs', type=int, default=1)
args = parser.parse_args()
# Polyaxon
experiment = Run(project='cnn-lstm')
experiment.create(framework='keras', tags=['examples'])
experiment.log_inputs(max_features=args.max_features,
skip_top=args.skip_top,
maxlen=args.maxlen,
epochs=args.epochs,
embedding_size=args.embedding_size,
pool_size=args.pool_size,
kernel_size=args.kernel_size,
filters=args.filters,
lstm_output_size=args.lstm_output_size,
batch_size=args.batch_size,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate)
logger.info('Loading data...')
(x_train, y_train), (x_test,
y_test) = imdb.load_data(num_words=args.max_features,
skip_top=args.skip_top)
logger.info('train sequences %s', len(x_train))
logger.info('test sequences %s', len(x_test))
# Polyaxon
parser.add_argument('--max_df',
type=float,
default=1.0,
help='the maximum document frequency.')
parser.add_argument(
'--C',
type=float,
default=1.0,
help='Inverse of regularization strength of LogisticRegression')
args = parser.parse_args()
# Polyaxon
experiment = Run(project='newsgroup')
experiment.create(tags=['examples', 'scikit-learn'])
experiment.log_inputs(ngram_range=(args.ngram, args.ngram),
max_features=args.max_features,
max_df=args.max_df,
C=args.C)
# Train and eval the model with given parameters.
# Polyaxon
metrics = train_and_eval(ngram_range=(args.ngram, args.ngram),
max_features=args.max_features,
max_df=args.max_df,
C=args.C)
# Logging metrics
print("Testing metrics: {}", metrics)
# Polyaxon
experiment.log_outputs(**metrics)