def orca_context_fixture():
from zoo.orca import init_orca_context, stop_orca_context
init_orca_context(cores=8,
init_ray_on_spark=True,
object_store_memory="1g")
yield
stop_orca_context()
def test_forecast_tcmf_distributed(self):
model = TCMFForecaster(y_iters=1,
init_FX_epoch=1,
max_FX_epoch=1,
max_TCN_epoch=1,
alt_iters=2)
horizon = np.random.randint(1, 50)
# construct data
id = np.arange(300)
data = np.random.rand(300, 480)
input = dict({'id': id, 'y': data})
from zoo.orca import init_orca_context, stop_orca_context
init_orca_context(cores=4, spark_log_level="INFO", init_ray_on_spark=True,
object_store_memory="1g")
model.fit(input, num_workers=4)
with tempfile.TemporaryDirectory() as tempdirname:
model.save(tempdirname)
loaded_model = TCMFForecaster.load(tempdirname, distributed=False)
yhat = model.predict(x=None, horizon=horizon, num_workers=4)
yhat_loaded = loaded_model.predict(x=None, horizon=horizon, num_workers=4)
yhat_id = yhat_loaded["id"]
assert (yhat_id == id).all()
yhat = yhat["prediction"]
yhat_loaded = yhat_loaded["prediction"]
assert yhat.shape == (300, horizon)
np.testing.assert_equal(yhat, yhat_loaded)
target_value = np.random.rand(300, horizon)
target_value = dict({"y": target_value})
assert model.evaluate(x=None, target_value=target_value, metric=['mse'])
stop_orca_context()
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--dir', default='/tmp/data', metavar='N',
help='the folder store mnist data')
parser.add_argument('--batch-size', type=int, default=256, metavar='N',
help='input batch size for training per executor(default: 256)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing per executor(default: 1000)')
parser.add_argument('--epochs', type=int, default=2, metavar='N',
help='number of epochs to train (default: 2)')
parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.001)')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--save-model', action='store_true', default=False,
help='For Saving the current Model')
parser.add_argument('--cluster_mode', type=str, default="local",
help='The mode for the Spark cluster. local or yarn.')
args = parser.parse_args()
torch.manual_seed(args.seed)
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.dir, train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.dir, train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.test_batch_size, shuffle=False)
if args.cluster_mode == "local":
init_orca_context(cores=1, memory="2g")
elif args.cluster_mode == "yarn":
init_orca_context(
cluster_mode="yarn-client", cores=4, num_nodes=2, memory="2g",
driver_memory="10g", driver_cores=1,
conf={"spark.rpc.message.maxSize": "1024",
"spark.task.maxFailures": "1",
"spark.driver.extraJavaOptions": "-Dbigdl.failure.retryTimes=1"})
model = LeNet()
model.train()
criterion = nn.NLLLoss()
adam = torch.optim.Adam(model.parameters(), args.lr)
est = Estimator.from_torch(model=model, optimizer=adam, loss=criterion)
est.fit(data=train_loader, epochs=args.epochs, validation_data=test_loader,
validation_metrics=[Accuracy()], checkpoint_trigger=EveryEpoch())
result = est.evaluate(data=test_loader, validation_metrics=[Accuracy()])
for r in result:
print(str(r))
stop_orca_context()
def main(max_epoch):
sc = init_orca_context(cores=4, memory="2g")
# get DataSet
# as_supervised returns tuple (img, label) instead of dict {'image': img, 'label':label}
mnist_train = tfds.load(name="mnist", split="train", as_supervised=True)
mnist_test = tfds.load(name="mnist", split="test", as_supervised=True)
# Normalizes images, unit8 -> float32
def normalize_img(image, label):
return tf.cast(image, tf.float32) / 255., label
mnist_train = mnist_train.map(
normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
mnist_test = mnist_test.map(
normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(20,
kernel_size=(5, 5),
strides=(1, 1),
activation='tanh',
input_shape=(28, 28, 1),
padding='valid'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
strides=(2, 2),
padding='valid'),
tf.keras.layers.Conv2D(50,
kernel_size=(5, 5),
strides=(1, 1),
activation='tanh',
padding='valid'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
strides=(2, 2),
padding='valid'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(500, activation='tanh'),
tf.keras.layers.Dense(10, activation='softmax'),
])
model.compile(optimizer=tf.keras.optimizers.RMSprop(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
est = Estimator.from_keras(keras_model=model)
est.fit(data=mnist_train,
batch_size=320,
epochs=max_epoch,
validation_data=mnist_test)
result = est.evaluate(mnist_test)
print(result)
est.save_keras_model("/tmp/mnist_keras.h5")
stop_orca_context()
def main():
parser = argparse.ArgumentParser(description='PyTorch Tensorboard Example')
parser.add_argument('--cluster_mode',
type=str,
default="local",
help='The cluster mode, such as local, yarn or k8s.')
args = parser.parse_args()
if args.cluster_mode == "local":
init_orca_context()
elif args.cluster_mode == "yarn":
init_orca_context(cluster_mode=args.cluster_mode, cores=4, num_nodes=2)
writer = SummaryWriter('runs/fashion_mnist_experiment_1')
# constant for classes
classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal',
'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')
# plot some random training images
dataiter = iter(train_data_creator(config={}))
images, labels = dataiter.next()
# create grid of images
img_grid = torchvision.utils.make_grid(images)
# show images
matplotlib_imshow(img_grid, one_channel=True)
# write to tensorboard
writer.add_image('four_fashion_mnist_images', img_grid)
# inspect the model using tensorboard
writer.add_graph(model_creator(config={}), images)
writer.close()
# training loss vs. epochs
criterion = nn.CrossEntropyLoss()
orca_estimator = Estimator.from_torch(model=model_creator,
optimizer=optimizer_creator,
loss=criterion,
backend="torch_distributed")
stats = orca_estimator.fit(train_data_creator, epochs=5, batch_size=4)
for stat in stats:
writer.add_scalar("training_loss", stat['train_loss'], stat['epoch'])
print("Train stats: {}".format(stats))
val_stats = orca_estimator.evaluate(validation_data_creator)
print("Validation stats: {}".format(val_stats))
orca_estimator.shutdown()
stop_orca_context()
def main(max_epoch):
sc = init_orca_context(cores=4, memory="2g")
# get DataSet
mnist_train = tfds.load(name="mnist", split="train")
mnist_test = tfds.load(name="mnist", split="test")
# Normalizes images
def normalize_img(data):
data['image'] = tf.cast(data["image"], tf.float32) / 255.
return data
mnist_train = mnist_train.map(
normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
mnist_test = mnist_test.map(
normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
# tensorflow inputs
images = tf.placeholder(dtype=tf.float32, shape=(None, 28, 28, 1))
# tensorflow labels
labels = tf.placeholder(dtype=tf.int32, shape=(None, ))
with slim.arg_scope(lenet.lenet_arg_scope()):
logits, end_points = lenet.lenet(images,
num_classes=10,
is_training=True)
loss = tf.reduce_mean(
tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels))
acc = accuracy(logits, labels)
# create an estimator
est = Estimator.from_graph(inputs=images,
outputs=logits,
labels=labels,
loss=loss,
optimizer=tf.train.AdamOptimizer(),
metrics={"acc": acc})
est.fit(data=mnist_train,
batch_size=320,
epochs=max_epoch,
validation_data=mnist_test)
result = est.evaluate(mnist_test)
print(result)
est.save_tf_checkpoint("/tmp/lenet/model")
stop_orca_context()
def orca_context_fixture(request):
import os
from zoo.orca import OrcaContext, init_orca_context, stop_orca_context
OrcaContext._eager_mode = True
access_key_id = os.getenv("AWS_ACCESS_KEY_ID")
secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY")
if access_key_id is not None and secret_access_key is not None:
env = {"AWS_ACCESS_KEY_ID": access_key_id,
"AWS_SECRET_ACCESS_KEY": secret_access_key}
else:
env = None
sc = init_orca_context(cores=4, spark_log_level="INFO",
env=env, object_store_memory="1g")
yield sc
stop_orca_context()
def orca_context_fixture():
sc = init_orca_context(cores=8)
def to_array_(v):
return v.toArray().tolist()
def flatten_(v):
result = []
for elem in v:
result.extend(elem.toArray().tolist())
return result
spark = SparkSession(sc)
spark.udf.register("to_array", to_array_, ArrayType(DoubleType()))
spark.udf.register("flatten", flatten_, ArrayType(DoubleType()))
yield
stop_orca_context()
def main(cluster_mode, max_epoch, file_path, batch_size, platform,
non_interactive):
import matplotlib
if not non_interactive and platform == "mac":
matplotlib.use('qt5agg')
if cluster_mode == "local":
init_orca_context(cluster_mode="local", cores=4, memory="3g")
elif cluster_mode == "yarn":
init_orca_context(cluster_mode="yarn-client",
num_nodes=2,
cores=2,
driver_memory="3g")
load_data(file_path)
img_dir = os.path.join(file_path, "train")
label_dir = os.path.join(file_path, "train_masks")
# Here we only take the first 1000 files for simplicity
df_train = pd.read_csv(os.path.join(file_path, 'train_masks.csv'))
ids_train = df_train['img'].map(lambda s: s.split('.')[0])
ids_train = ids_train[:1000]
x_train_filenames = []
y_train_filenames = []
for img_id in ids_train:
x_train_filenames.append(os.path.join(img_dir,
"{}.jpg".format(img_id)))
y_train_filenames.append(
os.path.join(label_dir, "{}_mask.gif".format(img_id)))
x_train_filenames, x_val_filenames, y_train_filenames, y_val_filenames = \
train_test_split(x_train_filenames, y_train_filenames, test_size=0.2, random_state=42)
def load_and_process_image(path):
array = mpimg.imread(path)
result = np.array(Image.fromarray(array).resize(size=(128, 128)))
result = result.astype(float)
result /= 255.0
return result
def load_and_process_image_label(path):
array = mpimg.imread(path)
result = np.array(Image.fromarray(array).resize(size=(128, 128)))
result = np.expand_dims(result[:, :, 1], axis=-1)
result = result.astype(float)
result /= 255.0
return result
train_images = np.stack(
[load_and_process_image(filepath) for filepath in x_train_filenames])
train_label_images = np.stack([
load_and_process_image_label(filepath)
for filepath in y_train_filenames
])
val_images = np.stack(
[load_and_process_image(filepath) for filepath in x_val_filenames])
val_label_images = np.stack([
load_and_process_image_label(filepath) for filepath in y_val_filenames
])
train_shards = XShards.partition({
"x": train_images,
"y": train_label_images
})
val_shards = XShards.partition({"x": val_images, "y": val_label_images})
# Build the U-Net model
def conv_block(input_tensor, num_filters):
encoder = layers.Conv2D(num_filters, (3, 3),
padding='same')(input_tensor)
encoder = layers.Activation('relu')(encoder)
encoder = layers.Conv2D(num_filters, (3, 3), padding='same')(encoder)
encoder = layers.Activation('relu')(encoder)
return encoder
def encoder_block(input_tensor, num_filters):
encoder = conv_block(input_tensor, num_filters)
encoder_pool = layers.MaxPooling2D((2, 2), strides=(2, 2))(encoder)
return encoder_pool, encoder
def decoder_block(input_tensor, concat_tensor, num_filters):
decoder = layers.Conv2DTranspose(num_filters, (2, 2),
strides=(2, 2),
padding='same')(input_tensor)
decoder = layers.concatenate([concat_tensor, decoder], axis=-1)
decoder = layers.Activation('relu')(decoder)
decoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)
decoder = layers.Activation('relu')(decoder)
decoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)
decoder = layers.Activation('relu')(decoder)
return decoder
inputs = layers.Input(shape=(128, 128, 3)) # 128
encoder0_pool, encoder0 = encoder_block(inputs, 16) # 64
encoder1_pool, encoder1 = encoder_block(encoder0_pool, 32) # 32
encoder2_pool, encoder2 = encoder_block(encoder1_pool, 64) # 16
encoder3_pool, encoder3 = encoder_block(encoder2_pool, 128) # 8
center = conv_block(encoder3_pool, 256) # center
decoder3 = decoder_block(center, encoder3, 128) # 16
decoder2 = decoder_block(decoder3, encoder2, 64) # 32
decoder1 = decoder_block(decoder2, encoder1, 32) # 64
decoder0 = decoder_block(decoder1, encoder0, 16) # 128
outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder0)
net = models.Model(inputs=[inputs], outputs=[outputs])
# Define custom metrics
def dice_coeff(y_true, y_pred):
smooth = 1.
# Flatten
y_true_f = tf.reshape(y_true, [-1])
y_pred_f = tf.reshape(y_pred, [-1])
intersection = tf.reduce_sum(y_true_f * y_pred_f)
score = (2. * intersection + smooth) / \
(tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)
return score
# Define custom loss function
def dice_loss(y_true, y_pred):
loss = 1 - dice_coeff(y_true, y_pred)
return loss
def bce_dice_loss(y_true, y_pred):
loss = losses.binary_crossentropy(y_true, y_pred) + dice_loss(
y_true, y_pred)
return loss
# compile model
net.compile(optimizer=tf.keras.optimizers.Adam(2e-3), loss=bce_dice_loss)
print(net.summary())
# create an estimator from keras model
est = Estimator.from_keras(keras_model=net)
# fit with estimator
est.fit(data=train_shards, batch_size=batch_size, epochs=max_epoch)
# evaluate with estimator
result = est.evaluate(val_shards)
print(result)
# predict with estimator
val_shards.cache()
val_image_shards = val_shards.transform_shard(
lambda val_dict: {"x": val_dict["x"]})
pred_shards = est.predict(data=val_image_shards, batch_size=batch_size)
pred = pred_shards.collect()[0]["prediction"]
val_image_label = val_shards.collect()[0]
val_image = val_image_label["x"]
val_label = val_image_label["y"]
if not non_interactive:
# visualize 5 predicted results
plt.figure(figsize=(10, 20))
for i in range(5):
img = val_image[i]
label = val_label[i]
predicted_label = pred[i]
plt.subplot(5, 3, 3 * i + 1)
plt.imshow(img)
plt.title("Input image")
plt.subplot(5, 3, 3 * i + 2)
plt.imshow(label[:, :, 0], cmap='gray')
plt.title("Actual Mask")
plt.subplot(5, 3, 3 * i + 3)
plt.imshow(predicted_label, cmap='gray')
plt.title("Predicted Mask")
plt.suptitle("Examples of Input Image, Label, and Prediction")
plt.show()
stop_orca_context()
def teardown_method(self, method):
stop_orca_context()
def main():
anchors = yolo_anchors
anchor_masks = yolo_anchor_masks
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir",
dest="data_dir",
help="Required. The path where data locates.")
parser.add_argument(
"--output_data",
dest="output_data",
default=tempfile.mkdtemp(),
help="Required. The path where voc parquet data locates.")
parser.add_argument("--data_year",
dest="data_year",
default="2009",
help="Required. The voc data date.")
parser.add_argument("--split_name_train",
dest="split_name_train",
default="train",
help="Required. Split name.")
parser.add_argument("--split_name_test",
dest="split_name_test",
default="val",
help="Required. Split name.")
parser.add_argument("--names",
dest="names",
help="Required. The path where class names locates.")
parser.add_argument("--weights",
dest="weights",
default="./checkpoints/yolov3.weights",
help="Required. The path where weights locates.")
parser.add_argument("--checkpoint",
dest="checkpoint",
default="./checkpoints/yolov3.tf",
help="Required. The path where checkpoint locates.")
parser.add_argument(
"--checkpoint_folder",
dest="checkpoint_folder",
default="./checkpoints",
help="Required. The path where saved checkpoint locates.")
parser.add_argument("--epochs",
dest="epochs",
type=int,
default=2,
help="Required. epochs.")
parser.add_argument("--batch_size",
dest="batch_size",
type=int,
default=16,
help="Required. epochs.")
parser.add_argument("--cluster_mode",
dest="cluster_mode",
default="local",
help="Required. Run on local/yarn/k8s mode.")
parser.add_argument("--class_num",
dest="class_num",
type=int,
default=20,
help="Required. class num.")
parser.add_argument(
"--worker_num",
type=int,
default=1,
help="The number of slave nodes to be used in the cluster."
"You can change it depending on your own cluster setting.")
parser.add_argument(
"--cores",
type=int,
default=4,
help="The number of cpu cores you want to use on each node. "
"You can change it depending on your own cluster setting.")
parser.add_argument(
"--memory",
type=str,
default="20g",
help="The memory you want to use on each node. "
"You can change it depending on your own cluster setting.")
parser.add_argument(
"--object_store_memory",
type=str,
default="10g",
help="The memory you want to use on each node. "
"You can change it depending on your own cluster setting.")
parser.add_argument('--k8s_master',
type=str,
default="",
help="The k8s master. "
"It should be k8s://https://<k8s-apiserver-host>: "
"<k8s-apiserver-port>.")
parser.add_argument("--container_image",
type=str,
default="",
help="The runtime k8s image. ")
parser.add_argument('--k8s_driver_host',
type=str,
default="",
help="The k8s driver localhost.")
parser.add_argument('--k8s_driver_port',
type=str,
default="",
help="The k8s driver port.")
options = parser.parse_args()
# convert yolov3 weights
yolo = YoloV3(classes=80)
load_darknet_weights(yolo, options.weights)
yolo.save_weights(options.checkpoint)
def model_creator(config):
model = YoloV3(DEFAULT_IMAGE_SIZE,
training=True,
classes=options.class_num)
anchors = yolo_anchors
anchor_masks = yolo_anchor_masks
model_pretrained = YoloV3(DEFAULT_IMAGE_SIZE,
training=True,
classes=80)
model_pretrained.load_weights(options.checkpoint)
model.get_layer('yolo_darknet').set_weights(
model_pretrained.get_layer('yolo_darknet').get_weights())
freeze_all(model.get_layer('yolo_darknet'))
optimizer = tf.keras.optimizers.Adam(lr=1e-3)
loss = [
YoloLoss(anchors[mask], classes=options.class_num)
for mask in anchor_masks
]
model.compile(optimizer=optimizer, loss=loss, run_eagerly=False)
return model
# prepare data
class_map = {
name: idx
for idx, name in enumerate(open(options.names).read().splitlines())
}
dataset_path = os.path.join(options.data_dir, "VOCdevkit")
voc_train_path = os.path.join(options.output_data, "train_dataset")
voc_val_path = os.path.join(options.output_data, "val_dataset")
write_parquet(format="voc",
voc_root_path=dataset_path,
output_path="file://" + voc_train_path,
splits_names=[(options.data_year, options.split_name_train)],
classes=class_map)
write_parquet(format="voc",
voc_root_path=dataset_path,
output_path="file://" + voc_val_path,
splits_names=[(options.data_year, options.split_name_test)],
classes=class_map)
output_types = {
"image": tf.string,
"label": tf.float32,
"image_id": tf.string
}
output_shapes = {"image": (), "label": (None, 5), "image_id": ()}
def train_data_creator(config, batch_size):
train_dataset = read_parquet(format="tf_dataset",
path=voc_train_path,
output_types=output_types,
output_shapes=output_shapes)
train_dataset = train_dataset.map(
lambda data_dict: (data_dict["image"], data_dict["label"]))
train_dataset = train_dataset.map(parse_data_train)
train_dataset = train_dataset.shuffle(buffer_size=512)
train_dataset = train_dataset.batch(batch_size)
train_dataset = train_dataset.map(lambda x, y: (
transform_images(x, DEFAULT_IMAGE_SIZE),
transform_targets(y, anchors, anchor_masks, DEFAULT_IMAGE_SIZE)))
train_dataset = train_dataset.prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
return train_dataset
def val_data_creator(config, batch_size):
val_dataset = read_parquet(format="tf_dataset",
path=voc_val_path,
output_types=output_types,
output_shapes=output_shapes)
val_dataset = val_dataset.map(lambda data_dict:
(data_dict["image"], data_dict["label"]))
val_dataset = val_dataset.map(parse_data_train)
val_dataset = val_dataset.batch(batch_size)
val_dataset = val_dataset.map(lambda x, y: (
transform_images(x, DEFAULT_IMAGE_SIZE),
transform_targets(y, anchors, anchor_masks, DEFAULT_IMAGE_SIZE)))
return val_dataset
callbacks = [
ReduceLROnPlateau(verbose=1),
EarlyStopping(patience=3, verbose=1),
ModelCheckpoint(options.checkpoint_folder + '/yolov3_train_{epoch}.tf',
verbose=1,
save_weights_only=True),
TensorBoard(log_dir='logs')
]
if options.cluster_mode == "local":
init_orca_context(cluster_mode="local",
cores=options.cores,
num_nodes=options.worker_num,
memory=options.memory,
init_ray_on_spark=True,
enable_numa_binding=False,
object_store_memory=options.object_store_memory)
elif options.cluster_mode == "k8s":
init_orca_context(cluster_mode="k8s",
master=options.k8s_master,
container_image=options.container_image,
init_ray_on_spark=True,
enable_numa_binding=False,
num_nodes=options.worker_num,
cores=options.cores,
memory=options.memory,
object_store_memory=options.object_store_memory,
conf={
"spark.driver.host": options.driver_host,
"spark.driver.port": options.driver_port
})
elif options.cluster_mode == "yarn":
init_orca_context(cluster_mode="yarn-client",
cores=options.cores,
num_nodes=options.worker_num,
memory=options.memory,
init_ray_on_spark=True,
enable_numa_binding=False,
object_store_memory=options.object_store_memory)
trainer = Estimator.from_keras(model_creator=model_creator)
trainer.fit(train_data_creator,
epochs=options.epochs,
batch_size=options.batch_size,
steps_per_epoch=3473 // options.batch_size,
callbacks=callbacks,
validation_data=val_data_creator,
validation_steps=3581 // options.batch_size)
stop_orca_context()