'location of the training dataset in the local filesystem (will be downloaded if needed)'
)
args = parser.parse_args()
# Initialize SparkSession
conf = SparkConf().setAppName('keras_spark_mnist').set(
'spark.sql.shuffle.partitions', '16')
if args.master:
conf.setMaster(args.master)
elif args.num_proc:
conf.setMaster('local[{}]'.format(args.num_proc))
spark = SparkSession.builder.config(conf=conf).getOrCreate()
# Setup our store for intermediate data
store = Store.create(args.work_dir)
# Download MNIST dataset
data_url = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/mnist.bz2'
libsvm_path = os.path.join(args.data_dir, 'mnist.bz2')
if not os.path.exists(libsvm_path):
subprocess.check_output(['wget', data_url, '-O', libsvm_path])
# Load dataset into a Spark DataFrame
df = spark.read.format('libsvm') \
.option('numFeatures', '784') \
.load(libsvm_path)
# One-hot encode labels into SparseVectors
encoder = OneHotEncoderEstimator(inputCols=['label'],
outputCols=['label_vec'],
def train(
max_sales: float,
vocab: Dict[str, List[Any]],
hp: Hyperparameters,
work_dir: FlyteDirectory,
train_df: pyspark.sql.DataFrame,
working_dir: FlyteDirectory,
):
print("==============")
print("Model training")
print("==============")
# a method to determine root mean square percentage error of exponential of predictions
def exp_rmspe(y_true, y_pred):
"""Competition evaluation metric, expects logarmithic inputs."""
pct = tf.square((tf.exp(y_true) - tf.exp(y_pred)) / tf.exp(y_true))
# compute mean excluding stores with zero denominator
x = tf.reduce_sum(tf.where(y_true > 0.001, pct, tf.zeros_like(pct)))
y = tf.reduce_sum(
tf.where(y_true > 0.001, tf.ones_like(pct), tf.zeros_like(pct)))
return tf.sqrt(x / y)
def act_sigmoid_scaled(x):
"""Sigmoid scaled to logarithm of maximum sales scaled by 20%."""
return tf.nn.sigmoid(x) * tf.math.log(max_sales) * 1.2
# NOTE: exp_rmse and act_sigmoid_scaled functions are not placed at the module level
# this is because we cannot explicitly send max_sales as an argument to act_sigmoid_scaled since it is an activation function
# two of them are custom objects, and placing one at the module level and the other within the function doesn't really add up
all_cols = CATEGORICAL_COLS + CONTINUOUS_COLS
CUSTOM_OBJECTS = {
"exp_rmspe": exp_rmspe,
"act_sigmoid_scaled": act_sigmoid_scaled
}
# disable GPUs when building the model to prevent memory leaks
if LooseVersion(tf.__version__) >= LooseVersion("2.0.0"):
# See https://github.com/tensorflow/tensorflow/issues/33168
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
else:
K.set_session(
tf.Session(config=tf.ConfigProto(device_count={"GPU": 0})))
# build the Keras model
inputs = {col: Input(shape=(1, ), name=col) for col in all_cols}
embeddings = [
Embedding(len(vocab[col]), 10, input_length=1,
name="emb_" + col)(inputs[col]) for col in CATEGORICAL_COLS
]
continuous_bn = Concatenate()([
Reshape((1, 1), name="reshape_" + col)(inputs[col])
for col in CONTINUOUS_COLS
])
continuous_bn = BatchNormalization()(continuous_bn)
x = Concatenate()(embeddings + [continuous_bn])
x = Flatten()(x)
x = Dense(1000,
activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
x = Dense(1000,
activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
x = Dense(1000,
activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
x = Dense(500,
activation="relu",
kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
x = Dropout(0.5)(x)
# specify element-wise activation
output = Dense(1, activation=act_sigmoid_scaled)(x)
model = tf.keras.Model([inputs[f] for f in all_cols], output)
# display the details of the Keras model
model.summary()
opt = tf.keras.optimizers.Adam(lr=hp.learning_rate, epsilon=1e-3)
# checkpoint callback to specify the options for the returned Keras model
ckpt_callback = BestModelCheckpoint(monitor="val_loss",
mode="auto",
save_freq="epoch")
# create an object of Store class
store = Store.create(work_dir.remote_source)
# 'SparkBackend' uses `horovod.spark.run` to execute the distributed training function, and
# returns a list of results by running 'train' on every worker in the cluster
backend = SparkBackend(
num_proc=hp.num_proc,
stdout=sys.stdout,
stderr=sys.stderr,
prefix_output_with_timestamp=True,
)
# define a Spark Estimator that fits Keras models to a DataFrame
keras_estimator = hvd.KerasEstimator(
backend=backend,
store=store,
model=model,
optimizer=opt,
loss="mae",
metrics=[exp_rmspe],
custom_objects=CUSTOM_OBJECTS,
feature_cols=all_cols,
label_cols=["Sales"],
validation="Validation",
batch_size=hp.batch_size,
epochs=hp.epochs,
verbose=2,
checkpoint_callback=ckpt_callback,
)
# The Estimator hides the following details:
# 1. Binding Spark DataFrames to a deep learning training script
# 2. Reading data into a format that can be interpreted by the training framework
# 3. Distributed training using Horovod
# the user would provide a Keras model to the `KerasEstimator``
# this `KerasEstimator`` will fit the data and store it in a Spark DataFrame
keras_model = keras_estimator.fit(train_df).setOutputCols(["Sales_output"])
# retrieve the model training history
history = keras_model.getHistory()
best_val_rmspe = min(history["val_exp_rmspe"])
print("Best RMSPE: %f" % best_val_rmspe)
# save the trained model
keras_model.save(os.path.join(working_dir, hp.local_checkpoint_file))
print("Written checkpoint to %s" %
os.path.join(working_dir, hp.local_checkpoint_file))
# the Estimator returns a Transformer representation of the trained model once training is complete
return keras_model
def train_model(args):
# do not run this test for pytorch lightning below min supported verson
import pytorch_lightning as pl
if LooseVersion(pl.__version__) < LooseVersion(MIN_PL_VERSION):
print("Skip test for pytorch_ligthning=={}, min support version is {}".format(pl.__version__, MIN_PL_VERSION))
return
# Initialize SparkSession
conf = SparkConf().setAppName('pytorch_spark_mnist').set('spark.sql.shuffle.partitions', '16')
if args.master:
conf.setMaster(args.master)
elif args.num_proc:
conf.setMaster('local[{}]'.format(args.num_proc))
spark = SparkSession.builder.config(conf=conf).getOrCreate()
# Setup our store for intermediate data
store = Store.create(args.work_dir)
# Download MNIST dataset
data_url = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/mnist.bz2'
libsvm_path = os.path.join(args.data_dir, 'mnist.bz2')
if not os.path.exists(libsvm_path):
subprocess.check_output(['wget', data_url, '-O', libsvm_path])
# Load dataset into a Spark DataFrame
df = spark.read.format('libsvm') \
.option('numFeatures', '784') \
.load(libsvm_path)
# One-hot encode labels into SparseVectors
encoder = OneHotEncoder(inputCols=['label'],
outputCols=['label_vec'],
dropLast=False)
model = encoder.fit(df)
train_df = model.transform(df)
# Train/test split
train_df, test_df = train_df.randomSplit([0.9, 0.1])
# Define the PyTorch model without any Horovod-specific parameters
class Net(LightningModule):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = x.float().reshape((-1, 1, 28, 28))
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, -1)
def configure_optimizers(self):
return optim.SGD(self.parameters(), lr=0.01, momentum=0.5)
def training_step(self, batch, batch_idx):
if batch_idx == 0:
print(f"training data batch size: {batch['label'].shape}")
x, y = batch['features'], batch['label']
y_hat = self(x)
loss = F.nll_loss(y_hat, y.long())
self.log('train_loss', loss)
return loss
def validation_step(self, batch, batch_idx):
if batch_idx == 0:
print(f"validation data batch size: {batch['label'].shape}")
x, y = batch['features'], batch['label']
y_hat = self(x)
loss = F.nll_loss(y_hat, y.long())
self.log('val_loss', loss)
def validation_epoch_end(self, outputs):
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean() if len(outputs) > 0 else float('inf')
self.log('avg_val_loss', avg_loss)
model = Net()
# Train a Horovod Spark Estimator on the DataFrame
backend = SparkBackend(num_proc=args.num_proc,
stdout=sys.stdout, stderr=sys.stderr,
prefix_output_with_timestamp=True)
from pytorch_lightning.callbacks import Callback
epochs = args.epochs
class MyDummyCallback(Callback):
def __init__(self):
self.epcoh_end_counter = 0
self.train_epcoh_end_counter = 0
self.validation_epoch_end_counter = 0
def on_init_start(self, trainer):
print('Starting to init trainer!')
def on_init_end(self, trainer):
print('Trainer is initialized.')
def on_epoch_end(self, trainer, model):
print('A train or eval epoch ended.')
self.epcoh_end_counter += 1
def on_train_epoch_end(self, trainer, model, unused=None):
print('A train epoch ended.')
self.train_epcoh_end_counter += 1
def on_validation_epoch_end(self, trainer, model, unused=None):
print('A val epoch ended.')
self.validation_epoch_end_counter += 1
def on_train_end(self, trainer, model):
print("Training ends:"
f"epcoh_end_counter={self.epcoh_end_counter}, "
f"train_epcoh_end_counter={self.train_epcoh_end_counter}, "
f"validation_epoch_end_counter={self.validation_epoch_end_counter} \n")
assert self.train_epcoh_end_counter <= epochs
assert self.epcoh_end_counter == self.train_epcoh_end_counter + self.validation_epoch_end_counter
callbacks = [MyDummyCallback()]
# added EarlyStopping and ModelCheckpoint
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
callbacks.append(ModelCheckpoint(monitor='val_loss', mode="min",
save_top_k=1, verbose=True))
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
callbacks.append(EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=3,
verbose=True,
mode='min'))
torch_estimator = hvd.TorchEstimator(backend=backend,
store=store,
model=model,
input_shapes=[[-1, 1, 28, 28]],
feature_cols=['features'],
label_cols=['label'],
batch_size=args.batch_size,
epochs=args.epochs,
validation=0.1,
verbose=1,
callbacks=callbacks,
profiler="simple" if args.enable_profiler else None)
torch_model = torch_estimator.fit(train_df).setOutputCols(['label_prob'])
# Evaluate the model on the held-out test DataFrame
pred_df = torch_model.transform(test_df)
argmax = udf(lambda v: float(np.argmax(v)), returnType=T.DoubleType())
pred_df = pred_df.withColumn('label_pred', argmax(pred_df.label_prob))
evaluator = MulticlassClassificationEvaluator(predictionCol='label_pred', labelCol='label', metricName='accuracy')
print('Test accuracy:', evaluator.evaluate(pred_df))
spark.stop()
def train_model(args):
# do not run this test for pytorch lightning below min supported verson
import pytorch_lightning as pl
if LooseVersion(pl.__version__) < LooseVersion(MIN_PL_VERSION):
print("Skip test for pytorch_ligthning=={}, min support version is {}".
format(pl.__version__, MIN_PL_VERSION))
return
# Initialize SparkSession
conf = SparkConf().setAppName('keras_spark_mnist').set(
'spark.sql.shuffle.partitions', '16')
if args.master:
conf.setMaster(args.master)
elif args.num_proc:
conf.setMaster('local[{}]'.format(args.num_proc))
spark = SparkSession.builder.config(conf=conf).getOrCreate()
# Setup our store for intermediate data
store = Store.create(args.work_dir)
# Download MNIST dataset
data_url = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/mnist.bz2'
libsvm_path = os.path.join(args.data_dir, 'mnist.bz2')
if not os.path.exists(libsvm_path):
subprocess.check_output(['wget', data_url, '-O', libsvm_path])
# Load dataset into a Spark DataFrame
df = spark.read.format('libsvm') \
.option('numFeatures', '784') \
.load(libsvm_path)
# One-hot encode labels into SparseVectors
encoder = OneHotEncoderEstimator(inputCols=['label'],
outputCols=['label_vec'],
dropLast=False)
model = encoder.fit(df)
train_df = model.transform(df)
# Train/test split
train_df, test_df = train_df.randomSplit([0.9, 0.1])
# Define the PyTorch model without any Horovod-specific parameters
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = x.float()
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x)
model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
loss = nn.NLLLoss()
# Train a Horovod Spark Estimator on the DataFrame
torch_estimator = hvd.TorchEstimator(
num_proc=args.num_proc,
store=store,
model=model,
optimizer=optimizer,
loss=lambda input, target: loss(input, target.long()),
input_shapes=[[-1, 1, 28, 28]],
feature_cols=['features'],
label_cols=['label'],
batch_size=args.batch_size,
epochs=args.epochs,
verbose=1)
torch_model = torch_estimator.fit(train_df).setOutputCols(['label_prob'])
# Evaluate the model on the held-out test DataFrame
pred_df = torch_model.transform(test_df)
argmax = udf(lambda v: float(np.argmax(v)), returnType=T.DoubleType())
pred_df = pred_df.withColumn('label_pred', argmax(pred_df.label_prob))
evaluator = MulticlassClassificationEvaluator(predictionCol='label_pred',
labelCol='label',
metricName='accuracy')
print('Test accuracy:', evaluator.evaluate(pred_df))
spark.stop()
