def test_estimator_keras_xshards_checkpoint(self):
import zoo.orca.data.pandas
tf.reset_default_graph()
model = self.create_model()
file_path = os.path.join(self.resource_path, "orca/learn/ncf.csv")
data_shard = zoo.orca.data.pandas.read_csv(file_path)
def transform(df):
result = {
"x": (df['user'].to_numpy().reshape([-1, 1]),
df['item'].to_numpy().reshape([-1, 1])),
"y":
df['label'].to_numpy()
}
return result
data_shard = data_shard.transform_shard(transform)
temp = tempfile.mkdtemp()
model_dir = os.path.join(temp, "test_model")
est = Estimator.from_keras(keras_model=model, model_dir=model_dir)
est.fit(data=data_shard,
batch_size=8,
epochs=6,
validation_data=data_shard,
checkpoint_trigger=SeveralIteration(4))
eval_result = est.evaluate(data_shard)
print(eval_result)
tf.reset_default_graph()
model = self.create_model()
est = Estimator.from_keras(keras_model=model, model_dir=model_dir)
est.load_latest_checkpoint(model_dir)
est.fit(data=data_shard,
batch_size=8,
epochs=10,
validation_data=data_shard,
checkpoint_trigger=SeveralIteration(4))
eval_result = est.evaluate(data_shard)
print(eval_result)
shutil.rmtree(temp)
def test_estimator_graph_checkpoint(self):
import zoo.orca.data.pandas
tf.reset_default_graph()
model = SimpleModel()
file_path = os.path.join(resource_path, "orca/learn/ncf.csv")
data_shard = zoo.orca.data.pandas.read_csv(file_path)
def transform(df):
result = {
"x": (df['user'].to_numpy(), df['item'].to_numpy()),
"y": df['label'].to_numpy()
}
return result
data_shard = data_shard.transform_shard(transform)
temp = tempfile.mkdtemp()
model_dir = os.path.join(temp, "test_model")
est = Estimator.from_graph(
inputs=[model.user, model.item],
labels=[model.label],
loss=model.loss,
optimizer=tf.train.AdamOptimizer(),
metrics={"loss": model.loss},
model_dir=model_dir
)
est.fit(data=data_shard,
batch_size=8,
epochs=6,
validation_data=data_shard,
checkpoint_trigger=SeveralIteration(4))
est.sess.close()
tf.reset_default_graph()
model = SimpleModel()
est = Estimator.from_graph(
inputs=[model.user, model.item],
labels=[model.label],
loss=model.loss,
optimizer=tf.train.AdamOptimizer(),
metrics={"loss": model.loss},
model_dir=model_dir
)
est.load_latest_checkpoint(model_dir)
est.fit(data=data_shard,
batch_size=8,
epochs=10,
validation_data=data_shard)
result = est.evaluate(data_shard)
assert "loss" in result
print(result)
shutil.rmtree(temp)
def __init__(self, interval):
from bigdl.optim.optimizer import SeveralIteration
self.trigger = SeveralIteration(interval)