ipex-llm/docs/readthedocs/source/doc/Orca/Overview/orca.md

Orca in 5 minutes

Overview

Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger data set in a distributed fashion. The Orca library seamlessly scales out your single node Python notebook across large clusters (so as to process distributed Big Data).

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Tensorflow Bite-sized Example

This section uses TensorFlow 1.15, and you should install TensorFlow before running this example:

pip install tensorflow==1.15

First, initialize Orca Context:

from bigdl.orca import init_orca_context, OrcaContext

# cluster_mode can be "local", "k8s" or "yarn"
sc = init_orca_context(cluster_mode="local", cores=4, memory="10g", num_nodes=1)

Next, perform data-parallel processing in Orca (supporting standard Spark Dataframes, TensorFlow Dataset, PyTorch DataLoader, Pandas, etc.):

from pyspark.sql.functions import array

spark = OrcaContext.get_spark_session()
df = spark.read.parquet(file_path)
df = df.withColumn('user', array('user')) \
       .withColumn('item', array('item'))

Finally, use sklearn-style Estimator APIs in Orca to perform distributed TensorFlow, PyTorch, Keras and BigDL training and inference:

from tensorflow import keras
from bigdl.orca.learn.tf.estimator import Estimator

user = keras.layers.Input(shape=[1])
item = keras.layers.Input(shape=[1])
feat = keras.layers.concatenate([user, item], axis=1)
predictions = keras.layers.Dense(2, activation='softmax')(feat)
model = keras.models.Model(inputs=[user, item], outputs=predictions)
model.compile(optimizer='rmsprop',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

est = Estimator.from_keras(keras_model=model)
est.fit(data=df,
        batch_size=64,
        epochs=4,
        feature_cols=['user', 'item'],
        label_cols=['label'])