update databricks doc (#6035)
Co-authored-by: Zhou <jian.zhou@intel.com>
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Jian Zhou
GitHub
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1 changed files with 8 additions and 128 deletions
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@ -27,8 +27,8 @@ init_script = """
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# install other necessary libraries, here we install libraries needed in this tutorial
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/databricks/python/bin/pip install tensorflow==2.9.1
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/databricks/python/bin/pip install pyarrow==8.0.0
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/databricks/python/bin/pip install psutil
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/databricks/python/bin/pip install tqdm
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/databricks/python/bin/pip install torch==1.11.0+cpu torchvision==0.12.0+cpu tensorboard -f https://download.pytorch.org/whl/torch_stable.html
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# copy bigdl jars to databricks
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cp /databricks/python/lib/python3.8/site-packages/bigdl/share/*/lib/*.jar /databricks/jars
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@ -54,8 +54,8 @@ Create a file **init.sh**(or any other filename) in your computer, the file cont
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# install other necessary libraries, here we install libraries needed in this tutorial
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/databricks/python/bin/pip install tensorflow==2.9.1
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/databricks/python/bin/pip install pyarrow==8.0.0
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/databricks/python/bin/pip install psutil
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/databricks/python/bin/pip install tqdm
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/databricks/python/bin/pip install torch==1.11.0+cpu torchvision==0.12.0+cpu tensorboard -f https://download.pytorch.org/whl/torch_stable.html
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# copy bigdl jars to databricks
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cp /databricks/python/lib/python3.8/site-packages/bigdl/share/*/lib/*.jar /databricks/jars
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@ -103,132 +103,12 @@ Output on Databricks:
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**Run ncf_train example on Databricks**
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**Run Examples**
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Create a notebook and run the following example. Note that to make things simple, we are just generating some dummy data for this example.
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- [Keras example on Databricks](https://github.com/intel-analytics/BigDL/blob/main/python/orca/tutorial/databricks/tf_keras_ncf.ipynb)
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- [Pytorch example on Databricks](https://github.com/intel-analytics/BigDL/blob/main/python/orca/tutorial/databricks/pytorch_fashion_mnist.ipynb)
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```python
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import math
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import argparse
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import os
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import random
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from bigdl.orca import init_orca_context, stop_orca_context, OrcaContext
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from bigdl.orca.learn.tf2.estimator import Estimator
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from pyspark.sql.types import StructType, StructField, IntegerType
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def build_model(num_users, num_items, class_num, layers=[20, 10], include_mf=True, mf_embed=20):
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import tensorflow as tf
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from tensorflow.keras.layers import Input, Embedding, Dense, Flatten, concatenate, multiply
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num_layer = len(layers)
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user_input = Input(shape=(1,), dtype='int32', name='user_input')
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item_input = Input(shape=(1,), dtype='int32', name='item_input')
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mlp_embed_user = Embedding(input_dim=num_users, output_dim=int(layers[0] / 2),
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input_length=1)(user_input)
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mlp_embed_item = Embedding(input_dim=num_items, output_dim=int(layers[0] / 2),
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input_length=1)(item_input)
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user_latent = Flatten()(mlp_embed_user)
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item_latent = Flatten()(mlp_embed_item)
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mlp_latent = concatenate([user_latent, item_latent], axis=1)
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for idx in range(1, num_layer):
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layer = Dense(layers[idx], activation='relu',
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name='layer%d' % idx)
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mlp_latent = layer(mlp_latent)
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if include_mf:
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mf_embed_user = Embedding(input_dim=num_users,
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output_dim=mf_embed,
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input_length=1)(user_input)
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mf_embed_item = Embedding(input_dim=num_users,
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output_dim=mf_embed,
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input_length=1)(item_input)
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mf_user_flatten = Flatten()(mf_embed_user)
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mf_item_flatten = Flatten()(mf_embed_item)
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mf_latent = multiply([mf_user_flatten, mf_item_flatten])
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concated_model = concatenate([mlp_latent, mf_latent], axis=1)
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prediction = Dense(class_num, activation='softmax', name='prediction')(concated_model)
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else:
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prediction = Dense(class_num, activation='softmax', name='prediction')(mlp_latent)
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model = tf.keras.Model([user_input, item_input], prediction)
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return model
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if __name__ == '__main__':
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executor_cores = 2
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lr = 0.001
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epochs = 5
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batch_size = 8000
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model_dir = "/dbfs/FileStore/model/ncf/"
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backend = "ray" # ray or spark
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data_dir = './'
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save_path = model_dir + "ncf.h5"
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sc = init_orca_context(cluster_mode="spark-submit")
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spark = OrcaContext.get_spark_session()
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num_users, num_items = 6000, 3000
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rdd = sc.range(0, 50000).map(
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lambda x: [random.randint(0, num_users - 1), random.randint(0, num_items - 1), random.randint(0, 4)])
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schema = StructType([StructField("user", IntegerType(), False),
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StructField("item", IntegerType(), False),
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StructField("label", IntegerType(), False)])
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data = spark.createDataFrame(rdd, schema)
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train, test = data.randomSplit([0.8, 0.2], seed=1)
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config = {"lr": lr, "inter_op_parallelism": 4, "intra_op_parallelism": executor_cores}
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def model_creator(config):
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import tensorflow as tf
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model = build_model(num_users, num_items, 5)
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print(model.summary())
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optimizer = tf.keras.optimizers.Adam(config["lr"])
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model.compile(optimizer=optimizer,
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loss='sparse_categorical_crossentropy',
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metrics=['sparse_categorical_crossentropy', 'accuracy'])
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return model
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steps_per_epoch = math.ceil(train.count() / batch_size)
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val_steps = math.ceil(test.count() / batch_size)
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estimator = Estimator.from_keras(model_creator=model_creator,
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verbose=False,
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config=config,
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backend=backend,
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model_dir=model_dir)
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estimator.fit(train,
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batch_size=batch_size,
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epochs=epochs,
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feature_cols=['user', 'item'],
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label_cols=['label'],
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steps_per_epoch=steps_per_epoch,
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validation_data=test,
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validation_steps=val_steps)
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predictions = estimator.predict(test,
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batch_size=batch_size,
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feature_cols=['user', 'item'],
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steps=val_steps)
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print("Predictions on validation dataset:")
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predictions.show(5, truncate=False)
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print("Saving model to: ", save_path)
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estimator.save(save_path)
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# load with estimator.load(save_path)
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stop_orca_context()
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```
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> Note that if you want to save model to DBFS, or load model from DBFS, the save/load path should be the **File API Format** on Databricks, which means your save/load path should start with `/dbfs`.
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### **6. Other ways to install third-party libraries on Databricks if necessary**
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