a703ae4eba
* update databricks doc * update databricks doc * update databricks doc * update databricks doc * update databricks doc * update databricks doc Co-authored-by: Zhou <jian.zhou@intel.com>
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Markdown
287 lines
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11 KiB
Markdown
# Databricks User Guide
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---
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You can run BigDL program on the [Databricks](https://databricks.com/) cluster as follows.
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### **1. Create a Databricks Cluster**
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- Create either an [AWS Databricks](https://docs.databricks.com/getting-started/try-databricks.html) workspace or an [Azure Databricks](https://docs.microsoft.com/en-us/azure/azure-databricks/) workspace.
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- Create a Databricks [cluster](https://docs.databricks.com/clusters/create.html) using the UI. Choose Databricks runtime version. This guide is tested on Runtime 9.1 LTS (includes Apache Spark 3.1.2, Scala 2.12).
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### 2. Generate initialization script
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[Init script](https://learn.microsoft.com/en-us/azure/databricks/clusters/init-scripts) is used to Install BigDL or other libraries. First, you need to put the **init script** into [DBFS](https://docs.databricks.com/dbfs/index.html), you can use one of the following ways.
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**a. Generate init script in Databricks notebook**
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Create a Databricks notebook and execute
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```python
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init_script = """
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#!/bin/bash
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# install bigdl-orca, add other bigdl modules if you need
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/databricks/python/bin/pip install pip install --pre --upgrade bigdl-orca-spark3[ray]
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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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# 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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"""
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# Change the first parameter to your DBFS path
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dbutils.fs.put("dbfs:/FileStore/scripts/init.sh", init_script, True)
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```
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To make sure the init script is in DBFS, in the left panel, click **Data > DBFS > check your script save path**.
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> if you do not see DBFS in your panel, see [Appendix A](#appendix-a).
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**b. Create init script in local and upload to DBFS**
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Create a file **init.sh**(or any other filename) in your computer, the file content is
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```bash
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#!/bin/bash
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# install bigdl-orca, add other bigdl modules if you need
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/databricks/python/bin/pip install pip install --pre --upgrade bigdl-orca-spark3[ray]
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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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# 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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```
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Then upload **init.sh** to DBFS. In Databricks left panel, click **Data > DBFS > Choose or create upload directory > Right click > Upload here**.
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Now the init script is in DBFS, right click the init.sh and choose **Copy path**, copy the **Spark API Format** path.
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### 3. Set Spark configuration
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In the left panel, click **Compute > Choose your cluster > edit > Advanced options > Spark > Confirm**. You can provide custom [Spark configuration properties](https://spark.apache.org/docs/latest/configuration.html) in a cluster configuration. Please set it according to your cluster resource and program needs.
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See below for an example of Spark config setting **needed** by BigDL. Here it sets 2 core per executor. Note that "spark.cores.max" needs to be properly set below.
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```
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spark.executor.cores 2
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spark.cores.max 4
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```
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### 4. Install BigDL Libraries
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Use the init script from [step 2](#2-generate-initialization-script) to install BigDL libraries. In the left panel, click **Compute > Choose your cluster > edit > Advanced options > Init Scripts > Paste init script path > Add > Confirm**.
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Then start or restart the cluster. After starting/restarting the cluster, the libraries specified in the init script are all installed.
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### **5. Run BigDL on Databricks**
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Open a new notebook, and call `init_orca_context` at the beginning of your code (with `cluster_mode` set to "spark-submit").
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```python
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from bigdl.orca import init_orca_context, stop_orca_context
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init_orca_context(cluster_mode="spark-submit")
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```
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Output on Databricks:
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**Run ncf_train example on Databricks**
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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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```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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### **6. Other ways to install third-party libraries on Databricks if necessary**
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If you want to use other ways to install third-party libraries, check related Databricks documentation of [libraries for AWS Databricks](https://docs.databricks.com/libraries/index.html) and [libraries for Azure Databricks](https://docs.microsoft.com/en-us/azure/databricks/libraries/).
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### Appendix A
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If there is no DBFS in your panel, go to **User profile > Admin Console > Workspace settings > Advanced > Enabled DBFS File Browser**
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### Appendix B
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Use **Databricks CLI** to upload file to DBFS. When you upload a large file to DBFS, using Databricks CLI could be faster than using the Databricks web UI.
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**Install and config Azure Databricks CLI**
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1. Install Python, need Python version 2.7.9 and above if you’re using Python 2 or Python 3.6 and above if you’re using Python 3.
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2. Run `pip install databricks-cli`
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3. Set authentication, Click **user profile icon > User Settings > Access tokens > Generate new token > generate > copy the token**, make sure to **copy** the token and store it in a secure location, **it won't show again**.
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4. Copy the URL of Databricks host, the format is `https://adb-<workspace-id>.<random-number>.azuredatabricks.net`, you can copy it from your Databricks web page URL.
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5. In cmd run `dbfs config --token` as shown below:
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```
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dbfs configure --token
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Databricks Host (should begin with https://): https://your.url.from.step.4
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Token: your-token-from-step-3
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```
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6. Verify whether you are able to connect to DBFS, run "databricks fs ls".
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**Upload through Databricks CLI**
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Now, we can use Databricks CLI to upload file to DBFS. run command:
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```
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dbfs cp /your/local/filepath/bigdl-assembly-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar dbfs:/FileStore/jars/stable/bigdl-assembly-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar
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```
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After command finished, check DBFS in Databricks, in left panel, click **Data > DBFS > your upload directory**, if you do not see DBFS in your panel, see [Appendix A](#appendix-a).
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**Install package from DBFS**
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In the left panel, click **Compute > choose your cluster > Libraries > Install new > Library Source(DBFS/ADLS) > Library Type(your package type)**.
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