111 lines
4 KiB
Markdown
111 lines
4 KiB
Markdown
# Scale Keras 2.3 Applications
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---
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[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/keras_lenet_mnist.ipynb) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/keras_lenet_mnist.ipynb)
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---
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**In this guide we will describe how to scale out _Keras 2.3_ programs using Orca in 4 simple steps.**
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### Step 0: Prepare Environment
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We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the environment. Please refer to the [install guide](../Overview/install.md) for more details.
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```bash
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conda create -n py37 python=3.7 # "py37" is conda environment name, you can use any name you like.
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conda activate py37
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pip install bigdl-orca
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pip install tensorflow==1.15.0
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pip install tensorflow-datasets==2.1.0
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pip install psutil
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pip install pandas
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pip install scikit-learn
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```
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### Step 1: Init Orca Context
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```python
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from bigdl.orca import init_orca_context, stop_orca_context
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if cluster_mode == "local": # For local machine
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init_orca_context(cluster_mode="local", cores=4, memory="4g")
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elif cluster_mode == "k8s": # For K8s cluster
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init_orca_context(cluster_mode="k8s", num_nodes=2, cores=2, memory="4g", master=..., container_image=...)
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elif cluster_mode == "yarn": # For Hadoop/YARN cluster
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init_orca_context(cluster_mode="yarn", num_nodes=2, cores=2, memory="4g")
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```
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This is the only place where you need to specify local or distributed mode. View [Orca Context](../Overview/orca-context.md) for more details.
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Please check the tutorials if you want to run on [Kubernetes](../Tutorial/k8s.md) or [Hadoop/YARN](../Tutorial/yarn.md) clusters.
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### Step 2: Define the Model
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You may define your model, loss and metrics in the same way as in any standard (single node) Keras program.
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```python
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from tensorflow import keras
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model = keras.Sequential(
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[keras.layers.Conv2D(20, kernel_size=(5, 5), strides=(1, 1), activation='tanh',
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input_shape=(28, 28, 1), padding='valid'),
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keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),
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keras.layers.Conv2D(50, kernel_size=(5, 5), strides=(1, 1), activation='tanh',
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padding='valid'),
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keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),
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keras.layers.Flatten(),
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keras.layers.Dense(500, activation='tanh'),
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keras.layers.Dense(10, activation='softmax'),
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]
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)
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model.compile(optimizer=keras.optimizers.RMSprop(),
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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```
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### Step 3: Define Train Dataset
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You can define the dataset using standard [tf.data.Dataset](https://www.tensorflow.org/api_docs/python/tf/data/Dataset). Orca also supports [Spark DataFrame](./spark-dataframe.md) and [Orca XShards](./xshards-pandas.md).
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```python
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import tensorflow as tf
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import tensorflow_datasets as tfds
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def preprocess(data):
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data['image'] = tf.cast(data["image"], tf.float32) / 255.
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return data['image'], data['label']
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# get DataSet
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mnist_train = tfds.load(name="mnist", split="train", data_dir=...)
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mnist_test = tfds.load(name="mnist", split="test", data_dir=...)
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mnist_train = mnist_train.map(preprocess)
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mnist_test = mnist_test.map(preprocess)
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```
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### Step 4: Fit with Orca Estimator
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First, create an Orca Estimator for TensorFlow.
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```python
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from bigdl.orca.learn.tf.estimator import Estimator
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est = Estimator.from_keras(keras_model=model)
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```
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Next, fit and evaluate using the Estimator.
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```python
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est.fit(data=mnist_train,
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batch_size=320,
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epochs=5,
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validation_data=mnist_test)
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result = est.evaluate(mnist_test)
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print(result)
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```
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**Note:** You should call `stop_orca_context()` when your program finishes.
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That's it, the same code can run seamlessly on your local laptop and scale to [Kubernetes](../Tutorial/k8s.md) or [Hadoop/YARN](../Tutorial/yarn.md) clusters.
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