Learn BIGDL with Real Code Examples

from bigdl.nn.layer import Sequential, Linear, ReLU, SoftMax
from bigdl.optim.optimizer import SGD, Top1Accuracy
from pyspark.sql import SparkSession

# Initialize Spark
spark = SparkSession.builder.appName('BigDLExample').getOrCreate()

# Define model
model = Sequential().add(Linear(4, 10)).add(ReLU()).add(Linear(10, 3)).add(SoftMax())

# Define optimizer and train (pseudo-code)
optimizer = SGD(model=model, learningrate=0.01)
# optimizer.train(data)  # Replace with actual RDD or DataFrame pipeline

print('Model defined and ready for training on Spark cluster.')

A minimal BigDL example defining and training a simple feedforward neural network on Spark.

from bigdl.nn.layer import Sequential, Conv2D, ReLU, MaxPooling2D, Linear, SoftMax
from bigdl.optim.optimizer import SGD

# Define CNN model
model = Sequential()
model.add(Conv2D(1, 32, kernel_size=3, stride=1, padding=1)).add(ReLU())
model.add(MaxPooling2D(pool_size=2, stride=2))
model.add(Linear(32*14*14, 128)).add(ReLU())
model.add(Linear(128, 10)).add(SoftMax())

# Define optimizer
optimizer = SGD(model=model, learningrate=0.01)
# optimizer.train(data)  # RDD/DataFrame pipeline

print('CNN model ready for image classification.')

Defining a CNN using BigDL for image classification tasks.

from bigdl.nn.layer import Sequential, Recurrent, LSTM, Linear, SoftMax
from bigdl.optim.optimizer import SGD

# Define RNN model
model = Sequential()
model.add(Recurrent().add(LSTM(input_size=10, hidden_size=20)))
model.add(Linear(20, 5)).add(SoftMax())

# Define optimizer
optimizer = SGD(model=model, learningrate=0.01)
# optimizer.train(data)  # Replace with actual sequence RDD

print('RNN model defined and ready for training.')

Building a simple RNN using BigDL for sequence prediction.

from bigdl.nn.layer import Sequential, Linear, ReLU
from bigdl.optim.optimizer import SGD

# Define autoencoder model
model = Sequential()
model.add(Linear(20, 10)).add(ReLU())
model.add(Linear(10, 20))

# Define optimizer
optimizer = SGD(model=model, learningrate=0.01)
# optimizer.train(data)

print('Autoencoder ready for training.')

Creating a simple autoencoder using BigDL for feature compression.

from bigdl.nn.layer import Sequential, Recurrent, LSTM, Linear
from bigdl.optim.optimizer import SGD

# Define model
model = Sequential()
model.add(Recurrent().add(LSTM(input_size=1, hidden_size=50)))
model.add(Linear(50, 1))

# Define optimizer
optimizer = SGD(model=model, learningrate=0.01)
# optimizer.train(time_series_data)

print('LSTM model ready for time series forecasting.')

Using LSTM for time series forecasting in BigDL.

from bigdl.nn.layer import Sequential, Linear, SoftMax
from bigdl.optim.optimizer import SGD

# Load pretrained model (pseudo-code)
pretrained_model = load_model('resnet50_bigdl')

# Fine-tune by adding a new output layer
model = Sequential()
model.add(pretrained_model)
model.add(Linear(1000, 10)).add(SoftMax())

# Optimizer
optimizer = SGD(model=model, learningrate=0.001)
# optimizer.train(custom_data)

print('Pretrained model fine-tuned.')

Using a pretrained BigDL model and fine-tuning it for a custom task.

from bigdl.nn.layer import Sequential, Linear, ReLU, SoftMax
from bigdl.optim.optimizer import SGD
from pyspark.sql import SparkSession

# Initialize Spark
spark = SparkSession.builder.appName('BigDLDistributed').getOrCreate()

# Define model
model = Sequential().add(Linear(10, 50)).add(ReLU()).add(Linear(50, 5)).add(SoftMax())

# Distributed optimizer
optimizer = SGD(model=model, learningrate=0.01, use_distributed=True)
# optimizer.train(distributed_rdd_data)

print('Distributed training setup complete.')

Illustrating distributed training using BigDL with Spark.

from bigdl.nn.layer import Sequential, Linear, ReLU, SoftMax
from bigdl.optim.optimizer import SGD
from bigdl.nn.criterion import Criterion

# Custom loss function
class MyLoss(Criterion):
    def forward(self, input, target):
        # define loss computation
        return ((input - target)**2).mean()

# Define model
model = Sequential().add(Linear(10, 20)).add(ReLU()).add(Linear(20, 5)).add(SoftMax())

# Optimizer with custom loss
optimizer = SGD(model=model, criterion=MyLoss(), learningrate=0.01)
# optimizer.train(data)

print('Model ready with custom loss.')

Defining a custom loss function in BigDL for training a neural network.

from bigdl.nn.layer import Sequential, Conv2D, ReLU, MaxPooling2D, Recurrent, LSTM, Linear, SoftMax
from bigdl.optim.optimizer import SGD

# Define hybrid model
model = Sequential()
model.add(Conv2D(1, 16, kernel_size=3, stride=1, padding=1)).add(ReLU())
model.add(MaxPooling2D(pool_size=2, stride=2))
model.add(Recurrent().add(LSTM(16*14*14, 50)))
model.add(Linear(50, 10)).add(SoftMax())

# Optimizer
optimizer = SGD(model=model, learningrate=0.01)
# optimizer.train(spatio_temporal_data)

print('Hybrid Conv+LSTM model ready.')

Combining convolutional and LSTM layers for spatio-temporal data in BigDL.

from bigdl.nn.layer import Sequential, Linear, ReLU, SoftMax
from bigdl.optim.optimizer import SGD

# Define simple model
model = Sequential().add(Linear(10, 50)).add(ReLU()).add(Linear(50, 5)).add(SoftMax())

# Optimizer
optimizer = SGD(model=model, learningrate=0.01)

# AutoML pseudo-code
# for hyperparams in hyperparam_grid:
#     optimizer.train(data)
#     evaluate performance

print('AutoML-style BigDL pipeline ready.')

Illustrating an AutoML-style pipeline using BigDL for automated model training.