Simple Neural Network Example - Bigdl Typing CST Test
Loading…
Simple Neural Network Example — Bigdl Code
A minimal BigDL example defining and training a simple feedforward neural network on Spark.
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.')Bigdl Language Guide
BigDL is an open-source distributed deep learning library for Apache Spark, enabling users to build, train, and deploy deep learning models at scale on big data clusters using standard Spark or Hadoop environments.
Primary Use Cases
- ▸Distributed training of deep learning models on Spark/Hadoop clusters
- ▸Large-scale image, text, and time-series analysis
- ▸Recommendation engines and predictive analytics on big datasets
- ▸Integrating deep learning with existing big data pipelines
- ▸Deploying AI models directly on big data infrastructure for inference
Notable Features
- ▸High-performance distributed deep learning on Apache Spark
- ▸Supports CNNs, RNNs, LSTMs, and standard neural networks
- ▸Keras-style high-level APIs for model development
- ▸Zero-copy data transformation between Spark RDDs/DataFrames and tensors
- ▸Integration with TensorFlow, PyTorch, and ONNX for model import/export
Origin & Creator
BigDL was developed by Intel in 2016 to bring deep learning capabilities to Apache Spark clusters efficiently, aiming to leverage big data infrastructure for AI workloads.
Industrial Note
BigDL is primarily used in industries requiring large-scale AI training on big data, such as finance, telecom, healthcare, and recommendation systems, where in-place model training and low-latency inference on Spark clusters are crucial.