Unstructured data—documents, images, and multimedia—demands scalable deep learning (DL) and large language model (LLM) pipelines. While Apache Spark excels at big data processing, integrating GPU-accelerated inference has long been a challenge for engineers and data scientists. In this guide, we explore how Spark 3.4’s predict_batch_udf API, combined with NVIDIA Triton Inference Server and vLLM integration, empowers you to deploy distributed deep learning and LLM inference workflows in the cloud. Whether you’re using Databricks, Dataproc, or another cloud-based platform, this post provides actionable insights and code examples to accelerate your critical ML workloads.
Why Batch Inference for DL/LLMs?
When handling massive data volumes, batch processing is often more efficient than real-time inference. Batch inference pipelines are ideal for:
- Semantic Search: Generating embeddings and metadata to improve search accuracy.
- Data Transformation: Converting unstructured information into structured formats for deeper analysis.
- Content Creation: Automatically producing product descriptions, captions, or social media content over large datasets.
This methodology not only scales but also leverages Spark’s inherent strengths in parallel data processing. By integrating DL/LLM models, you can significantly enhance your enterprise data workflows. For more background on Spark’s capabilities, check out the Distributed Deep Learning Made Easy with Spark 3.4 article.
Basic Deployment: Using predict_batch_udf
Spark 3.4 introduces the predict_batch_udf API, which automates the conversion of Spark DataFrame columns into batched NumPy inputs. This data-parallel approach allows each worker to load its own copy of the model onto the GPU, making it straightforward to port your code from popular frameworks like PyTorch or TensorFlow.
For example, consider this snippet that uses Hugging Face’s Sentence Transformers:
from pyspark.sql.functions import predict_batch_udf
from pyspark.sql.types import ArrayType, FloatType
def predict_batch_fn():
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('paraphrase-MiniLM-L6-v2', device='cuda')
def predict(inputs):
return model.encode(inputs)
return predict
embed_udf = predict_batch_udf(predict_batch_fn,
return_type=ArrayType(FloatType()),
batch_size=128)
df = spark.read.parquet('/path/to/text_data')
embeddings_df = df.withColumn('embedding', embed_udf('text'))
embeddings_df.write.parquet('/path/to/embeddings')
This straightforward approach is well-suited for prototyping and small to medium-sized models. However, loading multiple model copies on the GPU can become a bottleneck, especially when dealing with large LLMs.
Advanced Deployment: Inference Serving for Seamless GPU Utilization
To overcome GPU memory constraints, the advanced approach decouples GPU execution from Spark’s task scheduling through dedicated inference servers deployed on each executor. This method not only prevents excessive GPU memory usage but also offers additional benefits such as dynamic batching and improved model management.
Serving with NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is an industry-standard solution for model serving. Traditionally run in Docker containers, Triton can now be deployed using the PyTriton interface, giving you the flexibility to embed the server within your Python process. This decoupling allows Spark to handle CPU tasks in parallel while a dedicated inference server manages GPU tasks.
For example, using Triton is as simple as implementing a server utility in your Spark cluster. The Spark-RAPIDS-Examples DL Inference repo provides extensive guidance on setting up such an environment.
Optimizing LLM Inference with vLLM
While Triton is ideal for varied inference tasks, vLLM is optimized specifically for large language models. It offers an OpenAI-compatible interface that simplifies deployment on Spark clusters, thereby enhancing the throughput of LLM inference tasks. The approach is similar to Triton’s, with the vLLM server decoupling the heavy GPU computations from the Spark scheduler.
Using vLLM, you can start a server on each executor with minimal adjustments. This ensures that your LLMs, even those that are extremely large, run smoothly on scaled GPU infrastructures.
Deploying on Cloud Platforms: Databricks and Dataproc
For real-world applications, deploying these solutions on cloud platforms such as Databricks and Dataproc is essential. Cloud environments not only offer scalability but also simplify resource management. When deploying on the cloud, consider using GPU instances such as A10/L4 for moderate workloads or A100/H100 for larger models.
Our comprehensive guides—such as the Spark deep learning guide—offer detailed instructions on configuring your cluster and tuning Spark settings (like spark.executor.resource.gpu.amount and tensor_parallel_size) for optimal performance.
Conclusion and Next Steps
In summary, whether you are prototyping using the predict_batch_udf API or scaling up with advanced inference servers such as NVIDIA Triton or vLLM, Apache Spark provides a unified platform for both deep learning and large language model inference. By optimizing your DL pipelines for cloud environments, you can achieve unprecedented efficiency and scale.
Ready to transform your ML workflows? Explore the GitHub repo for our Spark-RAPIDS examples and learn how to deploy these solutions on your cloud cluster today. For further reading, check out our detailed guides on configuring GPU instances and troubleshooting common issues.
Call to Action: Deploy these techniques on your next cloud-based Spark cluster and unlock the true power of GPU-accelerated inference for deep learning and LLM applications. Whether you are optimizing semantic searches or scaling advanced LLM models, Spark’s integration with modern DL tools is your gateway to enhanced performance and scalability.