Here’s a comprehensive breakdown of using GPUs in Databricks:

What are GPUs, and why are they used in Databricks?

• GPUs (Graphics Processing Units): Designed for parallel processing, GPUs excel at computationally intensive tasks everyday in machine learning, deep learning, and large-scale data analysis.
• Databricks: A cloud-based data engineering, analytics, and machine learning platform. Databricks integrates with GPUs to significantly accelerate your data-intensive workloads.

Key Use Cases

• Deep Learning: Training complex neural networks is vastly faster with GPUs. Libraries like TensorFlow and PyTorch are optimized for GPU acceleration.
• Machine Learning:  Many ML algorithms (e.g., XGBoost, sci-kit-learn) offer GPU-accelerated versions for faster model training.
• Data Processing:  Processing large datasets in parallel using libraries like RAPIDS can be dramatically faster on GPUs.

How to Use GPUs in Databricks

1. Choose a GPU-enabled Databricks Runtime:
   • Databricks Runtime ML for GPU: Pre-configured with popular ML libraries optimized for GPUs.
   • Standard Databricks Runtime + Databricks Container Services: Use this to build a highly customized environment with specific GPU libraries.
2. Create a GPU-enabled Cluster:
   • Select a worker instance type with the desired number of GPUs (e.g., AWS p3 instances, Azure NC-series, GCP n1 with GPUs).
   • Necessary: Configure the cluster to allow only one task/executor per node. This ensures each task can fully utilize all GPUs on the node.
3. Install GPU-Compatible Libraries:
   • Runtime ML for GPU: Comes pre-installed.
   • Standard Runtime: Use pip or conda to install GPU versions of your libraries (e.g., tensorflow-gpu, pytorch-gpu).
4. Write GPU-Aware Code:
   • TensorFlow/PyTorch: These libraries automatically detect and utilize GPUs if available.
   • RAPIDS: This library provides GPU-accelerated equivalents of Pandas and other data processing tools.
   • Distributed Training: Use libraries like Horovod or DeepSpeed with Databricks to scale your training across multiple GPUs and nodes.

Example (PyTorch)

Python

import torch

# Check for GPU availability
if torch.cuda.is_available():
device = torch.device(“cuda”)
else:
device = torch.device(“cpu”)

# Create tensors and models on the designated device
my_tensor = torch.rand(1000, 1000).to(device)
model = MyModel().to(device)

# Training loop will utilize the GPU if available
…

Important Considerations

• Cost: GPU instances are typically more expensive than standard CPU instances. Assess the cost-benefit tradeoff for your workload.
• Library Compatibility: Ensure the GPU-accelerated versions of your desired libraries are compatible with your chosen Databricks runtime.
• Data Transfer: Moving large volumes of data between the CPU and GPU can create bottlenecks. Strategize your data loading to minimize this overhead.