Today, in partnership with NVIDIA, Google Cloud announced Dataflow is bringing GPUs to the world of big data processing to unlock new possibilities.
Overview
The article discusses the integration of NVIDIA GPUs with Google Cloud Dataflow to enhance machine learning model inference, specifically using the BERT model. It outlines the steps to set up the environment, run inference jobs on both CPU and GPU, and highlights the performance improvements and cost savings achieved through GPU acceleration.
What You'll Learn
1
How to set up a virtual environment for running Dataflow jobs
2
How to run TensorFlow inference jobs on Google Cloud Dataflow using GPUs
3
Why using NVIDIA TensorRT can significantly improve inference performance
4
How to compare performance metrics between CPU and GPU runs in Dataflow
Prerequisites & Requirements
- Basic understanding of machine learning and TensorFlow
- Familiarity with Google Cloud and Dataflow(optional)
- Experience with Python programming
Key Questions Answered
How can I leverage NVIDIA GPUs for machine learning inference on Google Cloud Dataflow?
You can leverage NVIDIA GPUs by using Dataflow's GPU support, which allows you to run TensorFlow inference jobs on GPUs, significantly improving performance and reducing costs. The article provides steps to set up your environment, run inference jobs, and optimize them using NVIDIA TensorRT.
What are the performance benefits of using TensorRT with BERT on Dataflow?
Using TensorRT with BERT on Dataflow can achieve up to 38 times lower costs and 17 times faster execution times compared to running on CPUs. The article details the performance metrics comparing TensorFlow CPU, TensorFlow GPU, and TensorRT runs.
What steps are involved in creating a custom Dockerfile for Dataflow?
Creating a custom Dockerfile involves using a GPU-optimized NGC TensorFlow container, installing necessary packages, and copying your model files into the container. The article provides a sample Dockerfile and commands for building and pushing it to Google Container Registry.
What is the recommended way to run TensorFlow jobs on Dataflow?
To run TensorFlow jobs on Dataflow, you should set parameters for worker accelerators and manage autoscaling options. The article emphasizes the importance of setting a job name and provides scripts for running jobs on both CPU and GPU.
Key Statistics & Figures
Total execution time for TF-CPU
2:46:00
This is the execution time for a TensorFlow inference job using CPU.
Total execution time for TF-GPU
0:35:51
This shows the execution time for a TensorFlow inference job using GPU.
Total execution time for TensorRT
0:09:51
This is the execution time for a TensorRT optimized inference job.
TCO Improvement for TF-GPU over TF-CPU
9.2x
This indicates the total cost of ownership improvement when using TF-GPU compared to TF-CPU.
TCO Improvement for TensorRT over TF-GPU
38x
This shows the total cost of ownership improvement when using TensorRT compared to TF-GPU.
Technologies & Tools
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Cloud Service
Google Cloud Dataflow
Used for executing data processing patterns including machine learning inference.
Optimization Library
Nvidia Tensorrt
Used to optimize deep learning models for inference.
Machine Learning Framework
Tensorflow
Used for building and running machine learning models.
Data Processing Framework
Apache Beam
Used for creating Dataflow pipelines.
Containerization
Docker
Used for creating custom containers to run Dataflow jobs.
Key Actionable Insights
1Utilizing NVIDIA GPUs for machine learning inference can drastically reduce execution time and costs.By switching from CPU to GPU, users can achieve up to 9.2 times faster execution times and 38 times lower costs, making it a compelling option for large-scale ML tasks.
2Implementing NVIDIA TensorRT can further optimize inference performance beyond standard GPU usage.TensorRT can accelerate inference by optimizing models for low latency and high throughput, which is crucial for real-time applications.
3Setting up a proper environment is critical for successful Dataflow job execution.Aligning Python and Apache Beam SDK versions with Dataflow runtime is essential to avoid unexpected errors during job execution.
Common Pitfalls
1
Failing to align the Python version in your local environment with the Dataflow runtime version can lead to unexpected errors.
This happens because discrepancies in versions can cause compatibility issues, resulting in job failures. Always verify that your local setup matches the runtime environment.
2
Not setting a job name can make it difficult to track and manage Dataflow jobs.
Using auto-generated job names can lead to confusion, especially when monitoring multiple jobs. Setting a descriptive job name helps in identifying and managing jobs effectively.
Related Concepts
Machine Learning Optimization Techniques
Google Cloud Services
Containerization With Docker
Nvidia GPU Acceleration