MLTransform Image Embedding CPU Performance
Model: Sentence Transformers — clip-ViT-B-32 (image) Accelerator: CPU with Dataflow Prime right-fitting (16 GB min RAM) Host: Dataflow Prime with throughput-based autoscaling
This batch pipeline reads image URIs from GCS, decodes images with Pillow,
generates image embeddings through MLTransform with
SentenceTransformerEmbeddings(image_model=True), and writes results to
BigQuery using batch file loads.
See the glossary for definitions.
Full pipeline implementation is available here.
What is the estimated cost to run the pipeline?
RunTime and EstimatedCost
How has various metrics changed when running the pipeline for different Beam SDK versions?
AvgThroughputBytesPerSec by Version
AvgThroughputElementsPerSec by Version
How has various metrics changed over time when running the pipeline?
AvgThroughputBytesPerSec by Date
AvgThroughputElementsPerSec by Date
See also MLTransform Image Embedding GPU for the Tesla T4 GPU variant of this pipeline.
Last updated on 2026/07/11
Have you found everything you were looking for?
Was it all useful and clear? Is there anything that you would like to change? Let us know!