You can run the Meridian code programmatically using sample data with the
Getting started colab.
The Meridian model uses a holistic MCMC sampling approach called No U Turn
Sampler
(NUTS)
which can be compute intensive. To help with this, GPU support has been
developed across the library (out-of-the-box) using tensors. We recommend
running your Meridian model on GPUs to get real time optimization results and
significantly reduce training time.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2026-04-30 UTC."],[],["Meridian requires Python 3.11 or 3.12 and recommends at least 1 GPU (V100 or T4 with 16GB RAM tested). Installation via `pip` uses: `python3 -m pip install --upgrade 'google-meridian[and-cuda]'` (Linux/GPU), `google-meridian` (macOS/CPU). CPU-only install also uses `google-meridian`. To verify the install, run: `python3 -c \"import meridian; print(meridian.__version__)\"`. The library, which uses No U-Turn Sampler, is compute-intensive, thus GPU usage is recommended for real-time optimization and faster training.\n"]]