From 525d53a6d4a2a0e133ecf8b4acfd2284249a0f51 Mon Sep 17 00:00:00 2001 From: kramstrom Date: Fri, 26 Jul 2024 10:51:53 +0200 Subject: [PATCH] update --- 10_integrations/dbt_modal_inference/dbt_modal_inference.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/10_integrations/dbt_modal_inference/dbt_modal_inference.py b/10_integrations/dbt_modal_inference/dbt_modal_inference.py index a9df24f53..d242756bd 100644 --- a/10_integrations/dbt_modal_inference/dbt_modal_inference.py +++ b/10_integrations/dbt_modal_inference/dbt_modal_inference.py @@ -3,8 +3,6 @@ # In this example we demonstrate how you could combine [dbt's python models](https://docs.getdbt.com/docs/build/python-models) # with LLM inference models powered by Modal, allowing you to run serverless gpu workloads within dbt. # -# ## Overview -# # This example runs [dbt](https://docs.getdbt.com/docs/introduction) with a [DuckDB](https://duckdb.org) # backend directly on top of Modal, but could be translated to run on any dbt-compatible # database that supports python models. Similarly you could make these requests from UDFs @@ -15,7 +13,6 @@ # for free-text product reviews and aggregate them in subsequent dbt sql models. These product names, descriptions and reviews # were also generated by an LLM running on Modal! # -# # ## Configure Modal and dbt # # We set up the environment variables necessary for dbt and @@ -35,7 +32,7 @@ TARGET_PATH = f"{VOL_PATH}/target" # We also define the environment our application will run in -- -# a container image, as in Docker. +# a container image, similar to Docker. # See [this guide](https://modal.com/docs/guide/custom-container) for details. dbt_image = ( # start from a slim Linux image