From 1de0dfaab58ffb1d86d13083ec5d92ee45431c8e Mon Sep 17 00:00:00 2001 From: Ihar Hrachyshka Date: Wed, 14 May 2025 03:37:07 -0400 Subject: [PATCH] docs: Clarify kfp provider is both inline and remote (#2144) The provider selling point *is* using the same provider for both. Signed-off-by: Ihar Hrachyshka Signed-off-by: Ihar Hrachyshka --- docs/source/providers/external.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/providers/external.md b/docs/source/providers/external.md index ee36ebc3c..6c36901ee 100644 --- a/docs/source/providers/external.md +++ b/docs/source/providers/external.md @@ -53,7 +53,7 @@ Here's a list of known external providers that you can use with Llama Stack: | Name | Description | API | Type | Repository | |------|-------------|-----|------|------------| | KubeFlow Training | Train models with KubeFlow | Post Training | Remote | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) | -| KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) | +| KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Inline **and** Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) | | RamaLama | Inference models with RamaLama | Inference | Remote | [ramalama-stack](https://github.com/containers/ramalama-stack) | | TrustyAI LM-Eval | Evaluate models with TrustyAI LM-Eval | Eval | Remote | [llama-stack-provider-lmeval](https://github.com/trustyai-explainability/llama-stack-provider-lmeval) |