From 159a2ccb3130f3508a94ec6b6022f4e8264a1a20 Mon Sep 17 00:00:00 2001 From: Marwan Sarieddine Date: Fri, 23 Feb 2024 16:21:55 -0800 Subject: [PATCH] create a template for RAG Dev Bootcamp 2024 --- configs/rag-dev-bootcamp-mar2024/aws.yaml | 10 ++++++++++ configs/rag-dev-bootcamp-mar2024/gce.yaml | 11 +++++++++++ templates/rag-dev-bootcamp-mar2024/README.md | 19 +++++++++++++++++++ 3 files changed, 40 insertions(+) create mode 100644 configs/rag-dev-bootcamp-mar2024/aws.yaml create mode 100644 configs/rag-dev-bootcamp-mar2024/gce.yaml create mode 100644 templates/rag-dev-bootcamp-mar2024/README.md diff --git a/configs/rag-dev-bootcamp-mar2024/aws.yaml b/configs/rag-dev-bootcamp-mar2024/aws.yaml new file mode 100644 index 000000000..ceeb3c3fa --- /dev/null +++ b/configs/rag-dev-bootcamp-mar2024/aws.yaml @@ -0,0 +1,10 @@ +head_node_type: + name: head_node_type + instance_type: g5.4xlarge +worker_node_types: +- name: gpu_worker + instance_type: g5.4xlarge + min_workers: 1 + max_workers: 1 + use_spot: false +auto_select_worker_config: true diff --git a/configs/rag-dev-bootcamp-mar2024/gce.yaml b/configs/rag-dev-bootcamp-mar2024/gce.yaml new file mode 100644 index 000000000..532ede4cb --- /dev/null +++ b/configs/rag-dev-bootcamp-mar2024/gce.yaml @@ -0,0 +1,11 @@ +# n1-standard-8-nvidia-t4-16gb-1 --> 8 CPUs, 1 GPU +head_node_type: + name: head_node_type + instance_type: n1-standard-8-nvidia-t4-16gb-1 +worker_node_types: +- name: gpu_worker + instance_type: n1-standard-8-nvidia-t4-16gb-1 + min_workers: 1 + max_workers: 1 + use_spot: false +auto_select_worker_config: true diff --git a/templates/rag-dev-bootcamp-mar2024/README.md b/templates/rag-dev-bootcamp-mar2024/README.md new file mode 100644 index 000000000..083ee7e49 --- /dev/null +++ b/templates/rag-dev-bootcamp-mar2024/README.md @@ -0,0 +1,19 @@ +# RAG Developer Bootcamp Training Program + +Level up your coding skills at the RAG Developer Bootcamp! + + + + +## Overview + +In recent months, Retrieval Augmented Generation or RAG became a central concept in the LLM apps development. Developers are using RAG to reduce hallucinations by grounding LLM output and adding context that wasn’t captured in the LLM training data. + + +## Training Objectives + +The RAG Developer Bootcamp is a 3-part training program designed to help developers understand and implement RAG in their LLM apps. The training program will cover three main areas: +- Part 1: RAG apps overview and quick-start with Canopy +- Part 2: RAG Development: embeddings at scale +- Part 3: RAG Development: evaluations +