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Dynamic Pipeline

This example shows the psuedo-code for a Dagster pipeline that:

1. Accepts the path to a raw dataset as a string
2. Runs a step to break the raw dataset into partitions
3. For each partition, the pipeline runs a series of two processing steps. Each processing step is a call out to a Docker container to run supplying the partition key as an input argument. The partitions are run together in parallel before being collected in a final processing step that operates on all the partitions.

To run the pipeline:

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with events as (select distinct

    DATE_FORMAT(timestamp, '%Y-%m')  as event_month,
    dagster_event_type,
    coalesce(run_id, '||', step_key) as step_id,
    count(1) as credits

from event_logs
where dagster_event_type = 'STEP_START'

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Dynamo

Executive Summary

This report provides a comprehensive comparison of NVIDIA Dynamo with other frameworks, including vLLM and NVIDIA Triton Server, for large language model (LLM) inference workloads. The key findings highlight Dynamo's superior performance in multi-GPU setups, achieving higher throughput and lower latency compared to vLLM. Additionally, Dynamo's disaggregated serving approach offers more flexible performance tuning, especially for large models and variable workload conditions. In comparison to NVIDIA Triton Server, Dynamo is optimized for low-latency generative AI/LLM workloads, while Triton Server excels in multi-model inference serving. The report also delves into Dynamo's technical architecture, which is designed to accelerate inference workloads through components such as disaggregated serving, smart routing, and distributed KV cache management. Benchmarking methodologies and key performance metrics are also discussed, emphasizing the importance of standardized evaluation for

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---
title: \textbf{USING RSTUDIO WITH TERADATA \\ \large RStudio makes it easy to access and analyze your data with R}
geometry: margin=0.6in
output:
  pdf_document:
    fig_caption: false
    pandoc_args: [
      "-V", "classoption=twocolumn"
    ]
---

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from gql import Client, gql
from gql.transport.requests import RequestsHTTPTransport
import os
import pandas as pd
from datetime import datetime, timedelta


USER_GRANTS_QUERY = """
query UsersByRole {
     usersOrError {

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A modification of the LangChain SQL Q&A tutorial https://python.langchain.com/docs/tutorials/sql_qa/.

The changes are:

• uses Pydantic to type the state and inputs/outputs
• uses duckDB on the palmerpenguin dataset
• uses a Nvidia NIM for the LLM
• instead of a sequence write_query -> run_query -> gen_answer, this graph adds a LLM that checks the write_query output for validity and to see if it answers the question, leading to a more dynamic graph that looks like this:

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See https://loppsided.blog/posts/2024-12-27-a-basic-tool-calling-agent/

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See https://loppsided.blog/posts/2024-12-26-a-rag-to-chat-with-your-favorite-blogger/

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This simple streamlit app uses the Google Maps and Places API, along with a hosted Nvidia NIM wrapper of the Llama model, to help you find coffee shops near an address.

To run

1. Install dependencies