slopp

Nemotron Ultra Deep Agents: ChatNVIDIA patches used in addition to the harness profile

This note documents the client/runtime patches we used in addition to the downloaded Nemotron Ultra Deep Agents harness profile.

The harness profile itself is registered with:

--harness-profile nemotron-ultra-downloaded

slopp

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

slopp

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:

slopp

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

slopp

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

slopp

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

slopp

Torch Experiment

Goal

• First attempt at using Torch for some type of "deep" learning
• Take advantage of modal to access serverless Python compute, including GPUs

Approach

slopp

from typing import Any
from dagster import ConfigurableResource, ConfigurableIOManager, InputContext, OutputContext, asset, Definitions, ResourceDependency, EnvVar
from pydantic import Field

# https://docs.dagster.io/concepts/resources#resources-that-depend-on-other-resources

class myResource(ConfigurableResource):
    username: str = Field(description="the username")
    password: str = Field(description="the password")

slopp

import os

from dagster import define_asset_job, load_assets_from_package_module, repository, with_resources, op, job, ScheduleDefinition
from my_dagster_project import assets
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.metrics_api import MetricsApi
from datadog_api_client.v2.model.metric_intake_type import MetricIntakeType
from datadog_api_client.v2.model.metric_payload import MetricPayload
from datadog_api_client.v2.model.metric_point import MetricPoint
from datetime import datetime

slopp

import datetime

import pins
import os
import seaborn as sns
from dagster import asset, asset_check, AssetCheckResult
from posit import connect  # install as uv pip install posit-sdk
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline