In this tutorial, we build an autonomous data science agent around DeepAnalyze-8B and run it. We begin by preparing a stable runtime, installing the required machine-learning dependencies, and loading the DeepAnalyze tokenizer and model in 4-bit mode to keep the workflow practical on limited GPU memory. We then create a sandboxed execution environment that allows the model to generate Python code, execute it safely, observe the results, and continue its analysis in an agentic loop. By the end of the workflow, we give the agent a realistic multi-file e-commerce workspace and let it clean, join, analyze, visualize, and summarize the data as a structured analyst-grade report.

Installing DeepAnalyze-8B Runtime Dependencies

import os, sys, subprocess

os.environ["MPLBACKEND"] = "Agg"

def _pip(*args):