Objective: get the most useful agentic-coding agent out of one DGX Spark — long tool-using runs, big contexts, hours at a time — and write down what actually moved the needle.
Comments very welcome. Much of this was found the hard way, and some of it is probably still wrong or leaving performance on the table. If you run a Spark (or any GB10 / unified-memory box) and know better, please say so — that is why this is public. Open questions are listed at the end.
You need Github Copilot and VS Code for this method
git_repos/eptr2mcp.json to your project's .vscode/mcp.json, replace eptr2_REPOSITORY_PATH with that absolute path.
| from redis import Redis | |
| from dotenv import load_dotenv | |
| import os | |
| ### .env dosyasına Redis bağlantı bilgilerini ekleyebilirsiniz. | |
| load_dotenv() | |
| ## Redis bağlantısı kurun | |
| r = Redis( | |
| host=os.getenv("REDIS_HOST"), |
| ## Fiyat ve maliyet bilgisi alırken 2026 tahmini maliyetlerini de alma opsiyonu | |
| ## Bu opsiyon v1.3.2.dev0'dan itibaren geçerlidir ve 2026 yılı içerisinde değiştirilecektir | |
| from eptr2 import EPTR2 | |
| eptr = EPTR2(recycle_tgt=True) | |
| df = get_hourly_price_and_cost_data( | |
| eptr=eptr, | |
| start_date="2025-11-01", |
| from typing import Any | |
| import httpx | |
| from mcp.server.fastmcp import FastMCP | |
| from eptr2.main import eptr_w_tgt_wrapper, EPTR2 | |
| import pandas as pd | |
| # Initialize FastMCP server | |
| mcp = FastMCP("eptr2") | |
| eptr = eptr_w_tgt_wrapper() |
| from eptr2.util.costs import calculate_diff_cost | |
| res = calculate_diff_cost( | |
| actual=50, forecast=100, mcp=1000.0, smp=2500.0, prod_source="solar" | |
| ) | |
| ## print(res) | |
| # { | |
| # "imbalances": { | |
| # "diff": -50, |
| library(tidyverse) | |
| library(reticulate) | |
| if(FALSE){ | |
| print("Installing python packages") | |
| reticulate::py_install("eptr2") | |
| reticulate::py_install("pandas") | |
| reticulate::py_install("urllib3") | |
| } | |
| eptr2 <- reticulate::import("eptr2") | |
| eptr_obj <- eptr2$EPTR2() |
| ## GİP Hacim Karşılaştırma | |
| from eptr2 import EPTR2 | |
| import re | |
| import pandas as pd | |
| eptr = EPTR2() | |
| idm_qty_24 = eptr.call("idm-qty", start_date="2024-01-01", end_date="2024-01-03") | |
| idm_qty_23 = eptr.call("idm-qty", start_date="2023-01-01", end_date="2023-01-03") | |
| idm_vol_24 = eptr.call("idm-volume", start_date="2024-01-01", end_date="2024-01-03") |
| { | |
| // Use IntelliSense to learn about possible attributes. | |
| // Hover to view descriptions of existing attributes. | |
| // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 | |
| "version": "0.2.0", | |
| "configurations": [ | |
| { | |
| "name": "Streamlit: App", | |
| "type": "python", | |
| "request": "launch", |
| FROM python:3.10-slim | |
| ENV APP_HOME /app | |
| WORKDIR $APP_HOME | |
| # Allow statements and log messages to immediately appear in the Knative logs | |
| ENV PYTHONUNBUFFERED True | |
| ENV PORT 8080 | |
| RUN pip install --no-cache-dir --upgrade shiny |