- Keep the architecture lean, concise and simple.
- Always keep a check on the number of lines in the file. If the file exceeds the limit of 800 lines, break it down into multiple files.
- Always keep function within 100 lines of code and it should follow single responsibility principle
rajvermacas
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| //@version=5 | |
| indicator("ADR Percentage", shorttitle="ADR%", overlay=false) | |
| len = input.int(20, title="Length", minval=1) | |
| tf = input.timeframe("1D", title="Timeframe") | |
| threshold = input.float(5.0, title="Threshold %", minval=0.0) | |
| adrPct = request.security(syminfo.tickerid, tf, 100 * (ta.sma(high / low, len) - 1)) | |
| plot(adrPct, title="ADR%", color=color.blue, linewidth=2) |
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| Complete Summary: Deploying a Scikit-Learn Model on Databricks Model Serving | |
| What We Accomplished | |
| Successfully trained, registered, and deployed a scikit-learn Linear Regression model as a REST API endpoint on Databricks Model Serving. | |
| Step-by-Step Process | |
| 1. Initial Setup & Understanding | |
| Question: Can scikit-learn models trained on Databricks be served using Model Serving? | |
| Answer: Yes! Databricks supports custom models packaged in MLflow format, including scikit-learn. | |
| 2. Reviewed Existing Notebook (PocML) | |
| Cell 1: Basic model training |
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| javascript:(function(){ | |
| if (!window._capturingLogs) { | |
| window._capturingLogs = true; | |
| window._logs = []; | |
| const originalLog = console.log; | |
| console.log = function(...args) { | |
| window._logs.push(args.map(a => { | |
| try { return JSON.parse(JSON.stringify(a)); } catch(e) { return String(a); } | |
| })); | |
| originalLog.apply(console, args); |
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| import os | |
| from openai import OpenAI | |
| # Setup client | |
| client = OpenAI( | |
| api_key=os.getenv("AZURE_OPENAI_API_KEY"), | |
| base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/" | |
| ) | |
| # Send a message to o3 |
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| # filepath="C:\Users\<username>\.wslconfig" - remove this line | |
| [wsl2] | |
| memory=16GB |
Here are the essential tmux commands to get you started:
tmux- Start a new sessiontmux new -s sessionname- Start a new named sessiontmux ls- List all sessionstmux attach -t sessionname- Attach to a sessiontmux kill-session -t sessionname- Kill a sessiontmux kill-server- Kill the tmux server
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| description="Compact session scratchpad" | |
| prompt = """ | |
| Compact @.dev-resources/context/session-scratchpad.md from session 1 to session N-1 where N is the latest session's latest part. the idea is to reduce the number of tokens for the file. | |
| Make use of below template: | |
| # [Project Name] - Session Summary | |
| ## Session Overview | |
| [to be provided by the agent - brief description of main activities and timestamp] |
Adopt an extremely detailed, comprehensive, and methodical approach to explaining technical concepts. Break down complex ideas into their smallest constituent parts, explaining each component with exhaustive thoroughness. Use crystal-clear, precision-engineered language and provide multi-layered explanations that cover not just the main points, but also intricate edge cases, potential exceptions, theoretical variations, and deeply nuanced technical details. Approach explanations with a systematic, almost architectural methodology, building understanding incrementally through nested layers of explanation. Utilize sophisticated analogies, visual conceptual mappings, and concrete real-world examples to illuminate abstract or complex theoretical frameworks. Prioritize absolute clarity, profound depth, and a comprehensive, almost forensic exploration of the topic that leaves no conceptual stone unturned. Create an interactive educational artifact demonstrating a beautiful, modern visual interface that incorporates
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| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Modern Web Communication Patterns - Interactive Guide</title> | |
| <style> | |
| * { | |
| margin: 0; | |
| padding: 0; |
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