goddoe

apiVersion: kubeflow.org/v1
kind: PyTorchJob
metadata:
  name: pytorch-rlyx-trainer
  namespace: p-ksj
spec:
  elasticPolicy:
    rdzvId: pytorch-job-rlyx-trainer
    rdzvBackend: c10d
    minReplicas: 4

goddoe

#!/bin/bash

set -e  # Exit on error

echo "=========================================="
echo "Installing tmux, docker, and uv"
echo "=========================================="
echo ""

# Check if running as root

goddoe

SRS(Software Requirements Specification) 구성

IEEE 830, ISO/IEC 29148 같은 표준을 따르기도 하는데, 보통 다음 섹션들이 많이 쓰임

1. 서론 (Introduction)

• 목적 (Purpose)
• 범위 (Scope)
• 정의 및 약어 (Definitions, Acronyms, Abbreviations)
• 참조 문서 (References)

2. 전체 설명 (Overall Description)

• 제품 관점 (Product Perspective) → 시스템 아키텍처, 기존 시스템과 관계

goddoe

// Claude Code is a Beta product per Anthropic's Commercial Terms of Service.
// By using Claude Code, you agree that all code acceptance or rejection decisions you make,
// and the associated conversations in context, constitute Feedback under Anthropic's Commercial Terms,
// and may be used to improve Anthropic's products, including training models.
// You are responsible for reviewing any code suggestions before use.

// (c) Anthropic PBC. All rights reserved. Use is subject to Anthropic's Commercial Terms of Service (https://www.anthropic.com/legal/commercial-terms).

// Version: 0.2.9

goddoe

You are a powerful agentic AI coding assistant, powered by Claude 3.5 Sonnet. You operate exclusively in Cursor, the world's best IDE.

You are pair programming with a USER to solve their coding task.
The task may require creating a new codebase, modifying or debugging an existing codebase, or simply answering a question.
Each time the USER sends a message, we may automatically attach some information about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more.
This information may or may not be relevant to the coding task, it is up for you to decide.
Your main goal is to follow the USER's instructions at each message, denoted by the <user_query> tag.

<communication>
1. Be conversational but professional.

goddoe

Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach

goddoe

pip freeze를 통해 나온 결과에서 특정 패키지 목록에 해당하는 패키지만 필터링하려면 아래와 같은 쉘 스크립트를 사용할 수 있습니다. 이 예시는 Unix 기반 시스템(Linux, macOS)에서 사용할 수 있습니다.

1. pip freeze의 결과를 installed_packages.txt 파일에 저장합니다.
2. 패키지 목록이 포함된 텍스트 파일을 packages_list.txt라고 가정합니다.

# 패키지 목록을 파일로 저장
pip freeze > installed_packages.txt

# 파일에 있는 패키지 목록을 기반으로 필터링

goddoe

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

# 예제 데이터 생성
X, _ = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)

# SSE 값을 저장할 리스트
sse = []

goddoe

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer


input_path = "./model_in"
output_path = "./model_out"
max_shard_size = "5GB"
new_max_length = 4096

print("load model...start")

goddoe

def extract_code(inputs: dict) -> dict:
  text = inputs["text"]
  result = re.search(r'```.*?\n(.*?)\n```', text, re.DOTALL)
  result = result.group(1) if result else text
  return {"output": result}