Guides - Production Best Practices

https://beta.openai.com/docs/guides/production-best-practices

OpenAI API

Production Best Practices

This guide provides a comprehensive set of best practices to help you transition from prototype to production. Whether you are a seasoned machine learning engineer or a recent enthusiast, this guide should provide you with the tools you need to successfully put the platform to work in a production setting: from securing access to our API to designing a robust architecture that can handle high traffic volumes. Use this guide to help develop a plan for deploying your application as smoothly and effectively as possible.

 

이 가이드는 프로토타입에서 프로덕션으로 전환하는 데 도움이 되는 포괄적인 모범 사례를 제공합니다. 노련한 기계 학습 엔지니어이든 최근에 열광하는 사람이든 관계 없이 이 가이드는 플랫폼을 생산 환경에서 성공적으로 작동시키는 데 필요한 도구를 제공합니다. API에 대한 액세스 보안에서 높은 수준을 처리할 수 있는 강력한 아키텍처 설계에 이르기까지 교통량을 핸들링 할 수 있는 아키텍쳐를 제공합니다. 이 가이드를 사용하면 애플리케이션을 최대한 원활하고 효과적으로 배포하기 위한 계획을 개발하는 데 도움이 됩니다.

 

Setting up your organization

Once you log in to your OpenAI account, you can find your organization name and ID in your organization settings. The organization name is the label for your organization, shown in user interfaces. The organization ID is the unique identifier for your organization which can be used in API requests.

 

OpenAI 계정에 로그인하면 조직 설정에서 조직 이름과 ID를 찾을 수 있습니다. 조직 이름은 사용자 인터페이스에 표시되는 조직의 레이블입니다. 조직 ID는 API 요청에 사용할 수 있는 조직의 고유 식별자입니다.

 

Users who belong to multiple organizations can pass a header to specify which organization is used for an API request. Usage from these API requests will count against the specified organization's quota. If no header is provided, the default organization will be billed. You can change your default organization in your user settings.

 

여러 조직에 속한 사용자는 헤더를 전달하여 API 요청에 사용되는 조직을 지정할 수 있습니다. 이러한 API 요청의 사용량은 지정된 조직의 할당량에 포함됩니다. 헤더가 제공되지 않으면 기본 조직에 요금이 청구됩니다. 사용자 설정에서 기본 조직을 변경할 수 있습니다.

 

You can invite new members to your organization from the members settings page. Members can be readers or owners. Readers can make API requests and view basic organization information, while owners can modify billing information and manage members within an organization.

 

구성원 설정 페이지에서 조직에 새 구성원을 초대할 수 있습니다. 구성원은 독자 또는 소유자일 수 있습니다. 독자는 API 요청을 하고 기본 조직 정보를 볼 수 있으며 소유자는 청구 정보를 수정하고 조직 내 구성원을 관리할 수 있습니다.

 

Managing billing limits

New free trial users receive an initial credit of $18 that expires after three months. Once the credit has been used or expires, you can choose to enter billing information to continue your use of the API. If no billing information is entered, you will still have login access but will be unable to make any further API requests.

 

새로운 무료 평가판 사용자는 3개월 후에 만료되는 $18의 초기 크레딧을 받습니다. 크레딧이 사용되었거나 만료되면 결제 정보를 입력하여 API를 계속 사용할 수 있습니다. 결제 정보를 입력하지 않으면 로그인 액세스 권한은 계속 유지되지만 더 이상 API 요청을 할 수 없습니다.

 

Once you’ve entered your billing information, you will have an approved usage limit of $120 per month, which is set by OpenAI. To increase your quota beyond the $120 monthly billing limit, please submit a quota increase request.

 

청구 정보를 입력하면 OpenAI에서 설정한 월 $120의 사용 한도가 승인됩니다. $120 월 청구 한도 이상으로 할당량을 늘리려면 할당량 증가 요청을 제출하십시오.

 

If you’d like to be notified when your usage exceeds a certain amount, you can set a soft limit through the usage limits page. When the soft limit is reached, the owners of the organization will receive an email notification. You can also set a hard limit so that, once the hard limit is reached, any subsequent API requests will be rejected. Note that these limits are best effort, and there may be 5 to 10 minutes of delay between the usage and the limits being enforced.

 

사용량이 일정량을 초과할 때 알림을 받으려면 사용량 한도 페이지를 통해 소프트 한도를 설정할 수 있습니다. 소프트 제한에 도달하면 조직 소유자는 이메일 알림을 받게 됩니다. 하드 제한에 도달하면 후속 API 요청이 거부되도록 하드 제한을 설정할 수도 있습니다. 이러한 제한은 최선의 노력이며 사용량과 적용되는 제한 사이에 5~10분의 지연이 있을 수 있습니다.

 

API keys

The OpenAI API uses API keys for authentication. Visit your API keys page to retrieve the API key you'll use in your requests.

 

OpenAI API는 인증을 위해 API 키를 사용합니다. 요청에 사용할 API 키를 검색하려면 API 키 페이지를 방문하세요.

 

This is a relatively straightforward way to control access, but you must be vigilant about securing these keys. Avoid exposing the API keys in your code or in public repositories; instead, store them in a secure location. You should expose your keys to your application using environment variables or secret management service, so that you don't need to hard-code them in your codebase. Read more in our Best practices for API key safety.

 

이것은 액세스를 제어하는 비교적 간단한 방법이지만 이러한 키를 보호하는 데 주의를 기울여야 합니다. 코드 또는 공개 리포지토리에 API 키를 노출하지 마십시오. 대신 안전한 장소에 보관하십시오. 코드베이스에서 키를 하드 코딩할 필요가 없도록 환경 변수 또는 비밀 관리 서비스를 사용하여 키를 애플리케이션에 노출해야 합니다. API 키 안전을 위한 모범 사례에서 자세히 알아보세요.

 

Staging accounts

As you scale, you may want to create separate organizations for your staging and production environments. Please note that you can sign up using two separate email addresses like bob+prod@widgetcorp.com and bob+dev@widgetcorp.com to create two organizations. This will allow you to isolate your development and testing work so you don't accidentally disrupt your live application. You can also limit access to your production organization this way.

 

규모를 조정함에 따라 스테이징 및 프로덕션 환경에 대해 별도의 조직을 생성할 수 있습니다. bob+prod@widgetcorp.com 및 bob+dev@widgetcorp.com과 같은 두 개의 개별 이메일 주소를 사용하여 가입하여 두 개의 조직을 만들 수 있습니다. 이를 통해 개발 및 테스트 작업을 분리하여 실수로 라이브 애플리케이션을 중단하지 않도록 할 수 있습니다. 이러한 방식으로 프로덕션 조직에 대한 액세스를 제한할 수도 있습니다.

 

Building your prototype

If you haven’t gone through the quickstart guide, we recommend you start there before diving into the rest of this guide.

 

빠른 시작 가이드를 살펴보지 않은 경우 이 가이드의 나머지 부분을 시작하기 전에 여기에서 시작하는 것이 좋습니다.

 

For those new to the OpenAI API, our playground can be a great resource for exploring its capabilities. Doing so will help you learn what's possible and where you may want to focus your efforts. You can also explore our example prompts.

 

OpenAI API를 처음 사용하는 사용자에게 Playground는 해당 기능을 탐색하는 데 유용한 리소스가 될 수 있습니다. 그렇게 하면 무엇이 가능한지, 어디에 노력을 집중해야 하는지 알 수 있습니다. 예제 프롬프트를 탐색할 수도 있습니다.

 

While the playground is a great place to prototype, it can also be used as an incubation area for larger projects. The playground also makes it easy to export code snippets for API requests and share prompts with collaborators, making it an integral part of your development process.

 

놀이터(playground)는 프로토타입을 만들기에 좋은 장소이지만 대규모 프로젝트를 위한 인큐베이션 영역으로도 사용할 수 있습니다. 또한 Playground를 사용하면 API 요청에 대한 코드 스니펫을 쉽게 내보내고 공동 작업자와 프롬프트를 공유하여 개발 프로세스의 필수적인 부분이 됩니다.

 

Additional tips

1. Start by determining the core functionalities you want your application to have. Consider the types of data inputs, outputs, and processes you will need. Aim to keep the prototype as focused as possible, so that you can iterate quickly and efficiently.
   
   애플리케이션에 원하는 핵심 기능을 결정하는 것부터 시작하십시오. 필요한 데이터 입력, 출력 및 프로세스 유형을 고려하십시오. 신속하고 효율적으로 반복할 수 있도록 가능한 한 프로토타입에 초점을 맞추는 것을 목표로 하십시오.
   
   
2. Choose the programming language and framework that you feel most comfortable with and that best aligns with your goals for the project. Some popular options include Python, Java, and Node.js. See library support page to learn more about the library bindings maintained both by our team and by the broader developer community.
   
   가장 편안하고 프로젝트 목표에 가장 부합하는 프로그래밍 언어와 프레임워크를 선택하세요. 인기 있는 옵션으로는 Python, Java 및 Node.js가 있습니다. 우리 팀과 광범위한 개발자 커뮤니티에서 유지 관리하는 라이브러리 바인딩에 대해 자세히 알아보려면 라이브러리 지원 페이지를 참조하세요.
   
   
3. Development environment and support: Set up your development environment with the right tools and libraries and ensure you have the resources you need to train your model. Leverage our documentation, community forum and our help center to get help with troubleshooting. If you are developing using Python, take a look at this structuring your project guide (repository structure is a crucial part of your project’s architecture). In order to connect with our support engineers, simply log in to your account and use the "Help" button to start a conversation.
   
   개발 환경 및 지원: 올바른 도구와 라이브러리로 개발 환경을 설정하고 모델 교육에 필요한 리소스가 있는지 확인합니다. 설명서, 커뮤니티 포럼 및 도움말 센터를 활용하여 문제 해결에 대한 도움을 받으세요. Python을 사용하는 개발하인 경우 이 프로젝트 구조화 가이드를 살펴보세요(리포지토리 구조는 프로젝트 아키텍처의 중요한 부분입니다). 지원 엔지니어와 연결하려면 계정에 로그인하고 "도움말" 버튼을 사용하여 대화를 시작하십시오.
   
   

Techniques for improving reliability around prompts

Even with careful planning, it's important to be prepared for unexpected issues when using GPT-3 in your application. In some cases, the model may fail on a task, so it's helpful to consider what you can do to improve the reliability of your application.

 

신중하게 계획하더라도 응용 프로그램에서 GPT-3을 사용할 때 예기치 않은 문제에 대비하는 것이 중요합니다. 경우에 따라 모델이 작업에 실패할 수 있으므로 애플리케이션의 안정성을 개선하기 위해 수행할 수 있는 작업을 고려하는 것이 좋습니다.

 

If your task involves logical reasoning or complexity, you may need to take additional steps to build more reliable prompts. For some helpful suggestions, consult our Techniques to improve reliability guide. Overall the recommendations revolve around:

 

작업에 논리적 추론이나 복잡성이 포함된 경우 보다 신뢰할 수 있는 프롬프트를 작성하기 위해 추가 단계를 수행해야 할 수 있습니다. 몇 가지 유용한 제안을 보려면 안정성 가이드를 개선하기 위한 기술을 참조하십시오. 전반적으로 권장 사항은 다음과 같습니다.

 

• Decomposing unreliable operations into smaller, more reliable operations (e.g., selection-inference prompting)
  신뢰할 수 없는 작업을 더 작고 더 안정적인 작업으로 분해(예: 선택-추론 프롬프팅)
  
  
• Using multiple steps or multiple relationships to make the system's reliability greater than any individual component (e.g., maieutic prompting)
  여러 단계 또는 여러 관계를 사용하여 시스템의 안정성을 개별 구성 요소보다 크게 만듭니다(예: 기계 프롬프트).

 

Evaluation and iteration

One of the most important aspects of developing a system for production is regular evaluation and iterative experimentation. This process allows you to measure performance, troubleshoot issues, and fine-tune your models to improve accuracy and efficiency. A key part of this process is creating an evaluation dataset for your functionality. Here are a few things to keep in mind:

 

생산 시스템 개발의 가장 중요한 측면 중 하나는 정기적인 평가와 반복 실험입니다. 이 프로세스를 통해 성능을 측정하고, 문제를 해결하고, 모델을 미세 조정하여 정확도와 효율성을 높일 수 있습니다. 이 프로세스의 핵심 부분은 기능에 대한 평가 데이터 세트를 만드는 것입니다. 다음은 염두에 두어야 할 몇 가지 사항입니다.

 

1. Make sure your evaluation set is representative of the data your model will be used on in the real world. This will allow you to assess your model's performance on data it hasn't seen before and help you understand how well it generalizes to new situations.
   
   평가 세트가 실제 세계에서 모델이 사용될 데이터를 대표하는지 확인하십시오. 이를 통해 이전에 본 적이 없는 데이터에 대한 모델의 성능을 평가하고 새로운 상황에 얼마나 잘 일반화되는지 이해할 수 있습니다.
   
   
2. Regularly update your evaluation set to ensure that it stays relevant as your model evolves and as new data becomes available.
   
   평가 세트를 정기적으로 업데이트하여 모델이 발전하고 새 데이터를 사용할 수 있을 때 관련성을 유지하도록 합니다.
   
   
3. Use a variety of metrics to evaluate your model's performance. Depending on your application and business outcomes, this could include accuracy, precision, recall, F1 score, or mean average precision (MAP). Additionally, you can sync your fine-tunes with Weights & Biases to track experiments, models, and datasets.
   
   다양한 메트릭을 사용하여 모델의 성능을 평가하십시오. 애플리케이션 및 비즈니스 결과에 따라 정확도, 정밀도, 재현율, F1 점수 또는 MAP(평균 평균 정밀도)가 포함될 수 있습니다. 또한 미세 조정을 Weights & Biases와 동기화하여 실험, 모델 및 데이터 세트를 추적할 수 있습니다.
   
   
4. Compare your model's performance against baseline. This will give you a better understanding of your model's strengths and weaknesses and can help guide your future development efforts.
   
   모델의 성능을 기준과 비교하십시오. 이렇게 하면 모델의 강점과 약점을 더 잘 이해할 수 있고 향후 개발 노력을 안내하는 데 도움이 될 수 있습니다.
   
   

By conducting regular evaluation and iterative experimentation, you can ensure that your GPT-powered application or prototype continues to improve over time.

 

정기적인 평가와 반복적인 실험을 통해 GPT 기반 애플리케이션 또는 프로토타입이 시간이 지남에 따라 지속적으로 개선되도록 할 수 있습니다.

 

Evaluating language models

Language models can be difficult to evaluate because evaluating the quality of generated language is often subjective, and there are many different ways to communicate the same message correctly in language. For example, when evaluating a model on the ability to summarize a long passage of text, there are many correct summaries. That being said, designing good evaluations is critical to making progress in machine learning.

 

언어 모델은 생성된 언어의 품질을 평가하는 것이 주관적인 경우가 많고 동일한 메시지를 언어로 올바르게 전달하는 다양한 방법이 있기 때문에 평가하기 어려울 수 있습니다. 예를 들어, 긴 텍스트 구절을 요약하는 능력에 대한 모델을 평가할 때 정확한 요약이 많이 있습니다. 즉, 좋은 평가를 설계하는 것은 기계 학습을 발전시키는 데 중요합니다.

 

An eval suite needs to be comprehensive, easy to run, and reasonably fast (depending on model size). It also needs to be easy to continue to add to the suite as what is comprehensive one month will likely be out of date in another month. We should prioritize having a diversity of tasks and tasks that identify weaknesses in the models or capabilities that are not improving with scaling.

 

평가 도구 모음은 포괄적이고 실행하기 쉬우며 합리적으로 빨라야 합니다(모델 크기에 따라 다름). 또한 한 달 동안 포괄적인 내용이 다음 달에는 구식이 될 수 있으므로 제품군에 계속 추가하기 쉬워야 합니다. 모델의 약점이나 확장으로 개선되지 않는 기능을 식별하는 다양한 작업과 작업을 우선시해야 합니다.

 

The simplest way to evaluate your system is to manually inspect its outputs. Is it doing what you want? Are the outputs high quality? Are they consistent?

 

시스템을 평가하는 가장 간단한 방법은 출력을 수동으로 검사하는 것입니다. 당신이 원하는대로하고 있습니까? 출력물이 고품질입니까? 일관성이 있습니까?

 

Automated evaluations

The best way to test faster is to develop automated evaluations. However, this may not be possible in more subjective applications like summarization tasks.

 

더 빠르게 테스트하는 가장 좋은 방법은 자동화된 평가를 개발하는 것입니다. 그러나 이것은 요약 작업과 같은 보다 주관적인 응용 프로그램에서는 불가능할 수 있습니다.

 

Automated evaluations work best when it’s easy to grade a final output as correct or incorrect. For example, if you’re fine-tuning a classifier to classify text strings as class A or class B, it’s fairly simple: create a test set with example input and output pairs, run your system on the inputs, and then grade the system outputs versus the correct outputs (looking at metrics like accuracy, F1 score, cross-entropy, etc.).

 

자동화된 평가는 최종 결과물을 정확하거나 부정확한 것으로 쉽게 등급을 매길 때 가장 잘 작동합니다. 예를 들어 텍스트 문자열을 클래스 A 또는 클래스 B로 분류하기 위해 분류자를 미세 조정하는 경우 매우 간단합니다. 예를 들어 입력 및 출력 쌍으로 테스트 세트를 만들고 입력에서 시스템을 실행한 다음 시스템 등급을 지정합니다. 출력 대 올바른 출력(정확도, F1 점수, 교차 엔트로피 등과 같은 메트릭 보기).

 

If your outputs are semi open-ended, as they might be for a meeting notes summarizer, it can be trickier to define success: for example, what makes one summary better than another? Here, possible techniques include:

 

출력물이 회의록 요약기용일 수 있으므로 반 개방형인 경우 성공을 정의하기가 더 까다로울 수 있습니다. 예를 들어 어떤 요약이 다른 요약보다 더 나은가요? 같은...  여기서 사용 가능한 기술은 다음과 같습니다.

 

• Writing a test with ‘gold standard’ answers and then measuring some sort of similarity score between each gold standard answer and the system output (we’ve seen embeddings work decently well for this)
  
  '골드 스탠다드' 답변으로 테스트를 작성한 다음 각 골드 스탠다드 답변과 시스템 출력 사이의 일종의 유사성 점수를 측정합니다(임베딩이 이에 대해 적절하게 작동하는 것을 확인했습니다).
  
  
• Building a discriminator system to judge / rank outputs, and then giving that discriminator a set of outputs where one is generated by the system under test (this can even be GPT model that is asked whether the question is answered correctly by a given output)
  
  출력을 판단하고 순위를 매기는 판별기 시스템을 구축한 다음 해당 판별기에 테스트 중인 시스템에서 생성된 출력 세트를 제공합니다(이는 주어진 출력에 의해 질문이 올바르게 대답되었는지 묻는 GPT 모델일 수도 있음)
  
  
• Building an evaluation model that checks for the truth of components of the answer; e.g., detecting whether a quote actually appears in the piece of given text
  
  답변 구성 요소의 진실성을 확인하는 평가 모델 구축 예를 들어 인용문이 주어진 텍스트에 실제로 나타나는지 여부를 감지합니다.
  
  

For very open-ended tasks, such as a creative story writer, automated evaluation is more difficult. Although it might be possible to develop quality metrics that look at spelling errors, word diversity, and readability scores, these metrics don’t really capture the creative quality of a piece of writing. In cases where no good automated metric can be found, human evaluations remain the best method.

창의적인 스토리 작가와 같이 제한이 없는 작업의 경우 자동화된 평가가 더 어렵습니다. 맞춤법 오류, 단어 다양성 및 가독성 점수를 살펴보는 품질 메트릭을 개발하는 것이 가능할 수 있지만 이러한 메트릭은 글의 창의적인 품질을 실제로 포착하지 못합니다. 좋은 자동 메트릭을 찾을 수 없는 경우 여전히 사람의 평가가 가장 좋은 방법입니다.

Example procedure for evaluating a GPT-3-based system

As an example, let’s consider the case of building a retrieval-based Q&A system.

 

예를 들어 검색 기반 Q&A 시스템을 구축하는 경우를 생각해 봅시다.

 

A retrieval-based Q&A system has two steps. First, a user’s query is used to rank potentially relevant documents in a knowledge base. Second, GPT-3 is given the top-ranking documents and asked to generate an answer to the query.

 

검색 기반 Q&A 시스템에는 두 단계가 있습니다. 첫째, 사용자의 쿼리는 지식 기반에서 잠재적으로 관련이 있는 문서의 순위를 매기는 데 사용됩니다. 둘째, GPT-3에게 최상위 문서를 제공하고 쿼리에 대한 답변을 생성하도록 요청합니다.

 

Evaluations can be made to measure the performance of each step.

 

평가는 각 단계의 성과를 측정하기 위해 이루어질 수 있습니다.

 

For the search step, one could:

 

검색 단계는 이렇게 진행 될 수 있습니다.

 

• First, generate a test set with ~100 questions and a set of correct documents for each
  
  먼저 ~100개의 질문과 각각에 대한 올바른 문서 세트로 테스트 세트를 생성합니다.
  
  
  • The questions can be sourced from user data if you have any; otherwise, you can invent a set of questions with diverse styles and difficulty.
  • 질문이 있는 경우 사용자 데이터에서 질문을 가져올 수 있습니다. 그렇지 않으면 다양한 스타일과 난이도의 일련의 질문을 만들 수 있습니다.
  • For each question, have a person manually search through the knowledge base and record the set of documents that contain the answer.
  • 각 질문에 대해 한 사람이 수동으로 기술 자료를 검색하고 답변이 포함된 문서 세트를 기록하도록 합니다.
• Second, use the test set to grade the system’s performance
  둘째, 테스트 세트를 사용하여 시스템의 성능 등급을 매깁니다.
  
  • For each question, use the system to rank the candidate documents (e.g., by cosine similarity of the document embeddings with the query embedding).
  • 각 질문에 대해 시스템을 사용하여 후보 문서의 순위를 매깁니다(예: 쿼리 임베딩과 문서 임베딩의 코사인 유사성 기준).
  • You can score the results with a binary accuracy score of 1 if the candidate documents contain at least 1 relevant document from the answer key and 0 otherwise
  • 후보 문서에 답변 키의 관련 문서가 1개 이상 포함되어 있으면 이진 정확도 점수 1로 결과를 채점하고 그렇지 않으면 0으로 점수를 매길 수 있습니다.
  • You can also use a continuous metric like Mean Reciprocal Rank which can help distinguish between answers that were close to being right or far from being right (e.g., a score of 1 if the correct document is rank 1, a score of ½ if rank 2, a score of ⅓ if rank 3, etc.)
  • 또한 평균 역수 순위와 같은 연속 메트릭을 사용하여 정답에 가까운 답변과 그렇지 않은 답변을 구별할 수 있습니다(예: 올바른 문서가 순위 1인 경우 점수 1점, 순위 2인 경우 점수 ½) , 3순위인 경우 ⅓점 등)

 

For the question answering step, one could:

 

이 질문에 대한 답변의 단계는 이럴 수 있습니다.

 

• First, generate a test set with ~100 sets of {question, relevant text, correct answer}
  먼저 {질문, 관련 텍스트, 정답}의 ~100세트로 테스트 세트를 생성합니다.
  
  • For the questions and relevant texts, use the above data
  • 질문 및 관련 텍스트는 위의 데이터를 사용하십시오.
  • For the correct answers, have a person write down ~100 examples of what a great answer looks like.
  • 정답을 찾기 위해 한 사람에게 훌륭한 답변이 어떤 것인지에 대한 ~100개의 예를 적어보라고 합니다.

 

Second, use the test set to grade the system’s performance
둘째, 테스트 세트를 사용하여 시스템의 성능 등급을 매깁니다.

• For each question & text pair, combine them into a prompt and submit the prompt to GPT-3
• 각 질문 및 텍스트 쌍에 대해 프롬프트로 결합하고 프롬프트를 GPT-3에 제출합니다.
• Next, compare GPT-3’s answers to the gold-standard answer written by a human
• 다음으로 GPT-3의 답변을 인간이 작성한 표준 답변과 비교합니다. 
  • This comparison can be manual, where humans look at them side by side and grade whether the GPT-3 answer is correct/high quality
  • 이 비교는 사람이 나란히 보고 GPT-3 답변이 올바른지/높은 품질인지 등급을 매기는 수동 방식일 수 있습니다.
  • This comparison can also be automated, by using embedding similarity scores or another method (automated methods will likely be noisy, but noise is ok as long as it’s unbiased and equally noisy across different types of models that you’re testing against one another)
  • 이 비교는 임베딩 유사성 점수 또는 다른 방법을 사용하여 자동화할 수도 있습니다. (자동화된 방법은 잡음이 많을 수 있지만 서로 테스트하는 여러 유형의 모델에서 편향되지 않고 잡음이 동일하다면 잡음은 괜찮습니다.)

Of course, N=100 is just an example, and in early stages, you might start with a smaller set that’s easier to generate, and in later stages, you might invest in a larger set that’s more costly but more statistically reliable.

 

물론 N=100은 예일 뿐이며 초기 단계에서는 생성하기 쉬운 더 작은 세트로 시작할 수 있고, 이후 단계에서는 비용이 더 많이 들지만 통계적으로 더 신뢰할 수 있는 더 큰 세트에 투자할 수 있습니다.

 

Scaling your solution architecture

When designing your application or service for production that uses our API, it's important to consider how you will scale to meet traffic demands. There are a few key areas you will need to consider regardless of the cloud service provider of your choice:

 

API를 사용하는 프로덕션용 애플리케이션 또는 서비스를 설계할 때 트래픽 수요를 충족하기 위해 확장하는 방법을 고려하는 것이 중요합니다. 선택한 클라우드 서비스 공급자에 관계없이 고려해야 할 몇 가지 주요 영역이 있습니다.

 

• Horizontal scaling: You may want to scale your application out horizontally to accommodate requests to your application that come from multiple sources. This could involve deploying additional servers or containers to distribute the load. If you opt for this type of scaling, make sure that your architecture is designed to handle multiple nodes and that you have mechanisms in place to balance the load between them.
  
  
• 수평 확장: 여러 소스에서 오는 애플리케이션에 대한 요청을 수용하기 위해 애플리케이션을 수평으로 확장할 수 있습니다. 여기에는 로드를 분산하기 위해 추가 서버 또는 컨테이너를 배포하는 작업이 포함될 수 있습니다. 이러한 유형의 확장을 선택하는 경우 아키텍처가 여러 노드를 처리하도록 설계되었고 노드 간에 로드 균형을 유지하는 메커니즘이 있는지 확인하십시오.
  
  
• Vertical scaling: Another option is to scale your application up vertically, meaning you can beef up the resources available to a single node. This would involve upgrading your server's capabilities to handle the additional load. If you opt for this type of scaling, make sure your application is designed to take advantage of these additional resources.
  
  
• 수직 확장: 또 다른 옵션은 애플리케이션을 수직으로 확장하는 것입니다. 즉, 단일 노드에서 사용 가능한 리소스를 강화할 수 있습니다. 여기에는 추가 로드를 처리하기 위한 서버 기능 업그레이드가 포함됩니다. 이러한 유형의 확장을 선택하는 경우 애플리케이션이 이러한 추가 리소스를 활용하도록 설계되었는지 확인하십시오.
  
  
• Caching: By storing frequently accessed data, you can improve response times without needing to make repeated calls to our API. Your application will need to be designed to use cached data whenever possible and invalidate the cache when new information is added. There are a few different ways you could do this. For example, you could store data in a database, filesystem, or in-memory cache, depending on what makes the most sense for your application.
  
  
• 캐싱: 자주 액세스하는 데이터를 저장하면 API를 반복적으로 호출하지 않고도 응답 시간을 개선할 수 있습니다. 애플리케이션은 가능할 때마다 캐시된 데이터를 사용하고 새 정보가 추가되면 캐시를 무효화하도록 설계해야 합니다. 이를 수행할 수 있는 몇 가지 방법이 있습니다. 예를 들어 애플리케이션에 가장 적합한 항목에 따라 데이터베이스, 파일 시스템 또는 메모리 내 캐시에 데이터를 저장할 수 있습니다.
  
  
• Load balancing: Finally, consider load-balancing techniques to ensure requests are distributed evenly across your available servers. This could involve using a load balancer in front of your servers or using DNS round-robin. Balancing the load will help improve performance and reduce bottlenecks.
  
  
• 로드 밸런싱: 마지막으로 로드 밸런싱 기술을 고려하여 요청이 사용 가능한 서버에 고르게 분산되도록 합니다. 여기에는 서버 앞에서 로드 밸런서를 사용하거나 DNS 라운드 로빈을 사용하는 것이 포함될 수 있습니다. 로드 균형을 조정하면 성능을 개선하고 병목 현상을 줄이는 데 도움이 됩니다.
  
  

Managing rate limits and latency

When using our API, it's important to understand and plan for rate limits. Exceeding these limits will result in error messages and can disrupt your application's performance. Rate limits are in place for a variety of reasons, from preventing abuse to ensuring everyone has fair access to the API.

 

API를 사용할 때 속도 제한을 이해하고 계획하는 것이 중요합니다. 이러한 제한을 초과하면 오류 메시지가 표시되고 애플리케이션 성능이 저하될 수 있습니다. 속도 제한은 악용 방지에서 모든 사람이 API에 공정하게 액세스할 수 있도록 보장하는 등 다양한 이유로 적용됩니다.

 

To avoid running into them, keep these tips in mind:

 

이러한 문제가 발생하지 않도록 하려면 다음 팁을 염두에 두십시오.

• Monitor your API usage to stay within your rate limit thresholds. Consider implementing exponential backoff and retry logic so your application can pause and retry requests if it hits a rate limit. (see example below).
  
  
• API 사용량을 모니터링하여 속도 제한 임계값을 유지하세요. 애플리케이션이 속도 제한에 도달하면 요청을 일시 중지하고 다시 시도할 수 있도록 지수 백오프 및 재시도 로직을 구현하는 것을 고려하십시오. (아래 예 참조).
  
  
• Use caching strategies to reduce the number of requests your application needs to make.
  
  
• 캐싱 전략을 사용하여 애플리케이션에 필요한 요청 수를 줄입니다.
  
  
• If your application consistently runs into rate limits, you may need to adjust your architecture or strategies to use the API in a less demanding manner.
  
  
• 애플리케이션이 지속적으로 속도 제한에 도달하는 경우 덜 까다로운 방식으로 API를 사용하도록 아키텍처 또는 전략을 조정해야 할 수 있습니다.
  
  

To avoid hitting the rate limit, we generally recommend implementing exponential backoff to your request code. For additional tips and guidance, consult our How to handle rate limits guide. In Python, an exponential backoff solution might look like this:

 

속도 제한에 도달하지 않으려면 일반적으로 요청 코드에 지수 백오프를 구현하는 것이 좋습니다. 추가 팁 및 지침은 속도 제한 처리 방법 가이드를 참조하세요. Python에서 지수 백오프 솔루션은 다음과 같습니다.

 

 

import backoff
import openai
from openai.error import RateLimitError

@backoff.on_exception(backoff.expo, RateLimitError)
def completions_with_backoff(**kwargs):
    response = openai.Completion.create(**kwargs)
    return response

 

 

(Please note: The backoff library is a third-party tool. We encourage all our users to do their due diligence when it comes to validating any external code for their projects.)

 

(참고: 백오프 라이브러리는 타사 도구입니다. 우리는 모든 사용자가 자신의 프로젝트에 대한 외부 코드의 유효성을 검사할 때 실사를 수행할 것을 권장합니다.)

 

As noted in the cookbook, If you're processing real-time requests from users, backoff and retry is a great strategy to minimize latency while avoiding rate limit errors. However, if you're processing large volumes of batch data, where throughput matters more than latency, you can do a few other things in addition to backoff and retry, for example, proactively adding delay between requests and batching requests. Note that our API has separate limits for requests per minute and tokens per minute.

 

cookbook에서 언급했듯이 사용자의 실시간 요청을 처리하는 경우 백오프 및 재시도는 속도 제한 오류를 피하면서 대기 시간을 최소화하는 훌륭한 전략입니다. 그러나 대기 시간보다 처리량이 더 중요한 대량의 배치 데이터를 처리하는 경우 백오프 및 재시도 외에 몇 가지 다른 작업을 수행할 수 있습니다(예: 요청 및 배치 요청 사이에 사전에 지연 추가). API에는 분당 요청과 분당 토큰에 대한 별도의 제한이 있습니다.

 

Managing costs

To monitor your costs, you can set a soft limit in your account to receive an email alert once you pass a certain usage threshold. You can also set a hard limit. Please be mindful of the potential for a hard limit to cause disruptions to your application/users. Use the usage tracking dashboard to monitor your token usage during the current and past billing cycles.

 

비용을 모니터링하기 위해 특정 사용 임계값을 초과하면 이메일 알림을 받도록 계정에 소프트 한도를 설정할 수 있습니다. 하드 제한을 설정할 수도 있습니다. 엄격한 제한으로 인해 애플리케이션/사용자가 중단될 가능성이 있음을 염두에 두십시오. 사용량 추적 대시보드를 사용하여 현재 및 과거 청구 주기 동안 토큰 사용량을 모니터링하십시오.

 

Text generation

One of the challenges of moving your prototype into production is budgeting for the costs associated with running your application. OpenAI offers a pay-as-you-go pricing model, with prices per 1,000 tokens (roughly equal to 750 words). To estimate your costs, you will need to project the token utilization. Consider factors such as traffic levels, the frequency with which users will interact with your application, and the amount of data you will be processing.

 

프로토타입을 프로덕션으로 옮기는 데 따르는 문제 중 하나는 애플리케이션 실행과 관련된 비용을 위한 예산을 책정하는 것입니다. OpenAI는 1,000개 토큰(대략 750단어에 해당)당 가격으로 종량제 가격 책정 모델을 제공합니다. 비용을 추정하려면 토큰 활용도를 예측해야 합니다. 트래픽 수준, 사용자가 애플리케이션과 상호 작용하는 빈도, 처리할 데이터 양과 같은 요소를 고려하십시오.

 

One useful framework for thinking about reducing costs is to consider costs as a function of the number of tokens and the cost per token. There are two potential avenues for reducing costs using this framework. First, you could work to reduce the cost per token by switching to smaller models for some tasks in order to reduce costs. Alternatively, you could try to reduce the number of tokens required. There are a few ways you could do this, such as by using shorter prompts, fine-tuning models, or caching common user queries so that they don't need to be processed repeatedly.

 

비용 절감에 대해 생각할 수 있는 유용한 프레임워크 중 하나는 토큰 수와 토큰당 비용의 함수로 비용을 고려하는 것입니다. 이 프레임워크를 사용하여 비용을 절감할 수 있는 두 가지 잠재적 방법이 있습니다. 첫째, 비용을 줄이기 위해 일부 작업에 대해 더 작은 모델로 전환하여 토큰당 비용을 줄이는 작업을 할 수 있습니다. 또는 필요한 토큰 수를 줄일 수 있습니다. 짧은 프롬프트를 사용하거나, 모델을 미세 조정하거나, 반복적으로 처리할 필요가 없도록 일반 사용자 쿼리를 캐싱하는 등의 몇 가지 방법이 있습니다.

 

You can experiment with our interactive tokenizer tool to help you estimate costs. The API and playground also returns token counts as part of the response. Once you’ve got things working with our most capable model, you can see if the other models can produce the same results with lower latency and costs. Learn more in our token usage help article.

 

대화형 토크나이저 도구를 실험하여 비용을 추정할 수 있습니다. API와 놀이터는 또한 응답의 일부로 토큰 수를 반환합니다. 가장 유능한 모델로 작업을 수행하면 다른 모델이 더 낮은 대기 시간과 비용으로 동일한 결과를 생성할 수 있는지 확인할 수 있습니다. 토큰 사용 도움말 문서에서 자세히 알아보세요.

 

MLOps strategy

As you move your prototype into production, you may want to consider developing an MLOps strategy. MLOps (machine learning operations) refers to the process of managing the end-to-end life cycle of your machine learning models, including any models you may be fine-tuning using our API. There are a number of areas to consider when designing your MLOps strategy. These include

프로토타입을 프로덕션으로 옮길 때 MLOps 전략 개발을 고려할 수 있습니다. MLOps(기계 학습 작업)는 API를 사용하여 미세 조정할 수 있는 모든 모델을 포함하여 기계 학습 모델의 종단 간 수명 주기를 관리하는 프로세스를 나타냅니다. MLOps 전략을 설계할 때 고려해야 할 여러 영역이 있습니다. 여기에는 다음이 포함됩니다.

 

• Data and model management: managing the data used to train or fine-tune your model and tracking versions and changes.
  
  
• 데이터 및 모델 관리: 모델 훈련 또는 미세 조정에 사용되는 데이터를 관리하고 버전 및 변경 사항을 추적합니다.
  
  
• Model monitoring: tracking your model's performance over time and detecting any potential issues or degradation.
  
  
• 모델 모니터링: 시간이 지남에 따라 모델의 성능을 추적하고 잠재적인 문제 또는 성능 저하를 감지합니다.
  
  
• Model retraining: ensuring your model stays up to date with changes in data or evolving requirements and retraining or fine-tuning it as needed.
  
  
• 모델 재훈련: 데이터의 변화나 진화하는 요구 사항에 따라 모델을 최신 상태로 유지하고 필요에 따라 모델을 재훈련하거나 미세 조정합니다.
  
  
• Model deployment: automating the process of deploying your model and related artifacts into production.
  
  
• 모델 배포: 모델 및 관련 아티팩트를 프로덕션에 배포하는 프로세스를 자동화합니다.
  
  

Thinking through these aspects of your application will help ensure your model stays relevant and performs well over time.

 

응용 프로그램의 이러한 측면을 고려하면 모델이 관련성을 유지하고 시간이 지남에 따라 잘 수행되도록 하는 데 도움이 됩니다.

 

Security and compliance

As you move your prototype into production, you will need to assess and address any security and compliance requirements that may apply to your application. This will involve examining the data you are handling, understanding how our API processes data, and determining what regulations you must adhere to. For reference, here is our Privacy Policy and Terms of Use.

 

프로토타입을 프로덕션으로 이동하면 애플리케이션에 적용될 수 있는 모든 보안 및 규정 준수 요구 사항을 평가하고 해결해야 합니다. 여기에는 귀하가 처리하는 데이터를 검토하고 API가 데이터를 처리하는 방법을 이해하고 준수해야 하는 규정을 결정하는 것이 포함됩니다. 참고로 개인 정보 보호 정책 및 이용 약관은 다음과 같습니다.

 

Some common areas you'll need to consider include data storage, data transmission, and data retention. You might also need to implement data privacy protections, such as encryption or anonymization where possible. In addition, you should follow best practices for secure coding, such as input sanitization and proper error handling.

 

고려해야 할 몇 가지 공통 영역에는 데이터 스토리지, 데이터 전송 및 데이터 보존이 포함됩니다. 가능한 경우 암호화 또는 익명화와 같은 데이터 개인 정보 보호를 구현해야 할 수도 있습니다. 또한 입력 삭제 및 적절한 오류 처리와 같은 안전한 코딩을 위한 모범 사례를 따라야 합니다.

 

Safety best practices

When creating your application with our API, consider our safety best practices to ensure your application is safe and successful. These recommendations highlight the importance of testing the product extensively, being proactive about addressing potential issues, and limiting opportunities for misuse.

 

API로 애플리케이션을 생성할 때 안전 모범 사례를 고려하여 애플리케이션이 안전하고 성공적인지 확인하세요. 이러한 권장 사항은 제품을 광범위하게 테스트하고 잠재적인 문제를 사전에 해결하며 오용 기회를 제한하는 것의 중요성을 강조합니다.