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BentoML — model serving and deployment. Build prediction services from any ML framework with OpenAPI/Swagger. Containerize, deploy to Kubernetes, AWS, GCP, Azure. Adaptive batching and GPU support.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mkurman/zorai --skill bentoml命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Use when provisioning Prime Intellect GPU compute, managing pods/disks/sandboxes, running hosted RL training via prime lab, installing or publishing RL environments, or exposing local services via Prime Tunnel. Covers the `prime` CLI (PyPI: prime) for all Prime Intellect platform operations.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
基于 SOC 职业分类
| name | bentoml |
| description | BentoML — model serving and deployment. Build prediction services from any ML framework with OpenAPI/Swagger. Containerize, deploy to Kubernetes, AWS, GCP, Azure. Adaptive batching and GPU support. |
| tags | ["bentoml","model-serving","deployment","mlops","api","kubernetes","zorai"] |
BentoML packages ML models with service definitions, dependencies, environment config, and deployment targets into a portable "Bento." Deploy to Kubernetes (Kserve, Seldon), AWS SageMaker, GCP Vertex AI, or as a standalone Docker container.
uv pip install bentoml
import bentoml
from bentoml.io import JSON
import numpy as np
iris_clf = bentoml.sklearn.get("iris_model:latest")
@bentoml.service
class IrisClassifier:
def __init__(self):
self.model = iris_clf.to_runner()
self.model.init_local()
@bentoml.api(input=JSON(), output=JSON())
def classify(self, input_data):
result = self.model.run(np.array([input_data["features"]]))
return {"class": int(result[0]), "probabilities": result[1].tolist()}
bentoml build # creates a Bento
bentoml containerize iris_classifier:latest # Docker image
docker run -p 3000:3000 iris_classifier:latest