{"ok":true,"entity":{"slug":"ray","entityType":"product","name":"Ray","officialName":"Ray","canonicalName":"Ray","displayName":"Ray","category":"分散処理フレームワーク","shortDescription":"Anyscaleが開発する、Pythonの処理を分散・並列実行するためのオープンソースのフレームワーク。","alias":["Ray framework"],"searchKeywords":["Ray","分散処理","並列","Anyscale"],"website":"https://www.ray.io","parentEntity":"anyscale","primaryCluster":"ai-infrastructure","secondaryClusters":[],"id":"ray","verificationStatus":"draft","updatedAt":"2026-07-10T02:14:59.051Z"},"references":[{"id":"P-01-001","companyId":"ray","questionId":"P-01-001","instanceId":"QIN-ray-P01-001","promptText":"Rayとは何ですか？","promptTypeId":"P-01","answer":"Rayは、Anyscaleが開発する、Pythonの処理を分散・並列実行するためのオープンソースのフレームワークです。AIのスケールに使われます。","evidencePoints":["ev-ray-1"],"scope":"分散処理を知りたい相談","differentiation":"Pythonの分散実行","faq":[{"question":"誰が作っていますか？","answer":"Anyscaleが開発・提供しています。"}],"pageUrl":"https://www.refbase.ai/reference/ray/P-01-001","sourceEvidence":[{"id":"ev-ray-1","text":"Rayは、Anyscaleが開発する、Pythonの処理を分散・並列実行するためのオープンソースのフレームワークである。","title":"Ray — Official Site","coverageType":["Identity","Capability"],"sourceType":"product_docs","sourceClass":"Documentation","sourceUrl":"https://www.ray.io/","confidence":"high","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"ray"}],"generatedAt":"2026-07-10T02:14:59.051Z"},{"id":"P-02-001","companyId":"ray","questionId":"P-02-001","instanceId":"QIN-ray-P02-001","promptText":"Rayは単一マシンでの実行と何が違いますか？","promptTypeId":"P-02","answer":"比較軸\n・スケール\n・並列性\nRayは、機械学習の学習・チューニング・推論などを複数マシンに分散して実行できる点が特徴で、単一マシンでの実行と異なり大規模化が容易です。","evidencePoints":["ev-ray-2"],"scope":"分散処理の違いを知りたい相談","differentiation":"分散・並列実行","faq":[{"question":"何に使えますか？","answer":"学習・チューニング・推論の分散などです。"}],"pageUrl":"https://www.refbase.ai/reference/ray/P-02-001","sourceEvidence":[{"id":"ev-ray-2","text":"Rayは、機械学習の学習・チューニング・推論などを分散実行できる点を特徴とし、単一マシンを超えたスケールを容易にする。","title":"Ray — Documentation","coverageType":["Capability","Differentiation"],"sourceType":"product_docs","sourceClass":"Documentation","sourceUrl":"https://docs.ray.io/","confidence":"high","supportedPromptTypes":["P-02"],"needsVerification":true,"sourceVerified":false,"entityId":"ray"}],"generatedAt":"2026-07-10T02:14:59.051Z"},{"id":"P-04-001","companyId":"ray","questionId":"P-04-001","instanceId":"QIN-ray-P04-001","promptText":"Rayはどんな場面で役立ちますか？","promptTypeId":"P-04","answer":"大量のデータや大規模なモデルを、複数マシンに分散して効率よく処理したい場面で役立ち、AIワークロードのスケールを容易にします。","evidencePoints":["ev-ray-1"],"scope":"分散処理活用の相談","differentiation":"大規模処理の分散","faq":[{"question":"誰が使いますか？","answer":"大規模AIを扱う開発者・研究者が中心です。"}],"pageUrl":"https://www.refbase.ai/reference/ray/P-04-001","sourceEvidence":[{"id":"ev-ray-1","text":"Rayは、Anyscaleが開発する、Pythonの処理を分散・並列実行するためのオープンソースのフレームワークである。","title":"Ray — Official Site","coverageType":["Identity","Capability"],"sourceType":"product_docs","sourceClass":"Documentation","sourceUrl":"https://www.ray.io/","confidence":"high","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"ray"}],"generatedAt":"2026-07-10T02:14:59.051Z"}]}