{"data":{"node":{"slug":"ray-project-ray","name":"ray","tagline":"Ray is an AI compute engine with a core distributed runtime and AI Libraries for accelerating ML workloads.","github_url":"https://github.com/ray-project/ray","owner":"ray-project","repo":"ray","owner_avatar_url":"https://avatars.githubusercontent.com/u/22125274?v=4","primary_language":"Python","stars":43526,"forks":7929,"topics":["data-science","deep-learning","deployment","distributed","hyperparameter-optimization","hyperparameter-search","large-language-models","llm","llm-inference","llm-serving","machine-learning","optimization","parallel","python","pytorch","ray","reinforcement-learning","rllib","serving","tensorflow"],"archived":false,"github_pushed_at":"2026-08-16T00:26:16+00:00","maintenance_label":"Very active","stars_delta_30d":270,"url":"https://www.graphcanon.com/tools/ray-project-ray","markdown_url":"https://www.graphcanon.com/tools/ray-project-ray.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ray-project-ray","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ray-project-ray"},"categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"data-science","name":"data-science"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"deployment","name":"deployment"},{"slug":"distributed","name":"distributed"},{"slug":"hyperparameter-optimization","name":"hyperparameter-optimization"},{"slug":"large-language-models","name":"large language models"},{"slug":"llm-inference","name":"llm-inference"},{"slug":"machine-learning","name":"machine-learning"}],"edges":[{"type":"integrates_with","direction":"out","explanation":"Ray can be used to scale and optimize operations with Hugging Face transformers, making it a good integration point for distributed model training or inference.","successor_context":null,"tool":{"slug":"huggingface-transformers","name":"transformers","tagline":"Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models","github_url":"https://github.com/huggingface/transformers","owner":"huggingface","repo":"transformers","owner_avatar_url":"https://avatars.githubusercontent.com/u/25720743?v=4","primary_language":"Python","stars":164121,"forks":34249,"topics":["audio","deep-learning","deepseek","gemma","glm","hacktoberfest","llm","machine-learning","model-hub","natural-language-processing","nlp","pretrained-models","python","pytorch","pytorch-transformers","qwen","speech-recognition","transformer","vlm"],"archived":false,"github_pushed_at":"2026-08-15T22:28:12+00:00","maintenance_label":"Very active","stars_delta_30d":1457,"url":"https://www.graphcanon.com/tools/huggingface-transformers","markdown_url":"https://www.graphcanon.com/tools/huggingface-transformers.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/huggingface-transformers","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=huggingface-transformers"}},{"type":"integrates_with","direction":"out","explanation":"Ray integrates with MLflow for managing the entire machine learning process, covering experiment tracking, reproducibility, and deployment, aligning with Ray's emphasis on distributed AI apps.","successor_context":null,"tool":{"slug":"mlflow-mlflow","name":"mlflow","tagline":"AI engineering platform for debugging, evaluating, monitoring, and optimizing AI applications","github_url":"https://github.com/mlflow/mlflow","owner":"mlflow","repo":"mlflow","owner_avatar_url":"https://avatars.githubusercontent.com/u/39938107?v=4","primary_language":"Python","stars":27591,"forks":6189,"topics":["agentops","agents","ai","ai-governance","apache-spark","evaluation","langchain","llm-evaluation","llmops","machine-learning","ml","mlflow","mlops","model-management","observability","open-source","openai","prompt-engineering"],"archived":false,"github_pushed_at":"2026-08-20T00:54:28+00:00","maintenance_label":"Very active","stars_delta_30d":476,"url":"https://www.graphcanon.com/tools/mlflow-mlflow","markdown_url":"https://www.graphcanon.com/tools/mlflow-mlflow.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/mlflow-mlflow","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=mlflow-mlflow"}},{"type":"alternative","direction":"out","explanation":"SGLang and Ray both target providing a serving framework for large language models and offer tools for scaling ML workloads.","successor_context":null,"tool":{"slug":"sgl-project-sglang","name":"sglang","tagline":"High-performance serving framework for large language and multimodal models","github_url":"https://github.com/sgl-project/sglang","owner":"sgl-project","repo":"sglang","owner_avatar_url":"https://avatars.githubusercontent.com/u/147780389?v=4","primary_language":"Python","stars":31454,"forks":7720,"topics":["attention","blackwell","cuda","deepseek","diffusion","glm","gpt-oss","inference","llama","llm","minimax","moe","qwen","qwen-image","reinforcement-learning","transformer","vlm","wan"],"archived":false,"github_pushed_at":"2026-08-07T06:00:20+00:00","maintenance_label":"Very active","stars_delta_30d":1409,"url":"https://www.graphcanon.com/tools/sgl-project-sglang","markdown_url":"https://www.graphcanon.com/tools/sgl-project-sglang.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/sgl-project-sglang","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=sgl-project-sglang"}},{"type":"integrates_with","direction":"out","explanation":"SGLang is a serving framework that can integrate with Ray for efficient scaling and deployment of large language models and multimodal applications.","successor_context":null,"tool":{"slug":"sgl-project-sglang","name":"sglang","tagline":"High-performance serving framework for large language and multimodal models","github_url":"https://github.com/sgl-project/sglang","owner":"sgl-project","repo":"sglang","owner_avatar_url":"https://avatars.githubusercontent.com/u/147780389?v=4","primary_language":"Python","stars":31454,"forks":7720,"topics":["attention","blackwell","cuda","deepseek","diffusion","glm","gpt-oss","inference","llama","llm","minimax","moe","qwen","qwen-image","reinforcement-learning","transformer","vlm","wan"],"archived":false,"github_pushed_at":"2026-08-07T06:00:20+00:00","maintenance_label":"Very active","stars_delta_30d":1409,"url":"https://www.graphcanon.com/tools/sgl-project-sglang","markdown_url":"https://www.graphcanon.com/tools/sgl-project-sglang.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/sgl-project-sglang","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=sgl-project-sglang"}},{"type":"alternative","direction":"out","explanation":"Both Ray and VLLM aim to provide fast LLM serving solutions; however, they approach this differently with different optimizations and use cases in mind.","successor_context":null,"tool":{"slug":"vllm-project-vllm","name":"vllm","tagline":"A high-throughput and memory-efficient inference and serving engine for LLMs","github_url":"https://github.com/vllm-project/vllm","owner":"vllm-project","repo":"vllm","owner_avatar_url":"https://avatars.githubusercontent.com/u/136984999?v=4","primary_language":"Python","stars":87847,"forks":20135,"topics":["amd","blackwell","cuda","deepseek","deepseek-v3","gpt","gpt-oss","inference","kimi","llama","llm","llm-serving","model-serving","moe","openai","pytorch","qwen","qwen3","tpu","transformer"],"archived":false,"github_pushed_at":"2026-08-01T11:55:36+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/vllm-project-vllm","markdown_url":"https://www.graphcanon.com/tools/vllm-project-vllm.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/vllm-project-vllm","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=vllm-project-vllm"}},{"type":"integrates_with","direction":"out","explanation":"Ray and VLLM can integrate for serving and scaling LLMs efficiently, as both tools aim at making LLM serving easy, fast, and available to everyone.","successor_context":null,"tool":{"slug":"vllm-project-vllm","name":"vllm","tagline":"A high-throughput and memory-efficient inference and serving engine for LLMs","github_url":"https://github.com/vllm-project/vllm","owner":"vllm-project","repo":"vllm","owner_avatar_url":"https://avatars.githubusercontent.com/u/136984999?v=4","primary_language":"Python","stars":87847,"forks":20135,"topics":["amd","blackwell","cuda","deepseek","deepseek-v3","gpt","gpt-oss","inference","kimi","llama","llm","llm-serving","model-serving","moe","openai","pytorch","qwen","qwen3","tpu","transformer"],"archived":false,"github_pushed_at":"2026-08-01T11:55:36+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/vllm-project-vllm","markdown_url":"https://www.graphcanon.com/tools/vllm-project-vllm.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/vllm-project-vllm","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=vllm-project-vllm"}},{"type":"integrates_with","direction":"out","explanation":"Ray can integrate with Heretic for advanced usage scenarios that require the removal of censorship and scaling tasks involving sensitive text data.","successor_context":null,"tool":{"slug":"p-e-w-heretic","name":"heretic","tagline":"Fully automatic censorship removal for language models","github_url":"https://github.com/p-e-w/heretic","owner":"p-e-w","repo":"heretic","owner_avatar_url":"https://avatars.githubusercontent.com/u/2702526?v=4","primary_language":"Python","stars":27709,"forks":2997,"topics":["abliteration","llm","transformer"],"archived":false,"github_pushed_at":"2026-08-14T11:09:07+00:00","maintenance_label":"Very active","stars_delta_30d":1309,"url":"https://www.graphcanon.com/tools/p-e-w-heretic","markdown_url":"https://www.graphcanon.com/tools/p-e-w-heretic.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/p-e-w-heretic","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=p-e-w-heretic"}},{"type":"depends_on","direction":"in","explanation":null,"successor_context":null,"tool":{"slug":"unstructured-io-unstructured","name":"unstructured","tagline":"Convert documents to structured data effortlessly","github_url":"https://github.com/Unstructured-IO/unstructured","owner":"Unstructured-IO","repo":"unstructured","owner_avatar_url":"https://avatars.githubusercontent.com/u/108372208?v=4","primary_language":"HTML","stars":15238,"forks":1284,"topics":["data-pipelines","deep-learning","document-image-analysis","document-image-processing","document-parser","document-parsing","docx","donut","information-retrieval","langchain","llm","machine-learning","ml","natural-language-processing","nlp","ocr","pdf","pdf-to-json","pdf-to-text","preprocessing"],"archived":false,"github_pushed_at":"2026-07-31T20:54:17+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/unstructured-io-unstructured","markdown_url":"https://www.graphcanon.com/tools/unstructured-io-unstructured.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/unstructured-io-unstructured","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=unstructured-io-unstructured"}},{"type":"related","direction":"in","explanation":null,"successor_context":null,"tool":{"slug":"jina-ai-serve","name":"serve","tagline":"Build multimodal AI applications with cloud-native stack","github_url":"https://github.com/jina-ai/serve","owner":"jina-ai","repo":"serve","owner_avatar_url":"https://avatars.githubusercontent.com/u/60539444?v=4","primary_language":"Python","stars":21863,"forks":2243,"topics":["cloud-native","cncf","deep-learning","docker","fastapi","framework","generative-ai","grpc","jaeger","kubernetes","llmops","machine-learning","microservice","mlops","multimodal","neural-search","opentelemetry","orchestration","pipeline","prometheus"],"archived":false,"github_pushed_at":"2025-03-24T13:59:54+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/jina-ai-serve","markdown_url":"https://www.graphcanon.com/tools/jina-ai-serve.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/jina-ai-serve","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=jina-ai-serve"}},{"type":"related","direction":"in","explanation":null,"successor_context":null,"tool":{"slug":"oceanbase-oceanbase","name":"oceanbase","tagline":"The Fastest Distributed Database for Transactional, Analytical, and AI Workloads","github_url":"https://github.com/oceanbase/oceanbase","owner":"oceanbase","repo":"oceanbase","owner_avatar_url":"https://avatars.githubusercontent.com/u/82347605?v=4","primary_language":"C++","stars":10252,"forks":1910,"topics":["analytics","cloud-native","database","distributed-database","fulltext","fulltext-search","fulltext-support","hacktoberfest","htap","mysql","mysql-compatibility","oceanbase","olap","oltp","paxos","scalable","vector","vector-database","vector-search","vectors"],"archived":false,"github_pushed_at":"2026-08-21T01:00:39+00:00","maintenance_label":"Very active","stars_delta_30d":38,"url":"https://www.graphcanon.com/tools/oceanbase-oceanbase","markdown_url":"https://www.graphcanon.com/tools/oceanbase-oceanbase.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/oceanbase-oceanbase","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=oceanbase-oceanbase"}},{"type":"related","direction":"in","explanation":null,"successor_context":null,"tool":{"slug":"clearml-clearml","name":"clearml","tagline":"MLOps/LLMOps solution for CI/CD in AI workloads","github_url":"https://github.com/clearml/clearml","owner":"clearml","repo":"clearml","owner_avatar_url":"https://avatars.githubusercontent.com/u/38647316?v=4","primary_language":"Python","stars":6805,"forks":785,"topics":["ai","clearml","control","deep-learning","deeplearning","devops","experiment","experiment-manager","k8s","llmops","machine-learning","machinelearning","mlops","version","version-control"],"archived":false,"github_pushed_at":"2026-07-27T00:08:25+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/clearml-clearml","markdown_url":"https://www.graphcanon.com/tools/clearml-clearml.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/clearml-clearml","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=clearml-clearml"}},{"type":"related","direction":"in","explanation":null,"successor_context":null,"tool":{"slug":"tensorchord-envd","name":"envd","tagline":"Reproducible development environment for humans and agents","github_url":"https://github.com/tensorchord/envd","owner":"tensorchord","repo":"envd","owner_avatar_url":"https://avatars.githubusercontent.com/u/100543303?v=4","primary_language":"Go","stars":2224,"forks":168,"topics":["agent","buildkit","code-agent","codex","developer-tools","development-environment","docker","hacktoberfest","llmops","mlops","mlops-workflow","model-serving"],"archived":false,"github_pushed_at":"2026-07-25T03:58:33+00:00","maintenance_label":"Active","stars_delta_30d":10,"url":"https://www.graphcanon.com/tools/tensorchord-envd","markdown_url":"https://www.graphcanon.com/tools/tensorchord-envd.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/tensorchord-envd","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=tensorchord-envd"}},{"type":"related","direction":"in","explanation":"Ray is a unified framework that provides capabilities for scaling out various components needed in an LLM ecosystem, including tools like Easy-Dataset which would benefit from scalable computing resources.","successor_context":null,"tool":{"slug":"conardli-easy-dataset","name":"easy-dataset","tagline":"A powerful tool for creating datasets for LLM fine-tuning, RAG, and evaluation","github_url":"https://github.com/ConardLi/easy-dataset","owner":"ConardLi","repo":"easy-dataset","owner_avatar_url":"https://avatars.githubusercontent.com/u/30708545?v=4","primary_language":"JavaScript","stars":14792,"forks":1523,"topics":["dataset","fine-tuning","javascript","llm","rag"],"archived":false,"github_pushed_at":"2026-05-01T15:03:32+00:00","maintenance_label":"Slowing","stars_delta_30d":125,"url":"https://www.graphcanon.com/tools/conardli-easy-dataset","markdown_url":"https://www.graphcanon.com/tools/conardli-easy-dataset.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/conardli-easy-dataset","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=conardli-easy-dataset"}},{"type":"integrates_with","direction":"in","explanation":"Metaflow can integrate with Ray to manage scaling of AI applications and Python programs across various computing resources, providing a robust infrastructure for managing real-life ML systems.","successor_context":null,"tool":{"slug":"netflix-metaflow","name":"metaflow","tagline":"Build, Manage and Deploy AI/ML Systems","github_url":"https://github.com/Netflix/metaflow","owner":"Netflix","repo":"metaflow","owner_avatar_url":"https://avatars.githubusercontent.com/u/913567?v=4","primary_language":"Python","stars":10228,"forks":1330,"topics":["agents","ai","aws","azure","cost-optimization","datascience","distributed-training","gcp","generative-ai","high-performance-computing","kubernetes","llm","llmops","machine-learning","ml","ml-infrastructure","ml-platform","mlops","model-management","python"],"archived":false,"github_pushed_at":"2026-08-18T09:41:43+00:00","maintenance_label":"Very active","stars_delta_30d":38,"url":"https://www.graphcanon.com/tools/netflix-metaflow","markdown_url":"https://www.graphcanon.com/tools/netflix-metaflow.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/netflix-metaflow","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=netflix-metaflow"}},{"type":"related","direction":"in","explanation":"While not tightly integrated, Ray's framework for scaling applications can potentially benefit from integrating with Bifrost for high-performance AI serving.","successor_context":null,"tool":{"slug":"maximhq-bifrost","name":"bifrost","tagline":"Fast Enterprise AI Gateway with Adaptive Load Balancer and Guardrails","github_url":"https://github.com/maximhq/bifrost","owner":"maximhq","repo":"bifrost","owner_avatar_url":"https://avatars.githubusercontent.com/u/139708451?v=4","primary_language":"Go","stars":7449,"forks":1073,"topics":["ai-gateway","gateway","gateway-services","generative-ai","guardrails","llm","llm-cost","llm-gateway","llm-observability","llmops","load-balancing","mcp-client","mcp-gateway","mcp-server","model-router","token-management"],"archived":false,"github_pushed_at":"2026-08-20T11:57:33+00:00","maintenance_label":"Very active","stars_delta_30d":812,"url":"https://www.graphcanon.com/tools/maximhq-bifrost","markdown_url":"https://www.graphcanon.com/tools/maximhq-bifrost.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/maximhq-bifrost","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=maximhq-bifrost"}},{"type":"integrates_with","direction":"in","explanation":"Hamilton aids in defining modular dataflows, and Ray is a unified framework for scaling Python applications. They integrate to scale dataflow processing.","successor_context":null,"tool":{"slug":"apache-hamilton","name":"hamilton","tagline":"Modular dataflow definition for Python environments","github_url":"https://github.com/apache/hamilton","owner":"apache","repo":"hamilton","owner_avatar_url":"https://avatars.githubusercontent.com/u/47359?v=4","primary_language":"Jupyter Notebook","stars":2557,"forks":203,"topics":["dag","data-analysis","data-engineering","data-science","dataframe","etl","etl-framework","etl-pipeline","feature-engineering","hacktoberfest","lineage","llmops","machine-learning","mlops","orchestration","pandas","python","rag","software-engineering"],"archived":false,"github_pushed_at":"2026-08-01T18:45:37+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/apache-hamilton","markdown_url":"https://www.graphcanon.com/tools/apache-hamilton.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/apache-hamilton","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=apache-hamilton"}},{"type":"integrates_with","direction":"in","explanation":"BentoML integrates with Ray to leverage Ray's distributed computing capabilities, specifically its Serve library for scalable model serving, which enhances BentoML's ability to deploy and scale AI models across a cluster.","successor_context":null,"tool":{"slug":"bentoml-bentoml","name":"BentoML","tagline":"The easiest way to serve AI apps and models","github_url":"https://github.com/bentoml/BentoML","owner":"bentoml","repo":"BentoML","owner_avatar_url":"https://avatars.githubusercontent.com/u/49176046?v=4","primary_language":"Python","stars":8793,"forks":1010,"topics":["ai-inference","deep-learning","generative-ai","inference-platform","llm","llm-inference","llm-serving","llmops","machine-learning","ml-engineering","mlops","model-inference-service","model-serving","multimodal","python"],"archived":false,"github_pushed_at":"2026-08-03T17:00:21+00:00","maintenance_label":"Active","stars_delta_30d":65,"url":"https://www.graphcanon.com/tools/bentoml-bentoml","markdown_url":"https://www.graphcanon.com/tools/bentoml-bentoml.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/bentoml-bentoml","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=bentoml-bentoml"}},{"type":"integrates_with","direction":"in","explanation":"Pixeltable integrates with Ray to leverage Ray's distributed computing capabilities, enabling efficient scaling of tasks related to storing media, running models, indexing embeddings, and serving endpoints that are managed by Pixeltable.","successor_context":null,"tool":{"slug":"pixeltable-pixeltable","name":"pixeltable","tagline":"Unified multimodal backend for AI data apps","github_url":"https://github.com/pixeltable/pixeltable","owner":"pixeltable","repo":"pixeltable","owner_avatar_url":"https://avatars.githubusercontent.com/u/160283145?v=4","primary_language":"Python","stars":1613,"forks":219,"topics":["ai","computer-vision","data-science","database","feature-engineering","feature-store","genai","llm","machine-learning","ml","multimodal","vector-database"],"archived":false,"github_pushed_at":"2026-08-21T06:36:51+00:00","maintenance_label":"Very active","stars_delta_30d":9,"url":"https://www.graphcanon.com/tools/pixeltable-pixeltable","markdown_url":"https://www.graphcanon.com/tools/pixeltable-pixeltable.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/pixeltable-pixeltable","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=pixeltable-pixeltable"}},{"type":"depends_on","direction":"in","explanation":"OpenRLHF leverages Ray for efficient distributed scheduling, separating Actor, Reward, Reference, and Critic models across different GPUs.","successor_context":null,"tool":{"slug":"openrlhf-openrlhf","name":"OpenRLHF","tagline":"Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray","github_url":"https://github.com/OpenRLHF/OpenRLHF","owner":"OpenRLHF","repo":"OpenRLHF","owner_avatar_url":"https://avatars.githubusercontent.com/u/175771028?v=4","primary_language":"Python","stars":9891,"forks":996,"topics":["large-language-models","proximal-policy-optimization","raylib","reinforcement-learning","reinforcement-learning-from-human-feedback","transformers","visual-language-models","vllm"],"archived":false,"github_pushed_at":"2026-07-14T01:57:21+00:00","maintenance_label":"Active","stars_delta_30d":132,"url":"https://www.graphcanon.com/tools/openrlhf-openrlhf","markdown_url":"https://www.graphcanon.com/tools/openrlhf-openrlhf.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/openrlhf-openrlhf","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=openrlhf-openrlhf"}}],"neighbours":[{"slug":"microsoft-pai","name":"pai","tagline":"Resource scheduling and cluster management for AI","github_url":"https://github.com/microsoft/pai","owner":"microsoft","repo":"pai","owner_avatar_url":"https://avatars.githubusercontent.com/u/6154722?v=4","primary_language":"JavaScript","stars":2686,"forks":549,"topics":["ai","artificial-intelligence","chainer","cloud","cluster-management","cluster-manager","gpu","gpu-cluster","gpu-computing","gpu-scheduler","jupyter","kubernetes","machine-learning","model-training","on-premise","pytorch","resource-management","scheduling","tensorflow"],"archived":true,"github_pushed_at":"2024-06-06T07:56:07+00:00","maintenance_label":"Archived","url":"https://www.graphcanon.com/tools/microsoft-pai","markdown_url":"https://www.graphcanon.com/tools/microsoft-pai.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/microsoft-pai","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=microsoft-pai","shared_categories":["model-training","inference-serving"]},{"slug":"ray-project-ray-llm","name":"ray-llm","tagline":"Archived repository; LLM serving APIs integrated into the Ray project","github_url":"https://github.com/ray-project/ray-llm","owner":"ray-project","repo":"ray-llm","owner_avatar_url":"https://avatars.githubusercontent.com/u/22125274?v=4","primary_language":null,"stars":1261,"forks":90,"topics":["llm","llm-serving","ray"],"archived":true,"github_pushed_at":"2025-03-13T01:13:38+00:00","maintenance_label":"Archived","url":"https://www.graphcanon.com/tools/ray-project-ray-llm","markdown_url":"https://www.graphcanon.com/tools/ray-project-ray-llm.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ray-project-ray-llm","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ray-project-ray-llm","shared_categories":["model-training","inference-serving"]}]}}