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Your ML & Data Knowledge Wiki. Best practices and expert-level knowledge for Machine Learning and Data Engineering, covering 1000+ frameworks and libraries from training to deployment.
Browse implementation patterns, configuration guides, debugging heuristics, and battle-tested defaults for frameworks like vLLM, DeepSpeed, Megatron-LM, FlashAttention, Triton, Unsloth, LangChain, and many more. Every page is structured so both humans and AI agents can find what they need fast.
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| Category | Description | Browse |
|---|---|---|
| Workflows | Step-by-step processes and procedures | Browse All |
| Principles | Core ideas and foundational knowledge | Browse All |
| Implementations | Code-level details and modules | Browse All |
| Heuristics | Best practices and guidelines | Browse All |
| Environments | Setup and configuration guides | Browse All |
Explore Pages
Workflows
- Workflow:LLMBook zh LLMBook zh github io Data Preprocessing Pipeline
- Workflow:Langchain ai Langchain Streaming Responses
- Workflow:Openai Openai python Embeddings Generation
- Workflow:Shiyu coder Kronos Qlib Finetuning
- Workflow:Ggml org Llama cpp HF to GGUF Model Conversion
- Workflow:Fede1024 Rust rdkafka Produce Consume Roundtrip
- Workflow:PeterL1n BackgroundMattingV2 Video matting inference
- Workflow:FlowiseAI Flowise Document Store Ingestion
- Workflow:Mistralai Client python Chat Completion
- Workflow:Ray project Ray Actor Lifecycle Management
Principles
- Principle:PacktPublishing LLM Engineers Handbook Prompt Engineering For Dataset Generation
- Principle:Astronomer Astronomer cosmos Execution Configuration
- Principle:Avdvg InjectGuard Vector Store Construction
- Principle:Protectai Llm guard Output Scanner Factory Pattern
- Principle:Anthropics Anthropic sdk python Provider API Interaction
- Principle:Microsoft Autogen Agent Specialization
- Principle:Apache Shardingsphere In Memory Rule Rebuild
- Principle:SeleniumHQ Selenium CDP Session Management
- Principle:Microsoft Semantic kernel Event Routing
- Principle:LaurentMazare Tch rs Top K Prediction
Implementations
- Implementation:Openai Openai node Batches Resource
- Implementation:Dotnet Machinelearning TrialResult Properties
- Implementation:Hpcaitech ColossalAI Train RM Script
- Implementation:Ggml org Llama cpp Convert Llama2c To GGML
- Implementation:Ray project Ray Buildkite Aarch64 Pipeline
- Implementation:Run llama Llama index MetadataReplacementPostProcessor
- Implementation:Farama Foundation Gymnasium HumanoidStandupEnv V4
- Implementation:Helicone Helicone XAI Model Definitions
- Implementation:Sgl project Sglang LWS Prefill Node Manifest
- Implementation:Infiniflow Ragflow Log Utils
Heuristics
- Heuristic:MarketSquare Robotframework browser Shared Node Process For Parallel
- Heuristic:Marker Inc Korea AutoRAG Module Selection Strategies
- Heuristic:OpenHands OpenHands Fail Open Rate Limiting
- Heuristic:Vespa engine Vespa Log Level Inheritance Polling
- Heuristic:Microsoft Onnxruntime ORTModule Wrapping Order
- Heuristic:Apache Airflow Task Idempotency Pattern
- Heuristic:Mlflow Mlflow Async Logging Best Practices
- Heuristic:FMInference FlexLLMGen Weight Compression 4bit
- Heuristic:Scikit learn Scikit learn Random State Management
- Heuristic:Intel Ipex llm LoRA Target All Linear Layers
Environments
- Environment:Axolotl ai cloud Axolotl Multi GPU
- Environment:Promptfoo Promptfoo SQLite Database
- Environment:Deepspeedai DeepSpeed CPU Environment
- Environment:Shiyu coder Kronos Qlib Data Environment
- Environment:Openclaw Openclaw Docker Deployment Environment
- Environment:Apache Kafka Release Toolchain Environment
- Environment:ThreeSR Awesome Inference Time Scaling Git CLI Environment
- Environment:Google deepmind Dm control EGL Headless Rendering
- Environment:Dagster io Dagster GRPC Communication
- Environment:AUTOMATIC1111 Stable diffusion webui Python And PyTorch Runtime