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Welcome to Leeroopedia
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 |
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Workflows
- Workflow:PrefectHQ Prefect Dbt Model Orchestration
- Workflow:MaterializeInc Materialize CI Pipeline Generation
- Workflow:FlagOpen FlagEmbedding Benchmark Evaluation
- Workflow:OWASP Www project top 10 for large language model applications Agentic Security Assessment
- Workflow:Microsoft Playwright Browser automation CLI
- Workflow:OpenHands OpenHands Third Party Runtime Integration
- Workflow:Protectai Llm guard API Server Deployment
- Workflow:Risingwavelabs Risingwave Sink Connector Pipeline
- Workflow:Kserve Kserve Deploying InferenceService
- Workflow:Neuml Txtai Agent Execution
Principles
- Principle:DataTalksClub Data engineering zoomcamp Spark Session Initialization
- Principle:Duckdb Duckdb Source Package Building
- Principle:Protectai Llm guard JSON Output Validation
- Principle:CarperAI Trlx Synthetic Environment Design
- Principle:Speechbrain Speechbrain WER CER Evaluation For Whisper
- Principle:Ggml org Ggml SYCL GPU Computation
- Principle:Mit han lab Llm awq Pseudo Quantization
- Principle:Openai Openai python Client Initialization
- Principle:Mit han lab Llm awq Per Channel Scaling Search
- Principle:Cohere ai Cohere python Bedrock Response Transformation
Implementations
- Implementation:Apache Kafka Kafka Run Class JVM Options
- Implementation:ArroyoSystems Arroyo Webhook Connector
- Implementation:Alibaba MNN PyMNN CV Preprocessing
- Implementation:Apache Paimon SnapshotCommit
- Implementation:Haosulab ManiSkill PartNetChairMeta
- Implementation:Huggingface Datatrove TokensCounter
- Implementation:Facebookresearch Habitat lab BaselineRegistry
- Implementation:Togethercomputer Together python Result Integration Pattern
- Implementation:Danijar Dreamerv3 Checkpoint Operations
- Implementation:Nightwatchjs Nightwatch Nightwatch Configuration
Heuristics
- Heuristic:Avdvg InjectGuard Sim K Threshold Tuning
- Heuristic:Microsoft DeepSpeedExamples LoRA Learning Rate Scaling
- Heuristic:DevExpress Testcafe Docker Chrome Tab Retry
- Heuristic:Fede1024 Rust rdkafka Partitioner Must Not Block
- Heuristic:Openclaw Openclaw Config Cascade Resolution
- Heuristic:Hiyouga LLaMA Factory Gradient Checkpointing Memory Optimization
- Heuristic:PacktPublishing LLM Engineers Handbook LoRA Finetuning Parameters
- Heuristic:Deepspeedai DeepSpeed ZeRO Pipeline Incompatibility
- Heuristic:Huggingface Trl Disable Dropout For RL Training
- Heuristic:OpenGVLab InternVL LoRA Alpha Scaling
Environments
- Environment:Bigscience workshop Petals Python Transformers
- Environment:Huggingface Peft GPU Hardware Detection
- Environment:FlagOpen FlagEmbedding Python PyTorch Environment
- Environment:Arize ai Phoenix Phoenix Server Runtime
- Environment:Intel Ipex llm RAG LangChain Environment
- Environment:NVIDIA DALI PyTorch Environment
- Environment:Dotnet Machinelearning Platform Architecture Support
- Environment:Run llama Llama index Python LlamaIndex Core
- Environment:Vespa engine Vespa CMake Cpp23 Build Environment
- Environment:CarperAI Trlx Python Accelerate