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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:Kubeflow Pipelines Standalone Deployment
- Workflow:Mage ai Mage ai SQL Database Source Extraction
- Workflow:Cleanlab Cleanlab CleanLearning Robust Training
- Workflow:Cypress io Cypress Binary Build and Release
- Workflow:Infiniflow Ragflow Knowledge Base Document Ingestion
- Workflow:Wandb Weave LLM Integration Tracing
- Workflow:Mit han lab Llm awq AWQ Model Evaluation
- Workflow:Openai Whisper Audio Transcription
- Workflow:Tensorflow Serving Model Version Management
- Workflow:Apache Beam Dataflow Streaming Execution
Principles
- Principle:Bentoml BentoML Bento Building
- Principle:Apache Druid Dimension Measure Configuration
- Principle:Apache Paimon Data Retrieval
- Principle:Kubeflow Pipelines KFP UI Access
- Principle:Open compass VLMEvalKit Results Summarization
- Principle:Rapidsai Cuml Linear Model Fitting
- Principle:Helicone Helicone MCP Type Generation
- Principle:Ray project Ray Cross Language Task Submission
- Principle:Mit han lab Llm awq Multimodal Model Configuration
- Principle:Openai Whisper Output Formatting
Implementations
- Implementation:Heibaiying BigData Notes Maven Packaging for Storm
- Implementation:Openai Openai agents python Execute Tools And Side Effects
- Implementation:FlowiseAI Flowise Dropdown
- Implementation:InternLM Lmdeploy Gemm Cast
- Implementation:PrefectHQ Prefect Worker CLI Commands
- Implementation:Scikit learn Scikit learn RandomForestClassifier Init
- Implementation:NVIDIA TransformerEngine Fused Attn F16 Arbitrary Seqlen
- Implementation:Kornia Kornia Charbonnier Loss
- Implementation:LaurentMazare Tch rs Adam Build
- Implementation:Infiniflow Ragflow UseGraphStore Zustand
Heuristics
- Heuristic:Teamcapybara Capybara Async Waiting And Retry
- Heuristic:Tencent Ncnn Letterbox Vs Direct Resize
- Heuristic:Mit han lab Llm awq AWQ Grid Search Tuning
- Heuristic:Datahub project Datahub Spark Databricks Coalescing
- Heuristic:Evidentlyai Evidently Drift Detection Thresholds
- Heuristic:Fede1024 Rust rdkafka Queue Buffering Priority
- Heuristic:Vespa engine Vespa RPM Zstd Compression Settings
- Heuristic:Pyro ppl Pyro Gradient Variance Reduction
- Heuristic:Langchain ai Langgraph Stream Mode Selection
- Heuristic:Gretelai Gretel synthetics Gumbel Softmax NaN Retry
Environments
- Environment:Google deepmind Dm control GLFW Desktop Rendering
- Environment:Facebookresearch Habitat lab SLURM Distributed Environment
- Environment:Microsoft Autogen Extension Optional Dependencies
- Environment:Huggingface Peft BitsAndBytes Quantization
- Environment:InternLM Lmdeploy CUDA GPU Runtime
- Environment:Risingwavelabs Risingwave Java Connector Environment
- Environment:Eventual Inc Daft Ray Distributed Runner
- Environment:Teamcapybara Capybara Selenium WebDriver Environment
- Environment:Intel Ipex llm XPU Serving Environment
- Environment:FlagOpen FlagEmbedding GPU Accelerator Environment