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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:Microsoft DeepSpeedExamples SuperOffload Finetuning
- Workflow:Huggingface Open r1 Dataset Pass Rate Filtering
- Workflow:Mlfoundations Open flamingo Few Shot Evaluation
- Workflow:Tensorflow Tfjs Pretrained Model Conversion And Inference
- Workflow:Trailofbits Fickling PyTorch Format Identification
- Workflow:NVIDIA TransformerEngine FP8 Training Quickstart
- Workflow:Intel Ipex llm DPO Finetuning
- Workflow:Google deepmind Dm control Locomotion Task Setup
- Workflow:Nightwatchjs Nightwatch Custom Commands And Assertions
- Workflow:NVIDIA DALI Image Classification Training PyTorch
Principles
- Principle:Apache Dolphinscheduler Query Execution Through Connection
- Principle:Bentoml BentoML Deployment Lifecycle Management
- Principle:OWASP Www project top 10 for large language model applications Application Architecture Review
- Principle:Getgauge Taiko Gauge Specification Authoring
- Principle:Microsoft BIPIA Dataset Preparation
- Principle:Huggingface Trl SFT Training Execution
- Principle:ContextualAI HALOs Supervised Finetuning
- Principle:Apache Dolphinscheduler Parameter Validation And Transformation
- Principle:Onnx Onnx External Data Saving
- Principle:Sdv dev SDV Metadata Loading
Implementations
- Implementation:SeleniumHQ Selenium Proxy
- Implementation:NVIDIA TransformerEngine Ops Fuser
- Implementation:Dotnet Machinelearning AutoML SweepablePipeline
- Implementation:Facebookresearch Habitat lab Datasets download CLI
- Implementation:OpenGVLab InternVL Phi3Config
- Implementation:Fastai Fastbook NN Sequential
- Implementation:Confident ai Deepeval ToolUseMetric
- Implementation:CrewAIInc CrewAI Tavily Extractor Tool
- Implementation:Webdriverio Webdriverio DeleteSession Command
- Implementation:Google deepmind Mujoco mj loadModel
Heuristics
- Heuristic:Vllm project Vllm GPU Memory Utilization Tuning
- Heuristic:Axolotl ai cloud Axolotl Attention Mechanism Selection
- Heuristic:Infiniflow Ragflow Hybrid Search Fallback Strategy
- Heuristic:Huggingface Diffusers Dtype Precision Selection
- Heuristic:Sktime Pytorch forecasting Batch Size Selection
- Heuristic:Googleapis Python genai API Retry Backoff Strategy
- Heuristic:Microsoft BIPIA OpenAI Rate Limit Retry
- Heuristic:Princeton nlp SimPO BOS Token Handling
- Heuristic:Ggml org Ggml Thread Count Selection
- Heuristic:Apache Airflow DAG Top Level Code Avoidance
Environments
- Environment:Snorkel team Snorkel PyTorch
- Environment:Openclaw Openclaw Node 22 Runtime
- Environment:LLMBook zh LLMBook zh github io Bitsandbytes Quantization Environment
- Environment:Confident ai Deepeval Python 3 9 Runtime
- Environment:Langgenius Dify Node Frontend Environment
- Environment:FMInference FlexLLMGen CUDA GPU
- Environment:Treeverse LakeFS Web UI Environment
- Environment:Tensorflow Serving Docker Runtime Environment
- Environment:Microsoft Agent framework Core Package Dependencies
- Environment:PrefectHQ Prefect Prefect Server Database