[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MAFZNMS019JTMAPA712MX5":3,"skill-same:01M6MAFZNMS019JTMAPA712MX5":99},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6MAFZNMS019JTMAPA712MX5","mlflow",null,"Tracks machine learning experiments and manages model lifecycles with MLflow, covering mlflow.log_param, log_metric and log_artifact, autologging for scikit-learn, PyTorch Lightning, XGBoost and HuggingFace Transformers, the Model Registry with versions and stage transitions, run searching, and local or cloud model serving. Use when logging parameters, metrics and artifacts for training runs, comparing runs across experiments, registering and promoting model versions from Staging to Production, serving a logged model for inference, or reproducing an experiment from an MLflow project. Not for hyperparameter-sweep dashboards or general data versioning; use a dedicated tool for those.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1559,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmlflow\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"tool","for","project","local","artifact","general","log","use","versioning","experiments","learning","lifecycle","boto3","HuggingFace","mlops","registry","run","deployment","training","2026-11-10 10:12:30",false,"---\nname: mlflow\ndescription: Tracks machine learning experiments and manages model lifecycles with MLflow, covering mlflow.log_param, log_metric and log_artifact, autologging for scikit-learn, PyTorch Lightning, XGBoost and HuggingFace Transformers, the Model Registry with versions and stage transitions, run searching, and local or cloud model serving. Use when logging parameters, metrics and artifacts for training runs, comparing runs across experiments, registering and promoting model versions from Staging to Production, serving a logged model for inference, or reproducing an experiment from an MLflow project. Not for hyperparameter-sweep dashboards or general data versioning; use a dedicated tool for those.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  category: ml-inference-and-ops\n  maintainer: Kalaris Labs\n  tags: MLOps, MLflow, Experiment Tracking, Model Registry, ML Lifecycle, Deployment, Model Versioning, PyTorch, TensorFlow, Scikit-Learn, HuggingFace\n  dependencies: mlflow, sqlalchemy, boto3\n---\n\n# MLflow: ML Lifecycle Management Platform\n\n## When to Use This Skill\n\nUse MLflow when you need to:\n- **Track ML experiments** with parameters, metrics, and artifacts\n- **Manage model registry** with versioning and stage transitions\n- **Deploy models** to various platforms (local, cloud, serving)\n- **Reproduce experiments** with project configurations\n- **Compare model versions** and performance metrics\n- **Collaborate** on ML projects with team workflows\n- **Integrate** with any ML framework (framework-agnostic)\n\n\n## Installation\n\n```bash\n# Install MLflow\npip install mlflow\n\n# Install with extras\npip install mlflow[extras]  # Includes SQLAlchemy, boto3, etc.\n\n# Start MLflow UI\nmlflow ui\n\n# Access at http:\u002F\u002Flocalhost:5000\n```\n\n## Quick Start\n\n### Basic Tracking\n\n```python\nimport mlflow\n\n# Start a run\nwith mlflow.start_run():\n    # Log parameters\n    mlflow.log_param(\"learning_rate\", 0.001)\n    mlflow.log_param(\"batch_size\", 32)\n\n    # Your training code\n    model = train_model()\n\n    # Log metrics\n    mlflow.log_metric(\"train_loss\", 0.15)\n    mlflow.log_metric(\"val_accuracy\", 0.92)\n\n    # Log model\n    mlflow.sklearn.log_model(model, \"model\")\n```\n\n### Autologging (Automatic Tracking)\n\n```python\nimport mlflow\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Enable autologging\nmlflow.autolog()\n\n# Train (automatically logged)\nmodel = RandomForestClassifier(n_estimators=100, max_depth=5)\nmodel.fit(X_train, y_train)\n\n# Metrics, parameters, and model logged automatically!\n```\n\n## Core Concepts\n\n### 1. Experiments and Runs\n\n**Experiment**: Logical container for related runs\n**Run**: Single execution of ML code (parameters, metrics, artifacts)\n\n```python\nimport mlflow\n\n# Create\u002Fset experiment\nmlflow.set_experiment(\"my-experiment\")\n\n# Start a run\nwith mlflow.start_run(run_name=\"baseline-model\"):\n    # Log params\n    mlflow.log_param(\"model\", \"ResNet50\")\n    mlflow.log_param(\"epochs\", 10)\n\n    # Train\n    model = train()\n\n    # Log metrics\n    mlflow.log_metric(\"accuracy\", 0.95)\n\n    # Log model\n    mlflow.pytorch.log_model(model, \"model\")\n\n# Run ID is automatically generated\nprint(f\"Run ID: {mlflow.active_run().info.run_id}\")\n```\n\n### 2. Logging Parameters\n\n```python\nwith mlflow.start_run():\n    # Single parameter\n    mlflow.log_param(\"learning_rate\", 0.001)\n\n    # Multiple parameters\n    mlflow.log_params({\n        \"batch_size\": 32,\n        \"epochs\": 50,\n        \"optimizer\": \"Adam\",\n        \"dropout\": 0.2\n    })\n\n    # Nested parameters (as dict)\n    config = {\n        \"model\": {\n            \"architecture\": \"ResNet50\",\n            \"pretrained\": True\n        },\n        \"training\": {\n            \"lr\": 0.001,\n            \"weight_decay\": 1e-4\n        }\n    }\n\n    # Log as JSON string or individual params\n    for key, value in config.items():\n        mlflow.log_param(key, str(value))\n```\n\n### 3. Logging Metrics\n\n```python\nwith mlflow.start_run():\n    # Training loop\n    for epoch in range(NUM_EPOCHS):\n        train_loss = train_epoch()\n        val_loss = validate()\n\n        # Log metrics at each step\n        mlflow.log_metric(\"train_loss\", train_loss, step=epoch)\n        mlflow.log_metric(\"val_loss\", val_loss, step=epoch)\n\n        # Log multiple metrics\n        mlflow.log_metrics({\n            \"train_accuracy\": train_acc,\n            \"val_accuracy\": val_acc\n        }, step=epoch)\n\n    # Log final metrics (no step)\n    mlflow.log_metric(\"final_accuracy\", final_acc)\n```\n\n### 4. Logging Artifacts\n\n```python\nwith mlflow.start_run():\n    # Log file\n    model.save('model.pkl')\n    mlflow.log_artifact('model.pkl')\n\n    # Log directory\n    os.makedirs('plots', exist_ok=True)\n    plt.savefig('plots\u002Floss_curve.png')\n    mlflow.log_artifacts('plots')\n\n    # Log text\n    with open('config.txt', 'w') as f:\n        f.write(str(config))\n    mlflow.log_artifact('config.txt')\n\n    # Log dict as JSON\n    mlflow.log_dict({'config': config}, 'config.json')\n```\n\n### 5. Logging Models\n\n```python\n# PyTorch\nimport mlflow.pytorch\n\nwith mlflow.start_run():\n    model = train_pytorch_model()\n    mlflow.pytorch.log_model(model, \"model\")\n\n# Scikit-learn\nimport mlflow.sklearn\n\nwith mlflow.start_run():\n    model = train_sklearn_model()\n    mlflow.sklearn.log_model(model, \"model\")\n\n# Keras\u002FTensorFlow\nimport mlflow.keras\n\nwith mlflow.start_run():\n    model = train_keras_model()\n    mlflow.keras.log_model(model, \"model\")\n\n# HuggingFace Transformers\nimport mlflow.transformers\n\nwith mlflow.start_run():\n    mlflow.transformers.log_model(\n        transformers_model={\n            \"model\": model,\n            \"tokenizer\": tokenizer\n        },\n        artifact_path=\"model\"\n    )\n```\n\n## Autologging\n\nDetails, code examples and parameter tables: [references\u002Fautologging.md](references\u002Fautologging.md). Read it when this step applies.\n\n## Model Registry\n\nDetails, code examples and parameter tables: [references\u002Fmodel-registry-2.md](references\u002Fmodel-registry-2.md). Read it when this step applies.\n\n## Searching Runs\n\nFind runs programmatically.\n\n```python\nfrom mlflow.tracking import MlflowClient\n\nclient = MlflowClient()\n\n# Search all runs in experiment\nexperiment_id = client.get_experiment_by_name(\"my-experiment\").experiment_id\nruns = client.search_runs(\n    experiment_ids=[experiment_id],\n    filter_string=\"metrics.accuracy > 0.9\",\n    order_by=[\"metrics.accuracy DESC\"],\n    max_results=10\n)\n\nfor run in runs:\n    print(f\"Run ID: {run.info.run_id}\")\n    print(f\"Accuracy: {run.data.metrics['accuracy']}\")\n    print(f\"Params: {run.data.params}\")\n\n# Search with complex filters\nruns = client.search_runs(\n    experiment_ids=[experiment_id],\n    filter_string=\"\"\"\n        metrics.accuracy > 0.9 AND\n        params.model = 'ResNet50' AND\n        tags.dataset = 'ImageNet'\n    \"\"\",\n    order_by=[\"metrics.f1_score DESC\"]\n)\n```\n\n## Integration Examples\n\nDetails, code examples and parameter tables: [references\u002Fintegration-examples.md](references\u002Fintegration-examples.md). Read it when this step applies.\n\n## Best Practices\n\n### 1. Organize with Experiments\n\n```python\n# ✅ Good: Separate experiments for different tasks\nmlflow.set_experiment(\"sentiment-analysis\")\nmlflow.set_experiment(\"image-classification\")\nmlflow.set_experiment(\"recommendation-system\")\n\n# ❌ Bad: Everything in one experiment\nmlflow.set_experiment(\"all-models\")\n```\n\n### 2. Use Descriptive Run Names\n\n```python\n# ✅ Good: Descriptive names\nwith mlflow.start_run(run_name=\"resnet50-imagenet-lr0.001-bs32\"):\n    train()\n\n# ❌ Bad: No name (auto-generated UUID)\nwith mlflow.start_run():\n    train()\n```\n\n### 3. Log Comprehensive Metadata\n\n```python\nwith mlflow.start_run():\n    # Log hyperparameters\n    mlflow.log_params({\n        \"learning_rate\": 0.001,\n        \"batch_size\": 32,\n        \"epochs\": 50\n    })\n\n    # Log system info\n    mlflow.set_tags({\n        \"dataset\": \"ImageNet\",\n        \"framework\": \"PyTorch 2.0\",\n        \"gpu\": \"A100\",\n        \"git_commit\": get_git_commit()\n    })\n\n    # Log data info\n    mlflow.log_param(\"train_samples\", len(train_dataset))\n    mlflow.log_param(\"val_samples\", len(val_dataset))\n```\n\n### 4. Track Model Lineage\n\n```python\n# Link runs to understand lineage\nwith mlflow.start_run(run_name=\"preprocessing\"):\n    data = preprocess()\n    mlflow.log_artifact(\"data.csv\")\n    preprocessing_run_id = mlflow.active_run().info.run_id\n\nwith mlflow.start_run(run_name=\"training\"):\n    # Reference parent run\n    mlflow.set_tag(\"preprocessing_run_id\", preprocessing_run_id)\n    model = train(data)\n```\n\n### 5. Use Model Registry for Deployment\n\n```python\n# ✅ Good: Use registry for production\nmodel_uri = \"models:\u002Fmy-classifier\u002FProduction\"\nmodel = mlflow.pyfunc.load_model(model_uri)\n\n# ❌ Bad: Hard-code run IDs\nmodel_uri = \"runs:\u002Fabc123\u002Fmodel\"\nmodel = mlflow.pyfunc.load_model(model_uri)\n```\n\n## Deployment\n\n### Serve Model Locally\n\n```bash\n# Serve registered model\nmlflow models serve -m \"models:\u002Fmy-classifier\u002FProduction\" -p 5001\n\n# Serve from run\nmlflow models serve -m \"runs:\u002F\u003CRUN_ID>\u002Fmodel\" -p 5001\n\n# Test endpoint\ncurl http:\u002F\u002F127.0.0.1:5001\u002Finvocations -H 'Content-Type: application\u002Fjson' -d '{\n  \"inputs\": [[1.0, 2.0, 3.0, 4.0]]\n}'\n```\n\n### Deploy to Cloud\n\n```bash\n# Deploy to AWS SageMaker\nmlflow sagemaker deploy -m \"models:\u002Fmy-classifier\u002FProduction\" --region-name us-west-2\n\n# Deploy to Azure ML\nmlflow azureml deploy -m \"models:\u002Fmy-classifier\u002FProduction\"\n```\n\n## Configuration\n\n### Tracking Server\n\n```bash\n# Start tracking server with backend store\nmlflow server \\\n  --backend-store-uri postgresql:\u002F\u002Fuser:password@localhost\u002Fmlflow \\\n  --default-artifact-root s3:\u002F\u002Fmy-bucket\u002Fmlflow \\\n  --host 0.0.0.0 \\\n  --port 5000\n```\n\n### Client Configuration\n\n```python\nimport mlflow\n\n# Set tracking URI\nmlflow.set_tracking_uri(\"http:\u002F\u002Flocalhost:5000\")\n\n# Or use environment variable\n# export MLFLOW_TRACKING_URI=http:\u002F\u002Flocalhost:5000\n```\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fmlflow.org\u002Fdocs\u002Flatest\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fmlflow\u002Fmlflow\n- **Examples**: https:\u002F\u002Fgithub.com\u002Fmlflow\u002Fmlflow\u002Ftree\u002Fmaster\u002Fexamples\n- **Community**: https:\u002F\u002Fmlflow.org\u002Fcommunity\n\n## See Also\n\n- `references\u002Ftracking.md` - Comprehensive tracking guide\n- `references\u002Fmodel-registry.md` - Model lifecycle management\n- `references\u002Fdeployment.md` - Production deployment patterns\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm hardware, framework and server versions, model format, and expected load.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Serve or log a single request or run end to end before scaling.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Measure latency, throughput and output correctness against a reference; check resource usage and costs.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| The server fails to start or OOMs | Check model size versus memory, quantization and parallelism settings. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Do not expose services or credentials publicly; confirm cloud costs before provisioning.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n","# MLflow: ML Lifecycle Management Platform\n\n## When to Use This Skill\n\nUse MLflow when you need to:\n- **Track ML experiments** with parameters, metrics, and artifacts\n- **Manage model registry** with versioning and stage transitions\n- **Deploy models** to various platforms (local, cloud, serving)\n- **Reproduce experiments** with project configurations\n- **Compare model versions** and performance metrics\n- **Collaborate** on ML projects with team workflows\n- **Integrate** with any ML framework (framework-agnostic)\n\n\n## Installation\n\n```bash\n# Install MLflow\npip install mlflow\n\n# Install with extras\npip install mlflow[extras]  # Includes SQLAlchemy, boto3, etc.\n\n# Start MLflow UI\nmlflow ui\n\n# Access at http:\u002F\u002Flocalhost:5000\n```\n\n## Quick Start\n\n### Basic Tracking\n\n```python\nimport mlflow\n\n# Start a run\nwith mlflow.start_run():\n    # Log parameters\n    mlflow.log_param(\"learning_rate\", 0.001)\n    mlflow.log_param(\"batch_size\", 32)\n\n    # Your training code\n    model = train_model()\n\n    # Log metrics\n    mlflow.log_metric(\"train_loss\", 0.15)\n    mlflow.log_metric(\"val_accuracy\", 0.92)\n\n    # Log model\n    mlflow.sklearn.log_model(model, \"model\")\n```\n\n### Autologging (Automatic Tracking)\n\n```python\nimport mlflow\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Enable autologging\nmlflow.autolog()\n\n# Train (automatically logged)\nmodel = RandomForestClassifier(n_estimators=100, max_depth=5)\nmodel.fit(X_train, y_train)\n\n# Metrics, parameters, and model logged automatically!\n```\n\n## Core Concepts\n\n### 1. Experiments and Runs\n\n**Experiment**: Logical container for related runs\n**Run**: Single execution of ML code (parameters, metrics, artifacts)\n\n```python\nimport mlflow\n\n# Create\u002Fset experiment\nmlflow.set_experiment(\"my-experiment\")\n\n# Start a run\nwith mlflow.start_run(run_name=\"baseline-model\"):\n    # Log params\n    mlflow.log_param(\"model\", \"ResNet50\")\n    mlflow.log_param(\"epochs\", 10)\n\n    # Train\n    model = train()\n\n    # Log metrics\n    mlflow.log_metric(\"accuracy\", 0.95)\n\n    # Log model\n    mlflow.pytorch.log_model(model, \"model\")\n\n# Run ID is automatically generated\nprint(f\"Run ID: {mlflow.active_run().info.run_id}\")\n```\n\n### 2. Logging Parameters\n\n```python\nwith mlflow.start_run():\n    # Single parameter\n    mlflow.log_param(\"learning_rate\", 0.001)\n\n    # Multiple parameters\n    mlflow.log_params({\n        \"batch_size\": 32,\n        \"epochs\": 50,\n        \"optimizer\": \"Adam\",\n        \"dropout\": 0.2\n    })\n\n    # Nested parameters (as dict)\n    config = {\n        \"model\": {\n            \"architecture\": \"ResNet50\",\n            \"pretrained\": True\n        },\n        \"training\": {\n            \"lr\": 0.001,\n            \"weight_decay\": 1e-4\n        }\n    }\n\n    # Log as JSON string or individual params\n    for key, value in config.items():\n        mlflow.log_param(key, str(value))\n```\n\n### 3. Logging Metrics\n\n```python\nwith mlflow.start_run():\n    # Training loop\n    for epoch in range(NUM_EPOCHS):\n        train_loss = train_epoch()\n        val_loss = validate()\n\n        # Log metrics at each step\n        mlflow.log_metric(\"train_loss\", train_loss, step=epoch)\n        mlflow.log_metric(\"val_loss\", val_loss, step=epoch)\n\n        # Log multiple metrics\n        mlflow.log_metrics({\n            \"train_accuracy\": train_acc,\n            \"val_accuracy\": val_acc\n        }, step=epoch)\n\n    # Log final metrics (no step)\n    mlflow.log_metric(\"final_accuracy\", final_acc)\n```\n\n### 4. Logging Artifacts\n\n```python\nwith mlflow.start_run():\n    # Log file\n    model.save('model.pkl')\n    mlflow.log_artifact('model.pkl')\n\n    # Log directory\n    os.makedirs('plots', exist_ok=True)\n    plt.savefig('plots\u002Floss_curve.png')\n    mlflow.log_artifacts('plots')\n\n    # Log text\n    with open('config.txt', 'w') as f:\n        f.write(str(config))\n    mlflow.log_artifact('config.txt')\n\n    # Log dict as JSON\n    mlflow.log_dict({'config': config}, 'config.json')\n```\n\n### 5. Logging Models\n\n```python\n# PyTorch\nimport mlflow.pytorch\n\nwith mlflow.start_run():\n    model = train_pytorch_model()\n    mlflow.pytorch.log_model(model, \"model\")\n\n# Scikit-learn\nimport mlflow.sklearn\n\nwith mlflow.start_run():\n    model = train_sklearn_model()\n    mlflow.sklearn.log_model(model, \"model\")\n\n# Keras\u002FTensorFlow\nimport mlflow.keras\n\nwith mlflow.start_run():\n    model = train_keras_model()\n    mlflow.keras.log_model(model, \"model\")\n\n# HuggingFace Transformers\nimport mlflow.transformers\n\nwith mlflow.start_run():\n    mlflow.transformers.log_model(\n        transformers_model={\n            \"model\": model,\n            \"tokenizer\": tokenizer\n        },\n        artifact_path=\"model\"\n    )\n```\n\n## Autologging\n\nDetails, code examples and parameter tables: [references\u002Fautologging.md](references\u002Fautologging.md). Read it when this step applies.\n\n## Model Registry\n\nDetails, code examples and parameter tables: [references\u002Fmodel-registry-2.md](references\u002Fmodel-registry-2.md). Read it when this step applies.\n\n## Searching Runs\n\nFind runs programmatically.\n\n```python\nfrom mlflow.tracking import MlflowClient\n\nclient = MlflowClient()\n\n# Search all runs in experiment\nexperiment_id = client.get_experiment_by_name(\"my-experiment\").experiment_id\nruns = client.search_runs(\n    experiment_ids=[experiment_id],\n    filter_string=\"metrics.accuracy > 0.9\",\n    order_by=[\"metrics.accuracy DESC\"],\n    max_results=10\n)\n\nfor run in runs:\n    print(f\"Run ID: {run.info.run_id}\")\n    print(f\"Accuracy: {run.data.metrics['accuracy']}\")\n    print(f\"Params: {run.data.params}\")\n\n# Search with complex filters\nruns = client.search_runs(\n    experiment_ids=[experiment_id],\n    filter_string=\"\"\"\n        metrics.accuracy > 0.9 AND\n        params.model = 'ResNet50' AND\n        tags.dataset = 'ImageNet'\n    \"\"\",\n    order_by=[\"metrics.f1_score DESC\"]\n)\n```\n\n## Integration Examples\n\nDetails, code examples and parameter tables: [references\u002Fintegration-examples.md](references\u002Fintegration-examples.md). Read it when this step applies.\n\n## Best Practices\n\n### 1. Organize with Experiments\n\n```python\n# ✅ Good: Separate experiments for different tasks\nmlflow.set_experiment(\"sentiment-analysis\")\nmlflow.set_experiment(\"image-classification\")\nmlflow.set_experiment(\"recommendation-system\")\n\n# ❌ Bad: Everything in one experiment\nmlflow.set_experiment(\"all-models\")\n```\n\n### 2. Use Descriptive Run Names\n\n```python\n# ✅ Good: Descriptive names\nwith mlflow.start_run(run_name=\"resnet50-imagenet-lr0.001-bs32\"):\n    train()\n\n# ❌ Bad: No name (auto-generated UUID)\nwith mlflow.start_run():\n    train()\n```\n\n### 3. Log Comprehensive Metadata\n\n```python\nwith mlflow.start_run():\n    # Log hyperparameters\n    mlflow.log_params({\n        \"learning_rate\": 0.001,\n        \"batch_size\": 32,\n        \"epochs\": 50\n    })\n\n    # Log system info\n    mlflow.set_tags({\n        \"dataset\": \"ImageNet\",\n        \"framework\": \"PyTorch 2.0\",\n        \"gpu\": \"A100\",\n        \"git_commit\": get_git_commit()\n    })\n\n    # Log data info\n    mlflow.log_param(\"train_samples\", len(train_dataset))\n    mlflow.log_param(\"val_samples\", len(val_dataset))\n```\n\n### 4. Track Model Lineage\n\n```python\n# Link runs to understand lineage\nwith mlflow.start_run(run_name=\"preprocessing\"):\n    data = preprocess()\n    mlflow.log_artifact(\"data.csv\")\n    preprocessing_run_id = mlflow.active_run().info.run_id\n\nwith mlflow.start_run(run_name=\"training\"):\n    # Reference parent run\n    mlflow.set_tag(\"preprocessing_run_id\", preprocessing_run_id)\n    model = train(data)\n```\n\n### 5. Use Model Registry for Deployment\n\n```python\n# ✅ Good: Use registry for production\nmodel_uri = \"models:\u002Fmy-classifier\u002FProduction\"\nmodel = mlflow.pyfunc.load_model(model_uri)\n\n# ❌ Bad: Hard-code run IDs\nmodel_uri = \"runs:\u002Fabc123\u002Fmodel\"\nmodel = mlflow.pyfunc.load_model(model_uri)\n```\n\n## Deployment\n\n### Serve Model Locally\n\n```bash\n# Serve registered model\nmlflow models serve -m \"models:\u002Fmy-classifier\u002FProduction\" -p 5001\n\n# Serve from run\nmlflow models serve -m \"runs:\u002F\u003CRUN_ID>\u002Fmodel\" -p 5001\n\n# Test endpoint\ncurl http:\u002F\u002F127.0.0.1:5001\u002Finvocations -H 'Content-Type: application\u002Fjson' -d '{\n  \"inputs\": [[1.0, 2.0, 3.0, 4.0]]\n}'\n```\n\n### Deploy to Cloud\n\n```bash\n# Deploy to AWS SageMaker\nmlflow sagemaker deploy -m \"models:\u002Fmy-classifier\u002FProduction\" --region-name us-west-2\n\n# Deploy to Azure ML\nmlflow azureml deploy -m \"models:\u002Fmy-classifier\u002FProduction\"\n```\n\n## Configuration\n\n### Tracking Server\n\n```bash\n# Start tracking server with backend store\nmlflow server \\\n  --backend-store-uri postgresql:\u002F\u002Fuser:password@localhost\u002Fmlflow \\\n  --default-artifact-root s3:\u002F\u002Fmy-bucket\u002Fmlflow \\\n  --host 0.0.0.0 \\\n  --port 5000\n```\n\n### Client Configuration\n\n```python\nimport mlflow\n\n# Set tracking URI\nmlflow.set_tracking_uri(\"http:\u002F\u002Flocalhost:5000\")\n\n# Or use environment variable\n# export MLFLOW_TRACKING_URI=http:\u002F\u002Flocalhost:5000\n```\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fmlflow.org\u002Fdocs\u002Flatest\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fmlflow\u002Fmlflow\n- **Examples**: https:\u002F\u002Fgithub.com\u002Fmlflow\u002Fmlflow\u002Ftree\u002Fmaster\u002Fexamples\n- **Community**: https:\u002F\u002Fmlflow.org\u002Fcommunity\n\n## See Also\n\n- `references\u002Ftracking.md` - Comprehensive tracking guide\n- `references\u002Fmodel-registry.md` - Model lifecycle management\n- `references\u002Fdeployment.md` - Production deployment patterns\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm hardware, framework and server versions, model format, and expected load.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Serve or log a single request or run end to end before scaling.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Measure latency, throughput and output correctness against a reference; check resource usage and costs.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| The server fails to start or OOMs | Check model size versus memory, quantization and parallelism settings. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Do not expose services or credentials publicly; confirm cloud costs before provisioning.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n",{"name":5,"license":15,"metadata":43,"description":7},{"tags":44,"version":45,"category":46,"maintainer":47,"dependencies":48},"MLOps, MLflow, Experiment Tracking, Model Registry, ML Lifecycle, Deployment, Model Versioning, PyTorch, TensorFlow, Scikit-Learn, HuggingFace","1.0.0","ml-inference-and-ops","Kalaris Labs","mlflow, sqlalchemy, boto3","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmlflow",474,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-11 14:40:14",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill mlflow",{"tool":65,"command":66},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill mlflow",{"tool":68,"command":69},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill mlflow","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmlflow\u002FSKILL.md",true,[73,79,83,87,91,95],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fautologging.md","REFERENCE","参考文档",1306,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmlflow\u002Freferences\u002Fautologging.md",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fdeployment.md",16695,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmlflow\u002Freferences\u002Fdeployment.md",{"path":84,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fintegration-examples.md",2365,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmlflow\u002Freferences\u002Fintegration-examples.md",{"path":88,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fmodel-registry-2.md",2645,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmlflow\u002Freferences\u002Fmodel-registry-2.md",{"path":92,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fmodel-registry.md",17475,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmlflow\u002Freferences\u002Fmodel-registry.md",{"path":96,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Ftracking.md",14970,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fmlflow\u002Freferences\u002Ftracking.md",[100,109,116,123,150,174],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":108},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":108},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":122,"publishedAt":108},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":129,"publishedAt":38},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":156,"publishedAt":38},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[157,158,159,145,160,161,162,163,164,165,166,167,168,169,135,170,21,171,172,173],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","session","end","reflection",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":191,"publishedAt":38},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[180,185,188],{"categoryNo":181,"categoryName":182,"taxonomyType":183,"taxonomyTypeDesc":184},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":186,"categoryName":187,"taxonomyType":183,"taxonomyTypeDesc":184},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":189,"categoryName":190,"taxonomyType":183,"taxonomyTypeDesc":184},"01JBSEEDCAT000000000000010","后端与接口",[176,192,193,194,195,196,197,21,198,136,199,200,201,202,141,203,204,205,206,207],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]