[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QF643X31K6YNWC0X4FBHJB":3,"skill-same:01M6QF643X31K6YNWC0X4FBHJB":98},{"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":14,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6QF643X31K6YNWC0X4FBHJB","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.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-mlflow\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","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fkal-mlflow","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill mlflow",{"tool":64,"command":65},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill mlflow",{"tool":67,"command":68},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill mlflow","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-mlflow\u002FSKILL.md",true,[72,78,82,86,90,94],{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Fautologging.md","REFERENCE","参考文档",1306,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-mlflow\u002Freferences\u002Fautologging.md",{"path":79,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fdeployment.md",16695,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-mlflow\u002Freferences\u002Fdeployment.md",{"path":83,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fintegration-examples.md",2365,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-mlflow\u002Freferences\u002Fintegration-examples.md",{"path":87,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fmodel-registry-2.md",2645,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-mlflow\u002Freferences\u002Fmodel-registry-2.md",{"path":91,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fmodel-registry.md",17475,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-mlflow\u002Freferences\u002Fmodel-registry.md",{"path":95,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Ftracking.md",14970,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-mlflow\u002Freferences\u002Ftracking.md",[99,130,154,181,204,228],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":110,"publishedAt":38},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[105],{"categoryNo":106,"categoryName":107,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,20,127,128,129],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":136,"publishedAt":38},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[137,138,139,140,115,141,142,143,144,145,146,147,148,149,26,150,151,152,153,20],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","platform","grow","posting","competitive",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":163,"publishedAt":38},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[160],{"categoryNo":161,"categoryName":162,"taxonomyType":108,"taxonomyTypeDesc":109},"01JBSEEDCAT000000000000023","Skill与智能体开发",[164,148,165,166,167,168,169,170,26,171,172,49,173,174,175,176,177,178,179,180],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":187,"publishedAt":38},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[188,189,190,191,36,35,192,193,194,26,195,122,196,197,198,199,200,201,202,203],"wrap up","up","wrap","gemini","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":211,"publishedAt":38},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[210],{"categoryNo":161,"categoryName":162,"taxonomyType":108,"taxonomyTypeDesc":109},[212,213,214,215,216,166,35,217,218,219,220,221,148,222,121,223,224,225,226,227],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":232,"categories":233,"keywords":234,"publishedAt":38},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[235,236,237,238,239,240,241,242,243,220,244,245,246,247,248,21,123,249,250,203],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","sprint","cadence"]