> ## Documentation Index
> Fetch the complete documentation index at: https://docs.loom.aayanmishra.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Advanced Features Appendix

> Expanded advanced features: Tapestry, Weaver, container runtime, troubleshooting.

#### Example: Multi-Stage Pipeline

```python theme={null}
from core.tapestry import Pipeline, ModelStage

pipeline = Pipeline([
  ModelStage(model="Apollo-1-4B"),
  ModelStage(model="prism-verifier", postprocess=True)
])
result = await pipeline.run(input_data)
```

#### Advanced Tapestry Usage

Tapestry supports complex routing strategies, including conditional branching and dynamic model selection based on input characteristics.

##### Conditional Routing Example

```python theme={null}
from core.tapestry import ConditionalRouter, Pipeline

router = ConditionalRouter(
    condition=lambda input: len(input) > 1000,  # Route based on input length
  true_branch=ModelStage(model="Apollo-1-8B"),
  false_branch=ModelStage(model="Apollo-1-4B")
)

pipeline = Pipeline([router])
result = await pipeline.run(input_data)
```

##### Performance Optimization

* Use caching for frequently accessed models
* Implement request batching for high-throughput scenarios
* Monitor pipeline latency and optimize bottlenecks

### Weaver — Distributed Orchestration

Weaver is LoomOS's advanced orchestration engine for distributed training, hyperparameter search, and experiment management.

#### Features

* Distributed job graph execution
* Hyperparameter sweeps and grid/random search
* Fault-tolerant checkpointing and recovery
* Integration with Scheduler and LoomDB

#### Example: Hyperparameter Search

```python theme={null}
from core.weaver import Weaver, SweepConfig

sweep = SweepConfig(
    param_grid={"learning_rate": [1e-4, 3e-4, 1e-3]},
    max_trials=10
)
weaver = Weaver(sweep)
results = await weaver.run(job_spec)
print(results)
```

#### Advanced Weaver Configurations

Weaver supports Bayesian optimization, early stopping, and multi-objective optimization for complex experiments.

##### Bayesian Optimization Example

```python theme={null}
from core.weaver import BayesianOptimizer

optimizer = BayesianOptimizer(
    search_space={
        "learning_rate": {"type": "loguniform", "low": 1e-5, "high": 1e-2},
        "batch_size": {"type": "categorical", "choices": [16, 32, 64]}
    },
    objective="validation_accuracy",
    max_trials=50
)

weaver = Weaver(optimizer=optimizer)
results = await weaver.optimize(job_spec)
```

##### Multi-Objective Optimization

```python theme={null}
from core.weaver import MultiObjectiveOptimizer

optimizer = MultiObjectiveOptimizer(
    objectives=["accuracy", "latency", "memory_usage"],
    weights=[0.5, 0.3, 0.2],
    constraints={"latency": "< 100ms"}
)

weaver = Weaver(optimizer=optimizer)
pareto_front = await weaver.optimize(job_spec)
```

#### Integration with LoomDB

Weaver automatically logs all experiments, hyperparameters, and results to LoomDB for comprehensive tracking and analysis.

### Container Runtime Extensions

LoomOS supports custom container runtimes (Docker, WASM) for secure, reproducible execution.

#### Best Practices

* Use minimal, trusted base images
* Pin all dependencies for reproducibility
* Enable resource limits and security policies

#### Docker Runtime Configuration

```yaml theme={null}
# docker-compose.yml snippet
services:
  loomos-worker:
    image: loomos/worker:latest
    runtime: nvidia  # For GPU support
    security_opt:
      - no-new-privileges:true
    cap_drop:
      - ALL
    read_only: true
    tmpfs:
      - /tmp
```

#### WASM Runtime for Secure Execution

LoomOS supports WebAssembly for sandboxed model execution, providing additional security layers.

```python theme={null}
from core.runtime import WASMRuntime

runtime = WASMRuntime(model_path="model.wasm")
result = await runtime.execute(input_data)
```

#### Performance Tuning

* Use multi-stage builds to minimize image size
* Implement health checks and graceful shutdown
* Monitor resource usage with Prometheus metrics

### Troubleshooting Advanced Features

* **Verification failures**: Check test suite config and model outputs
* **Pipeline errors**: Validate all model stages and input/output schemas
* **Orchestration issues**: Inspect job graph and scheduler logs
* **Container runtime errors**: Check image compatibility and resource limits

#### Detailed Troubleshooting Guide

##### Prism Verification Issues

* **False positives**: Adjust threshold values in test configurations
* **Timeout errors**: Increase verification timeout or optimize test suite
* **Integration failures**: Ensure Prism is properly initialized and connected to LoomDB

##### Tapestry Pipeline Problems

* **Routing failures**: Debug conditional logic and input validation
* **Performance bottlenecks**: Profile each stage and optimize slow components
* **State management**: Ensure proper cleanup between pipeline runs

##### Weaver Orchestration Errors

* **Job scheduling failures**: Check resource availability and constraints
* **Checkpoint corruption**: Verify storage integrity and backup mechanisms
* **Optimization convergence**: Adjust search spaces and objective functions

##### Container Runtime Failures

* **Image pull errors**: Verify registry access and image tags
* **Resource exhaustion**: Monitor CPU, memory, and GPU usage
* **Security violations**: Review security policies and capabilities