Advanced Features Appendix

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Example: Multi-Stage Pipeline

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)

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Advanced Tapestry Usage

Conditional Routing Example

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

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Weaver — Distributed Orchestration

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Features

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

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Example: Hyperparameter Search

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)

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Advanced Weaver Configurations

Bayesian Optimization Example

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

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)

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Integration with LoomDB

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Container Runtime Extensions

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Best Practices

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

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Docker Runtime Configuration

# 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

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WASM Runtime for Secure Execution

from core.runtime import WASMRuntime

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

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Performance Tuning

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

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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

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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