Taskpack Architecture

taskpack/
├── manifest.yaml          # Package metadata and requirements
├── requirements.txt       # Python dependencies
├── Dockerfile            # Container definition (optional)
├── src/
│   ├── __init__.py
│   ├── task.py          # Main task entry point
│   ├── preprocessor.py   # Data preprocessing logic
│   ├── trainer.py       # Training logic
│   └── utils/
├── configs/
│   ├── default.yaml     # Default configuration
│   └── schema.json      # Configuration schema
├── tests/
│   └── test_task.py
└── README.md

# manifest.yaml
apiVersion: v1
kind: TaskPackage
metadata:
  name: fedavg-resnet50-pathology
  version: 1.0.0
  author: BitFlow Team
  description: Federated learning package for pathology image classification
 
spec:
  type: training # training | analysis | preprocessing | inference
  framework: pytorch # pytorch | tensorflow | custom
 
  requirements:
    python: ">=3.8"
    gpu: required # required | optional | none
    memory: 16GB
    storage: 50GB
 
  inputs:
    - name: images
      type: wsi # wsi | dicom | numpy | csv
      format: [".svs", ".tiff", ".ndpi"]
    - name: labels
      type: annotations
      format: [".json", ".xml"]
 
  outputs:
    - name: model_updates
      type: model_weights
      format: ".pth"
    - name: metrics
      type: metrics
      format: ".json"
 
  federated:
    protocol: fedavg # fedavg | fedprox | scaffold
    aggregation_compatible: true
    differential_privacy: optional

# manifest.yaml for LLM training
apiVersion: v1
kind: TaskPackage
metadata:
  name: pathology-llm-finetuning
  version: 1.0.0
  description: Fine-tune LLM on pathology reports and WSI descriptions
 
spec:
  type: training
  framework: transformers
 
  requirements:
    python: ">=3.9"
    gpu: required
    memory: 32GB
    gpu_memory: 24GB # For LLM
 
  inputs:
    - name: reports
      type: text
      format: [".txt", ".json"]
    - name: wsi_features
      type: embeddings
      format: ".npy"
    - name: qa_pairs
      type: json
      format: ".json"
 
  outputs:
    - name: model_adapter # LoRA/QLoRA adapters
      type: model_weights
      format: ".bin"
    - name: tokenizer
      type: tokenizer
      format: ".json"
 
  llm_config:
    base_model: "meta-llama/Llama-2-7b-hf"
    training_method: "lora" # lora | qlora | full
    lora_config:
      r: 16
      lora_alpha: 32
      target_modules: ["q_proj", "v_proj"]

# manifest.yaml for pharma biomarker discovery
apiVersion: v1
kind: TaskPackage
metadata:
  name: biomarker-discovery-spatial
  version: 2.0.0
  description: Spatial biomarker discovery for drug response prediction
 
spec:
  type: analysis
  framework: custom
 
  requirements:
    python: ">=3.8"
    gpu: optional
    memory: 32GB
 
  inputs:
    - name: wsi_images
      type: wsi
      format: [".svs", ".tiff"]
    - name: clinical_data
      type: structured
      format: [".csv", ".parquet"]
    - name: treatment_response
      type: structured
      format: ".json"
 
  outputs:
    - name: biomarker_candidates
      type: analysis_results
      format: ".json"
    - name: spatial_features
      type: features
      format: ".h5"
    - name: statistical_report
      type: report
      format: [".pdf", ".html"]
 
  analysis_config:
    methods:
      - spatial_statistics
      - cell_neighborhood_analysis
      - molecular_correlation
    privacy_preserving: true
    statistical_significance: 0.05

# src/task.py - Standard interface all packages must implement
 
from abc import ABC, abstractmethod
from typing import Dict, Any, Optional
 
class BitflowTask(ABC):
    """Base class for all BitFlow tasks"""
 
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.node_id = config.get('node_id')
        self.task_id = config.get('task_id')
        self.round = config.get('round', 0)
 
    @abstractmethod
    def validate_inputs(self, input_paths: Dict[str, str]) -> bool:
        """Validate input data meets requirements"""
        pass
 
    @abstractmethod
    def preprocess(self, input_paths: Dict[str, str]) -> Dict[str, Any]:
        """Preprocess data before main execution"""
        pass
 
    @abstractmethod
    def execute(self, preprocessed_data: Dict[str, Any]) -> Dict[str, Any]:
        """Main task execution"""
        pass
 
    @abstractmethod
    def postprocess(self, results: Dict[str, Any]) -> Dict[str, str]:
        """Postprocess results and prepare outputs"""
        pass
 
    def run(self, input_paths: Dict[str, str], output_dir: str) -> Dict[str, Any]:
        """Standard execution pipeline"""
        # Validate
        if not self.validate_inputs(input_paths):
            raise ValueError("Input validation failed")
 
        # Execute pipeline
        preprocessed = self.preprocess(input_paths)
        results = self.execute(preprocessed)
        outputs = self.postprocess(results)
 
        # Save outputs
        for name, path in outputs.items():
            self.save_output(path, output_dir, name)
 
        return {
            'status': 'success',
            'outputs': outputs,
            'metrics': results.get('metrics', {})
        }

# src/llm_training_task.py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, TaskType
 
class PathologyLLMTask(BitflowTask):
    def __init__(self, config):
        super().__init__(config)
        self.model = None
        self.tokenizer = None
 
    def preprocess(self, input_paths):
        # Load pathology reports and WSI descriptions
        reports = self.load_reports(input_paths['reports'])
        wsi_features = self.load_features(input_paths['wsi_features'])
 
        # Create training dataset
        dataset = self.create_dataset(reports, wsi_features)
        return {'dataset': dataset}
 
    def execute(self, preprocessed_data):
        # Initialize model with LoRA
        base_model = AutoModelForCausalLM.from_pretrained(
            self.config['base_model'],
            load_in_8bit=True,
            device_map="auto"
        )
 
        lora_config = LoraConfig(
            task_type=TaskType.CAUSAL_LM,
            r=self.config['lora_r'],
            lora_alpha=self.config['lora_alpha'],
            target_modules=self.config['target_modules']
        )
 
        model = get_peft_model(base_model, lora_config)
 
        # Train
        trainer = self.create_trainer(model, preprocessed_data['dataset'])
        trainer.train()
 
        return {
            'model': model,
            'metrics': trainer.state.log_history
        }

# src/biomarker_discovery_task.py
import numpy as np
from scipy import stats
from skimage import measure
import pandas as pd
 
class BiomarkerDiscoveryTask(BitflowTask):
    def preprocess(self, input_paths):
        # Load WSI and extract patches
        wsi_data = self.load_wsi(input_paths['wsi_images'])
        clinical = pd.read_csv(input_paths['clinical_data'])
 
        # Extract spatial features
        features = self.extract_spatial_features(wsi_data)
 
        return {
            'features': features,
            'clinical': clinical,
            'wsi_metadata': wsi_data['metadata']
        }
 
    def execute(self, preprocessed_data):
        features = preprocessed_data['features']
        clinical = preprocessed_data['clinical']
 
        # Spatial analysis
        spatial_patterns = self.analyze_spatial_patterns(features)
 
        # Correlation with treatment response
        biomarkers = self.correlate_with_response(
            spatial_patterns,
            clinical['treatment_response']
        )
 
        # Statistical validation
        validated = self.statistical_validation(biomarkers,
                                              significance=0.05)
 
        return {
            'biomarker_candidates': validated,
            'spatial_features': spatial_patterns,
            'statistics': self.generate_statistics(validated)
        }
 
    def analyze_spatial_patterns(self, features):
        """Analyze spatial relationships between cells"""
        patterns = []
 
        for slide_features in features:
            # Cell neighborhood analysis
            neighborhoods = self.compute_neighborhoods(slide_features)
 
            # Spatial statistics (Ripley's K, Moran's I)
            spatial_stats = self.compute_spatial_stats(neighborhoods)
 
            patterns.append({
                'neighborhoods': neighborhoods,
                'spatial_stats': spatial_stats
            })
 
        return patterns

# bitflow-registry.yaml
registry:
  url: https://registry.bitflow.ai
 
packages:
  - name: fedavg-resnet50-pathology
    versions:
      - version: 1.0.0
        url: s3://bitflow-packages/fedavg-resnet50-pathology-1.0.0.tar.gz
        checksum: sha256:abcd1234...
 
  - name: pathology-llm-finetuning
    versions:
      - version: 1.0.0
        url: s3://bitflow-packages/pathology-llm-finetuning-1.0.0.tar.gz
        requirements:
          base_model_download: true
 
  - name: biomarker-discovery-spatial
    versions:
      - version: 2.0.0
        url: s3://bitflow-packages/biomarker-discovery-spatial-2.0.0.tar.gz

# Mission authentication on Bitedge before accepting
class MissionAuthenticator:
    def authenticate_mission(self, mission_package: str) -> bool:
        """Verify this mission package is legitimate and safe"""
        print("📨 Incoming mission package...")
 
        # Verify mission source (IMF headquarters)
        manifest = self.decode_mission_briefing(mission_package)
        if not self.verify_mission_authority(manifest):
            print("⚠️ Mission source unverified. Aborting.")
            return False
 
        # Check agent capabilities match mission requirements
        if not self.verify_agent_equipment(manifest['spec']['requirements']):
            print("⚠️ Agent lacks required equipment. Mission declined.")
            return False
 
        # Security clearance
        if not self.security_clearance(mission_package):
            print("🚫 Security breach detected. Mission compromised.")
            return False
 
        # Mission simulation (dry run)
        if not self.simulate_mission(mission_package):
            print("❌ Mission simulation failed. Too dangerous.")
            return False
 
        print("✅ Mission authenticated. Good luck, agent.")
        print("⏰ This package will self-destruct after execution...")
        return True
 
    def secure_cleanup(self, mission_id: str):
        """Mission complete. Eliminate all traces."""
        # Clear sensitive data from memory
        # Remove temporary files
        # Wipe execution logs
        print(f"💥 Mission {mission_id} data destroyed.")

interface FlowConfig {
  // ... existing config
  taskPackage: {
    name: string;
    version: string;
    source: "registry" | "custom";
    customUrl?: string;
    parameters?: Record<string, any>;
  };
}
 
// In Flow Builder UI
const TaskPackageSelector = () => {
  return (
    <div>
      <Label>Select Mission Package</Label>
      <Tabs defaultValue="registry">
        <TabsList>
          <TabsTrigger value="registry">Mission Registry</TabsTrigger>
          <TabsTrigger value="custom">Custom Mission</TabsTrigger>
        </TabsList>
 
        <TabsContent value="registry">
          <Select>
            <SelectTrigger>
              <SelectValue placeholder="Choose a package" />
            </SelectTrigger>
            <SelectContent>
              <SelectGroup>
                <SelectLabel>🎯 Training Missions</SelectLabel>
                <SelectItem value="pathology-llm-finetuning">
                  Operation: LLM Enhancement
                </SelectItem>
                <SelectItem value="fedavg-resnet50-pathology">
                  Operation: Federated Vision
                </SelectItem>
              </SelectGroup>
              <SelectGroup>
                <SelectLabel>🔍 Discovery Missions</SelectLabel>
                <SelectItem value="biomarker-discovery-spatial">
                  Operation: Biomarker Hunt
                </SelectItem>
                <SelectItem value="drug-response-prediction">
                  Operation: Pharma Intel
                </SelectItem>
              </SelectGroup>
              <SelectGroup>
                <SelectLabel>🛡️ Special Operations</SelectLabel>
                <SelectItem value="privacy-preserving-analysis">
                  Operation: Ghost Protocol
                </SelectItem>
              </SelectGroup>
            </SelectContent>
          </Select>
        </TabsContent>
 
        <TabsContent value="custom">
          <div className="space-y-4">
            <Input placeholder="Package URL (tar.gz)" />
            <Button variant="outline">
              <Upload className="h-4 w-4 " />
              Upload Package
            </Button>
          </div>
        </TabsContent>
      </Tabs>
    </div>
  );
};