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2 archivos| name | cloud-neural |
| description | Neural network training and deployment in Flow Nexus cloud. Use for distributed ML training, model inference, and neural network lifecycle management. |
| version | 1.0.0 |
| category | cloud |
| type | skill |
| capabilities | ["neural_network_training","distributed_training","model_inference","cluster_management","template_deployment","model_validation"] |
| tools | ["mcp__flow-nexus__neural_train","mcp__flow-nexus__neural_predict","mcp__flow-nexus__neural_cluster_init","mcp__flow-nexus__neural_node_deploy","mcp__flow-nexus__neural_cluster_connect","mcp__flow-nexus__neural_train_distributed","mcp__flow-nexus__neural_cluster_status","mcp__flow-nexus__neural_predict_distributed","mcp__flow-nexus__neural_cluster_terminate","mcp__flow-nexus__neural_list_templates","mcp__flow-nexus__neural_deploy_template","mcp__flow-nexus__neural_training_status","mcp__flow-nexus__neural_list_models","mcp__flow-nexus__neural_validation_workflow","mcp__flow-nexus__neural_publish_template","mcp__flow-nexus__neural_rate_template","mcp__flow-nexus__neural_performance_benchmark"] |
| related_skills | ["cloud-swarm","cloud-sandbox","cloud-workflow"] |
Cloud Neural Network
Train, deploy, and manage neural networks at scale using Flow Nexus cloud-powered distributed computing.
Quick Start
mcp__flow-nexus__neural_train({
config: {
architecture: {
type: "feedforward",
layers: [
{ type: "dense", units: 128, activation: "relu" },
{ type: "dropout", rate: 0.2 },
{ type: "dense", units: 10, activation: "softmax" }
]
},
training: { epochs: 100, batch_size: 32, learning_rate: 0.001 }
},
tier: "small"
})
mcp__flow-nexus__neural_predict({
model_id: "trained_model_id",
input: [[0.5, 0.3, 0.2]]
})
When to Use
- Training neural networks for classification, regression, or generation tasks
- Deploying distributed training across multiple cloud sandboxes
- Running model inference on trained models
- Managing model lifecycle from training to production deployment
- Implementing federated learning or ensemble methods
- Fine-tuning pre-trained models for specific domains
Prerequisites
- Flow Nexus account with active session
- MCP server
flow-nexus configured
- Sufficient rUv credits for training tier selected
Core Concepts
Neural Architectures
| Type | Use Case |
|---|
| Feedforward | Classification, regression |
| LSTM/RNN | Time series, NLP sequences |
| Transformer | Advanced NLP, multimodal |
| CNN | Computer vision, image processing |
| GAN | Data generation, augmentation |
| Autoencoder | Dimensionality reduction, anomaly detection |
Training Tiers
| Tier | Resources | Cost |
|---|
nano | Minimal, quick tests | Low |
mini | Small models | Low |
small | Standard training | Medium |
medium | Large models | High |
large | Production scale | Highest |
Distributed Consensus Protocols
- proof-of-learning: Training contribution verification
- byzantine: Fault-tolerant distributed consensus
- raft: Leader-based coordination
- gossip: Decentralized information propagation
MCP Tools Reference
Single-Node Training
mcp__flow-nexus__neural_train({
config: {
architecture: {
type: "feedforward",
layers: [
{ type: "dense", units: 128, activation: "relu" },
{ type: "dropout", rate: 0.2 },
{ type: "dense", units: 10, activation: "softmax" }
]
},
training: {
epochs: 100,
batch_size: 32,
learning_rate: 0.001,
optimizer: "adam"
},
divergent: {
enabled: false,
pattern: "lateral",
factor: 0.1
}
},
tier: "small",
user_id: "user_id"
})
Distributed Cluster Training
mcp__flow-nexus__neural_cluster_init({
name: "training-cluster",
architecture: "transformer",
topology: "mesh",
consensus: "proof-of-learning",
daaEnabled: true,
wasmOptimization: true
})
mcp__flow-nexus__neural_node_deploy({
cluster_id: "cluster_id",
node_type: "worker",
model: "base",
capabilities: ["training", "inference"],
autonomy: 0.8
})
mcp__flow-nexus__neural_cluster_connect({
cluster_id: "cluster_id",
topology: "mesh"
})
mcp__flow-nexus__neural_train_distributed({
cluster_id: "cluster_id",
dataset: "dataset_id",
epochs: 10,
batch_size: 32,
learning_rate: 0.001,
optimizer: "adam",
federated: false
})
mcp__flow-nexus__neural_cluster_status({ cluster_id: "cluster_id" })
mcp__flow-nexus__neural_cluster_terminate({ cluster_id: "cluster_id" })
Inference
mcp__flow-nexus__neural_predict({
model_id: "model_id",
input: [[0.5, 0.3, 0.2]],
user_id: "user_id"
})
mcp__flow-nexus__neural_predict_distributed({
cluster_id: "cluster_id",
input_data: "[0.5, 0.3, 0.2]",
aggregation: "mean"
})
Template Management
mcp__flow-nexus__neural_list_templates({
category: "classification",
tier: "free",
search: "sentiment",
limit: 20
})
mcp__flow-nexus__neural_deploy_template({
template_id: "template_id",
custom_config: { epochs: 50 },
user_id: "user_id"
})
mcp__flow-nexus__neural_publish_template({
model_id: "model_id",
name: "Sentiment Analyzer",
description: "LSTM-based sentiment analysis model",
category: "nlp",
price: 0,
user_id: "user_id"
})
mcp__flow-nexus__neural_rate_template({
template_id: "template_id",
rating: 5,
review: "Excellent model, fast and accurate",
user_id: "user_id"
})
Model Management
mcp__flow-nexus__neural_list_models({
user_id: "user_id",
include_public: false
})
mcp__flow-nexus__neural_training_status({ job_id: "job_id" })
mcp__flow-nexus__neural_validation_workflow({
model_id: "model_id",
validation_type: "comprehensive",
user_id: "user_id"
})
mcp__flow-nexus__neural_performance_benchmark({
model_id: "model_id",
benchmark_type: "comprehensive"
})
Usage Examples
Example 1: Classification Model Training
const trainingJob = await mcp__flow-nexus__neural_train({
config: {
architecture: {
type: "feedforward",
layers: [
{ type: "dense", units: 256, activation: "relu" },
{ type: "batch_norm" },
{ type: "dropout", rate: 0.3 },
{ type: "dense", units: 128, activation: "relu" },
{ type: "dropout", rate: 0.2 },
{ type: "dense", units: 10, activation: "softmax" }
]
},
training: {
epochs: 100,
batch_size: 64,
learning_rate: 0.001,
optimizer: "adam"
}
},
tier: "small"
});
const status = await mcp__flow-nexus__neural_training_status({
job_id: trainingJob.job_id
});
console.log(`Epoch: ${status.current_epoch}, Loss: ${status.loss}`);
const prediction = await mcp__flow-nexus__neural_predict({
model_id: trainingJob.model_id,
input: [[0.1, 0.2, 0.3, 0.4, 0.5]]
});
Example 2: Distributed Transformer Training
const cluster = await mcp__flow-nexus__neural_cluster_init({
name: "transformer-cluster",
architecture: "transformer",
topology: "mesh",
consensus: "proof-of-learning",
daaEnabled: true,
wasmOptimization: true
});
for (let i = 0; i < 4; i++) {
await mcp__flow-nexus__neural_node_deploy({
cluster_id: cluster.cluster_id,
node_type: "worker",
model: "large",
capabilities: ["training", "inference"]
});
}
await mcp__flow-nexus__neural_node_deploy({
cluster_id: cluster.cluster_id,
node_type: "parameter_server",
model: "base"
});
await mcp__flow-nexus__neural_cluster_connect({
cluster_id: cluster.cluster_id
});
await mcp__flow-nexus__neural_train_distributed({
cluster_id: cluster.cluster_id,
dataset: "large_nlp_dataset",
epochs: 50,
batch_size: 128,
learning_rate: 0.0001,
optimizer: "adam"
});
const clusterStatus = await mcp__flow-nexus__neural_cluster_status({
cluster_id: cluster.cluster_id
});
await mcp__flow-nexus__neural_cluster_terminate({
cluster_id: cluster.cluster_id
});
Example 3: Using Pre-built Templates
const templates = await mcp__flow-nexus__neural_list_templates({
category: "nlp",
tier: "free",
search: "sentiment"
});
const deployment = await mcp__flow-nexus__neural_deploy_template({
template_id: templates.templates[0].id,
custom_config: {
epochs: 25,
learning_rate: 0.0005
}
});
await mcp__flow-nexus__neural_validation_workflow({
model_id: deployment.model_id,
validation_type: "comprehensive"
});
const benchmark = await mcp__flow-nexus__neural_performance_benchmark({
model_id: deployment.model_id,
benchmark_type: "comprehensive"
});
console.log(`Inference latency: ${benchmark.inference_latency_ms}ms`);
Execution Checklist
Best Practices
- Start Small: Begin with
nano or mini tier for testing, scale up for production
- Proper Validation: Always run validation workflow before production deployment
- Hyperparameter Tuning: Use grid search or Bayesian optimization for best results
- Distributed Training: Use for large models; single-node for smaller experiments
- Checkpoint Frequently: Enable checkpointing for long training runs
- Monitor Drift: Implement drift detection for production models
Error Handling
| Error | Cause | Solution |
|---|
training_failed | Invalid architecture config | Verify layer compatibility and types |
cluster_init_failed | Invalid topology or architecture | Check supported combinations |
insufficient_credits | Training tier exceeds balance | Reduce tier or add credits |
model_not_found | Invalid model_id | Use neural_list_models to verify |
node_deploy_failed | Cluster capacity reached | Terminate unused nodes |
Metrics & Success Criteria
- Training Convergence: Loss decreasing over epochs
- Validation Accuracy: Target >90% for classification
- Inference Latency: <100ms for production
- Memory Efficiency: <80% resource utilization
- Model Size: Appropriate for deployment target
Integration Points
With Swarms
await mcp__flow-nexus__agent_spawn({
type: "analyst",
name: "ML Analyst",
capabilities: ["neural_training", "model_evaluation"]
});
With Workflows
await mcp__flow-nexus__workflow_create({
name: "ML Training Pipeline",
steps: [
{ id: "preprocess", action: "data_prep" },
{ id: "train", action: "neural_train", depends: ["preprocess"] },
{ id: "validate", action: "neural_validate", depends: ["train"] },
{ id: "deploy", action: "neural_deploy", depends: ["validate"] }
]
});
Related Skills
References
Version History
- 1.0.0 (2026-01-02): Initial release - converted from flow-nexus-neural agent
