Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
npxskills add ruvnet/claude-flow--skill flow-nexus-neuralLoading…
Deploy, train, and manage neural networks in distributed E2B sandbox environments. Train custom models with multiple architectures (feedforward, LSTM, GAN, transformer) or use pre-built templates from the marketplace.
# Add Flow Nexus MCP server
claude mcp add flow-nexus npx flow-nexus@latest mcp start
# Register and login
npx flow-nexus@latest register
npx flow-nexus@latest login
Train neural networks with custom architectures and configurations.
Available Architectures:
feedforward - Standard fully-connected networkslstm - Long Short-Term Memory for sequencesgan - Generative Adversarial Networksautoencoder - Dimensionality reductiontransformer - Attention-based modelsTraining Tiers:
nano - Minimal resources (fast, limited)mini - Small modelssmall - Standard modelsmedium - Complex modelslarge - Large-scale trainingmcp__flow-nexus__neural_train({
config: {
architecture: {
type: "feedforward",
layers: [
{ type: "dense", units: 256, activation: "relu" },
{ type: "dropout", rate: 0.3 },
{ type: "dense", units: 128, activation: "relu" },
{ type: "dropout", rate: 0.2 },
{ type: "dense", units: 64, activation: "relu" },
{ type: "dense", units: 10, activation: "softmax" }
]
},
training: {
epochs: 100,
batch_size: 32,
learning_rate: 0.001,
optimizer: "adam"
},
divergent: {
enabled: true,
pattern: "lateral", // quantum, chaotic, associative, evolutionary
factor: 0.5
}
},
tier: "small",
user_id: "your_user_id"
})
mcp__flow-nexus__neural_train({
config: {
architecture: {
type: "lstm",
layers: [
{ type: "lstm", units: 128, return_sequences: true },
{ type: "dropout", rate: 0.2 },
{ type: "lstm", units: 64 },
{ type: "dense", units: 1, activation: "linear" }
]
},
training: {
epochs: 150,
batch_size: 64,
learning_rate: 0.01,
optimizer: "adam"
}
},
tier: "medium"
})
mcp__flow-nexus__neural_train({
config: {
architecture: {
type: "transformer",
layers: [
{ type: "embedding", vocab_size: 10000, embedding_dim: 512 },
{ type: "transformer_encoder", num_heads: 8, ff_dim: 2048 },
{ type: "global_average_pooling" },
{ type: "dense", units: 128, activation: "relu" },
{ type: "dense", units: 2, activation: "softmax" }
]
},
training: {
epochs: 50,
batch_size: 16,
learning_rate: 0.0001,
optimizer: "adam"
}
},
tier: "large"
})
Run predictions on trained models.
mcp__flow-nexus__neural_predict({
model_id: "model_abc123",
input: [
[0.5, 0.3, 0.2, 0.1],
[0.8, 0.1, 0.05, 0.05],
[0.2, 0.6, 0.15, 0.05]
],
user_id: "your_user_id"
})
Response:
{
"predictions": [
[0.12, 0.85, 0.03],
[0.89, 0.08, 0.03],
[0.05, 0.92, 0.03]
],
"inference_time_ms": 45,
"model_version": "1.0.0"
}
Browse and deploy pre-trained models from the marketplace.
mcp__flow-nexus__neural_list_templates({
category: "classification", // timeseries, regression, nlp, vision, anomaly, generative
tier: "free", // or "paid"
search: "sentiment",
limit: 20
})
Response:
{
"templates": [
{
"id": "sentiment-analysis-v2",
"name": "Sentiment Analysis Classifier",
"description": "Pre-trained BERT model for sentiment analysis",
"category": "nlp",
"accuracy": 0.94,
"downloads": 1523,
"tier": "free"
},
{
"id": "image-classifier-resnet",
"name": "ResNet Image Classifier",
"description": "ResNet-50 for image classification",
"category": "vision",
"accuracy": 0.96,
"downloads": 2341,
"tier": "paid"
}
]
}
mcp__flow-nexus__neural_deploy_template({
template_id: "sentiment-analysis-v2",
custom_config: {
training: {
epochs: 50,
learning_rate: 0.0001
}
},
user_id: "your_user_id"
})
Train large models across multiple E2B sandboxes with distributed computing.
mcp__flow-nexus__neural_cluster_init({
name: "large-model-cluster",
architecture: "transformer", // transformer, cnn, rnn, gnn, hybrid
topology: "mesh", // mesh, ring, star, hierarchical
consensus: "proof-of-learning", // byzantine, raft, gossip
daaEnabled: true, // Decentralized Autonomous Agents
wasmOptimization: true
})
Response:
{
"cluster_id": "cluster_xyz789",
"name": "large-model-cluster",
"status": "initializing",
"topology": "mesh",
"max_nodes": 100,
"created_at": "2025-10-19T10:30:00Z"
}
// Deploy parameter server
mcp__flow-nexus__neural_node_deploy({
cluster_id: "cluster_xyz789",
node_type: "parameter_server",
model: "large",
template: "nodejs",
capabilities: ["parameter_management", "gradient_aggregation"],
autonomy: 0.8
})
// Deploy worker nodes
mcp__flow-nexus__neural_node_deploy({
cluster_id: "cluster_xyz789",
node_type: "worker",
model: "xl",
role: "worker",
capabilities: ["training", "inference"],
layers: [
{ type: "transformer_encoder", num_heads: 16 },
{ type: "feed_forward", units: 4096 }
],
autonomy: 0.9
})
// Deploy aggregator
mcp__flow-nexus__neural_node_deploy({
cluster_id: "cluster_xyz789",
node_type: "aggregator",
model: "large",
capabilities: ["gradient_aggregation", "model_synchronization"]
})
mcp__flow-nexus__neural_cluster_connect({
cluster_id: "cluster_xyz789",
topology: "mesh" // Override default if needed
})
mcp__flow-nexus__neural_train_distributed({
cluster_id: "cluster_xyz789",
dataset: "imagenet", // or custom dataset identifier
epochs: 100,
batch_size: 128,
learning_rate: 0.001,
optimizer: "adam", // sgd, rmsprop, adagrad
federated: true // Enable federated learning
})
Federated Learning Example:
mcp__flow-nexus__neural_train_distributed({
cluster_id: "cluster_xyz789",
dataset: "medical_images_distributed",
epochs: 200,
batch_size: 64,
learning_rate: 0.0001,
optimizer: "adam",
federated: true, // Data stays on local nodes
aggregation_rounds: 50,
min_nodes_per_round: 5
})
mcp__flow-nexus__neural_cluster_status({
cluster_id: "cluster_xyz789"
})
Response:
{
"cluster_id": "cluster_xyz789",
"status": "training",
"nodes": [
{
"node_id": "node_001",
"type": "parameter_server",
"status": "active",
"cpu_usage": 0.75,
"memory_usage": 0.82
},
{
"node_id": "node_002",
"type": "worker",
"status": "active",
"training_progress": 0.45
}
],
"training_metrics": {
"current_epoch": 45,
"total_epochs": 100,
"loss": 0.234,
"accuracy": 0.891
}
}
mcp__flow-nexus__neural_predict_distributed({
cluster_id: "cluster_xyz789",
input_data: JSON.stringify([
[0.1, 0.2, 0.3],
[0.4, 0.5, 0.6]
]),
aggregation: "ensemble" // mean, majority, weighted, ensemble
})
mcp__flow-nexus__neural_cluster_terminate({
cluster_id: "cluster_xyz789"
})
mcp__flow-nexus__neural_list_models({
user_id: "your_user_id",
include_public: true
})
Response:
{
"models": [
{
"model_id": "model_abc123",
"name": "Custom Classifier v1",
"architecture": "feedforward",
"accuracy": 0.92,
"created_at": "2025-10-15T14:20:00Z",
"status": "trained"
},
{
"model_id": "model_def456",
"name": "LSTM Forecaster",
"architecture": "lstm",
"mse": 0.0045,
"created_at": "2025-10-18T09:15:00Z",
"status": "training"
}
]
}
mcp__flow-nexus__neural_training_status({
job_id: "job_training_xyz"
})
Response:
{
"job_id": "job_training_xyz",
"status": "training",
"progress": 0.67,
"current_epoch": 67,
"total_epochs": 100,
"current_loss": 0.234,
"estimated_completion": "2025-10-19T12:45:00Z"
}
mcp__flow-nexus__neural_performance_benchmark({
model_id: "model_abc123",
benchmark_type: "comprehensive" // inference, throughput, memory, comprehensive
})
Response:
{
"model_id": "model_abc123",
"benchmarks": {
"inference_latency_ms": 12.5,
"throughput_qps": 8000,
"memory_usage_mb": 245,
"gpu_utilization": 0.78,
"accuracy": 0.92,
"f1_score": 0.89
},
"timestamp": "2025-10-19T11:00:00Z"
}
mcp__flow-nexus__neural_validation_workflow({
model_id: "model_abc123",
user_id: "your_user_id",
validation_type: "comprehensive" // performance, accuracy, robustness, comprehensive
})
mcp__flow-nexus__neural_publish_template({
model_id: "model_abc123",
name: "High-Accuracy Sentiment Classifier",
description: "Fine-tuned BERT model for sentiment analysis with 94% accuracy",
category: "nlp",
price: 0, // 0 for free, or credits amount
user_id: "your_user_id"
})
mcp__flow-nexus__neural_rate_template({
template_id: "sentiment-analysis-v2",
rating: 5,
review: "Excellent model! Achieved 95% accuracy on my dataset.",
user_id: "your_user_id"
})
// Initialize cluster for large-scale image training
const cluster = await mcp__flow-nexus__neural_cluster_init({
name: "image-classification-cluster",
architecture: "cnn",
topology: "hierarchical",
wasmOptimization: true
})
// Deploy worker nodes
await mcp__flow-nexus__neural_node_deploy({
cluster_id: cluster.cluster_id,
node_type: "worker",
model: "large",
capabilities: ["training", "data_augmentation"]
})
// Start training
await mcp__flow-nexus__neural_train_distributed({
cluster_id: cluster.cluster_id,
dataset: "custom_images",
epochs: 100,
batch_size: 64,
learning_rate: 0.001,
optimizer: "adam"
})
// Use pre-built template
const deployment = await mcp__flow-nexus__neural_deploy_template({
template_id: "sentiment-analysis-v2",
custom_config: {
training: {
epochs: 30,
batch_size: 16
}
}
})
// Run inference
const result = await mcp__flow-nexus__neural_predict({
model_id: deployment.model_id,
input: ["This product is amazing!", "Terrible experience."]
})
// Train LSTM model
const training = await mcp__flow-nexus__neural_train({
config: {
architecture: {
type: "lstm",
layers: [
{ type: "lstm", units: 128, return_sequences: true },
{ type: "dropout", rate: 0.2 },
{ type: "lstm", units: 64 },
{ type: "dense", units: 1 }
]
},
training: {
epochs: 150,
batch_size: 64,
learning_rate: 0.01,
optimizer: "adam"
}
},
tier: "medium"
})
// Monitor progress
const status = await mcp__flow-nexus__neural_training_status({
job_id: training.job_id
})
// Initialize federated cluster
const cluster = await mcp__flow-nexus__neural_cluster_init({
name: "federated-medical-cluster",
architecture: "transformer",
topology: "mesh",
consensus: "proof-of-learning",
daaEnabled: true
})
// Deploy nodes across different locations
for (let i = 0; i < 5; i++) {
await mcp__flow-nexus__neural_node_deploy({
cluster_id: cluster.cluster_id,
node_type: "worker",
model: "large",
autonomy: 0.9
})
}
// Train with federated learning (data never leaves nodes)
await mcp__flow-nexus__neural_train_distributed({
cluster_id: cluster.cluster_id,
dataset: "medical_records_distributed",
epochs: 200,
federated: true,
aggregation_rounds: 100
})
Best for: Classification, regression, simple pattern recognition
{
type: "feedforward",
layers: [
{ type: "dense", units: 256, activation: "relu" },
{ type: "dropout", rate: 0.3 },
{ type: "dense", units: 128, activation: "relu" },
{ type: "dense", units: 10, activation: "softmax" }
]
}
Best for: Time series, sequences, forecasting
{
type: "lstm",
layers: [
{ type: "lstm", units: 128, return_sequences: true },
{ type: "lstm", units: 64 },
{ type: "dense", units: 1 }
]
}
Best for: NLP, attention mechanisms, large-scale text
{
type: "transformer",
layers: [
{ type: "embedding", vocab_size: 10000, embedding_dim: 512 },
{ type: "transformer_encoder", num_heads: 8, ff_dim: 2048 },
{ type: "global_average_pooling" },
{ type: "dense", units: 2, activation: "softmax" }
]
}
Best for: Generative tasks, image synthesis
{
type: "gan",
generator_layers: [...],
discriminator_layers: [...]
}
Best for: Dimensionality reduction, anomaly detection
{
type: "autoencoder",
encoder_layers: [
{ type: "dense", units: 128, activation: "relu" },
{ type: "dense", units: 64, activation: "relu" }
],
decoder_layers: [
{ type: "dense", units: 128, activation: "relu" },
{ type: "dense", units: input_dim, activation: "sigmoid" }
]
}
nano or mini tiers for experimentation// Check cluster status
const status = await mcp__flow-nexus__neural_cluster_status({
cluster_id: "cluster_id"
})
// Terminate and restart if needed
await mcp__flow-nexus__neural_cluster_terminate({
cluster_id: "cluster_id"
})
flow-nexus-sandbox - E2B sandbox managementflow-nexus-swarm - AI swarm orchestrationflow-nexus-workflow - Workflow automationNote: Distributed training requires authentication. Register at https:/$flow-nexus.ruv.io or use npx flow-nexus@latest register.
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).