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Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
name flow-nexus-neural description Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus version 1.0.0 category ai-ml tags ["neural-networks","distributed-training","machine-learning","deep-learning","flow-nexus","e2b-sandboxes"] requires_auth true mcp_server flow-nexus hooks {"pre":"echo \"🧠 Flow Nexus Neural activated\"\nif [ -d \"/workspaces/ruvector/.claude/intelligence\" ]; then\n cd /workspaces/ruvector/.claude/intelligence\n INTELLIGENCE_MODE=treatment node cli.js pre-edit \"$FILE\" 2>/dev/null || true\nfi\n","post":"echo \"✅ Flow Nexus Neural complete\"\nif [ -d \"/workspaces/ruvector/.claude/intelligence\" ]; then\n cd /workspaces/ruvector/.claude/intelligence\n INTELLIGENCE_MODE=treatment node cli.js post-edit \"$FILE\" \"true\" 2>/dev/null || true\nfi\n"}
Flow Nexus Neural Networks
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.
Self-Learning Intelligence
Integrates with RuVector's Q-learning and vector memory for improved performance.
CLI: node .claude/intelligence/cli.js stats
Prerequisites
claude mcp add flow-nexus npx flow-nexus@latest mcp start
npx flow-nexus@latest register
npx flow-nexus@latest login
Core Capabilities
1. Single-Node Neural Training
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 models
Training Tiers:
nano - Minimal resources (fast, limited)mini - Small modelssmall - Standard modelsmedium - Complex modelslarge - Large-scale training
Example: Train Custom Classifier
mcp__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"
factor
0.5
tier
"small"
user_id
"your_user_id"
Example: LSTM for Time Series 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"
})
Example: Transformer Architecture 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"
})
2. Model Inference 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"
})
{
"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"
}
3. Template Marketplace Browse and deploy pre-trained models from the marketplace.
List Available Templates mcp__flow-nexus__neural_list_templates ({
category : "classification" ,
tier : "free" ,
search : "sentiment" ,
limit : 20
})
{
"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"
}
]
}
Deploy Template 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"
})
4. Distributed Training Clusters Train large models across multiple E2B sandboxes with distributed computing.
Initialize Cluster mcp__flow-nexus__neural_cluster_init ({
name : "large-model-cluster" ,
architecture : "transformer" ,
topology : "mesh" ,
consensus : "proof-of-learning" ,
daaEnabled : true ,
wasmOptimization : true
})
{
"cluster_id" : "cluster_xyz789" ,
"name" : "large-model-cluster" ,
"status" : "initializing" ,
"topology" : "mesh" ,
"max_nodes" : 100 ,
"created_at" : "2025-10-19T10:30:00Z"
}
Deploy Worker Nodes
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
})
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
})
mcp__flow-nexus__neural_node_deploy ({
cluster_id : "cluster_xyz789" ,
node_type : "aggregator" ,
model : "large" ,
capabilities : ["gradient_aggregation" , "model_synchronization" ]
})
Connect Cluster Topology mcp__flow-nexus__neural_cluster_connect ({
cluster_id : "cluster_xyz789" ,
topology : "mesh"
})
Start Distributed Training mcp__flow-nexus__neural_train_distributed ({
cluster_id : "cluster_xyz789" ,
dataset : "imagenet" ,
epochs : 100 ,
batch_size : 128 ,
learning_rate : 0.001 ,
optimizer : "adam" ,
federated : true
})
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 ,
aggregation_rounds : 50 ,
min_nodes_per_round : 5
})
Monitor Cluster Status mcp__flow-nexus__neural_cluster_status ({
cluster_id : "cluster_xyz789"
})
{
"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
}
}
Run Distributed Inference 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"
})
Terminate Cluster mcp__flow-nexus__neural_cluster_terminate ({
cluster_id : "cluster_xyz789"
})
5. Model Management
List Your Models mcp__flow-nexus__neural_list_models ({
user_id : "your_user_id" ,
include_public : true
})
{
"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"
}
]
}
Check Training Status mcp__flow-nexus__neural_training_status ({
job_id : "job_training_xyz"
})
{
"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"
}
Performance Benchmarking mcp__flow-nexus__neural_performance_benchmark ({
model_id : "model_abc123" ,
benchmark_type : "comprehensive"
})
{
"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"
}
Create Validation Workflow mcp__flow-nexus__neural_validation_workflow ({
model_id : "model_abc123" ,
user_id : "your_user_id" ,
validation_type : "comprehensive"
})
6. Publishing and Marketplace
Publish Model as Template 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 ,
user_id : "your_user_id"
})
Rate a Template 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"
})
Common Use Cases
Image Classification with CNN
const cluster = await mcp__flow-nexus__neural_cluster_init ({
name : "image-classification-cluster" ,
architecture : "cnn" ,
topology : "hierarchical" ,
wasmOptimization : true
})
await mcp__flow-nexus__neural_node_deploy ({
cluster_id : cluster.cluster_id ,
node_type : "worker" ,
model : "large" ,
capabilities : ["training" , "data_augmentation" ]
})
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"
})
NLP Sentiment Analysis
const deployment = await mcp__flow-nexus__neural_deploy_template ({
template_id : "sentiment-analysis-v2" ,
custom_config : {
training : {
epochs : 30 ,
batch_size : 16
}
}
})
const result = await mcp__flow-nexus__neural_predict ({
model_id : deployment.model_id ,
input : ["This product is amazing!" , "Terrible experience." ]
})
Time Series Forecasting
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"
})
const status = await mcp__flow-nexus__neural_training_status ({
job_id : training.job_id
})
Federated Learning for Privacy
const cluster = await mcp__flow-nexus__neural_cluster_init ({
name : "federated-medical-cluster" ,
architecture : "transformer" ,
topology : "mesh" ,
consensus : "proof-of-learning" ,
daaEnabled : true
})
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
})
}
await mcp__flow-nexus__neural_train_distributed ({
cluster_id : cluster.cluster_id ,
dataset : "medical_records_distributed" ,
epochs : 200 ,
federated : true ,
aggregation_rounds : 100
})
Architecture Patterns
Feedforward Networks 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" }
]
}
LSTM Networks Best for: Time series, sequences, forecasting
{
type : "lstm" ,
layers : [
{ type : "lstm" , units : 128 , return_sequences : true },
{ type : "lstm" , units : 64 },
{ type : "dense" , units : 1 }
]
}
Transformers 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" }
]
}
GANs Best for: Generative tasks, image synthesis
{
type : "gan" ,
generator_layers : [...],
discriminator_layers : [...]
}
Autoencoders 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" }
]
}
Best Practices
Start Small : Begin with nano or mini tiers for experimentationUse Templates : Leverage marketplace templates for common tasksMonitor Training : Check status regularly to catch issues earlyBenchmark Models : Always benchmark before production deploymentDistributed Training : Use clusters for large models (>1B parameters)Federated Learning : Use for privacy-sensitive dataVersion Models : Publish successful models as templates for reuseValidate Thoroughly : Use validation workflows before deployment
Troubleshooting
Training Stalled
const status = await mcp__flow-nexus__neural_cluster_status ({
cluster_id : "cluster_id"
})
await mcp__flow-nexus__neural_cluster_terminate ({
cluster_id : "cluster_id"
})
Low Accuracy
Increase epochs
Adjust learning rate
Add regularization (dropout)
Try different optimizer
Use data augmentation
Out of Memory
Reduce batch size
Use smaller model tier
Enable gradient accumulation
Use distributed training
Related Skills
flow-nexus-sandbox - E2B sandbox managementflow-nexus-swarm - AI swarm orchestrationflow-nexus-workflow - Workflow automation
Resources
Note : Distributed training requires authentication. Register at https://flow-nexus.ruv.io or use npx flow-nexus@latest register.
