neurenix monitor

The monitor command provides real-time monitoring of model training progress, tracking metrics like loss and accuracy, with optional plot generation.

Usage

neurenix monitor [options]

Options

Option	Type	Default	Description
`--log-dir`	string	`logs`	Directory containing training logs
`--refresh-rate`	float	`1.0`	Refresh rate in seconds
`--metrics`	string	`loss,accuracy`	Metrics to display (comma-separated)
`--output`	string	None	Output file for monitoring data
`--plot`	flag	false	Generate plots for metrics

Examples

Basic monitoring

neurenix monitor

Monitoring training logs in 'logs'...
Metrics: loss, accuracy
Refresh rate: 1.0 seconds

Press Ctrl+C to stop monitoring.

Epoch 1:
  loss: 0.6234
  accuracy: 0.7812

Epoch 2:
  loss: 0.5123
  accuracy: 0.8234

Epoch 3:
  loss: 0.4567
  accuracy: 0.8456
...

Monitor specific metrics

neurenix monitor --metrics loss,accuracy,val_loss,val_accuracy

Monitoring training logs in 'logs'...
Metrics: loss, accuracy, val_loss, val_accuracy
Refresh rate: 1.0 seconds

Press Ctrl+C to stop monitoring.

Epoch 1:
  loss: 0.6234
  accuracy: 0.7812
  val_loss: 0.6543
  val_accuracy: 0.7656
...

Custom log directory

neurenix monitor --log-dir experiments/run_1/logs

Monitoring training logs in 'experiments/run_1/logs'...
Metrics: loss, accuracy
Refresh rate: 1.0 seconds

Press Ctrl+C to stop monitoring.
...

Faster refresh rate

neurenix monitor --refresh-rate 0.5

Monitoring training logs in 'logs'...
Metrics: loss, accuracy
Refresh rate: 0.5 seconds

Press Ctrl+C to stop monitoring.
...

Save monitoring data

neurenix monitor --output monitoring_data.csv

Monitoring training logs in 'logs'...
Metrics: loss, accuracy
Refresh rate: 1.0 seconds

Press Ctrl+C to stop monitoring.

Epoch 1:
  loss: 0.6234
  accuracy: 0.7812
...

Stopping monitoring...

This creates monitoring_data.csv:

epoch,loss,accuracy
1,0.6234,0.7812
2,0.5123,0.8234
3,0.4567,0.8456

Generate plots

neurenix monitor --plot

Monitoring training logs in 'logs'...
Metrics: loss, accuracy
Refresh rate: 1.0 seconds

Press Ctrl+C to stop monitoring.

Epoch 1:
  loss: 0.6234
  accuracy: 0.7812
...

Stopping monitoring...
Plots saved to logs/plots

Complete monitoring setup

neurenix monitor \
  --log-dir experiments/model_v1/logs \
  --metrics loss,accuracy,val_loss,val_accuracy,lr \
  --refresh-rate 2.0 \
  --output monitoring.csv \
  --plot

Log File Format

The monitor command reads JSON log files in the log directory:

{
  "epoch": 1,
  "loss": 0.6234,
  "accuracy": 0.7812,
  "val_loss": 0.6543,
  "val_accuracy": 0.7656,
  "lr": 0.001
}

Plot Generation

When --plot is enabled, individual plots are generated for each metric:

logs/plots/
├── loss_plot.png
├── accuracy_plot.png
├── val_loss_plot.png
└── val_accuracy_plot.png

Each plot shows the metric value versus epoch number.

Requirement: Plot generation requires matplotlib. Install with: pip install matplotlib

Real-time Monitoring Workflow

Terminal 1: Start training

neurenix run train.py --epochs 100

Terminal 2: Monitor progress

neurenix monitor --refresh-rate 1.0 --plot

Press Ctrl+C when training completes to save plots.

Error Handling

Log directory not found

neurenix monitor --log-dir missing_logs

Error: Log directory 'missing_logs' not found.

No log files

neurenix monitor

Monitoring training logs in 'logs'...
Metrics: loss, accuracy
Refresh rate: 1.0 seconds

Press Ctrl+C to stop monitoring.

No log files found. Waiting for logs...
No log files found. Waiting for logs...
...

Matplotlib not available

neurenix monitor --plot

...
Stopping monitoring...
Warning: matplotlib not available. Plots not generated.

Use Cases

1. Track long training runs

Monitor training that takes hours or days:

# Terminal 1
neurenix run train.py --epochs 200

# Terminal 2
neurenix monitor --refresh-rate 5.0 --output training_log.csv

2. Compare multiple metrics

Track training and validation metrics simultaneously:

neurenix monitor \
  --metrics loss,val_loss,accuracy,val_accuracy \
  --refresh-rate 1.0

3. Save training history

Export metrics for later analysis:

neurenix monitor \
  --output experiments/model_v1/metrics.csv \
  --plot

4. Monitor learning rate schedules

Track learning rate changes during training:

neurenix monitor --metrics loss,accuracy,lr

5. Remote training monitoring

Monitor training on a remote server:

# On remote server
neurenix run train.py

# Via SSH from local machine
ssh user@server "cd project && neurenix monitor --output -" | tee local_monitor.csv

Best Practices

1. Monitor multiple metrics

Track both training and validation metrics:

neurenix monitor \
  --metrics loss,accuracy,val_loss,val_accuracy

2. Save monitoring data

Always save metrics for later analysis:

neurenix monitor \
  --output experiments/$(date +%Y%m%d)/metrics.csv \
  --plot

3. Adjust refresh rate based on epoch time

# Fast epochs (< 10 seconds)
neurenix monitor --refresh-rate 1.0

# Medium epochs (10-60 seconds)
neurenix monitor --refresh-rate 5.0

# Slow epochs (> 60 seconds)
neurenix monitor --refresh-rate 15.0

4. Generate plots for presentations

neurenix monitor \
  --metrics loss,val_loss \
  --plot \
  --output final_metrics.csv

5. Use descriptive output paths

neurenix monitor \
  --log-dir experiments/resnet50_run1/logs \
  --output experiments/resnet50_run1/metrics.csv

Integration Examples

Monitoring script

#!/bin/bash

# Start training in background
neurenix run train.py --epochs 100 &
TRAIN_PID=$!

# Monitor training
neurenix monitor \
  --metrics loss,accuracy,val_loss,val_accuracy \
  --output metrics.csv \
  --plot

# Wait for training to complete
wait $TRAIN_PID

echo "Training completed. Metrics saved to metrics.csv"

Python integration

import subprocess
import threading

def monitor_training(log_dir, output_file):
    cmd = [
        "neurenix", "monitor",
        "--log-dir", log_dir,
        "--output", output_file,
        "--plot"
    ]
    subprocess.run(cmd)

# Start monitoring in separate thread
monitor_thread = threading.Thread(
    target=monitor_training,
    args=("logs", "metrics.csv")
)
monitor_thread.start()

# Start training
subprocess.run(["neurenix", "run", "train.py"])

# Wait for monitoring to finish
monitor_thread.join()

Keyboard Controls

Ctrl+C: Stop monitoring and save data/plots

Output Files

Monitoring can generate several output files:

.
├── monitoring_data.csv          # Metrics CSV (if --output specified)
└── logs/plots/                  # Generated plots (if --plot enabled)
    ├── loss_plot.png
    ├── accuracy_plot.png
    ├── val_loss_plot.png
    └── val_accuracy_plot.png

Commands

Usage

Options

Examples

Basic monitoring

Monitor specific metrics

Custom log directory

Faster refresh rate

Save monitoring data

Generate plots

Complete monitoring setup

Log File Format

Plot Generation

Real-time Monitoring Workflow

Terminal 1: Start training

Terminal 2: Monitor progress

Error Handling

Log directory not found

No log files

Matplotlib not available

Use Cases

1. Track long training runs

2. Compare multiple metrics

3. Save training history

4. Monitor learning rate schedules

5. Remote training monitoring

Best Practices

1. Monitor multiple metrics

2. Save monitoring data

3. Adjust refresh rate based on epoch time

4. Generate plots for presentations

5. Use descriptive output paths

Integration Examples

Monitoring script

Python integration

Keyboard Controls

Output Files

See Also

Build docs developers (and LLMs) love

Commands

Documentation Index

​Usage

​Options

​Examples

​Basic monitoring

​Monitor specific metrics

​Custom log directory

​Faster refresh rate

​Save monitoring data

​Generate plots

​Complete monitoring setup

​Log File Format

​Plot Generation

​Real-time Monitoring Workflow

​Terminal 1: Start training

​Terminal 2: Monitor progress

​Error Handling

​Log directory not found

​No log files

​Matplotlib not available

​Use Cases

​1. Track long training runs

​2. Compare multiple metrics

​3. Save training history

​4. Monitor learning rate schedules

​5. Remote training monitoring

​Best Practices

​1. Monitor multiple metrics

​2. Save monitoring data

​3. Adjust refresh rate based on epoch time

​4. Generate plots for presentations

​5. Use descriptive output paths

​Integration Examples

​Monitoring script

​Python integration

​Keyboard Controls

​Output Files

​See Also

Build docs developers (and LLMs) love

Usage

Options

Examples

Basic monitoring

Monitor specific metrics

Custom log directory

Faster refresh rate

Save monitoring data

Generate plots

Complete monitoring setup

Log File Format

Plot Generation

Real-time Monitoring Workflow

Terminal 1: Start training

Terminal 2: Monitor progress

Error Handling

Log directory not found

No log files

Matplotlib not available

Use Cases

1. Track long training runs

2. Compare multiple metrics

3. Save training history

4. Monitor learning rate schedules

5. Remote training monitoring

Best Practices

1. Monitor multiple metrics

2. Save monitoring data

3. Adjust refresh rate based on epoch time

4. Generate plots for presentations

5. Use descriptive output paths

Integration Examples

Monitoring script

Python integration

Keyboard Controls

Output Files

See Also