neurenix save

The save command saves the current project state by creating a checkpoint that includes models, configurations, and optionally data and logs.

Usage

neurenix save [options]

Options

Option	Type	Default	Description
`--name`	string	`checkpoint_{timestamp}`	Name for the checkpoint
`--include-data`	flag	false	Include data directory in the checkpoint
`--include-logs`	flag	false	Include logs directory in the checkpoint
`--output-dir`	string	`checkpoints`	Output directory for checkpoints

Checkpoint Contents

A checkpoint always includes:

models/: All saved model files
configs/: Configuration files (or config.json if it exists)
metadata.json: Checkpoint information (timestamp, name, Neurenix version, options)

Optionally includes:

data/: Training/validation data (with --include-data)
logs/: Training logs (with --include-logs)

Examples

Save basic checkpoint

neurenix save

Saving models...
Saving configurations...
Project state saved to checkpoints/checkpoint_1678901234

Save with custom name

neurenix save --name best_model_v1

Saving models...
Saving configurations...
Project state saved to checkpoints/best_model_v1

Save with data and logs

neurenix save --name experiment_1 --include-data --include-logs

Saving models...
Saving configurations...
Saving data...
Saving logs...
Project state saved to checkpoints/experiment_1

Save to custom directory

neurenix save --name milestone_v2 --output-dir backup/checkpoints

Saving models...
Saving configurations...
Project state saved to backup/checkpoints/milestone_v2

Full backup before deployment

neurenix save \
  --name production_deployment \
  --include-data \
  --include-logs \
  --output-dir backups

Saving models...
Saving configurations...
Saving data...
Saving logs...
Project state saved to backups/production_deployment

Checkpoint Metadata

Each checkpoint includes a metadata.json file with information about the save:

{
  "timestamp": 1678901234.567,
  "name": "best_model_v1",
  "neurenix_version": "0.1.0",
  "include_data": false,
  "include_logs": false
}

Use Cases

1. Save training milestones

Save checkpoints at key training milestones:

neurenix save --name epoch_50
neurenix save --name best_accuracy
neurenix save --name final_model

2. Version control for models

Create versioned checkpoints for model iterations:

neurenix save --name model_v1.0
neurenix save --name model_v1.1
neurenix save --name model_v2.0

3. Pre-deployment backup

Create a full backup before deploying to production:

neurenix save \
  --name pre_deployment_$(date +%Y%m%d) \
  --include-data \
  --include-logs

4. Experiment tracking

Save results of different experiments:

neurenix save --name baseline_model
neurenix save --name with_augmentation
neurenix save --name larger_batch_size

Restoring from Checkpoint

To restore a project from a checkpoint, copy the contents back to your project directory:

cp -r checkpoints/best_model_v1/* .

Or selectively restore components:

# Restore only models
cp -r checkpoints/best_model_v1/models/* models/

# Restore only configuration
cp checkpoints/best_model_v1/configs/config.json config.json

Best Practices

1. Use descriptive names

Give checkpoints meaningful names that indicate their purpose:

neurenix save --name baseline_acc_0.92
neurenix save --name after_hyperparameter_tuning
neurenix save --name final_production_model

2. Regular checkpoints during long training

Save periodic checkpoints during extended training sessions:

# In a training loop
for epoch in {10..100..10}; do
  neurenix save --name epoch_$epoch
done

3. Include data for reproducibility

When saving experiment results, include data to ensure reproducibility:

neurenix save --name paper_submission --include-data

4. Clean up old checkpoints

Regularly remove outdated checkpoints to save disk space:

# Keep only the 5 most recent checkpoints
ls -t checkpoints | tail -n +6 | xargs -I {} rm -r checkpoints/{}

Commands

Usage

Options

Checkpoint Contents

Examples

Save basic checkpoint

Save with custom name

Save with data and logs

Save to custom directory

Full backup before deployment

Checkpoint Metadata

Use Cases

1. Save training milestones

2. Version control for models

3. Pre-deployment backup

4. Experiment tracking

Restoring from Checkpoint

Best Practices

1. Use descriptive names

2. Regular checkpoints during long training

3. Include data for reproducibility

4. Clean up old checkpoints

See Also

​Usage

​Options

​Checkpoint Contents

​Examples

​Save basic checkpoint

​Save with custom name

​Save with data and logs

​Save to custom directory

​Full backup before deployment

​Checkpoint Metadata

​Use Cases

​1. Save training milestones

​2. Version control for models

​3. Pre-deployment backup

​4. Experiment tracking

​Restoring from Checkpoint

​Best Practices

​1. Use descriptive names

​2. Regular checkpoints during long training

​3. Include data for reproducibility

​4. Clean up old checkpoints

​See Also

Usage

Options

Checkpoint Contents

Examples

Save basic checkpoint

Save with custom name

Save with data and logs

Save to custom directory

Full backup before deployment

Checkpoint Metadata

Use Cases

1. Save training milestones

2. Version control for models

3. Pre-deployment backup

4. Experiment tracking

Restoring from Checkpoint

Best Practices

1. Use descriptive names

2. Regular checkpoints during long training

3. Include data for reproducibility

4. Clean up old checkpoints

See Also