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When to Use the CLI

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Best For

• Automation - Script repetitive tasks
• CI/CD pipelines - Integrate with deployment
• Remote servers - SSH into cloud instances
• Batch processing - Train multiple models
• Reproducibility - Save and share exact commands

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What It Looks Like

aitraining text-classification \
  --model bert-base-uncased \
  --data train.csv \
  --epochs 5

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Workflow Example

# Prepare data
python prepare_data.py

# Train model
aitraining train \
  --task text-classification \
  --data data/train.csv \
  --output models/v1

# Evaluate
aitraining evaluate \
  --model models/v1 \
  --test data/test.csv

# Deploy
aitraining serve --model models/v1

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Advantages

• Scriptable - Automate everything
• Reproducible - Save exact commands
• Version control - Track in git
• Remote friendly - Works over SSH
• Parallel execution - Run multiple trainings

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Limitations

• Learning curve - Must know command syntax
• No visual feedback - Text output only
• Error prone - Typos in commands
• Less discoverable - Must know options exist

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When to Switch

• Need visual feedback
• Want to explore options
• Teaching non-technical users
• Doing quick experiments

• Need custom logic
• Building applications
• Complex preprocessing
• Dynamic configuration

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Common Use Cases

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Hyperparameter Search

for lr in 1e-5 2e-5 5e-5; do
  aitraining train \
    --learning-rate $lr \
    --output models/lr_$lr
done

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Nightly Training

# In cron or scheduler
0 2 * * * /path/to/retrain.sh

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Remote Training

ssh gpu-server
screen -S training
aitraining train --data s3://bucket/data.csv
# Detach with Ctrl-A D

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CI/CD Integration

# .github/workflows/train.yml
- name: Train model
  run: aitraining train --config config.yaml

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Tips for CLI Users

1. Save commands - Keep a commands.txt file
2. Use configs - YAML files over long commands
3. Log output - Redirect to files
4. Use screen/tmux - For long-running jobs
5. Write scripts - Combine multiple steps

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CLI-Exclusive Features

• Pipe data from other commands
• Integrate with shell scripts
• Run on headless servers
• Batch process files
• Scheduled execution

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Essential Commands

# See all options
aitraining --help

# List available models
aitraining models list

# Check training status
aitraining status

# Resume interrupted training
aitraining train --resume

# Convert model formats
aitraining convert --from pytorch --to onnx

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Environment Variables

# Set defaults
export AITRAINING_MODEL=bert-base
export AITRAINING_EPOCHS=5

# Now simpler commands
aitraining train --data train.csv

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Next Steps