Trainwave CLI Guide

pip install trainwave-cli

wave --version

# Login to your account
wave auth login
 
# Create a new configuration
wave config
 
# Launch a training job
wave jobs launch
 
# Monitor the job
wave jobs logs -f

# Interactive browser login (recommended)
wave auth login
 
# Login with API key
wave auth set-token your-api-key
 
# Verify login
wave auth whoami

# Create a new API token
wave auth create-token --name "CI/CD Token"
 
# List active tokens
wave auth list-tokens
 
# Revoke a token
wave auth revoke-token token-id

# Launch with default config
wave jobs launch
 
# Launch with specific config file
wave jobs launch --config custom-config.toml
 
# Launch with overrides
wave jobs launch --gpu-type "RTX A5000" --gpus 2
 
# Launch in a specific project
wave jobs launch --project p-abc123

# List all jobs
wave jobs list
 
# List running jobs
wave jobs list --status running
 
# List jobs for a specific project
wave jobs list --project p-abc123
 
# Get detailed job status
wave jobs status j-xyz789
 
# Stream logs in real-time
wave jobs logs -f j-xyz789
 
# View GPU metrics
wave jobs metrics j-xyz789 --type gpu

# Cancel a job
wave jobs cancel j-xyz789
 
# Stop a job gracefully
wave jobs stop j-xyz789
 
# Restart a failed job
wave jobs restart j-xyz789

# Open web UI for configuration
wave config
 
# Create config in specific location
wave config --output custom-config.toml

# Validate current config
wave config validate
 
# Validate specific config file
wave config validate --file custom-config.toml

# List available GPU types
wave gpus list
 
# Show GPU pricing
wave gpus pricing
 
# Check GPU availability
wave gpus availability

# List artifacts
wave storage list
 
# Download artifacts
wave storage download j-xyz789 --output ./artifacts
 
# Clean up old artifacts
wave storage cleanup --older-than 30d

# Set a single secret
wave secrets set API_KEY=xyz123
 
# Set multiple secrets
wave secrets set \
  WANDB_API_KEY=abc123 \
  HUGGINGFACE_TOKEN=def456
 
# Set secret from file
wave secrets set-file GITHUB_KEY=./github_key.txt

# List all secrets
wave secrets list
 
# List secrets for specific project
wave secrets list --project p-abc123
 
# Delete a secret
wave secrets unset API_KEY
 
# Rotate a secret
wave secrets rotate API_KEY

# List projects
wave projects list
 
# Create new project
wave projects create "MNIST Training"
 
# Set default project
wave projects set-default p-abc123

# Cancel all running jobs
wave jobs list --status running | xargs -I {} wave jobs cancel {}
 
# Download artifacts from multiple jobs
wave jobs list --status completed | xargs -I {} wave storage download {}

# Non-interactive authentication
export TRAINWAVE_API_KEY=your-api-key
wave jobs launch --non-interactive
 
# Wait for job completion
wave jobs wait j-xyz789 --timeout 1h

# Get job info in JSON format
wave jobs status j-xyz789 --json
 
# Use with jq for parsing
wave jobs status j-xyz789 --json | jq .status

# Start training
JOB_ID=$(wave jobs launch --config train.toml --json | jq -r .id)
 
# Monitor progress
wave jobs logs -f $JOB_ID
 
# Download results
wave storage download $JOB_ID --output ./results

# Launch multiple experiments
for lr in 0.001 0.01 0.1; do
  wave jobs launch --config base.toml \
    --env LEARNING_RATE=$lr \
    --name "experiment-lr-${lr}"
done
 
# Monitor all experiments
wave jobs list --format wide

# Cancel old running jobs
wave jobs list --status running --older-than 24h | \
  xargs -I {} wave jobs cancel {}
 
# Clean up old artifacts
wave storage cleanup --older-than 7d