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CLI Flags and Efficiency

Stop using interactive mode - master CLI flags for fast, scriptable workflows.

From Interactive to Flags​

Beginner (interactive):

container deploy
# Menu appears, select project, options...

Intermediate (flags):

container deploy my-project --open

10x faster when you know what you want.

Universal Flags​

Every DS01 command supports:

--help, -h              Quick reference
--info                  Full documentation
--concepts              Learn concept first
--guided                Step-by-step walkthrough

Examples:

container-deploy --help
image-create --info
project-init --concepts
container-retire --guided

Container Deployment Flags​

container deploy <name> [flags]

--open                  Create and open terminal (default)
--background            Create but don't attach
--project=<name>        Mount specific project
--workspace=<path>      Custom workspace mount
--image=<name>          Use specific image
--gpu=<count>           Request N GPUs

Examples:

# Standard deploy
container deploy my-thesis --open

# Background deploy
container deploy training --background

# Custom workspace
container deploy analysis --workspace=/data/shared

# Multi-GPU
container deploy distributed --gpu=2

Project Commands Flags​

project init <name> [flags]

--type=<type>           ml, cv, nlp, rl, llm, ts, audio
--quick                 Skip interactive questions, use defaults
--no-git                Skip Git initialization
--blank                 Create blank directory (no structure)
project launch <name> [flags]

--open                  Launch and open (default)
--background            Launch without attaching
--rebuild               Force image rebuild

Examples:

# Quick project creation
project init cv-research --type=cv --quick

# Launch with rebuild
project launch cv-research --rebuild --open

Container Atomic Flags​

container-create <name> [flags]

--image=<name>          Docker image
--project=<name>        Project workspace
--gpu=<count>           GPU count
container-remove <name> [flags]

--force                 Skip confirmations
--stop                  Stop if running

Examples:

# Create with options
container-create exp-1 --image=aime/pytorch:2.8.0 --gpu=2

# Force remove
container-remove exp-1 --force --stop

Image Flags​

image-create <project> [flags]

-f, --framework <name>  Base framework (pytorch, tensorflow, jax)
-t, --type <type>       Use case type (cv, nlp, rl, ml, custom)
--no-cache              Rebuild from scratch
image-delete <name> [flags]

--force                 Skip confirmations

Examples:

# Fresh build
image-create my-project --no-cache

# Quick delete
image-delete old-project --force

List/Query Flags​

container-list [flags]

--all                   Include stopped
--format=<type>         table, json, simple
image-list [flags]

--all                   Include base images
--format=json           JSON output

Examples:

# All containers as JSON
container-list --all --format=json

# Parse with jq
container-list --format=json | jq '.[] | select(.status=="running")'

Flag Combinations​

Multiple flags together:

# Background deploy with custom project
container deploy training --background --project=research-2024

# Force stop and remove
container-remove old-container --stop --force

# Fresh image build with framework
image-create my-project --no-cache -f pytorch -t nlp

Scripting with Flags​

#!/bin/bash
# deploy-and-train.sh

PROJECT=$1
CONFIG=$2

# Deploy in background
container deploy $PROJECT --background --gpu=2 || exit 1

# Wait for startup
sleep 5

# Run training
docker exec $PROJECT._.$(id -u) python /workspace/train.py --config $CONFIG

# Check results
docker exec $PROJECT._.$(id -u) cat /workspace/results/metrics.txt

# Cleanup
container retire $PROJECT --force

Next Steps​