# Modal Sandbox Profiles Configuration
# =====================================
# This file defines different sandbox profiles for heterogeneous workloads.
# Copy to modal_profiles.yaml and customize as needed.
#
# Usage:
#   terminal_tool("python train.py", profile="pytorch-gpu")
#   terminal_tool("npm test", profile="node")
#
# Each profile can specify:
#   - image: Docker image to use
#   - gpu: GPU type (null, "T4", "A10G", "A100", "H100")
#   - cpu: CPU cores (float)
#   - memory: Memory in MB
#   - min_pool: Minimum warm sandboxes (cost vs latency tradeoff)
#   - max_pool: Maximum sandboxes (hard cost cap)
#   - idle_timeout: Server-side auto-cleanup in seconds
#   - max_lifetime: Maximum sandbox lifetime in seconds
#   - scale_down_idle: Client-side scale-down threshold in seconds
#   - workdir: Working directory inside container
#   - secrets: List of Modal Secret names to inject (created via dashboard/CLI)
#   - env_vars: Dict of environment variables to pass directly
#   - use_dotenv: If true, loads local .env file into sandbox
#
# SECRETS SETUP:
#   Create secrets via Modal dashboard or CLI:
#     modal secret create huggingface-token HF_TOKEN=hf_xxx
#     modal secret create openai-key OPENAI_API_KEY=sk-xxx
#   Then reference by name in profile's secrets list.

# Default profile used when no profile specified
default_profile: default

profiles:
  # Default Python environment - good for most tasks
  default:
    image: python:3.11
    gpu: null
    cpu: 1.0
    memory: 2048
    min_pool: 1        # Keep 1 warm for fast response
    max_pool: 5
    idle_timeout: 120  # Modal terminates if idle 2 min
    max_lifetime: 3600 # Max 1 hour
    scale_down_idle: 180
    workdir: /workspace
    secrets: []        # Add secret names here: ["my-api-keys"]
    env_vars: {}       # Add env vars here: {DEBUG: "1"}
    use_dotenv: false  # Set to true to load local .env

  # PyTorch with GPU for ML training/inference
  pytorch-gpu:
    image: pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime
    gpu: T4            # Options: T4, A10G, A100, H100
    cpu: 4.0
    memory: 16384      # 16GB
    min_pool: 0        # Don't keep GPU sandboxes warm (expensive!)
    max_pool: 2
    idle_timeout: 60   # Shorter idle timeout for GPU (cost)
    max_lifetime: 1800 # 30 min max for GPU tasks
    scale_down_idle: 60
    workdir: /workspace
    # ML-specific secrets
    secrets:
      - huggingface-token  # HF_TOKEN env var
      - wandb-key          # WANDB_API_KEY env var
    env_vars:
      CUDA_VISIBLE_DEVICES: "0"
      PYTORCH_CUDA_ALLOC_CONF: "expandable_segments:True"

  # High-end GPU for large models
  pytorch-a100:
    image: pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime
    gpu: A100
    cpu: 8.0
    memory: 65536      # 64GB
    min_pool: 0
    max_pool: 1        # Only 1 at a time (very expensive)
    idle_timeout: 30
    max_lifetime: 3600
    scale_down_idle: 30
    workdir: /workspace

  # Node.js for JavaScript/TypeScript tasks
  node:
    image: node:18
    gpu: null
    cpu: 1.0
    memory: 2048
    min_pool: 0        # Create on-demand
    max_pool: 3
    idle_timeout: 120
    max_lifetime: 3600
    scale_down_idle: 180
    workdir: /workspace

  # High memory for data processing
  high-memory:
    image: python:3.11
    gpu: null
    cpu: 4.0
    memory: 32768      # 32GB
    min_pool: 0
    max_pool: 2
    idle_timeout: 120
    max_lifetime: 3600
    scale_down_idle: 180
    workdir: /workspace

  # Rust development environment
  rust:
    image: rust:1.75
    gpu: null
    cpu: 2.0
    memory: 4096
    min_pool: 0
    max_pool: 2
    idle_timeout: 120
    max_lifetime: 3600
    scale_down_idle: 180
    workdir: /workspace

  # Go development environment
  golang:
    image: golang:1.21
    gpu: null
    cpu: 2.0
    memory: 4096
    min_pool: 0
    max_pool: 2
    idle_timeout: 120
    max_lifetime: 3600
    scale_down_idle: 180
    workdir: /workspace