592 lines
21 KiB
Python
592 lines
21 KiB
Python
"""Pinned remote environment and deployment identity helpers."""
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from __future__ import annotations
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import hashlib
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import json
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import logging
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import os
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import shlex
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any, Protocol
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from packaging.requirements import InvalidRequirement, Requirement
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from packaging.utils import canonicalize_name
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if __package__:
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from .settings import DEFAULT_MODAL_GPU, DEFAULT_MODAL_SECRET_NAME
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else: # pragma: no cover - the stable cloud entrypoint imports this module top-level.
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from settings import DEFAULT_MODAL_GPU, DEFAULT_MODAL_SECRET_NAME
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logger = logging.getLogger(__name__)
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REMOTE_APP_PROTOCOL_VERSION = 9
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REMOTE_PYTHON_VERSION = "3.13"
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REMOTE_MODAL_SDK_SPEC = "modal==1.4.2"
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REMOTE_HUGGINGFACE_HUB_SPEC = "huggingface-hub==1.28.0"
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REMOTE_HF_XET_SPEC = "hf-xet==1.6.0"
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REMOTE_VLLM_SPEC = "vllm==0.27.1"
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REMOTE_AV_SPEC = "av==18.1.0"
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REMOTE_VLLM_WHEEL_URL = (
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"https://wheels.vllm.ai/"
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"6e448d0ea9bf3d88d898b65449ca6dc2aec170ac/"
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"vllm-0.27.1-cp38-abi3-manylinux_2_28_x86_64.whl"
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)
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REMOTE_LLAMA_CPP_SERVER_IMAGE = (
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"ghcr.io/ggml-org/llama.cpp:server-cuda13@"
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"sha256:ff73838d023dde440bea6b953c284f327783111341cea8f5cc10eb1346636aa3"
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)
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REMOTE_NUMPY_SPEC = "numpy==2.3.5"
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REMOTE_OPENCV_SPEC = "opencv-python-headless==4.13.0.92"
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_REMOTE_APT_PACKAGES = (
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"libgl1",
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"libglib2.0-0",
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)
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_REMOTE_RUNTIME_PACKAGES = (
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"aiohttp==3.13.3",
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"accelerate==1.14.0",
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"alembic==1.18.4",
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REMOTE_AV_SPEC,
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"blake3==1.0.9",
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"comfy-angle==0.1.0",
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"comfy-aimdo==0.4.13",
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"comfy-kitchen==0.2.31",
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"einops==0.8.2",
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"kornia==0.8.2",
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REMOTE_NUMPY_SPEC,
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"num2words==0.5.14",
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REMOTE_OPENCV_SPEC,
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"packaging==26.0",
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"pillow==12.1.0",
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"psutil==7.2.2",
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"pypdf==6.16.1",
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"pydantic==2.12.5",
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"pydantic-settings==2.13.0",
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"pyopengl==3.1.10",
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"pyyaml==6.0.3",
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"requests==2.32.5",
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"safetensors==0.8.0",
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"scipy==1.17.0",
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"sentencepiece==0.2.1",
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"simpleeval==1.0.7",
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"spandrel==0.4.1",
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"sqlalchemy==2.0.46",
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"torchsde==0.2.6",
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"tqdm==4.67.3",
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"transformers==5.15.0",
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REMOTE_HUGGINGFACE_HUB_SPEC,
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REMOTE_HF_XET_SPEC,
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)
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_CUDA_130_TORCH_PACKAGES = ("torch==2.13.0", "torchvision==0.28.0")
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_CUDA_130_CPU_AUDIO_PACKAGES = ("torchaudio==2.11.0+cpu",)
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_DEPLOYMENT_OWNED_PACKAGE_NAMES = frozenset(
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{
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*(
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canonicalize_name(Requirement(requirement).name)
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for requirement in (
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*_REMOTE_RUNTIME_PACKAGES,
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*_CUDA_130_TORCH_PACKAGES,
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*_CUDA_130_CPU_AUDIO_PACKAGES,
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)
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),
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"opencv-python",
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"triton",
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"vllm",
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}
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)
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_IGNORED_DIRECTORY_NAMES = frozenset(
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{
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".cache",
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".git",
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".mypy_cache",
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".pytest_cache",
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".ruff_cache",
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".venv",
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"__pycache__",
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}
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)
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COMFYUI_RUNTIME_SOURCE_DIRECTORIES = frozenset(
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{
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"alembic_db",
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"api_server",
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"app",
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"blueprints",
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"comfy",
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"comfy_api",
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"comfy_api_nodes",
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"comfy_config",
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"comfy_execution",
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"comfy_extras",
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"middleware",
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"utils",
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"web",
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}
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)
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COMFYUI_RUNTIME_SOURCE_FILES = frozenset(
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{
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"alembic.ini",
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"manager_requirements.txt",
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"pyproject.toml",
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"requirements.txt",
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"uv.lock",
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}
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)
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class RemoteRuntimeSettings(Protocol):
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"""Describe settings that shape one deployed Modal runtime."""
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app_name: str
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buffer_containers: int | None
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enable_gpu_memory_snapshot: bool
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enable_memory_snapshot: bool
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execution_timeout_seconds: int
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invocation_dict_name: str
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invocation_result_inline_max_bytes: int
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interrupt_dict_name: str
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loader_prewarm_workers: int
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max_containers: int | None
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min_containers: int
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modal_gpu: str
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modal_secret_name: str
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node_output_cache_dict_name: str
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remote_storage_root: str
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scaledown_window_seconds: int
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session_bridge_dict_name: str
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bridge_inline_max_bytes: int
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snapshot_profile_dict_name: str
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startup_timeout_seconds: int
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stream_event_queue_maxsize: int
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sync_index_dict_name: str
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volume_name: str
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llm_max_resident_models: int
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llm_reserve_free_vram_gb: float
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llm_memory_recovery_timeout_seconds: float
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@dataclass(frozen=True)
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class RemoteRuntimeIdentity:
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"""Hold a deterministic fingerprint and its diagnostic manifest."""
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fingerprint: str
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manifest: dict[str, Any]
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@dataclass(frozen=True)
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class RemoteTorchInstallLayer:
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"""Describe one ordered package layer in a remote PyTorch installation."""
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index_url: str
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packages: tuple[str, ...]
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extra_options: str = ""
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@dataclass(frozen=True)
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class RemoteTorchBuild:
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"""Describe an ordered, pinned PyTorch installation for one Modal GPU type."""
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cuda_version: str
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install_layers: tuple[RemoteTorchInstallLayer, ...]
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@property
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def packages(self) -> tuple[str, ...]:
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"""Return every package pin installed for this PyTorch build."""
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return tuple(
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package
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for install_layer in self.install_layers
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for package in install_layer.packages
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)
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def validation_command(self) -> str:
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"""Return a build-time import and CUDA compatibility check."""
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validation_script = (
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"import torch, torchaudio, torchvision; "
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f"expected_cuda={self.cuda_version!r}; "
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"actual_cuda=torch.version.cuda; "
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"assert actual_cuda == expected_cuda, "
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"f'Expected PyTorch CUDA {expected_cuda}, found {actual_cuda}'; "
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"print('Validated Modal PyTorch stack:', "
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"torch.__version__, torchvision.__version__, torchaudio.__version__, "
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"'CUDA', actual_cuda)"
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)
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return f"python -c {shlex.quote(validation_script)}"
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def remote_runtime_packages() -> tuple[str, ...]:
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"""Return the exact ComfyUI support package set used by Modal images."""
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return _REMOTE_RUNTIME_PACKAGES
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def remote_runtime_validation_command() -> str:
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"""Return a build-time check for ComfyUI's required PyAV API."""
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validation_script = (
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"from importlib import metadata; "
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"from av.video.reformatter import ColorPrimaries; "
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f"expected_av={REMOTE_AV_SPEC.split('==', maxsplit=1)[1]!r}; "
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"actual_av=metadata.version('av'); "
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"assert actual_av == expected_av, "
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"f'Expected PyAV {expected_av}, found {actual_av}'; "
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"assert ColorPrimaries.BT709 is not None; "
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"print('Validated ComfyUI video stack:', 'PyAV', actual_av)"
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)
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return f"python -c {shlex.quote(validation_script)}"
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def remote_huggingface_packages() -> tuple[str, ...]:
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"""Return the pinned Hub client and its Xet transfer accelerator."""
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return (REMOTE_HUGGINGFACE_HUB_SPEC, REMOTE_HF_XET_SPEC)
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def remote_huggingface_validation_command() -> str:
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"""Return a build-time check for the remote Hugging Face transfer stack."""
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validation_script = (
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"from importlib import metadata; import hf_xet, huggingface_hub; "
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f"expected_hub={REMOTE_HUGGINGFACE_HUB_SPEC.split('==', maxsplit=1)[1]!r}; "
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f"expected_xet={REMOTE_HF_XET_SPEC.split('==', maxsplit=1)[1]!r}; "
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"actual_hub=metadata.version('huggingface-hub'); "
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"actual_xet=metadata.version('hf-xet'); "
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"assert actual_hub == expected_hub, "
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"f'Expected huggingface-hub {expected_hub}, found {actual_hub}'; "
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"assert actual_xet == expected_xet, "
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"f'Expected hf-xet {expected_xet}, found {actual_xet}'; "
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"print('Validated Hugging Face transfer stack:', "
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"'huggingface-hub', actual_hub, 'hf-xet', actual_xet)"
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)
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return f"python -c {shlex.quote(validation_script)}"
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def remote_accelerator_packages(modal_gpu: str) -> tuple[str, ...]:
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"""Return the immutable vLLM wheel installed for every supported GPU."""
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normalized_modal_gpu_type(modal_gpu)
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return (REMOTE_VLLM_WHEEL_URL,)
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def remote_accelerator_validation_command(modal_gpu: str) -> str:
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"""Return a build-time import and version check for the GPU inference stack."""
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normalized_modal_gpu_type(modal_gpu)
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validation_script = (
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"from importlib import metadata; import cv2, numpy, torch, vllm; "
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f"expected_vllm={REMOTE_VLLM_SPEC.split('==', maxsplit=1)[1]!r}; "
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f"expected_numpy={REMOTE_NUMPY_SPEC.split('==', maxsplit=1)[1]!r}; "
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f"expected_opencv={REMOTE_OPENCV_SPEC.split('==', maxsplit=1)[1]!r}; "
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"actual_vllm=metadata.version('vllm'); "
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"actual_numpy=metadata.version('numpy'); "
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"actual_opencv=metadata.version('opencv-python-headless'); "
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"assert actual_vllm == expected_vllm, "
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"f'Expected vLLM {expected_vllm}, found {actual_vllm}'; "
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"assert actual_numpy == expected_numpy, "
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"f'Expected NumPy {expected_numpy}, found {actual_numpy}'; "
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"assert actual_opencv == expected_opencv, "
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"f'Expected OpenCV {expected_opencv}, found {actual_opencv}'; "
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"print('Validated Modal accelerator stack:', "
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"'vLLM', actual_vllm, 'Torch', torch.__version__, "
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"'CUDA', torch.version.cuda, 'NumPy', numpy.__version__, "
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"'OpenCV', cv2.__version__)"
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)
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return f"python -c {shlex.quote(validation_script)}"
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def remote_apt_packages() -> tuple[str, ...]:
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"""Return system libraries required by Python packages in the Modal image."""
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return _REMOTE_APT_PACKAGES
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def normalized_modal_gpu_type(modal_gpu: str) -> str:
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"""Return a canonical Modal GPU type without an optional count suffix."""
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gpu_type = modal_gpu.strip().split(":", maxsplit=1)[0].upper()
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if not gpu_type:
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raise ValueError(
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"Modal GPU type cannot be empty when selecting a PyTorch build."
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)
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return gpu_type
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def select_remote_torch_build(modal_gpu: str) -> RemoteTorchBuild:
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"""Select the CUDA 13 stack shared by every supported Modal GPU."""
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normalized_modal_gpu_type(modal_gpu)
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return RemoteTorchBuild(
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cuda_version="13.0",
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install_layers=(
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RemoteTorchInstallLayer(
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index_url="https://download.pytorch.org/whl/cu130",
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packages=_CUDA_130_TORCH_PACKAGES,
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),
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RemoteTorchInstallLayer(
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index_url="https://download.pytorch.org/whl/cpu",
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packages=_CUDA_130_CPU_AUDIO_PACKAGES,
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extra_options="--no-deps",
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),
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),
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)
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def remote_torch_packages(modal_gpu: str = DEFAULT_MODAL_GPU) -> tuple[str, ...]:
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"""Return the exact PyTorch package set for one Modal GPU specification."""
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return select_remote_torch_build(modal_gpu).packages
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def _strip_requirement_comment(line: str) -> str:
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"""Remove a requirements.txt comment while preserving URL fragments."""
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for index, character in enumerate(line):
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if character != "#":
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continue
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previous = line[index - 1] if index > 0 else ""
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if not previous or previous.isspace():
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return line[:index].strip()
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return line.strip()
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def _custom_node_requirement_files(custom_nodes_dir: Path | None) -> tuple[Path, ...]:
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"""Return top-level custom-node requirement files in stable order."""
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if custom_nodes_dir is None or not custom_nodes_dir.exists():
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return ()
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requirement_files: list[Path] = []
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for entry_path in sorted(custom_nodes_dir.iterdir(), key=lambda path: path.name):
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if not entry_path.is_dir():
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continue
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requirements_path = entry_path / "requirements.txt"
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if requirements_path.is_file():
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requirement_files.append(requirements_path)
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return tuple(requirement_files)
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def _read_requirement_file(requirements_path: Path, seen: set[Path]) -> tuple[str, ...]:
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"""Read package specs from one requirement file, following relative includes."""
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resolved_path = requirements_path.resolve()
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if resolved_path in seen:
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return ()
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seen.add(resolved_path)
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requirements: list[str] = []
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for raw_line in requirements_path.read_text(encoding="utf-8").splitlines():
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line = _strip_requirement_comment(raw_line)
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if not line:
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continue
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if line.startswith(("-r ", "--requirement ")):
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_, include_path = line.split(maxsplit=1)
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requirements.extend(
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_read_requirement_file(
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(requirements_path.parent / include_path).resolve(), seen
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)
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)
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continue
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if line.startswith(("-c ", "--constraint ")):
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logger.info(
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"Skipping custom-node pip constraint line from %s: %s",
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requirements_path,
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line,
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)
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continue
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if line.startswith("-"):
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logger.info(
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"Skipping custom-node pip option line from %s: %s",
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requirements_path,
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line,
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)
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continue
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requirements.append(line)
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return tuple(requirements)
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def custom_node_runtime_packages(custom_nodes_dir: Path | None) -> tuple[str, ...]:
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"""Return deduplicated package specs declared by bundled custom nodes."""
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requirements: list[str] = []
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seen_specs: set[str] = set()
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seen_files: set[Path] = set()
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for requirements_path in _custom_node_requirement_files(custom_nodes_dir):
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for requirement in _read_requirement_file(requirements_path, seen_files):
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if _is_deployment_owned_requirement(requirement):
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logger.info(
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"Skipping custom-node requirement for deployment-owned package: %s",
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requirement,
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)
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continue
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if requirement in seen_specs:
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continue
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seen_specs.add(requirement)
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requirements.append(requirement)
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if requirements:
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logger.info(
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"Including %d custom-node Python requirement(s) in the Modal image from %s.",
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len(requirements),
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custom_nodes_dir,
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)
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return tuple(requirements)
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def _is_deployment_owned_requirement(requirement: str) -> bool:
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"""Return whether the immutable remote runtime owns this package version."""
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try:
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package_name = canonicalize_name(Requirement(requirement).name)
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except InvalidRequirement:
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return False
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return package_name in _DEPLOYMENT_OWNED_PACKAGE_NAMES
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def _tree_digest(
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root: Path | None,
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*,
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included_suffixes: frozenset[str],
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included_names: frozenset[str] = frozenset(),
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ignored_top_level_directories: frozenset[str] = frozenset(),
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included_top_level_directories: frozenset[str] | None = None,
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include_all_files_in_top_level_directories: bool = False,
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) -> str:
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"""Hash the selected files in one directory tree using stable relative paths."""
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if root is None or not root.is_dir():
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return "missing"
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digest = hashlib.sha256()
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resolved_root = root.resolve()
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for directory, directory_names, file_names in os.walk(resolved_root):
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directory_path = Path(directory)
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relative_directory = directory_path.relative_to(resolved_root)
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directory_names[:] = sorted(
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directory_name
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for directory_name in directory_names
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if directory_name not in _IGNORED_DIRECTORY_NAMES
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and not (
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relative_directory == Path(".")
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and directory_name in ignored_top_level_directories
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)
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and not (
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relative_directory == Path(".")
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and included_top_level_directories is not None
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and directory_name not in included_top_level_directories
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)
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)
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for file_name in sorted(file_names):
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file_path = directory_path / file_name
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relative_path = file_path.relative_to(resolved_root)
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inside_included_top_level_directory = bool(
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include_all_files_in_top_level_directories
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and included_top_level_directories is not None
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and relative_path.parts
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and relative_path.parts[0] in included_top_level_directories
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)
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if (
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not inside_included_top_level_directory
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and file_name not in included_names
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and file_path.suffix.lower() not in included_suffixes
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):
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continue
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relative_posix_path = relative_path.as_posix()
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digest.update(relative_posix_path.encode("utf-8"))
|
|
digest.update(b"\0")
|
|
with file_path.open("rb") as source_file:
|
|
for chunk in iter(lambda: source_file.read(1024 * 1024), b""):
|
|
digest.update(chunk)
|
|
digest.update(b"\0")
|
|
return digest.hexdigest()
|
|
|
|
|
|
def _runtime_options(settings: RemoteRuntimeSettings) -> dict[str, Any]:
|
|
"""Return deployment settings that materially shape the remote runtime."""
|
|
return {
|
|
"app_name": settings.app_name,
|
|
"buffer_containers": settings.buffer_containers,
|
|
"enable_gpu_memory_snapshot": settings.enable_gpu_memory_snapshot,
|
|
"enable_memory_snapshot": settings.enable_memory_snapshot,
|
|
"execution_timeout_seconds": settings.execution_timeout_seconds,
|
|
"invocation_dict_name": settings.invocation_dict_name,
|
|
"invocation_result_inline_max_bytes": settings.invocation_result_inline_max_bytes,
|
|
"interrupt_dict_name": settings.interrupt_dict_name,
|
|
"loader_prewarm_workers": getattr(
|
|
settings,
|
|
"loader_prewarm_workers",
|
|
2,
|
|
),
|
|
"max_containers": settings.max_containers,
|
|
"min_containers": settings.min_containers,
|
|
"modal_gpu": settings.modal_gpu,
|
|
"modal_secret_name": getattr(
|
|
settings,
|
|
"modal_secret_name",
|
|
DEFAULT_MODAL_SECRET_NAME,
|
|
),
|
|
"node_output_cache_dict_name": settings.node_output_cache_dict_name,
|
|
"remote_storage_root": settings.remote_storage_root,
|
|
"scaledown_window_seconds": settings.scaledown_window_seconds,
|
|
"session_bridge_dict_name": settings.session_bridge_dict_name,
|
|
"bridge_inline_max_bytes": settings.bridge_inline_max_bytes,
|
|
"snapshot_profile_dict_name": settings.snapshot_profile_dict_name,
|
|
"startup_timeout_seconds": settings.startup_timeout_seconds,
|
|
"stream_event_queue_maxsize": settings.stream_event_queue_maxsize,
|
|
"sync_index_dict_name": settings.sync_index_dict_name,
|
|
"volume_name": settings.volume_name,
|
|
"llm_max_resident_models": getattr(settings, "llm_max_resident_models", 2),
|
|
"llm_reserve_free_vram_gb": getattr(settings, "llm_reserve_free_vram_gb", 24.0),
|
|
"llm_compile_cache_volume_name": getattr(
|
|
settings,
|
|
"llm_compile_cache_volume_name",
|
|
f"{settings.volume_name}-llm-compile-cache",
|
|
),
|
|
"llm_vllm_execution_mode": getattr(
|
|
settings,
|
|
"llm_vllm_execution_mode",
|
|
"auto",
|
|
),
|
|
"llm_memory_recovery_timeout_seconds": getattr(
|
|
settings,
|
|
"llm_memory_recovery_timeout_seconds",
|
|
15.0,
|
|
),
|
|
}
|
|
|
|
|
|
def build_remote_runtime_identity(
|
|
*,
|
|
repo_root: Path,
|
|
comfyui_root: Path | None,
|
|
custom_nodes_dir: Path | None,
|
|
settings: RemoteRuntimeSettings,
|
|
) -> RemoteRuntimeIdentity:
|
|
"""Build the deterministic identity expected from one deployed Modal runtime."""
|
|
torch_build = select_remote_torch_build(settings.modal_gpu)
|
|
manifest: dict[str, Any] = {
|
|
"protocol_version": REMOTE_APP_PROTOCOL_VERSION,
|
|
"python_version": REMOTE_PYTHON_VERSION,
|
|
"modal_sdk_spec": REMOTE_MODAL_SDK_SPEC,
|
|
"apt_packages": list(remote_apt_packages()),
|
|
"runtime_packages": list(remote_runtime_packages()),
|
|
"accelerator_packages": list(remote_accelerator_packages(settings.modal_gpu)),
|
|
"torch_packages": list(torch_build.packages),
|
|
"torch_build": {
|
|
"cuda_version": torch_build.cuda_version,
|
|
"install_layers": [
|
|
{
|
|
"index_url": install_layer.index_url,
|
|
"packages": list(install_layer.packages),
|
|
"extra_options": install_layer.extra_options,
|
|
}
|
|
for install_layer in torch_build.install_layers
|
|
],
|
|
},
|
|
"custom_node_packages": list(custom_node_runtime_packages(custom_nodes_dir)),
|
|
"repo_source_digest": _tree_digest(
|
|
repo_root,
|
|
included_suffixes=frozenset({".py"}),
|
|
included_names=frozenset({"llm_profiles.json"}),
|
|
ignored_top_level_directories=frozenset({"tests"}),
|
|
),
|
|
"comfyui_source_digest": _tree_digest(
|
|
comfyui_root,
|
|
included_suffixes=frozenset({".py"}),
|
|
included_names=COMFYUI_RUNTIME_SOURCE_FILES,
|
|
included_top_level_directories=COMFYUI_RUNTIME_SOURCE_DIRECTORIES,
|
|
include_all_files_in_top_level_directories=True,
|
|
),
|
|
"runtime_options": _runtime_options(settings),
|
|
}
|
|
canonical_manifest = json.dumps(
|
|
manifest,
|
|
sort_keys=True,
|
|
separators=(",", ":"),
|
|
).encode("utf-8")
|
|
return RemoteRuntimeIdentity(
|
|
fingerprint=hashlib.sha256(canonical_manifest).hexdigest(),
|
|
manifest=manifest,
|
|
)
|