fix(apps): keep transitive torch out of GPU artifacts

[justinwlin]

GPU apps whose dependencies pull in torch (peft, accelerate, sentence-transformers) crash-loop after rp flash deploy. runpod-flash didn't have this problem, so Flash apps migrating to 2.0 break.

Cause

split_exclusions only skips torch-family packages listed as top-level requirements. pip still installs them as dependencies, and the vendored PyPI torch shadows the GPU image's build. A torch built off Linux has no NVIDIA libraries, so it fails to load (libcudart.so.13 / libcublasLt.so not found). runpod-flash removes these at archive time.

Change

• After vendoring, remove every installed excluded distribution, plus the wheels only the torch family needs (NVIDIA/CUDA libraries, sympy, networkx, and scripts like torchrun). This uses each wheel's RECORD and Requires-Dist, the same approach as fix(build): keep torch's dependency tree out of tarballs flash#390. Without complete metadata, or when a requirement has no name (URL, path), only the excluded packages themselves are removed.
• Auto-exclusion now applies when any resource is GPU on a builtin image and none uses a custom image. Before, every resource had to be GPU, so a mixed CPU+GPU app bundled torch and its GPU stage crash-looped. A vendored PyPI torch can't import on the builtin CPU images either, so CPU resources lose nothing.
• Workers pip-install manifest excludedPackages that their image lacks. In mixed apps, torch is listed only when the CPU resources' dependencies need it, and the build warns that those workers will install it at cold start. Otherwise a CPU stage that never uses torch tries to install CUDA torch onto its 10 GB disk and fails to boot.

GPU app with dependencies=["peft"]	Before	After
Artifact	645.6 MB, torch 2.14.1 bundled	80.7 MB, no torch
Worker	crash loop	image torch 2.9.1+cu128, CUDA available

Verification

• Live rp flash deploy of a GPU queue app with dependencies=["peft"]. The worker returned {"cuda": true, "file": "/usr/local/lib/python3.12/site-packages/torch/__init__.py", "torch": "2.9.1+cu128", "peft": "0.21.2"}.
• Live mixed app: a CPU queue (humanize) plus a GPU queue (torch, peft). Both returned results. Before the manifest change, the CPU worker failed bootstrap verify with No space left on device while installing torch.
• docker run python:3.12-slim with a macOS-vendored PyPI torch on PYTHONPATH fails with libcublasLt.so.*[0-9] not found, so vendoring torch never worked on the CPU images.
• Unit tests for pruning (shared nvidia/ dirs, ../../bin script paths, missing RECORD/METADATA, unnamed requirements, user --exclude dependencies, custom images, mixed apps, manifest contents). pytest tests: 1494 passed, coverage 91.94%.