indigo
b69eedbd54
A free-tier-friendly Modal app to validate the SCAIL-2 code path on real Linux/GPU without a local GPU or the 19B checkpoint. - modal/checks.py: device-aware (CPU/GPU auto) consolidation of the Phase 1-4 plumbing checks using tiny random-init models + synthetic data (no checkpoint, no dataset): driving concat, zero-init patchify_proj widening (output-preserving), a FlexibleStrategy driving+mask training step + loss, and inference conditioning assembly. Passes on CPU locally. - modal/app.py: builds the workspace via `uv sync` (skips CUDA-only ltx-kernels; attention falls back to SDPA). Functions: verify (CPU, ~free), smoke (T4, cents), train (A10G/A100, paid — runs the real trainer against a checkpoint + data on the scail-data Volume). - modal/README.md: free-tier setup (modal setup), run commands, cost table, the scale-up path, and the expected preprocessed dataset layout. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
"""Modal app for testing the SCAIL-2 LTX-2 training/inference path.
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Free-tier friendly: `verify` runs on CPU and `smoke` on a cheap T4, both using
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tiny random-init models + synthetic data (no checkpoint, no dataset) so a full run
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costs pennies. `train` is the scale-up entrypoint that runs the real trainer once
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you upload a checkpoint + preprocessed data to the `scail-data` Volume -- that one
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uses a real GPU and is NOT free.
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Setup (once):
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pip install modal && modal setup
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Run:
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modal run modal/app.py::verify # CPU plumbing check (~free)
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modal run modal/app.py::smoke # same checks on a T4 GPU (cents)
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modal run modal/app.py::train --config-rel configs/scail_animation_lora.yaml
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"""
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import subprocess
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from pathlib import Path
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import modal
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REPO = Path(__file__).parent.parent
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# Build the workspace once into the image. ltx-kernels (CUDA-compiled) is excluded
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# from the workspace and not needed -- attention falls back to SDPA.
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image = (
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modal.Image.debian_slim(python_version="3.11")
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.apt_install("git")
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.pip_install("uv")
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.env({"UV_LINK_MODE": "copy", "UV_PROJECT_ENVIRONMENT": "/root/LTX-2/.venv"})
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.add_local_dir(
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str(REPO),
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"/root/LTX-2",
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copy=True,
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ignore=[".git", ".venv", "**/__pycache__", "**/*.pyc", "outputs", "wandb", "**/.pytest_cache"],
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)
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.run_commands("cd /root/LTX-2 && uv sync --package ltx-trainer")
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)
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app = modal.App("scail-ltx2", image=image)
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# Persistent volume for the (large) checkpoint + preprocessed dataset used by `train`.
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data_volume = modal.Volume.from_name("scail-data", create_if_missing=True)
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_UV_PY = ["uv", "run", "--package", "ltx-trainer", "python"]
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def _run(args: list[str]) -> None:
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subprocess.run([*_UV_PY, *args], cwd="/root/LTX-2", check=True)
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@app.function(timeout=1800)
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def verify() -> None:
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"""Run the SCAIL-2 plumbing checks on CPU (near-free)."""
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_run(["modal/checks.py"])
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@app.function(gpu="T4", timeout=1800)
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def smoke() -> None:
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"""Run the same checks on a T4 GPU to validate the CUDA path (cents)."""
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_run(["modal/checks.py"])
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@app.function(gpu="A10G", timeout=60 * 60 * 6, volumes={"/data": data_volume})
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def train(config_rel: str = "configs/scail_animation_lora.yaml") -> None:
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"""Run the real SCAIL trainer. NOT free -- needs a real checkpoint + data.
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Upload your base checkpoint, Gemma text encoder, and preprocessed data to the
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``scail-data`` Volume (mounted at /data), and point the config's paths at /data.
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For the full 19B model use a bigger GPU (e.g. gpu="A100") and expect real cost.
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"""
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_run(["packages/ltx-trainer/scripts/train.py", config_rel])
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data_volume.commit()
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@app.local_entrypoint()
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def main(target: str = "verify", config_rel: str = "configs/scail_animation_lora.yaml") -> None:
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if target == "smoke":
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smoke.remote()
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elif target == "train":
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train.remote(config_rel)
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else:
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verify.remote()
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