ClaudeandClaude Opus 5.5
8064834c37
- test_refined_dfg::test_source_pin runs its mismatch negative controls first (now including window mode) so missing history cannot skip them. - test_re100: renamed to test_manifest_matches_its_pinned_commit, plus an explicit record that today's src differs from the pin. - Structural manifest checks run before any history-dependent check. - Real tamper test: a flipped digest raises via verify_manifest, and a path absent at the commit raises ValueError. - A blobless clone (tree present, blob missing) is reported as unavailable history, not as a missing path. - All history skips go through history_unavailable(): fatal when CELERIS_REQUIRE_HISTORY=1. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
677 lines
26 KiB
Python
677 lines
26 KiB
Python
#!/usr/bin/env python3
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"""Bounded jet-on periodic cylinder: actual AMR Jet API vs blocked validation adapter."""
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import argparse
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import hashlib
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import subprocess
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import json
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import os
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from pathlib import Path
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import sys
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import time
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import numpy as np
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from source_provenance import checkout_provenance
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PIN = "47b05bc8abfc957fe876b0c63bdb386e3c6cbaa5"
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HERE = Path(__file__).resolve().parent
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CX = np.array([0, 1, -1, 0, 0, 1, -1, 1, -1])
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CY = np.array([0, 0, 0, 1, -1, 1, -1, -1, 1])
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W = np.array([4 / 9] + [1 / 9] * 4 + [1 / 36] * 4)
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def case(scale=1, re=100, steps=800):
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u = 0.03 / scale
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d = 24.084 * scale
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return dict(
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scale=scale,
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re_d=re,
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beta=0.1,
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nx=192 * scale,
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ny=128 * scale,
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diameter=d,
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center=[96 * scale, 64 * scale],
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u=u,
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jet_speed=0.1 * u,
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nu=u * d / re,
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tau_root=0.5 + 3 * u * d / re,
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tau_fine=0.5 + 6 * u * d / re,
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bounds=[48 * scale, 32 * scale, 144 * scale, 96 * scale],
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contours=[
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[72 * scale, 44 * scale, 120 * scale, 84 * scale],
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[64 * scale, 40 * scale, 128 * scale, 88 * scale],
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],
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steps=steps * scale**2,
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window=100 * scale**2,
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dx_root=1 / scale,
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dt_root=1 / scale**2,
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fine_dx_root_units=0.5,
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fine_dt_root_units=0.5,
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mach=np.sqrt(3) * u,
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final_t_star=steps * scale**2 * u / d,
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)
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def state(shape, u):
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cu = CX[:, None, None] * u
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return (
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np.broadcast_to(
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W[:, None, None] * (1 + 3 * cu + 4.5 * cu**2 - 1.5 * u * u), shape
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)
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.astype(np.float32)
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.copy()
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)
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def moments(g):
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f = np.asarray(g, np.float64)
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rho = f.sum(axis=0)
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momentum = np.stack([np.einsum("q,qyx->yx", v, f) for v in (CX, CY)])
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return rho, momentum, momentum / rho
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def cv(g, fluid, origin, rectangle, nu, h=0.5):
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"""Cell-centred inventory and midpoint face quadrature, physical root units.
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Cauchy stress from centred velocity gradients, NOT postcollision non-equilibrium.
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Rectangle faces lie on cell edges; fluid mask excludes the staircase cylinder.
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"""
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return _cv_quantities(*moments(g), fluid, origin, rectangle, nu, h)
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def cv_from_macros(rho, ux, uy, fluid, origin, rectangle, nu, h=0.5):
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"""Same CV calculation from public macros, in one common length/time frame.
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``origin`` is the lower cell edge, ``h`` the cell width, and ``nu`` uses
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that frame. Returns [mass, Px, Py] inventory and outward total flux.
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Crop fields/mask together and translate origin before calling if desired.
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No boundary-exchange observation enters this independent estimator.
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"""
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rho = np.asarray(rho, np.float64)
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if rho.ndim != 2 or any(np.shape(v) != rho.shape for v in (ux, uy, fluid)):
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raise ValueError("CV macros and fluid mask must have matching 2D shapes")
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vel = np.asarray([ux, uy], np.float64)
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return _cv_quantities(rho, rho[None] * vel, vel, fluid, origin, rectangle, nu, h)
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def _cv_quantities(rho, mom, vel, fluid, origin, rectangle, nu, h):
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x0, y0, x1, y1 = np.rint((np.array(rectangle) - np.tile(origin, 2)) / h).astype(int)
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np.testing.assert_allclose(
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np.array([x0, y0, x1, y1]) * h + np.tile(origin, 2), rectangle, atol=1e-12
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)
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ny, nx = fluid.shape
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if not (2 <= x0 < x1 <= nx - 2 and 2 <= y0 < y1 <= ny - 2):
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raise ValueError("Contour needs two fluid-node stencil layers")
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# Every face and its velocity-gradient stencil must be active fluid.
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for ys, xs in [
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(slice(y0 - 2, y1 + 2), slice(x0 - 2, x0 + 2)),
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(slice(y0 - 2, y1 + 2), slice(x1 - 2, x1 + 2)),
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(slice(y0 - 2, y0 + 2), slice(x0 - 2, x1 + 2)),
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(slice(y1 - 2, y1 + 2), slice(x0 - 2, x1 + 2)),
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]:
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if not fluid[ys, xs].all():
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raise ValueError("Contour stencil intersects solid/inactive region")
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use = fluid[y0:y1, x0:x1]
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inventory = (
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np.array(
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[
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rho[y0:y1, x0:x1][use].sum(),
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mom[0, y0:y1, x0:x1][use].sum(),
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mom[1, y0:y1, x0:x1][use].sum(),
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]
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)
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* h
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* h
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)
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duy, dux = np.gradient(vel[0], h)
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dvy, dvx = np.gradient(vel[1], h)
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mu = rho * nu
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# Subtract constant reference pressure, whose closed-contour integral is zero.
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p = (rho - 1) / 3
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tensor = np.array(
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[
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[
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rho * vel[0] ** 2 + p - 2 * mu * dux,
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rho * vel[0] * vel[1] - mu * (duy + dvx),
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],
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[
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rho * vel[0] * vel[1] - mu * (duy + dvx),
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rho * vel[1] ** 2 + p - 2 * mu * dvy,
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],
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]
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)
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transport = np.concatenate(
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[mom[None], tensor], axis=0
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) # [mass,Px,Py] x normal axis
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flux = np.zeros(3)
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for i, sign in [(x0, -1), (x1, 1)]:
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flux += (
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sign
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* 0.5
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* (transport[:, 0, y0:y1, i - 1] + transport[:, 0, y0:y1, i]).sum(axis=1)
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* h
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)
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for j, sign in [(y0, -1), (y1, 1)]:
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flux += (
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sign
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* 0.5
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* (transport[:, 1, j - 1, x0:x1] + transport[:, 1, j, x0:x1]).sum(axis=1)
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* h
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)
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if not np.isfinite(np.r_[inventory, flux]).all():
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raise RuntimeError("Nonfinite CV quantities")
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return inventory, flux
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def aperture_probe(fluid, origin, center, radius, h=0.5):
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"""Independent true-arc quadrature with nearest active-fluid sampling.
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This is a first-order near-wall diagnostic, not certified wall reconstruction.
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No target velocity or raw-population ledger is used to measure the flux.
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"""
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z, w = np.polynomial.legendre.leggauss(64)
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result = []
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for angle in (np.pi / 2, 3 * np.pi / 2):
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theta = angle + np.pi / 12 * z
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er = np.column_stack((np.cos(theta), np.sin(theta)))
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points = np.asarray(center) + radius * er
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selected = []
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distance = []
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for point in points:
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ix, iy = np.rint((point - np.asarray(origin)) / h - 0.5).astype(int)
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candidates = [
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(y, x)
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for y in range(iy - 3, iy + 4)
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for x in range(ix - 3, ix + 4)
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if 0 <= y < fluid.shape[0] and 0 <= x < fluid.shape[1] and fluid[y, x]
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]
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if not candidates:
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raise ValueError("No active near-wall sampling node")
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yy, xx = np.array(candidates).T
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dist = np.linalg.norm(
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np.column_stack((xx, yy)) * h + np.asarray(origin) + h / 2 - point,
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axis=1,
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)
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k = np.argmin(dist)
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selected.append(candidates[k])
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distance.append(dist[k])
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yy, xx = np.array(selected).T
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result.append((yy, xx, er, w * radius * np.pi / 12, float(max(distance))))
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return result
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def measured_aperture_flux(g, probe):
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_, momentum, _ = moments(g)
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return np.array(
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[
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np.sum(np.einsum("in,ni->n", momentum[:, y, x], normal) * ds)
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for y, x, normal, ds, _ in probe
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]
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)
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def verify(repo):
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manifest = json.loads(
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(HERE / "data/jet_circle_cv/source_manifest.json").read_text()
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)
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if manifest["revision"] != PIN:
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raise RuntimeError("Wrong pinned revision")
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for path, sha in manifest["files"].items():
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if hashlib.sha256((repo / path).read_bytes()).hexdigest() != sha:
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raise RuntimeError("Source hash mismatch: " + path)
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return manifest
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def verify_historical(repo):
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"""The pinned manifest describes the PIN tree (read with git, not the working tree)."""
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from source_provenance import verify_manifest
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manifest = json.loads((HERE / "data/jet_circle_cv/source_manifest.json").read_text())
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if manifest["revision"] != PIN:
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raise RuntimeError("Wrong pinned revision")
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verify_manifest(repo, PIN, manifest["files"])
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return manifest
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def current_checkout(repo):
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"""Provenance for a run on the current checkout; never labelled as the pinned source."""
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files = subprocess.check_output(["git", "-C", str(repo), "ls-files", "src"],
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text=True).splitlines()
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return dict(source_mode="current_checkout",
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source_capture="current checkout, not the frozen pinned qualification",
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source_revision=checkout_provenance(repo)["checkout_revision"],
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src_tree=subprocess.check_output(["git", "-C", str(repo), "rev-parse",
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"HEAD:src"], text=True).strip(),
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src_sha256={f: hashlib.sha256((repo / f).read_bytes()).hexdigest()
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for f in files})
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def run_case(c, ctx, output, provenance, continuation=False):
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from CelerisLab.body.geometry.circle import CircleGeometry
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from CelerisLab.body.objects import SimObject
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from CelerisLab.body.jets import Jet, Sector, Radial
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from CelerisLab.config import LBMConfig
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from CelerisLab.lbm.blocks import BlockLayout2D
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from CelerisLab.lbm.block_stepper import PeriodicBlockStepper
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from CelerisLab.lbm.refinement_stepper import FixedPatchStepper
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from CelerisLab.lbm.descriptors import D2Q9_EX, D2Q9_EY
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import pycuda.driver as cuda
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np.testing.assert_array_equal(CX, D2Q9_EX)
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np.testing.assert_array_equal(CY, D2Q9_EY)
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nx, ny, u, nu = c["nx"], c["ny"], c["u"], c["nu"]
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center = np.array(c["center"], float)
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radius = c["diameter"] / 2
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cfg = LBMConfig(
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nx=nx,
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ny=ny,
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viscosity=nu,
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velocity=0.0,
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collision="SRT",
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streaming="double_buffer",
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store_precision="FP32",
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ddf_shifting=False,
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les_enabled=False,
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)
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fine_cfg = LBMConfig(
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nx=2 * nx,
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ny=2 * ny,
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viscosity=2 * nu,
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velocity=0.0,
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collision="SRT",
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streaming="double_buffer",
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store_precision="FP32",
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ddf_shifting=False,
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les_enabled=False,
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)
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obj = SimObject(
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0, CircleGeometry(*center, radius), center=tuple(center), radius=radius
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)
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fc = 2 * center - 0.5
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fobj = SimObject(
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0, CircleGeometry(*fc, 2 * radius), center=tuple(fc), radius=2 * radius
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)
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jets = [
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Jet(
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Sector(tuple(center), angle, np.pi / 12, radius + 0.1),
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Radial(tuple(center), inward=False),
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"uniform",
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sign * c["jet_speed"],
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)
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for angle, sign in [(np.pi / 2, 1), (3 * np.pi / 2, -1)]
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]
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layout = BlockLayout2D.build(2 * ny, 2 * nx, 8, 8)
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with (
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PeriodicBlockStepper(fine_cfg, layout, context=ctx, objects=[fobj]) as uniform,
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FixedPatchStepper(
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cfg,
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c["bounds"],
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context=ctx,
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base_block=(8, 8),
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fine_block=(8, 8),
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fine_objects=[obj],
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jets=jets,
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observe_raw_exchange=True,
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) as amr,
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):
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b = amr._level_boundaries[1]
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count = len(b.tables["cell"])
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if np.any(b.tables["q"] <= 0.010001) or np.any(b.tables["fallback"]):
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raise RuntimeError("Pilot requires unclamped q and no fallback")
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links = fobj.geometry.build_cut_links(2 * nx, 2 * ny)
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np.testing.assert_array_equal([v.dir for v in links], b.tables["dir"])
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np.testing.assert_allclose(
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[v.q for v in links], b.tables["q"], atol=2e-7, rtol=0
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)
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np.testing.assert_array_equal([v.fallback for v in links], b.tables["fallback"])
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np.testing.assert_allclose(
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np.array([[v.hit_x, v.hit_y] for v in links]) * 0.5 + 0.25,
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b.host.curved_hits,
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atol=2e-12,
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rtol=0,
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)
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# Same pre-existing optional-input adapter as test_raw_exchange_gpu.py.
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velocity = uniform._upload(b.tables["surface_velocity"])
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rx = uniform._upload(b.tables["rx"])
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ry = uniform._upload(b.tables["ry"])
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raw = uniform._allocate(count * 16)
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original = uniform._curved
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samples = []
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def boundary(*args, **kw):
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original(*args[:7], velocity, rx, ry, raw, **kw)
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uniform._stream.synchronize()
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a = np.empty((count, 4), np.float32)
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cuda.memcpy_dtoh(a, raw)
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samples.append(a)
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uniform._curved = boundary
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def initialize():
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uniform.initialize(state((9, 2 * ny, 2 * nx), u))
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amr.initialize(*(state(l.shape, u) for l in amr._levels))
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initialize()
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# Compile/launch all stepping and diagnostic paths, then reset physical time.
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amr.step()
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uniform.step(2)
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uniform.download()
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amr.download()
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amr.read_raw_exchange(0)
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initialize()
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samples.clear()
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jet_id = b.jet_cache[1]
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masks = {
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"wall": jet_id < 0,
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"aperture": jet_id >= 0,
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"total": np.ones(count, bool),
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"blowing": jet_id == 0,
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"suction": jet_id == 1,
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}
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if not masks["blowing"].any() or not masks["suction"].any():
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raise RuntimeError("Both apertures must resolve cut links")
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fluid_u = ~uniform.boundary["solid"]
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fluid_a = amr._leaf[1]
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origin_a = np.array(amr.patch.fine_origin)
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probes = {
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"uniform_adapter": aperture_probe(fluid_u, (0.0, 0.0), center, radius),
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"amr_api": aperture_probe(fluid_a, origin_a, center, radius),
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}
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recorder = None
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if continuation:
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from jet_cv_measurements import MeasurementRecorder, sample_steps
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recorder = MeasurementRecorder(
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c, output, fluid_u, amr._leaf, origin_a, CX, CY
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)
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scheduled = set(sample_steps(c).tolist())
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else:
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scheduled = set(range(c["steps"] + 1))
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cumulative_raw = {
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name: {k: np.zeros(4) for k in masks}
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for name in ("uniform_adapter", "amr_api")
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}
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def capture(step):
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ug = uniform.download()
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levels = amr.download()
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ag = levels[1]
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data = {}
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aperture = {}
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for name, g, fluid, origin in [
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("uniform_adapter", ug, fluid_u, (0.0, 0.0)),
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("amr_api", ag, fluid_a, origin_a),
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]:
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data[name] = [cv(g, fluid, origin, r, nu) for r in c["contours"]]
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aperture[name] = measured_aperture_flux(g, probes[name])
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rho, _, _ = moments(g)
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if not np.isfinite(rho[fluid]).all() or rho[fluid].min() <= 0:
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raise RuntimeError("Nonfinite/nonpositive fluid density")
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ox, oy = np.rint(2 * origin_a).astype(int)
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ay, ax = fluid_a.shape
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_, _, uv = moments(ug[:, oy : oy + ay, ox : ox + ax])
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_, _, av = moments(ag)
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error = float(
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np.linalg.norm((av - uv)[:, fluid_a])
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/ max(np.linalg.norm(uv[:, fluid_a]), 1e-30)
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)
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if recorder is not None:
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recorder.capture(step, ug, levels, data, aperture, error)
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recorder.records[-1]["raw_cumulative"] = np.array(
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[
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[cumulative_raw[name][k].copy() for k in masks]
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for name in ("uniform_adapter", "amr_api")
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]
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)
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return data, error, aperture
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previous, _, previous_aperture = capture(0)
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previous_sample_step = 0
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initial = previous
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integrated = {name: np.zeros((2, 3)) for name in previous}
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integrated_aperture = {name: np.zeros(2) for name in previous}
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accumulated = {name: {k: np.zeros(4) for k in masks} for name in previous}
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rows = []
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start = time.monotonic()
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print(
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json.dumps(
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dict(
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status="READY",
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case=c,
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links=count,
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actual_amr_jet_api=True,
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uniform_reference="private optional-input validation adapter",
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q_range=[float(b.tables["q"].min()), float(b.tables["q"].max())],
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fallback_count=int(np.count_nonzero(b.tables["fallback"])),
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)
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),
|
|
flush=True,
|
|
)
|
|
for step in range(1, c["steps"] + 1):
|
|
if time.monotonic() - start > c.get("post_ready_budget_seconds", 1800):
|
|
raise RuntimeError(
|
|
"Declared post-READY budget exceeded; partial report retained"
|
|
)
|
|
samples.clear()
|
|
amr.step()
|
|
uniform.step(2)
|
|
if len(samples) != 2:
|
|
raise RuntimeError("Uniform interval must contain two fine hops")
|
|
ur = sum(a.astype(float) * [0.25, 0.25, 0.125, 0.25] for a in samples)
|
|
ar = np.empty((count, 4), np.float64)
|
|
with ctx.activate():
|
|
cuda.memcpy_dtoh(ar, b.interval_integral)
|
|
if not np.isfinite(ar).all() or not np.isfinite(ur).all():
|
|
raise RuntimeError("Nonfinite raw exchange in completed base interval")
|
|
obs = amr.read_raw_exchange(0)
|
|
if obs["time_start"] != step - 1 or obs["time_end"] != step:
|
|
raise RuntimeError("Raw interval time mismatch")
|
|
np.testing.assert_allclose(
|
|
ar.sum(axis=0)[:2], obs["total"]["impulse"], atol=1e-13, rtol=1e-13
|
|
)
|
|
for name, rr in [("uniform_adapter", ur), ("amr_api", ar)]:
|
|
for k, mask in masks.items():
|
|
subtotal = rr[mask].sum(axis=0)
|
|
accumulated[name][k] += subtotal
|
|
cumulative_raw[name][k] += subtotal
|
|
if step not in scheduled:
|
|
continue
|
|
current, error, current_aperture = capture(step)
|
|
sample_dt = step - previous_sample_step
|
|
for name in current:
|
|
integrated_aperture[name] += (
|
|
0.5 * sample_dt * (previous_aperture[name] + current_aperture[name])
|
|
)
|
|
integrated[name] += (
|
|
0.5
|
|
* sample_dt
|
|
* (
|
|
np.array([x[1] for x in previous[name]])
|
|
+ np.array([x[1] for x in current[name]])
|
|
)
|
|
)
|
|
previous = current
|
|
previous_aperture = current_aperture
|
|
previous_sample_step = step
|
|
if step % c["window"] == 0 or step == c["steps"]:
|
|
end0 = rows[-1]["step_end"] if rows else 0
|
|
duration = step - end0
|
|
row = dict(
|
|
step_start=end0,
|
|
step_end=step,
|
|
t_star=step * u / c["diameter"],
|
|
fine_velocity_relative_l2=error,
|
|
elapsed_seconds=time.monotonic() - start,
|
|
measurements={},
|
|
)
|
|
for name in current:
|
|
delta = np.array([x[0] for x in current[name]]) - np.array(
|
|
[x[0] for x in initial[name]]
|
|
)
|
|
mass_cv = delta[:, 0] + integrated[name][:, 0]
|
|
reaction_cv = -delta[:, 1:] - integrated[name][:, 1:]
|
|
ledger = accumulated[name]
|
|
total = ledger["total"]
|
|
normal = 0.5 * u * u * c["diameter"]
|
|
row["measurements"][name] = dict(
|
|
raw_interval={k: v.tolist() for k, v in ledger.items()},
|
|
cv_mass_into_fluid=mass_cv.tolist(),
|
|
cv_reaction_impulse=reaction_cv.tolist(),
|
|
mass_balance_residual=(mass_cv - total[3]).tolist(),
|
|
reaction_balance_residual=(reaction_cv - total[:2]).tolist(),
|
|
reaction_residual_coefficient=(
|
|
(reaction_cv - total[:2]) / duration / normal
|
|
).tolist(),
|
|
raw_reaction_coefficient=(
|
|
total[:2] / duration / normal
|
|
).tolist(),
|
|
cv_reaction_coefficient=(
|
|
reaction_cv / duration / normal
|
|
).tolist(),
|
|
contour_reaction_spread_coefficient=(
|
|
(reaction_cv[1] - reaction_cv[0]) / duration / normal
|
|
).tolist(),
|
|
measured_arc_mass_rates=(
|
|
integrated_aperture[name] / duration
|
|
).tolist(),
|
|
arc_max_sample_distance=[v[-1] for v in probes[name]],
|
|
nominal_volume_fluxes=[
|
|
2 * np.pi / 12 * radius * c["jet_speed"],
|
|
-2 * np.pi / 12 * radius * c["jet_speed"],
|
|
],
|
|
aperture_mass_rates={
|
|
k: float(ledger[k][3] / duration)
|
|
for k in ("blowing", "suction", "wall", "total")
|
|
},
|
|
)
|
|
integrated[name].fill(0)
|
|
integrated_aperture[name].fill(0)
|
|
for v in accumulated[name].values():
|
|
v.fill(0)
|
|
rows.append(row)
|
|
initial = current
|
|
if recorder is not None:
|
|
recorder.flush()
|
|
payload = dict(
|
|
case=c,
|
|
**{"source_revision": PIN, **provenance},
|
|
status="PILOT_DIAGNOSTIC_ONLY",
|
|
windows=rows,
|
|
physics_validation_pass=False,
|
|
precision="FP32 evolution, FP64 host diagnostics",
|
|
)
|
|
(output / f"re{c['re_d']}_s{c['scale']}.json").write_text(
|
|
json.dumps(payload, indent=2) + "\n"
|
|
)
|
|
print(
|
|
json.dumps(
|
|
dict(
|
|
status="PROGRESS",
|
|
step=step,
|
|
steps=c["steps"],
|
|
elapsed_seconds=row["elapsed_seconds"],
|
|
fine_velocity_relative_l2=error,
|
|
)
|
|
),
|
|
flush=True,
|
|
)
|
|
return payload
|
|
|
|
|
|
def main():
|
|
p = argparse.ArgumentParser(description=__doc__)
|
|
p.add_argument("--repo", type=Path, required=True)
|
|
p.add_argument("--output", type=Path, required=True)
|
|
p.add_argument("--scale", type=int, choices=[1, 2, 4], default=1)
|
|
p.add_argument("--re", type=int, choices=[100, 400], default=100)
|
|
p.add_argument(
|
|
"--steps", type=int, default=800, help="Base s=1 steps; scales as s squared"
|
|
)
|
|
p.add_argument("--allow-re400", action="store_true")
|
|
p.add_argument(
|
|
"--continuation",
|
|
action="store_true",
|
|
help="Fixed measurement campaign: Re100, scale1/2, 3200 reference steps",
|
|
)
|
|
p.add_argument("--plan", action="store_true")
|
|
p.add_argument("--current-checkout", action="store_true",
|
|
help="Run on the current clean checkout and record its provenance "
|
|
"instead of requiring the frozen pinned source")
|
|
a = p.parse_args()
|
|
if a.steps <= 0 or a.steps > 3200:
|
|
p.error("steps must be 1..3200")
|
|
if a.re == 400 and not a.allow_re400:
|
|
p.error("Re400 requires explicit conditional-campaign opt-in")
|
|
if a.continuation:
|
|
if a.scale not in (1, 2) or a.re != 100 or a.steps != 3200:
|
|
p.error("--continuation requires --re 100 --scale 1/2 --steps 3200")
|
|
c = case(a.scale, a.re, a.steps)
|
|
if a.continuation:
|
|
from jet_cv_measurements import sample_steps, factors
|
|
|
|
c.update(
|
|
measurement_schema=1,
|
|
sample_count=len(sample_steps(c)),
|
|
startup_sample_dt_reference=1,
|
|
later_sample_dt_reference=4,
|
|
dense_startup_end_reference=100,
|
|
reference_conversion=factors(a.scale),
|
|
post_ready_budget_seconds=1200 if a.scale == 1 else 5400,
|
|
runtime_estimate_seconds=[210, 500] if a.scale == 1 else [900, 3200],
|
|
)
|
|
plan = dict(
|
|
case=c,
|
|
required_revision=None if a.current_checkout else PIN,
|
|
source_mode="current_checkout" if a.current_checkout else "pinned",
|
|
required_free_device_mib=1024,
|
|
estimated_peak_device_mib=128 * a.scale**2,
|
|
estimated_output_mib=64 if a.continuation else 2,
|
|
max_steps=c["steps"],
|
|
gpu_launch_owner="root; this runner does not select or reserve a GPU",
|
|
)
|
|
if a.plan:
|
|
print(json.dumps(plan, indent=2))
|
|
return
|
|
if (
|
|
not os.environ.get("CUDA_VISIBLE_DEVICES")
|
|
or "," in os.environ["CUDA_VISIBLE_DEVICES"]
|
|
):
|
|
p.error("Root must select one verified idle GPU via CUDA_VISIBLE_DEVICES")
|
|
repo = a.repo.resolve()
|
|
if a.current_checkout:
|
|
provenance = dict(**checkout_provenance(repo), **current_checkout(repo))
|
|
if provenance["checkout_dirty"]:
|
|
raise RuntimeError("Current-checkout runs require a clean checkout")
|
|
else:
|
|
source = verify(repo)
|
|
provenance = dict(
|
|
source_experiment=source["experiment"], source_mode="pinned",
|
|
**checkout_provenance(repo)
|
|
)
|
|
sys.path.insert(0, str(repo / "src"))
|
|
import CelerisLab
|
|
|
|
if Path(CelerisLab.__file__).resolve() != repo / "src/CelerisLab/__init__.py":
|
|
raise RuntimeError("Wrong source import")
|
|
from CelerisLab.cuda.context import CudaContext
|
|
import pycuda.driver as cuda
|
|
|
|
a.output.mkdir(parents=True, exist_ok=False)
|
|
(a.output / "plan.json").write_text(json.dumps(plan, indent=2) + "\n")
|
|
with CudaContext(0) as ctx:
|
|
with ctx.activate():
|
|
free, total = cuda.mem_get_info()
|
|
if free < max(1024, 2 * plan["estimated_peak_device_mib"]) * 1024**2:
|
|
raise RuntimeError("Insufficient free device memory")
|
|
run_case(c, ctx, a.output, provenance, continuation=a.continuation)
|
|
print(
|
|
json.dumps(
|
|
dict(status="COMPLETE_DIAGNOSTIC_ONLY", physics_validation_pass=False)
|
|
),
|
|
flush=True,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|