Temporarily diagnose fixed GPU smoke fixture

RELEASE_MANIFEST.json

@@ -67,7 +67,7 @@
     "vendor/hf-oauth/LICENSE": "d6e7b19451c1b3d1d66353bb4b1274138953b3bfd9ae60c56e77b1f548796982",
     "vendor/hf-oauth/index.js": "53282844000a171011e547f6472a3846bb4cecb5071adafea079fcac3a2aee7e",
     "vendor/hf-oauth/package.json": "fafd4d8891023a1961546a7d3bc25cd24f64923a2ebfc449fe6d516a2949bda8",
-    "worker.py": "934fb941a7f73a82c9bbcff5f3290103d4dd55e66542e71d56ae8fe7101f0e57"
+    "worker.py": "4e3509ccd767a586b5c7e7c2d656edb0bb4705be37c05ed6381d655d1859b86b"
   },
   "interface": "single-engine-runner-with-map",
   "upstream_revision": "b90b8af06ffc4ea4e4fd703becd1107a48b8e5e0"

worker.py

@@ -125,7 +125,19 @@ def run_native(track_bytes,network_bytes,*,binary=None,importer=None,scene_templ
             '--eliminate-on-wall' if config['collision'] else '--no-eliminate-on-wall',
             '--idle-eliminate' if config['idle'] else '--no-idle-eliminate']
         if gpu_library is not None: command.append('--gpu')
-        subprocess.run(command,cwd=work,env=env,stdout=subprocess.DEVNULL,stderr=subprocess.DEVNULL,timeout=25,check=True)
+        if (gpu_library is not None
+                and hashlib.sha256(track_bytes).hexdigest() == 'ca5749537450430794e2eade2e0ac72fa141a4f225e13e2f8ef842abb948ff15'
+                and hashlib.sha256(network_bytes).hexdigest() == '832361ca786f176d3af8dcad91454a6df173a1d630ee420a2fe95810360734f5'):
+            # Temporary diagnostic for a fixed public fixture. Never capture user jobs.
+            diagnostic = subprocess.run(command,cwd=work,env=env,stdout=subprocess.PIPE,
+                                        stderr=subprocess.PIPE,timeout=25,check=False)
+            if diagnostic.returncode:
+                raise RuntimeError('Fixed GPU probe exit '+str(diagnostic.returncode)+
+                                   ' stderr: '+diagnostic.stderr.decode('utf-8','replace')[-3000:]+
+                                   ' stdout: '+diagnostic.stdout.decode('utf-8','replace')[-1000:])
+        else:
+            subprocess.run(command,cwd=work,env=env,stdout=subprocess.DEVNULL,
+                           stderr=subprocess.DEVNULL,timeout=25,check=True)
         candidate=out/'candidate.json'
         if candidate.stat().st_size>1024*1024: raise ValueError('Output too large')
         trained=json.loads(candidate.read_text())