[py] Added a gpu queue that defaults to short_gpu.

bob/pipelines/config/distributed/sge_light.py → bob/pipelines/config/distributed/sge_gpu.py

 from dask.distributed import Client
 
 from bob.pipelines.distributed.sge import SGEMultipleQueuesCluster
-from bob.pipelines.distributed.sge_queues import QUEUE_LIGHT
+from bob.pipelines.distributed.sge_queues import QUEUE_GPU
 
-cluster = SGEMultipleQueuesCluster(min_jobs=20, sge_job_spec=QUEUE_LIGHT)
+cluster = SGEMultipleQueuesCluster(min_jobs=20, sge_job_spec=QUEUE_GPU)
 dask_client = Client(cluster)

bob/pipelines/distributed/sge_queues.py

@@ -50,14 +50,14 @@ default
 """
 
 
-QUEUE_LIGHT = {
+QUEUE_GPU = {
     "default": {
-        "queue": "q_1day",
-        "memory": "4GB",
+        "queue": "q_short_gpu",
+        "memory": "30GB",
         "io_big": False,
         "resource_spec": "",
-        "max_jobs": 96,
-        "resources": "",
+        "max_jobs": 45,
+        "resources": {"q_short_gpu": 1},
     },
     "q_1day": {
         "queue": "q_1day",
@@ -98,6 +98,5 @@ QUEUE_LIGHT = {
     },
 }
 """
-This queue setup has a light arrangement.
-For CPU jobs, it prioritizes all.q and not io_big
+This queue setup uses the q_short_gpu queue of the SGE.
 """

setup.py

@@ -46,7 +46,7 @@ setup(
         'dask.client': [
             'local-parallel  = bob.pipelines.config.distributed.local_parallel',
             'sge             = bob.pipelines.config.distributed.sge_default',
-            'sge-light       = bob.pipelines.config.distributed.sge_light',
+            'sge-gpu         = bob.pipelines.config.distributed.sge_gpu',
         ],
     },