Add a new experimental restart policy for large scale model training

Differential Revision: D58684341

Pull Request resolved: #922

torchx/specs/api.py

@@ -237,11 +237,15 @@ class RetryPolicy(str, Enum):
                 application to deal with failed replica departures and
                 replacement replica admittance.
     2. APPLICATION: Restarts the entire application.
-
+    3. HOT_SPARE: Restarts the replicas for a role as long as quorum (min_replicas)
+                is not violated using extra hosts as spares. It does not really support
+                elasticity and just uses the delta between num_replicas and min_replicas
+                as spares (EXPERIMENTAL).
     """
 
     REPLICA = "REPLICA"
     APPLICATION = "APPLICATION"
+    HOT_SPARE = "HOT_SPARE"
 
 
 class MountType(str, Enum):
@@ -340,6 +344,8 @@ class Role:
                 and num_replicas depending on the cluster resources and
                 policies. If the scheduler doesn't support auto scaling this
                 field is ignored and the job size will be num_replicas.
+                EXPERIMENTAL: For HOT_SPARE restart policy this field is used to
+                indicate the quorum required for the job to run.
             max_retries: max number of retries before giving up
             retry_policy: retry behavior upon replica failures
             resource: Resource requirement for the role. The role should be scheduled