Add RedisDataStore (#295)

* GraphManager.set_session also sets self.sess

* make sure that GraphManager.fetch_from_worker uses training phase

* remove unnecessary phase setting in training worker

* reorganize rollout worker

* provide default name to GlobalVariableSaver.__init__ since it isn't really used anyway

* allow dividing TrainingSteps and EnvironmentSteps

* add timestamps to the log

* added redis data store

* conflict merge fix

rl_coach/orchestrators/kubernetes_orchestrator.py

@@ -124,6 +124,11 @@ class Kubernetes(Deploy):
         """
 
         self.memory_backend.deploy()
+
+        if self.params.data_store_params.store_type == "redis":
+            self.data_store.params.redis_address = self.memory_backend.params.redis_address
+            self.data_store.params.redis_port = self.memory_backend.params.redis_port
+
         if not self.data_store.deploy():
             return False
         if self.params.data_store_params.store_type == "nfs":
@@ -146,6 +151,8 @@ class Kubernetes(Deploy):
         trainer_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)]
         name = "{}-{}".format(trainer_params.run_type, uuid.uuid4())
 
+        # TODO: instead of defining each container and template spec from scratch, loaded default
+        # configuration and modify them as necessary depending on the store type
         if self.params.data_store_params.store_type == "nfs":
             container = k8sclient.V1Container(
                 name=name,
@@ -171,7 +178,7 @@ class Kubernetes(Deploy):
                     restart_policy='Never'
                 ),
             )
-        else:
+        elif self.params.data_store_params.store_type == "s3":
             container = k8sclient.V1Container(
                 name=name,
                 image=trainer_params.image,
@@ -190,6 +197,34 @@ class Kubernetes(Deploy):
                     restart_policy='Never'
                 ),
             )
+        elif self.params.data_store_params.store_type == "redis":
+            container = k8sclient.V1Container(
+                name=name,
+                image=trainer_params.image,
+                command=trainer_params.command,
+                args=trainer_params.arguments,
+                image_pull_policy='Always',
+                stdin=True,
+                tty=True,
+                resources=k8sclient.V1ResourceRequirements(
+                    limits={
+                        "cpu": "40",
+                        "memory": "4Gi",
+                        "nvidia.com/gpu": "1",
+                    }
+                ),
+            )
+            template = k8sclient.V1PodTemplateSpec(
+                metadata=k8sclient.V1ObjectMeta(labels={'app': name}),
+                spec=k8sclient.V1PodSpec(
+                    containers=[container],
+                    restart_policy='Never'
+                ),
+            )
+        else:
+            raise ValueError("unexpected store_type {}. expected 's3', 'nfs', 'redis'".format(
+                self.params.data_store_params.store_type
+            ))
 
         job_spec = k8sclient.V1JobSpec(
             completions=1,
@@ -221,12 +256,17 @@ class Kubernetes(Deploy):
         if not worker_params:
             return False
 
+        # At this point, the memory backend and data store have been deployed and in the process,
+        # these parameters have been updated to include things like the hostname and port the
+        # service can be found at.
         worker_params.command += ['--memory_backend_params', json.dumps(self.params.memory_backend_parameters.__dict__)]
         worker_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)]
         worker_params.command += ['--num_workers', '{}'.format(worker_params.num_replicas)]
 
         name = "{}-{}".format(worker_params.run_type, uuid.uuid4())
 
+        # TODO: instead of defining each container and template spec from scratch, loaded default
+        # configuration and modify them as necessary depending on the store type
         if self.params.data_store_params.store_type == "nfs":
             container = k8sclient.V1Container(
                 name=name,
@@ -252,7 +292,7 @@ class Kubernetes(Deploy):
                     restart_policy='Never'
                 ),
             )
-        else:
+        elif self.params.data_store_params.store_type == "s3":
             container = k8sclient.V1Container(
                 name=name,
                 image=worker_params.image,
@@ -271,6 +311,32 @@ class Kubernetes(Deploy):
                     restart_policy='Never'
                 )
             )
+        elif self.params.data_store_params.store_type == "redis":
+            container = k8sclient.V1Container(
+                name=name,
+                image=worker_params.image,
+                command=worker_params.command,
+                args=worker_params.arguments,
+                image_pull_policy='Always',
+                stdin=True,
+                tty=True,
+                resources=k8sclient.V1ResourceRequirements(
+                    limits={
+                        "cpu": "8",
+                        "memory": "4Gi",
+                        # "nvidia.com/gpu": "0",
+                    }
+                ),
+            )
+            template = k8sclient.V1PodTemplateSpec(
+                metadata=k8sclient.V1ObjectMeta(labels={'app': name}),
+                spec=k8sclient.V1PodSpec(
+                    containers=[container],
+                    restart_policy='Never'
+                )
+            )
+        else:
+            raise ValueError('unexpected store type {}'.format(self.params.data_store_params.store_type))
 
         job_spec = k8sclient.V1JobSpec(
             completions=worker_params.num_replicas,