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docker/runner/README.md

@@ -21,7 +21,6 @@ After you have both kubernetes and ray, use the following command to create a cl
 'kubectl get pods'-> this is will give you the cluster name
 
 Use to forward the needed port to the ray service: kubectl port-forward svc/<cluster name> 8265:8265
-
 This is the port that will be used inside ray_jobs.py, where we submit the jobs to ray.
 
 

docker/runner/ray-cluster.yaml

@@ -30,7 +30,6 @@ spec:
               memory: 512Mi
           command: ["/bin/bash", "-c", "--"]
           args: ["ray start --head --port=6379 --dashboard-host=0.0.0.0 --block"]
-
           livenessProbe:
             exec:
               command:

roboteam_ai/src/RL/example_ray_job.py

@@ -1,5 +1,4 @@
 import ray
-
 #Case 1: the function will be distributed across the ray cluster
 @ray.remote
 def compute_square(x):
@@ -24,9 +23,7 @@ def increment(self):
 futures = [compute_square.remote(i) for i in range(10)]
 results = ray.get(futures)
 print(f"Results: {results}")
-
-
 #Testing case 2, using the class:
 counter = Counter.remote()
 future_count = counter.increment.remote()
-print(f"Count: {ray.get(future_count)}")
+print(f"Count: {ray.get(future_count)}")

roboteam_ai/src/RL/ray_jobs.py

@@ -12,8 +12,8 @@
 #result = ray.get(compute_square.remote(4))
 #print(result)
 
-
-
+#result
+#iiii
 job_payload = {
     "entrypoint": "python3 example_ray_job.py"
 }