Refactor

clearml_agent/glue/k8s.py

@@ -455,6 +455,9 @@ class K8sIntegration(Worker):
     def ports_mode_supported_for_task(self, task_id: str, task_data):
         return self.ports_mode
 
+    def get_default_docker_image(self, session, queue: str) -> str:
+        return str(ENV_DOCKER_IMAGE.get() or session.config.get("agent.default_docker.image", "nvidia/cuda"))
+
     def run_one_task(self, queue: Text, task_id: Text, worker_args=None, task_session=None, **_):
         print('Pulling task {} launching on kubernetes cluster'.format(task_id))
         session = task_session or self._session
@@ -509,9 +512,7 @@ class K8sIntegration(Worker):
 
         container = get_task_container(session, task_id)
         if not container.get('image'):
-            container['image'] = str(
+            container['image'] = self.get_default_docker_image(session, queue)
-                ENV_DOCKER_IMAGE.get() or session.config.get("agent.default_docker.image", "nvidia/cuda")
-            )
             container['arguments'] = session.config.get("agent.default_docker.arguments", None)
             set_task_container(
                 session, task_id, docker_image=container['image'], docker_arguments=container['arguments']

docker/k8s-glue/build-resources/clearml.conf

@@ -13,7 +13,7 @@ api {
 agent.git_user=""
 agent.git_pass=""
 
-# extra_index_url: ["https://allegroai.jfrog.io/clearml/api/pypi/public/simple"]
+# extra_index_url: ["https://clearml.jfrog.io/clearml/api/pypi/public/simple"]
 agent.package_manager.extra_index_url= [
 
 ]
@@ -68,7 +68,7 @@ agent {
         force_upgrade: false,
 
         # additional artifact repositories to use when installing python packages
-        # extra_index_url: ["https://allegroai.jfrog.io/clearmlai/api/pypi/public/simple"]
+        # extra_index_url: ["https://clearml.jfrog.io/clearmlai/api/pypi/public/simple"]
 
         # additional conda channels to use when installing with conda package manager
         conda_channels: ["pytorch", "conda-forge", "defaults", ]

examples/dynamic_cloud_cluster.ipynb

@@ -20,7 +20,7 @@
     "This notebook defines a cloud budget (currently only AWS is supported, but feel free to expand with PRs), and spins an instance the minute a job is waiting for execution. It will also spin down idle machines, saving you some $$$ :)\n",
     "\n",
     "> **Note:**\n",
-    "> This is just an example of how you can use ClearML Agent to implement custom autoscaling. For a more structured autoscaler script, see [here](https://github.com/allegroai/clearml/blob/master/clearml/automation/auto_scaler.py).\n",
+    "> This is just an example of how you can use ClearML Agent to implement custom autoscaling. For a more structured autoscaler script, see [here](https://github.com/clearml/clearml/blob/master/clearml/automation/auto_scaler.py).\n",
     "\n",
     "Configuration steps:\n",
     "- Define maximum budget to be used (instance type / number of instances).\n",

examples/k8s_glue_example.py

@@ -1,6 +1,6 @@
 """
 This example assumes you have preconfigured services with selectors in the form of
- "ai.allegro.agent.serial=pod-<number>" and a targetPort of 10022.
+ "ai.clearml.agent.serial=pod-<number>" and a targetPort of 10022.
 The K8sIntegration component will label each pod accordingly.
 """
 from argparse import ArgumentParser
@@ -22,7 +22,7 @@ def parse_args():
         action="store_true",
         default=False,
         help="Ports-Mode will add a label to the pod which can be used as service, in order to expose ports"
-             "Should not be used with max-pods"
+        "Should not be used with max-pods",
     )
     parser.add_argument(
         "--num-of-services",
@@ -35,14 +35,14 @@ def parse_args():
         type=int,
         help="Used in conjunction with ports-mode, specifies the base port exposed by the services. "
         "For pod #X, the port will be <base-port>+X. Note that pod number is calculated based on base-pod-num"
-             "e.g. if base-port=20000 and base-pod-num=3, the port for the first pod will be 20003"
+        "e.g. if base-port=20000 and base-pod-num=3, the port for the first pod will be 20003",
     )
     parser.add_argument(
         "--base-pod-num",
         type=int,
         default=1,
         help="Used in conjunction with ports-mode and base-port, specifies the base pod number to be used by the "
-             "service (default: %(default)s)"
+        "service (default: %(default)s)",
     )
     parser.add_argument(
         "--gateway-address",
@@ -62,7 +62,7 @@ def parse_args():
         "--template-yaml",
         type=str,
         help="YAML file containing pod template. If provided pod will be scheduled with kubectl apply "
-             "and overrides are ignored, otherwise it will be scheduled with kubectl run"
+        "and overrides are ignored, otherwise it will be scheduled with kubectl run",
     )
     parser.add_argument(
         "--ssh-server-port",
@@ -80,7 +80,7 @@ def parse_args():
         "--max-pods",
         type=int,
         help="Limit the maximum number of pods that this service can run at the same time."
-             "Should not be used with ports-mode"
+        "Should not be used with ports-mode",
     )
     parser.add_argument(
         "--use-owner-token",