|
19 | 19 | """
|
20 | 20 |
|
21 | 21 | from time import sleep
|
22 |
| -from typing import List, Optional, Tuple, Dict |
| 22 | +from typing import List, Optional, Tuple |
23 | 23 |
|
24 |
| -import openshift as oc |
25 | 24 | from kubernetes import config
|
26 | 25 | from ray.job_submission import JobSubmissionClient
|
27 |
| -import urllib3 |
28 | 26 |
|
29 | 27 | from .auth import config_check, api_config_handler
|
30 | 28 | from ..utils import pretty_print
|
@@ -58,8 +56,6 @@ class Cluster:
|
58 | 56 | Note that currently, the underlying implementation is a Ray cluster.
|
59 | 57 | """
|
60 | 58 |
|
61 |
| - torchx_scheduler = "ray" |
62 |
| - |
63 | 59 | def __init__(self, config: ClusterConfiguration):
|
64 | 60 | """
|
65 | 61 | Create the resource cluster object by passing in a ClusterConfiguration
|
@@ -472,20 +468,6 @@ def job_logs(self, job_id: str) -> str:
|
472 | 468 | """
|
473 | 469 | return self.job_client.get_job_logs(job_id)
|
474 | 470 |
|
475 |
| - def torchx_config( |
476 |
| - self, working_dir: str = None, requirements: str = None |
477 |
| - ) -> Dict[str, str]: |
478 |
| - dashboard_address = urllib3.util.parse_url(self.cluster_dashboard_uri()).host |
479 |
| - to_return = { |
480 |
| - "cluster_name": self.config.name, |
481 |
| - "dashboard_address": dashboard_address, |
482 |
| - } |
483 |
| - if working_dir: |
484 |
| - to_return["working_dir"] = working_dir |
485 |
| - if requirements: |
486 |
| - to_return["requirements"] = requirements |
487 |
| - return to_return |
488 |
| - |
489 | 471 | def from_k8_cluster_object(
|
490 | 472 | rc,
|
491 | 473 | mcad=True,
|
|
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