prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  cmle training component should output job_dir.txt
  It should output the file regardless user provides jodDir or not, otherwise argo will fail.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `component_sdk/python/kfp_component/google/ml_engine/_create_job.py`
  Content:
  ```
  1 # Copyright 2018 Google LLC
  2 #
  3 # Licensed under the Apache License, Version 2.0 (the "License");
  4 # you may not use this file except in compliance with the License.
  5 # You may obtain a copy of the License at
  6 #
  7 #      http://www.apache.org/licenses/LICENSE-2.0
  8 #
  9 # Unless required by applicable law or agreed to in writing, software
  10 # distributed under the License is distributed on an "AS IS" BASIS,
  11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12 # See the License for the specific language governing permissions and
  13 # limitations under the License.
  14 
  15 
  16 import json
  17 import logging
  18 import re
  19 import time
  20 
  21 from googleapiclient import errors
  22 
  23 from kfp_component.core import KfpExecutionContext, display
  24 from ._client import MLEngineClient
  25 from .. import common as gcp_common
  26 
  27 def create_job(project_id, job, job_id_prefix=None, wait_interval=30):
  28     """Creates a MLEngine job.
  29 
  30     Args:
  31         project_id: the ID of the parent project of the job.
  32         job: the payload of the job. Must have ``jobId`` 
  33             and ``trainingInput`` or ``predictionInput`.
  34         job_id_prefix: the prefix of the generated job id.
  35         wait_interval: optional wait interval between calls
  36             to get job status. Defaults to 30.
  37 
  38     Outputs:
  39         /tmp/kfp/output/ml_engine/job.json: The json payload of the create job.
  40         /tmp/kfp/output/ml_engine/job_id.txt: The ID of the created job.
  41         /tmp/kfp/output/ml_engine/job_dir.txt: The `jobDir` of the training job.
  42     """
  43     return CreateJobOp(project_id, job, job_id_prefix,
  44         wait_interval).execute_and_wait()
  45 
  46 class CreateJobOp:
  47     def __init__(self, project_id, job, job_id_prefix=None, wait_interval=30):
  48         self._ml = MLEngineClient()
  49         self._project_id = project_id
  50         self._job_id_prefix = job_id_prefix
  51         self._job_id = None
  52         self._job = job
  53         self._wait_interval = wait_interval
  54     
  55     def execute_and_wait(self):
  56         with KfpExecutionContext(on_cancel=self._cancel) as ctx:
  57             self._set_job_id(ctx.context_id())
  58             self._dump_metadata()
  59             self._create_job()
  60             finished_job = self._wait_for_done()
  61             self._dump_job(finished_job)
  62             if finished_job['state'] != 'SUCCEEDED':
  63                 raise RuntimeError('Job failed with state {}. Error: {}'.format(
  64                     finished_job['state'], finished_job.get('errorMessage', '')))
  65             return finished_job
  66 
  67     def _set_job_id(self, context_id):
  68         if self._job_id_prefix:
  69             job_id = self._job_id_prefix + context_id[:16]
  70         else:
  71             job_id = 'job_' + context_id
  72         job_id = gcp_common.normalize_name(job_id)
  73         self._job_id = job_id
  74         self._job['jobId'] = job_id
  75 
  76     def _cancel(self):
  77         try:
  78             logging.info('Cancelling job {}.'.format(self._job_id))
  79             self._ml.cancel_job(self._project_id, self._job_id)
  80             logging.info('Cancelled job {}.'.format(self._job_id))
  81         except errors.HttpError as e:
  82             # Best effort to cancel the job
  83             logging.error('Failed to cancel the job: {}'.format(e))
  84             pass
  85 
  86     def _create_job(self):
  87         try:
  88             self._ml.create_job(
  89                 project_id = self._project_id,
  90                 job = self._job
  91             )
  92         except errors.HttpError as e:
  93             if e.resp.status == 409:
  94                 if not self._is_dup_job():
  95                     logging.error('Another job has been created with same name before: {}'.format(self._job_id))
  96                     raise
  97                 logging.info('The job {} has been submitted before. Continue waiting.'.format(self._job_id))
  98             else:
  99                 logging.error('Failed to create job.\nPayload: {}\nError: {}'.format(self._job, e))
  100                 raise
  101 
  102     def _is_dup_job(self):
  103         existing_job = self._ml.get_job(self._project_id, self._job_id)
  104         return existing_job.get('trainingInput', None) == self._job.get('trainingInput', None) \
  105             and existing_job.get('predictionInput', None) == self._job.get('predictionInput', None)
  106 
  107     def _wait_for_done(self):
  108         while True:
  109             job = self._ml.get_job(self._project_id, self._job_id)
  110             if job.get('state', None) in ['SUCCEEDED', 'FAILED', 'CANCELLED']:
  111                 return job
  112             # Move to config from flag
  113             logging.info('job status is {}, wait for {}s'.format(
  114                 job.get('state', None), self._wait_interval))
  115             time.sleep(self._wait_interval)
  116 
  117     def _dump_metadata(self):
  118         display.display(display.Link(
  119             'https://console.cloud.google.com/mlengine/jobs/{}?project={}'.format(
  120                 self._job_id, self._project_id),
  121             'Job Details'
  122         ))
  123         display.display(display.Link(
  124             'https://console.cloud.google.com/logs/viewer?project={}&resource=ml_job/job_id/{}&interval=NO_LIMIT'.format(
  125                 self._project_id, self._job_id),
  126             'Logs'
  127         ))
  128         if 'trainingInput' in self._job and 'jobDir' in self._job['trainingInput']:
  129             display.display(display.Tensorboard(
  130                 self._job['trainingInput']['jobDir']))
  131 
  132     def _dump_job(self, job):
  133         logging.info('Dumping job: {}'.format(job))
  134         gcp_common.dump_file('/tmp/kfp/output/ml_engine/job.json', json.dumps(job))
  135         gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_id.txt', job['jobId'])
  136         if 'trainingInput' in job and 'jobDir' in job['trainingInput']:
  137             gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_dir.txt', 
  138                 job['trainingInput']['jobDir'])
  139
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/component_sdk/python/kfp_component/google/ml_engine/_create_job.py b/component_sdk/python/kfp_component/google/ml_engine/_create_job.py
  --- a/component_sdk/python/kfp_component/google/ml_engine/_create_job.py
  +++ b/component_sdk/python/kfp_component/google/ml_engine/_create_job.py
  @@ -133,6 +133,8 @@
           logging.info('Dumping job: {}'.format(job))
           gcp_common.dump_file('/tmp/kfp/output/ml_engine/job.json', json.dumps(job))
           gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_id.txt', job['jobId'])
  +        job_dir = ''
           if 'trainingInput' in job and 'jobDir' in job['trainingInput']:
  -            gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_dir.txt', 
  -                job['trainingInput']['jobDir'])
  +            job_dir = job['trainingInput']['jobDir']
  +        gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_dir.txt', 
  +            job_dir)
  
verification_info: {"golden_diff": "diff --git a/component_sdk/python/kfp_component/google/ml_engine/_create_job.py b/component_sdk/python/kfp_component/google/ml_engine/_create_job.py\n--- a/component_sdk/python/kfp_component/google/ml_engine/_create_job.py\n+++ b/component_sdk/python/kfp_component/google/ml_engine/_create_job.py\n@@ -133,6 +133,8 @@\n         logging.info('Dumping job: {}'.format(job))\n         gcp_common.dump_file('/tmp/kfp/output/ml_engine/job.json', json.dumps(job))\n         gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_id.txt', job['jobId'])\n+        job_dir = ''\n         if 'trainingInput' in job and 'jobDir' in job['trainingInput']:\n-            gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_dir.txt', \n-                job['trainingInput']['jobDir'])\n+            job_dir = job['trainingInput']['jobDir']\n+        gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_dir.txt', \n+            job_dir)\n", "issue": "cmle training component should output job_dir.txt\nIt should output the file regardless user provides jodDir or not, otherwise argo will fail.\n", "before_files": [{"content": "# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport json\nimport logging\nimport re\nimport time\n\nfrom googleapiclient import errors\n\nfrom kfp_component.core import KfpExecutionContext, display\nfrom ._client import MLEngineClient\nfrom .. import common as gcp_common\n\ndef create_job(project_id, job, job_id_prefix=None, wait_interval=30):\n    \"\"\"Creates a MLEngine job.\n\n    Args:\n        project_id: the ID of the parent project of the job.\n        job: the payload of the job. Must have ``jobId`` \n            and ``trainingInput`` or ``predictionInput`.\n        job_id_prefix: the prefix of the generated job id.\n        wait_interval: optional wait interval between calls\n            to get job status. Defaults to 30.\n\n    Outputs:\n        /tmp/kfp/output/ml_engine/job.json: The json payload of the create job.\n        /tmp/kfp/output/ml_engine/job_id.txt: The ID of the created job.\n        /tmp/kfp/output/ml_engine/job_dir.txt: The `jobDir` of the training job.\n    \"\"\"\n    return CreateJobOp(project_id, job, job_id_prefix,\n        wait_interval).execute_and_wait()\n\nclass CreateJobOp:\n    def __init__(self, project_id, job, job_id_prefix=None, wait_interval=30):\n        self._ml = MLEngineClient()\n        self._project_id = project_id\n        self._job_id_prefix = job_id_prefix\n        self._job_id = None\n        self._job = job\n        self._wait_interval = wait_interval\n    \n    def execute_and_wait(self):\n        with KfpExecutionContext(on_cancel=self._cancel) as ctx:\n            self._set_job_id(ctx.context_id())\n            self._dump_metadata()\n            self._create_job()\n            finished_job = self._wait_for_done()\n            self._dump_job(finished_job)\n            if finished_job['state'] != 'SUCCEEDED':\n                raise RuntimeError('Job failed with state {}. Error: {}'.format(\n                    finished_job['state'], finished_job.get('errorMessage', '')))\n            return finished_job\n\n    def _set_job_id(self, context_id):\n        if self._job_id_prefix:\n            job_id = self._job_id_prefix + context_id[:16]\n        else:\n            job_id = 'job_' + context_id\n        job_id = gcp_common.normalize_name(job_id)\n        self._job_id = job_id\n        self._job['jobId'] = job_id\n\n    def _cancel(self):\n        try:\n            logging.info('Cancelling job {}.'.format(self._job_id))\n            self._ml.cancel_job(self._project_id, self._job_id)\n            logging.info('Cancelled job {}.'.format(self._job_id))\n        except errors.HttpError as e:\n            # Best effort to cancel the job\n            logging.error('Failed to cancel the job: {}'.format(e))\n            pass\n\n    def _create_job(self):\n        try:\n            self._ml.create_job(\n                project_id = self._project_id,\n                job = self._job\n            )\n        except errors.HttpError as e:\n            if e.resp.status == 409:\n                if not self._is_dup_job():\n                    logging.error('Another job has been created with same name before: {}'.format(self._job_id))\n                    raise\n                logging.info('The job {} has been submitted before. Continue waiting.'.format(self._job_id))\n            else:\n                logging.error('Failed to create job.\\nPayload: {}\\nError: {}'.format(self._job, e))\n                raise\n\n    def _is_dup_job(self):\n        existing_job = self._ml.get_job(self._project_id, self._job_id)\n        return existing_job.get('trainingInput', None) == self._job.get('trainingInput', None) \\\n            and existing_job.get('predictionInput', None) == self._job.get('predictionInput', None)\n\n    def _wait_for_done(self):\n        while True:\n            job = self._ml.get_job(self._project_id, self._job_id)\n            if job.get('state', None) in ['SUCCEEDED', 'FAILED', 'CANCELLED']:\n                return job\n            # Move to config from flag\n            logging.info('job status is {}, wait for {}s'.format(\n                job.get('state', None), self._wait_interval))\n            time.sleep(self._wait_interval)\n\n    def _dump_metadata(self):\n        display.display(display.Link(\n            'https://console.cloud.google.com/mlengine/jobs/{}?project={}'.format(\n                self._job_id, self._project_id),\n            'Job Details'\n        ))\n        display.display(display.Link(\n            'https://console.cloud.google.com/logs/viewer?project={}&resource=ml_job/job_id/{}&interval=NO_LIMIT'.format(\n                self._project_id, self._job_id),\n            'Logs'\n        ))\n        if 'trainingInput' in self._job and 'jobDir' in self._job['trainingInput']:\n            display.display(display.Tensorboard(\n                self._job['trainingInput']['jobDir']))\n\n    def _dump_job(self, job):\n        logging.info('Dumping job: {}'.format(job))\n        gcp_common.dump_file('/tmp/kfp/output/ml_engine/job.json', json.dumps(job))\n        gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_id.txt', job['jobId'])\n        if 'trainingInput' in job and 'jobDir' in job['trainingInput']:\n            gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_dir.txt', \n                job['trainingInput']['jobDir'])\n", "path": "component_sdk/python/kfp_component/google/ml_engine/_create_job.py"}], "after_files": [{"content": "# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport json\nimport logging\nimport re\nimport time\n\nfrom googleapiclient import errors\n\nfrom kfp_component.core import KfpExecutionContext, display\nfrom ._client import MLEngineClient\nfrom .. import common as gcp_common\n\ndef create_job(project_id, job, job_id_prefix=None, wait_interval=30):\n    \"\"\"Creates a MLEngine job.\n\n    Args:\n        project_id: the ID of the parent project of the job.\n        job: the payload of the job. Must have ``jobId`` \n            and ``trainingInput`` or ``predictionInput`.\n        job_id_prefix: the prefix of the generated job id.\n        wait_interval: optional wait interval between calls\n            to get job status. Defaults to 30.\n\n    Outputs:\n        /tmp/kfp/output/ml_engine/job.json: The json payload of the create job.\n        /tmp/kfp/output/ml_engine/job_id.txt: The ID of the created job.\n        /tmp/kfp/output/ml_engine/job_dir.txt: The `jobDir` of the training job.\n    \"\"\"\n    return CreateJobOp(project_id, job, job_id_prefix,\n        wait_interval).execute_and_wait()\n\nclass CreateJobOp:\n    def __init__(self, project_id, job, job_id_prefix=None, wait_interval=30):\n        self._ml = MLEngineClient()\n        self._project_id = project_id\n        self._job_id_prefix = job_id_prefix\n        self._job_id = None\n        self._job = job\n        self._wait_interval = wait_interval\n    \n    def execute_and_wait(self):\n        with KfpExecutionContext(on_cancel=self._cancel) as ctx:\n            self._set_job_id(ctx.context_id())\n            self._dump_metadata()\n            self._create_job()\n            finished_job = self._wait_for_done()\n            self._dump_job(finished_job)\n            if finished_job['state'] != 'SUCCEEDED':\n                raise RuntimeError('Job failed with state {}. Error: {}'.format(\n                    finished_job['state'], finished_job.get('errorMessage', '')))\n            return finished_job\n\n    def _set_job_id(self, context_id):\n        if self._job_id_prefix:\n            job_id = self._job_id_prefix + context_id[:16]\n        else:\n            job_id = 'job_' + context_id\n        job_id = gcp_common.normalize_name(job_id)\n        self._job_id = job_id\n        self._job['jobId'] = job_id\n\n    def _cancel(self):\n        try:\n            logging.info('Cancelling job {}.'.format(self._job_id))\n            self._ml.cancel_job(self._project_id, self._job_id)\n            logging.info('Cancelled job {}.'.format(self._job_id))\n        except errors.HttpError as e:\n            # Best effort to cancel the job\n            logging.error('Failed to cancel the job: {}'.format(e))\n            pass\n\n    def _create_job(self):\n        try:\n            self._ml.create_job(\n                project_id = self._project_id,\n                job = self._job\n            )\n        except errors.HttpError as e:\n            if e.resp.status == 409:\n                if not self._is_dup_job():\n                    logging.error('Another job has been created with same name before: {}'.format(self._job_id))\n                    raise\n                logging.info('The job {} has been submitted before. Continue waiting.'.format(self._job_id))\n            else:\n                logging.error('Failed to create job.\\nPayload: {}\\nError: {}'.format(self._job, e))\n                raise\n\n    def _is_dup_job(self):\n        existing_job = self._ml.get_job(self._project_id, self._job_id)\n        return existing_job.get('trainingInput', None) == self._job.get('trainingInput', None) \\\n            and existing_job.get('predictionInput', None) == self._job.get('predictionInput', None)\n\n    def _wait_for_done(self):\n        while True:\n            job = self._ml.get_job(self._project_id, self._job_id)\n            if job.get('state', None) in ['SUCCEEDED', 'FAILED', 'CANCELLED']:\n                return job\n            # Move to config from flag\n            logging.info('job status is {}, wait for {}s'.format(\n                job.get('state', None), self._wait_interval))\n            time.sleep(self._wait_interval)\n\n    def _dump_metadata(self):\n        display.display(display.Link(\n            'https://console.cloud.google.com/mlengine/jobs/{}?project={}'.format(\n                self._job_id, self._project_id),\n            'Job Details'\n        ))\n        display.display(display.Link(\n            'https://console.cloud.google.com/logs/viewer?project={}&resource=ml_job/job_id/{}&interval=NO_LIMIT'.format(\n                self._project_id, self._job_id),\n            'Logs'\n        ))\n        if 'trainingInput' in self._job and 'jobDir' in self._job['trainingInput']:\n            display.display(display.Tensorboard(\n                self._job['trainingInput']['jobDir']))\n\n    def _dump_job(self, job):\n        logging.info('Dumping job: {}'.format(job))\n        gcp_common.dump_file('/tmp/kfp/output/ml_engine/job.json', json.dumps(job))\n        gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_id.txt', job['jobId'])\n        job_dir = ''\n        if 'trainingInput' in job and 'jobDir' in job['trainingInput']:\n            job_dir = job['trainingInput']['jobDir']\n        gcp_common.dump_file('/tmp/kfp/output/ml_engine/job_dir.txt', \n            job_dir)\n", "path": "component_sdk/python/kfp_component/google/ml_engine/_create_job.py"}]}