def _dump_job_metadata(project_id, job_id, job): display.display( display.Link( 'https://console.cloud.google.com/mlengine/jobs/{}?project={}'. format(job_id, project_id), 'Job Details')) display.display( display.Link( 'https://console.cloud.google.com/logs/viewer?project={}&resource=ml_job/job_id/{}&interval=NO_LIMIT' .format(project_id, job_id), 'Logs')) if 'trainingInput' in job and 'jobDir' in job['trainingInput']: display.display(display.Tensorboard(job['trainingInput']['jobDir']))
def _dump_metadata(job, region): display.display( display.Link( 'https://console.cloud.google.com/dataproc/jobs/{}?project={}®ion={}' .format( job.get('reference').get('jobId'), job.get('reference').get('projectId'), region), 'Job Details'))
def _dump_metadata(self): display.display(display.Link( 'https://console.cloud.google.com/mlengine/models/{}/versions/{}?project={}'.format( self._model_id, self._version_id, self._project_id), 'Version Details' )) display.display(display.Markdown(''' ## Online Prediction ### REST endpoint The REST endpoint for online prediction is as follows: ``` POST https://ml.googleapis.com/v1/{}:predict ``` Try the REST endpoint in [Google OAuth 2.0 Playgound](https://developers.google.com/oauthplayground/#step3\ &apisSelect=https://www.googleapis.com/auth/cloud-platform&postData={{"instances":[]}}\ &url=https://ml.googleapis.com/v1/{}:predict&content_type=application/json&http_method=POST). ### GCloud command ```bash gcloud ml-engine predict --model {} \ --version {} \ --json-instances instances.json ``` '''.format(self._version_name, self._version_name, self._model_id, self._version_id)))
def display_job_link(project_id, job): location = job.get('location') job_id = job.get('id') display.display( display.Link(href='https://console.cloud.google.com/dataflow/' 'jobsDetail/locations/{}/jobs/{}?project={}'.format( location, job_id, project_id), text='Job Details'))
def test_display_link(self, mock_open, mock_os, mock_json): mock_os.path.isfile.return_value = False display.display(display.Link('https://test/link', 'Test Link')) mock_json.dump.assert_called_with( { 'outputs': [{ 'type': 'web-app', 'html': '<a href="https://test/link">Test Link</a>' }] }, mock.ANY)
def test_display_link(self, mock_open, mock_os, mock_json): mock_os.path.isfile.return_value = False display.display(display.Link('https://test/link', 'Test Link')) mock_json.dump.assert_called_with( { 'outputs': [{ 'type': 'markdown', 'source': '## [Test Link](https://test/link)', 'storage': 'inline' }] }, mock.ANY)
def _display_job_link(project_id, job_id): display.display(display.Link( href= 'https://console.cloud.google.com/bigquery?project={}' '&j={}&page=queryresults'.format(project_id, job_id), text='Query Details' ))
def _dump_metadata(self): display.display(display.Link( 'https://console.cloud.google.com/mlengine/models/{}/versions/{}?project={}'.format( self._model_short_name, self._version_id, self._project_id), 'Version Details' ))
def test___repr__(self, mock_open, mock_os, mock_json): self.assertEqual('# Title', str(display.Markdown('# Title'))) self.assertEqual('Open Tensorboard at: gs://trained/model/', str(display.Tensorboard('gs://trained/model/'))) self.assertEqual('title: https://test/uri', str(display.Link('https://test/uri', 'title')))
def _dump_metadata(cluster, region): display.display(display.Link( 'https://console.cloud.google.com/dataproc/clusters/{}?project={}®ion={}'.format( cluster.get('clusterName'), cluster.get('projectId'), region), 'Cluster Details' ))
def _dump_metadata(self): display.display( display.Link( 'https://console.cloud.google.com/mlengine/models/{}?project={}' .format(self._model_id, self._project_id), 'Model Details'))