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'))
