def set_project_id(self, project_id):
""" Set the project_id for the context. """
self._project_id = project_id
if self == Context._global_context:
try:
from google.datalab import Context as new_context
new_context.default().set_project_id(project_id)
except ImportError:
# If the new library is not loaded, then we have nothing to do.
pass
def test_default_context(self, mock_save_project_id, mock_get_default_project_id,
mock_get_credentials):
mock_get_default_project_id.return_value = 'default_project'
mock_get_credentials.return_value = ''
c = Context.default()
default_project = c.project_id
self.assertEqual(default_project, 'default_project')
# deliberately change the default project and make sure it's reset
c.set_project_id('test_project4')
self.assertEqual(Context.default().project_id, 'default_project')
def _batch_predict(args, cell):
if args['cloud_config'] and not args['cloud']:
raise ValueError('"cloud_config" is provided but no "--cloud". '
'Do you want local run or cloud run?')
if args['cloud']:
parts = args['model'].split('.')
if len(parts) != 2:
raise ValueError('Invalid model name for cloud prediction. Use "model.version".')
version_name = ('projects/%s/models/%s/versions/%s' %
(Context.default().project_id, parts[0], parts[1]))
cloud_config = args['cloud_config'] or {}
job_id = cloud_config.pop('job_id', None)
job_request = {
'version_name': version_name,
'data_format': 'TEXT',
'input_paths': file_io.get_matching_files(args['prediction_data']['csv']),
'output_path': args['output'],
}
job_request.update(cloud_config)
job = datalab_ml.Job.submit_batch_prediction(job_request, job_id)
_show_job_link(job)
else:
print('local prediction...')
_local_predict.local_batch_predict(args['model'],
args['prediction_data']['csv'],
args['output'],
args['format'],
args['batch_size'])
print('done.')
def _show_job_link(job):
log_url_query_strings = {
'project': Context.default().project_id,
'resource': 'ml.googleapis.com/job_id/' + job.info['jobId']
}
log_url = 'https://console.developers.google.com/logs/viewer?' + \
urllib.urlencode(log_url_query_strings)
html = 'Job "%s" submitted.' % job.info['jobId']
html += '<p>Click <a href="%s" target="_blank">here</a> to view cloud log. <br/>' % log_url
IPython.display.display_html(html, raw=True)
def _show_job_link(job):
log_url_query_strings = {
'project': Context.default().project_id,
'resource': 'ml.googleapis.com/job_id/' + job.info['jobId']
}
log_url = 'https://console.developers.google.com/logs/viewer?' + \
urllib.urlencode(log_url_query_strings)
html = 'Job "%s" submitted.' % job.info['jobId']
html += '<p>Click <a href="%s" target="_blank">here</a> to view cloud log. <br/>' % log_url
IPython.display.display_html(html, raw=True)
def _batch_predict(args, cell):
env = google.datalab.utils.commands.notebook_environment()
cell_data = google.datalab.utils.commands.parse_config(cell, env)
required_keys = ['prediction_data']
if args['cloud']:
required_keys.append('cloud')
google.datalab.utils.commands.validate_config(cell_data,
required_keys=required_keys)
data = cell_data['prediction_data']
google.datalab.utils.commands.validate_config(
data, required_keys=['csv_file_pattern'])
if args['cloud']:
parts = args['model'].split('.')
if len(parts) != 2:
raise ValueError(
'Invalid model name for cloud prediction. Use "model.version".'
)
version_name = ('projects/%s/models/%s/versions/%s' %
(Context.default().project_id, parts[0], parts[1]))
cloud_config = cell_data['cloud']
job_id = cloud_config.pop('job_id', None)
job_request = {
'version_name': version_name,
'data_format': 'TEXT',
'input_paths':
file_io.get_matching_files(data['csv_file_pattern']),
'output_path': args['output_dir'],
}
job_request.update(cloud_config)
job = datalab_ml.Job.submit_batch_prediction(job_request, job_id)
_show_job_link(job)
else:
print('local prediction...')
_local_predict.local_batch_predict(args['model'],
data['csv_file_pattern'],
args['output_dir'],
args['output_format'],
args['batch_size'])
print('done.')
def test_default_project(self, mock_save_project_id, mock_get_default_project_id,
mock_get_credentials):
# verify setting the project on a default Context object sets
# the global default project
global default_project
default_project = ''
def save_project(project=None):
global default_project
default_project = project
def get_project():
global default_project
return default_project
mock_save_project_id.side_effect = save_project
mock_get_default_project_id.side_effect = get_project
mock_get_credentials.return_value = ''
c = Context.default()
dummy_project = 'test_project3'
c.set_project_id(dummy_project)
self.assertEqual(du.get_default_project_id(), dummy_project)
def _default_project(): from google.datalab import Context return Context.default().project_id