Move cloud trainer and predictor from their own classes to Job and Model respectively.

datalab/mlalpha/__init__.py

@@ -24,7 +24,7 @@
 from ._tensorboard import TensorBoard
 from ._dataset import CsvDataSet, BigQueryDataSet
 from ._package import Packager
-from ._cloud_models import CloudModels, CloudModelVersions
+from ._cloud_models import Models, ModelVersions
 from ._confusion_matrix import ConfusionMatrix
 from ._analysis import csv_to_dataframe
 from ._package_runner import PackageRunner

datalab/mlalpha/_cloud_models.py

@@ -14,35 +14,27 @@
 
 from googleapiclient import discovery
 import os
-import time
 import yaml
 
 import datalab.context
 import datalab.storage
 import datalab.utils
 
+from . import _util
 
-# TODO(qimingj) Remove once the API is public since it will no longer be needed
-_CLOUDML_DISCOVERY_URL = 'https://storage.googleapis.com/cloud-ml/discovery/' \
-                         'ml_v1beta1_discovery.json'
-
-
-class CloudModels(object):
+class Models(object):
   """Represents a list of Cloud ML models for a project."""
 
   def __init__(self, project_id=None):
-    """Initializes an instance of a CloudML Model list that is iteratable
-           ("for model in CloudModels()").
-
+    """
     Args:
       project_id: project_id of the models. If not provided, default project_id will be used.
     """
     if project_id is None:
       project_id = datalab.context.Context.default().project_id
     self._project_id = project_id
     self._credentials = datalab.context.Context.default().credentials
-    self._api = discovery.build('ml', 'v1alpha3', credentials=self._credentials,
-                                discoveryServiceUrl=_CLOUDML_DISCOVERY_URL)
+    self._api = discovery.build('ml', 'v1', credentials=self._credentials)
 
   def _retrieve_models(self, page_token, page_size):
     list_info = self._api.projects().models().list(
@@ -51,11 +43,13 @@ def _retrieve_models(self, page_token, page_size):
     page_token = list_info.get('nextPageToken', None)
     return models, page_token
 
-  def __iter__(self):
+  def get_iterator(self):
+    """Get iterator of models so it can be used as "for model in Models().get_iterator()".
+    """
     return iter(datalab.utils.Iterator(self._retrieve_models))
 
   def get_model_details(self, model_name):
-    """Get details of a model.
+    """Get details of the specified model from CloudML Service.
 
     Args:
       model_name: the name of the model. It can be a model full name
@@ -72,10 +66,16 @@ def create(self, model_name):
 
     Args:
       model_name: the short name of the model, such as "iris".
+    Returns:
+      If successful, returns informaiton of the model, such as
+      {u'regions': [u'us-central1'], u'name': u'projects/myproject/models/mymodel'}
+    Raises:
+      If the model creation failed.
     """
     body = {'name': model_name}
     parent = 'projects/' + self._project_id
-    self._api.projects().models().create(body=body, parent=parent).execute()
+    # Model creation is instant. If anything goes wrong, Exception will be thrown.
+    return self._api.projects().models().create(body=body, parent=parent).execute()
 
   def delete(self, model_name):
     """Delete a model.
@@ -87,7 +87,10 @@ def delete(self, model_name):
     full_name = model_name
     if not model_name.startswith('projects/'):
       full_name = ('projects/%s/models/%s' % (self._project_id, model_name))
-    return self._api.projects().models().delete(name=full_name).execute()
+    response = self._api.projects().models().delete(name=full_name).execute()
+    if 'name' not in response:
+      raise Exception('Invalid response from service. "name" is not found.')
+    _util.wait_for_long_running_operation(response['name'])
 
   def list(self, count=10):
     """List models under the current project in a table view.
@@ -121,13 +124,11 @@ def describe(self, model_name):
     print model_yaml
 
 
-class CloudModelVersions(object):
+class ModelVersions(object):
   """Represents a list of versions for a Cloud ML model."""
 
   def __init__(self, model_name, project_id=None):
-    """Initializes an instance of a CloudML model version list that is iteratable
-        ("for version in CloudModelVersions()").
-
+    """
     Args:
       model_name: the name of the model. It can be a model full name
           ("projects/[project_id]/models/[model_name]") or just [model_name].
@@ -137,8 +138,7 @@ def __init__(self, model_name, project_id=None):
     if project_id is None:
       self._project_id = datalab.context.Context.default().project_id
     self._credentials = datalab.context.Context.default().credentials
-    self._api = discovery.build('ml', 'v1alpha3', credentials=self._credentials,
-                                discoveryServiceUrl=_CLOUDML_DISCOVERY_URL)
+    self._api = discovery.build('ml', 'v1', credentials=self._credentials)
     if not model_name.startswith('projects/'):
       model_name = ('projects/%s/models/%s' % (self._project_id, model_name))
     self._full_model_name = model_name
@@ -152,7 +152,10 @@ def _retrieve_versions(self, page_token, page_size):
     page_token = list_info.get('nextPageToken', None)
     return versions, page_token
 
-  def __iter__(self):
+  def get_iterator(self):
+    """Get iterator of versions so it can be used as
+       "for v in ModelVersions(model_name).get_iterator()".
+    """
     return iter(datalab.utils.Iterator(self._retrieve_versions))
 
   def get_version_details(self, version_name):
@@ -165,21 +168,6 @@ def get_version_details(self, version_name):
     name = ('%s/versions/%s' % (self._full_model_name, version_name))
     return self._api.projects().models().versions().get(name=name).execute()
 
-  def _wait_for_long_running_operation(self, response):
-    if 'name' not in response:
-      raise Exception('Invaid response from service. Cannot find "name" field.')
-    print('Waiting for job "%s"' % response['name'])
-    while True:
-      response = self._api.projects().operations().get(name=response['name']).execute()
-      if 'done' not in response or response['done'] != True:
-        time.sleep(3)
-      else:
-        if 'error' in response:
-          print(response['error'])
-        else:
-          print('Done.')
-        break
-
   def deploy(self, version_name, path):
     """Deploy a model version to the cloud.
 
@@ -211,7 +199,9 @@ def deploy(self, version_name, path):
     }
     response = self._api.projects().models().versions().create(body=body,
                    parent=self._full_model_name).execute()
-    self._wait_for_long_running_operation(response)
+    if 'name' not in response:
+      raise Exception('Invalid response from service. "name" is not found.')
+    _util.wait_for_long_running_operation(response['name'])
 
   def delete(self, version_name):
     """Delete a version of model.
@@ -221,8 +211,37 @@ def delete(self, version_name):
     """
     name = ('%s/versions/%s' % (self._full_model_name, version_name))
     response = self._api.projects().models().versions().delete(name=name).execute()
-    self._wait_for_long_running_operation(response)
-
+    if 'name' not in response:
+      raise Exception('Invalid response from service. "name" is not found.')
+    _util.wait_for_long_running_operation(response['name'])
+
+  def predict(self, version_name, data):
+    """Get prediction results from features instances.
+
+    Args:
+      version_name: the name of the version used for prediction.
+      data: typically a list of instance to be submitted for prediction. The format of the
+          instance depends on the model. For example, structured data model may require
+          a csv line for each instance.
+          Note that online prediction only works on models that take one placeholder value,
+          such as a string encoding a csv line.
+    Returns:
+      A list of prediction results for given instances. Each element is a dictionary representing
+          output mapping from the graph.
+      An example:
+        [{"predictions": 1, "score": [0.00078, 0.71406, 0.28515]},
+         {"predictions": 1, "score": [0.00244, 0.99634, 0.00121]}]
+    """
+    full_version_name = ('%s/versions/%s' % (self._full_model_name, version_name))
+    request = self._api.projects().predict(body={'instances': data},
+                                           name=full_version_name)
+    request.headers['user-agent'] = 'GoogleCloudDataLab/1.0'
+    result = request.execute()
+    if 'predictions' not in result:
+      raise Exception('Invalid response from service. Cannot find "predictions" in response.')
+
+    return result['predictions']
+
   def describe(self, version_name):
     """Print information of a specified model.
 

datalab/mlalpha/_job.py

@@ -18,39 +18,22 @@
 from googleapiclient import discovery
 import yaml
 
-# TODO(qimingj) Remove once the API is public since it will no longer be needed
-_CLOUDML_DISCOVERY_URL = 'https://storage.googleapis.com/cloud-ml/discovery/' \
-                         'ml_v1beta1_discovery.json'
-
-import datalab.utils
-import datalab.context
-from googleapiclient import discovery
-import yaml
-
-# TODO(qimingj) Remove once the API is public since it will no longer be needed
-_CLOUDML_DISCOVERY_URL = 'https://storage.googleapis.com/cloud-ml/discovery/' \
-                         'ml_v1beta1_discovery.json'
-
 
 class Job(object):
   """Represents a Cloud ML job."""
 
-  def __init__(self, name, context=None, api=None):
+  def __init__(self, name, context=None):
     """Initializes an instance of a CloudML Job.
 
     Args:
       name: the name of the job. It can be an operation full name
-          ("projects/[project_id]/operations/[operation_name]") or just [operation_name].
+          ("projects/[project_id]/jobs/[operation_name]") or just [operation_name].
       context: an optional Context object providing project_id and credentials.
-      api: optional CloudML API client.
     """
     if context is None:
       context = datalab.context.Context.default()
     self._context = context
-    if api is None:
-      api = discovery.build('ml', 'v1beta1', credentials=self._context.credentials,
-                            discoveryServiceUrl=_CLOUDML_DISCOVERY_URL)
-    self._api = api
+    self._api = discovery.build('ml', 'v1', credentials=self._context.credentials)
     if not name.startswith('projects/'):
       name = 'projects/' + self._context.project_id + '/jobs/' + name
     self._name = name
@@ -68,6 +51,63 @@ def describe(self):
     job_yaml = yaml.safe_dump(self._info, default_flow_style=False)
     print job_yaml
 
+  @staticmethod 
+  def submit_training(job_request, job_id=None):
+    """Submit a training job.
+
+    Args:
+      job_request: the arguments of the training job in a dict. For example,
+          {
+            'package_uris':  'gs://my-bucket/iris/trainer-0.1.tar.gz',
+            'python_module': 'trainer.task',
+            'scale_tier': 'BASIC',
+            'region': 'us-central1',
+            'args': {
+              'train_data_paths': ['gs://mubucket/data/features_train'],
+              'eval_data_paths': ['gs://mubucket/data/features_eval'],
+              'metadata_path': 'gs://mubucket/data/metadata.yaml',
+              'output_path': 'gs://mubucket/data/mymodel/',
+            }
+          }
+          If 'args' is present in job_request and is a dict, it will be expanded to
+          --key value or --key list_item_0 --key list_item_1, ...
+      job_id: id for the training job. If None, an id based on timestamp will be generated.
+    Returns:
+      A Job object representing the cloud training job.
+    """
+    new_job_request = dict(job_request)
+    # convert job_args from dict to list as service required.
+    if 'args' in job_request and isinstance(job_request['args'], dict):
+      job_args = job_request['args']
+      args = []
+      for k,v in job_args.iteritems():
+        if isinstance(v, list):
+          for item in v:
+            args.append('--' + str(k))
+            args.append(str(item))
+        else:
+          args.append('--' + str(k))
+          args.append(str(v))
+      new_job_request['args'] = args
+
+    if job_id is None:
+      job_id = datetime.datetime.now().strftime('%y%m%d_%H%M%S')
+      if 'python_module' in new_job_request:
+        job_id = new_job_request['python_module'].replace('.', '_') + \
+            '_' + job_id
+
+    job = {
+        'job_id': job_id,
+        'training_input': new_job_request,
+    }
+    context = datalab.context.Context.default()
+    cloudml = discovery.build('ml', 'v1', credentials=context.credentials)
+    request = cloudml.projects().jobs().create(body=job,
+                                               parent='projects/' + context.project_id)
+    request.headers['user-agent'] = 'GoogleCloudDataLab/1.0'
+    request.execute()
+    return Job(job_id)
+
 
 class Jobs(object):
   """Represents a list of Cloud ML jobs for a project."""
@@ -82,8 +122,7 @@ def __init__(self, filter=None):
     """
     self._filter = filter
     self._context = datalab.context.Context.default()
-    self._api = discovery.build('ml', 'v1beta1', credentials=self._context.credentials,
-                                discoveryServiceUrl=_CLOUDML_DISCOVERY_URL)
+    self._api = discovery.build('ml', 'v1', credentials=self._context.credentials)
 
   def _retrieve_jobs(self, page_token, page_size):
     list_info = self._api.projects().jobs().list(parent='projects/' + self._context.project_id,
@@ -93,7 +132,9 @@ def _retrieve_jobs(self, page_token, page_size):
     page_token = list_info.get('nextPageToken', None)
     return jobs, page_token
 
-  def __iter__(self):
+  def get_iterator(self):
+    """Get iterator of jobs so it can be used as "for model in Jobs().get_iterator()".
+    """
     return iter(datalab.utils.Iterator(self._retrieve_jobs))
 
   def list(self, count=10):

datalab/mlalpha/_util.py

@@ -13,10 +13,29 @@
 # limitations under the License.
 
 import datetime
+from googleapiclient import discovery
 import os
 import shutil
 import subprocess
 import tempfile
+import time
+
+import datalab.context
+
+# TODO: Create an Operation class.
+def wait_for_long_running_operation(operation_full_name):
+  print('Waiting for operation "%s"' % operation_full_name)
+  api = discovery.build('ml', 'v1', credentials=datalab.context.Context.default().credentials)
+  while True:
+    response = api.projects().operations().get(name=operation_full_name).execute()
+    if 'done' not in response or response['done'] != True:
+      time.sleep(3)
+    else:
+      if 'error' in response:
+        print(response['error'])
+      else:
+        print('Done.')
+      break
 
 
 def package_and_copy(package_root_dir, setup_py, output_tar_path):

datalab/utils/_iterator.py

@@ -37,7 +37,8 @@ def __iter__(self):
 
       self._page_token = next_page_token
       self._first_page = False
-      self._count += len(items)
+      if self._count == 0:
+        self._count = len(items)
 
       for item in items:
         yield item