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[25/35] incubator-airflow-site git commit: 1.9.0
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+ <h1>Source code for airflow.contrib.operators.mlengine_operator</h1><div class="highlight"><pre>
+<span></span><span class="c1">#</span>
+<span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
+<span class="c1"># contributor license agreements. See the NOTICE file distributed with</span>
+<span class="c1"># this work for additional information regarding copyright ownership.</span>
+<span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
+<span class="c1"># (the 'License'); you may not use this file except in compliance with</span>
+<span class="c1"># the License. You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an 'AS IS' BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="kn">import</span> <span class="nn">re</span>
+
+<span class="kn">from</span> <span class="nn">airflow</span> <span class="k">import</span> <span class="n">settings</span>
+<span class="kn">from</span> <span class="nn">airflow.contrib.hooks.gcp_mlengine_hook</span> <span class="k">import</span> <span class="n">MLEngineHook</span>
+<span class="kn">from</span> <span class="nn">airflow.exceptions</span> <span class="k">import</span> <span class="n">AirflowException</span>
+<span class="kn">from</span> <span class="nn">airflow.operators</span> <span class="k">import</span> <span class="n">BaseOperator</span>
+<span class="kn">from</span> <span class="nn">airflow.utils.decorators</span> <span class="k">import</span> <span class="n">apply_defaults</span>
+<span class="kn">from</span> <span class="nn">apiclient</span> <span class="k">import</span> <span class="n">errors</span>
+
+<span class="kn">from</span> <span class="nn">airflow.utils.log.logging_mixin</span> <span class="k">import</span> <span class="n">LoggingMixin</span>
+
+<span class="n">log</span> <span class="o">=</span> <span class="n">LoggingMixin</span><span class="p">()</span><span class="o">.</span><span class="n">log</span>
+
+
+<span class="k">def</span> <span class="nf">_create_prediction_input</span><span class="p">(</span><span class="n">project_id</span><span class="p">,</span>
+ <span class="n">region</span><span class="p">,</span>
+ <span class="n">data_format</span><span class="p">,</span>
+ <span class="n">input_paths</span><span class="p">,</span>
+ <span class="n">output_path</span><span class="p">,</span>
+ <span class="n">model_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">version_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">uri</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">max_worker_count</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">runtime_version</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Create the batch prediction input from the given parameters.</span>
+
+<span class="sd"> Args:</span>
+<span class="sd"> A subset of arguments documented in __init__ method of class</span>
+<span class="sd"> MLEngineBatchPredictionOperator</span>
+
+<span class="sd"> Returns:</span>
+<span class="sd"> A dictionary representing the predictionInput object as documented</span>
+<span class="sd"> in https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs.</span>
+
+<span class="sd"> Raises:</span>
+<span class="sd"> ValueError: if a unique model/version origin cannot be determined.</span>
+<span class="sd"> """</span>
+ <span class="n">prediction_input</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'dataFormat'</span><span class="p">:</span> <span class="n">data_format</span><span class="p">,</span>
+ <span class="s1">'inputPaths'</span><span class="p">:</span> <span class="n">input_paths</span><span class="p">,</span>
+ <span class="s1">'outputPath'</span><span class="p">:</span> <span class="n">output_path</span><span class="p">,</span>
+ <span class="s1">'region'</span><span class="p">:</span> <span class="n">region</span>
+ <span class="p">}</span>
+
+ <span class="k">if</span> <span class="n">uri</span><span class="p">:</span>
+ <span class="k">if</span> <span class="n">model_name</span> <span class="ow">or</span> <span class="n">version_name</span><span class="p">:</span>
+ <span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
+ <span class="s1">'Ambiguous model origin: Both uri and model/version name are provided.'</span>
+ <span class="p">)</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Ambiguous model origin.'</span><span class="p">)</span>
+ <span class="n">prediction_input</span><span class="p">[</span><span class="s1">'uri'</span><span class="p">]</span> <span class="o">=</span> <span class="n">uri</span>
+ <span class="k">elif</span> <span class="n">model_name</span><span class="p">:</span>
+ <span class="n">origin_name</span> <span class="o">=</span> <span class="s1">'projects/</span><span class="si">{}</span><span class="s1">/models/</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">project_id</span><span class="p">,</span> <span class="n">model_name</span><span class="p">)</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="n">version_name</span><span class="p">:</span>
+ <span class="n">prediction_input</span><span class="p">[</span><span class="s1">'modelName'</span><span class="p">]</span> <span class="o">=</span> <span class="n">origin_name</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">prediction_input</span><span class="p">[</span><span class="s1">'versionName'</span><span class="p">]</span> <span class="o">=</span> \
+ <span class="n">origin_name</span> <span class="o">+</span> <span class="s1">'/versions/</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">version_name</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
+ <span class="s1">'Missing model origin: Batch prediction expects a model, '</span>
+ <span class="s1">'a model & version combination, or a URI to savedModel.'</span><span class="p">)</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Missing model origin.'</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="n">max_worker_count</span><span class="p">:</span>
+ <span class="n">prediction_input</span><span class="p">[</span><span class="s1">'maxWorkerCount'</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_worker_count</span>
+ <span class="k">if</span> <span class="n">runtime_version</span><span class="p">:</span>
+ <span class="n">prediction_input</span><span class="p">[</span><span class="s1">'runtimeVersion'</span><span class="p">]</span> <span class="o">=</span> <span class="n">runtime_version</span>
+
+ <span class="k">return</span> <span class="n">prediction_input</span>
+
+
+<span class="k">def</span> <span class="nf">_normalize_mlengine_job_id</span><span class="p">(</span><span class="n">job_id</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Replaces invalid MLEngine job_id characters with '_'.</span>
+
+<span class="sd"> This also adds a leading 'z' in case job_id starts with an invalid</span>
+<span class="sd"> character.</span>
+
+<span class="sd"> Args:</span>
+<span class="sd"> job_id: A job_id str that may have invalid characters.</span>
+
+<span class="sd"> Returns:</span>
+<span class="sd"> A valid job_id representation.</span>
+<span class="sd"> """</span>
+ <span class="n">match</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="sa">r</span><span class="s1">'\d'</span><span class="p">,</span> <span class="n">job_id</span><span class="p">)</span>
+ <span class="k">if</span> <span class="n">match</span> <span class="ow">and</span> <span class="n">match</span><span class="o">.</span><span class="n">start</span><span class="p">()</span> <span class="ow">is</span> <span class="mi">0</span><span class="p">:</span>
+ <span class="n">job_id</span> <span class="o">=</span> <span class="s1">'z_</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">job_id</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s1">'[^0-9a-zA-Z]+'</span><span class="p">,</span> <span class="s1">'_'</span><span class="p">,</span> <span class="n">job_id</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="MLEngineBatchPredictionOperator"><a class="viewcode-back" href="../../../../integration.html#airflow.contrib.operators.mlengine_operator.MLEngineBatchPredictionOperator">[docs]</a><span class="k">class</span> <span class="nc">MLEngineBatchPredictionOperator</span><span class="p">(</span><span class="n">BaseOperator</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Start a Google Cloud ML Engine prediction job.</span>
+
+<span class="sd"> NOTE: For model origin, users should consider exactly one from the</span>
+<span class="sd"> three options below:</span>
+<span class="sd"> 1. Populate 'uri' field only, which should be a GCS location that</span>
+<span class="sd"> points to a tensorflow savedModel directory.</span>
+<span class="sd"> 2. Populate 'model_name' field only, which refers to an existing</span>
+<span class="sd"> model, and the default version of the model will be used.</span>
+<span class="sd"> 3. Populate both 'model_name' and 'version_name' fields, which</span>
+<span class="sd"> refers to a specific version of a specific model.</span>
+
+<span class="sd"> In options 2 and 3, both model and version name should contain the</span>
+<span class="sd"> minimal identifier. For instance, call</span>
+<span class="sd"> MLEngineBatchPredictionOperator(</span>
+<span class="sd"> ...,</span>
+<span class="sd"> model_name='my_model',</span>
+<span class="sd"> version_name='my_version',</span>
+<span class="sd"> ...)</span>
+<span class="sd"> if the desired model version is</span>
+<span class="sd"> "projects/my_project/models/my_model/versions/my_version".</span>
+
+
+<span class="sd"> :param project_id: The Google Cloud project name where the</span>
+<span class="sd"> prediction job is submitted.</span>
+<span class="sd"> :type project_id: string</span>
+
+<span class="sd"> :param job_id: A unique id for the prediction job on Google Cloud</span>
+<span class="sd"> ML Engine.</span>
+<span class="sd"> :type job_id: string</span>
+
+<span class="sd"> :param data_format: The format of the input data.</span>
+<span class="sd"> It will default to 'DATA_FORMAT_UNSPECIFIED' if is not provided</span>
+<span class="sd"> or is not one of ["TEXT", "TF_RECORD", "TF_RECORD_GZIP"].</span>
+<span class="sd"> :type data_format: string</span>
+
+<span class="sd"> :param input_paths: A list of GCS paths of input data for batch</span>
+<span class="sd"> prediction. Accepting wildcard operator *, but only at the end.</span>
+<span class="sd"> :type input_paths: list of string</span>
+
+<span class="sd"> :param output_path: The GCS path where the prediction results are</span>
+<span class="sd"> written to.</span>
+<span class="sd"> :type output_path: string</span>
+
+<span class="sd"> :param region: The Google Compute Engine region to run the</span>
+<span class="sd"> prediction job in.:</span>
+<span class="sd"> :type region: string</span>
+
+<span class="sd"> :param model_name: The Google Cloud ML Engine model to use for prediction.</span>
+<span class="sd"> If version_name is not provided, the default version of this</span>
+<span class="sd"> model will be used.</span>
+<span class="sd"> Should not be None if version_name is provided.</span>
+<span class="sd"> Should be None if uri is provided.</span>
+<span class="sd"> :type model_name: string</span>
+
+<span class="sd"> :param version_name: The Google Cloud ML Engine model version to use for</span>
+<span class="sd"> prediction.</span>
+<span class="sd"> Should be None if uri is provided.</span>
+<span class="sd"> :type version_name: string</span>
+
+<span class="sd"> :param uri: The GCS path of the saved model to use for prediction.</span>
+<span class="sd"> Should be None if model_name is provided.</span>
+<span class="sd"> It should be a GCS path pointing to a tensorflow SavedModel.</span>
+<span class="sd"> :type uri: string</span>
+
+<span class="sd"> :param max_worker_count: The maximum number of workers to be used</span>
+<span class="sd"> for parallel processing. Defaults to 10 if not specified.</span>
+<span class="sd"> :type max_worker_count: int</span>
+
+<span class="sd"> :param runtime_version: The Google Cloud ML Engine runtime version to use</span>
+<span class="sd"> for batch prediction.</span>
+<span class="sd"> :type runtime_version: string</span>
+
+<span class="sd"> :param gcp_conn_id: The connection ID used for connection to Google</span>
+<span class="sd"> Cloud Platform.</span>
+<span class="sd"> :type gcp_conn_id: string</span>
+
+<span class="sd"> :param delegate_to: The account to impersonate, if any.</span>
+<span class="sd"> For this to work, the service account making the request must</span>
+<span class="sd"> have doamin-wide delegation enabled.</span>
+<span class="sd"> :type delegate_to: string</span>
+
+<span class="sd"> Raises:</span>
+<span class="sd"> ValueError: if a unique model/version origin cannot be determined.</span>
+<span class="sd"> """</span>
+
+ <span class="n">template_fields</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="s2">"prediction_job_request"</span><span class="p">,</span>
+ <span class="p">]</span>
+
+ <span class="nd">@apply_defaults</span>
+ <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+ <span class="n">project_id</span><span class="p">,</span>
+ <span class="n">job_id</span><span class="p">,</span>
+ <span class="n">region</span><span class="p">,</span>
+ <span class="n">data_format</span><span class="p">,</span>
+ <span class="n">input_paths</span><span class="p">,</span>
+ <span class="n">output_path</span><span class="p">,</span>
+ <span class="n">model_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">version_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">uri</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">max_worker_count</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">runtime_version</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">gcp_conn_id</span><span class="o">=</span><span class="s1">'google_cloud_default'</span><span class="p">,</span>
+ <span class="n">delegate_to</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="o">*</span><span class="n">args</span><span class="p">,</span>
+ <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+ <span class="nb">super</span><span class="p">(</span><span class="n">MLEngineBatchPredictionOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">project_id</span> <span class="o">=</span> <span class="n">project_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">gcp_conn_id</span> <span class="o">=</span> <span class="n">gcp_conn_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">delegate_to</span> <span class="o">=</span> <span class="n">delegate_to</span>
+
+ <span class="k">try</span><span class="p">:</span>
+ <span class="n">prediction_input</span> <span class="o">=</span> <span class="n">_create_prediction_input</span><span class="p">(</span>
+ <span class="n">project_id</span><span class="p">,</span> <span class="n">region</span><span class="p">,</span> <span class="n">data_format</span><span class="p">,</span> <span class="n">input_paths</span><span class="p">,</span> <span class="n">output_path</span><span class="p">,</span>
+ <span class="n">model_name</span><span class="p">,</span> <span class="n">version_name</span><span class="p">,</span> <span class="n">uri</span><span class="p">,</span> <span class="n">max_worker_count</span><span class="p">,</span>
+ <span class="n">runtime_version</span><span class="p">)</span>
+ <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
+ <span class="s1">'Cannot create batch prediction job request due to: </span><span class="si">%s</span><span class="s1">'</span><span class="p">,</span>
+ <span class="n">e</span>
+ <span class="p">)</span>
+ <span class="k">raise</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">prediction_job_request</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'jobId'</span><span class="p">:</span> <span class="n">_normalize_mlengine_job_id</span><span class="p">(</span><span class="n">job_id</span><span class="p">),</span>
+ <span class="s1">'predictionInput'</span><span class="p">:</span> <span class="n">prediction_input</span>
+ <span class="p">}</span>
+
+ <span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span>
+ <span class="n">hook</span> <span class="o">=</span> <span class="n">MLEngineHook</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gcp_conn_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">delegate_to</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="nf">check_existing_job</span><span class="p">(</span><span class="n">existing_job</span><span class="p">):</span>
+ <span class="k">return</span> <span class="n">existing_job</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'predictionInput'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> \
+ <span class="bp">self</span><span class="o">.</span><span class="n">prediction_job_request</span><span class="p">[</span><span class="s1">'predictionInput'</span><span class="p">]</span>
+ <span class="k">try</span><span class="p">:</span>
+ <span class="n">finished_prediction_job</span> <span class="o">=</span> <span class="n">hook</span><span class="o">.</span><span class="n">create_job</span><span class="p">(</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">project_id</span><span class="p">,</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">prediction_job_request</span><span class="p">,</span>
+ <span class="n">check_existing_job</span><span class="p">)</span>
+ <span class="k">except</span> <span class="n">errors</span><span class="o">.</span><span class="n">HttpError</span><span class="p">:</span>
+ <span class="k">raise</span>
+
+ <span class="k">if</span> <span class="n">finished_prediction_job</span><span class="p">[</span><span class="s1">'state'</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">'SUCCEEDED'</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
+ <span class="s1">'Batch prediction job failed: </span><span class="si">%s</span><span class="s1">'</span><span class="p">,</span>
+ <span class="nb">str</span><span class="p">(</span><span class="n">finished_prediction_job</span><span class="p">))</span>
+ <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">finished_prediction_job</span><span class="p">[</span><span class="s1">'errorMessage'</span><span class="p">])</span>
+
+ <span class="k">return</span> <span class="n">finished_prediction_job</span><span class="p">[</span><span class="s1">'predictionOutput'</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="MLEngineModelOperator"><a class="viewcode-back" href="../../../../integration.html#airflow.contrib.operators.mlengine_operator.MLEngineModelOperator">[docs]</a><span class="k">class</span> <span class="nc">MLEngineModelOperator</span><span class="p">(</span><span class="n">BaseOperator</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Operator for managing a Google Cloud ML Engine model.</span>
+
+<span class="sd"> :param project_id: The Google Cloud project name to which MLEngine</span>
+<span class="sd"> model belongs.</span>
+<span class="sd"> :type project_id: string</span>
+
+<span class="sd"> :param model: A dictionary containing the information about the model.</span>
+<span class="sd"> If the `operation` is `create`, then the `model` parameter should</span>
+<span class="sd"> contain all the information about this model such as `name`.</span>
+
+<span class="sd"> If the `operation` is `get`, the `model` parameter</span>
+<span class="sd"> should contain the `name` of the model.</span>
+<span class="sd"> :type model: dict</span>
+
+<span class="sd"> :param operation: The operation to perform. Available operations are:</span>
+<span class="sd"> 'create': Creates a new model as provided by the `model` parameter.</span>
+<span class="sd"> 'get': Gets a particular model where the name is specified in `model`.</span>
+
+<span class="sd"> :param gcp_conn_id: The connection ID to use when fetching connection info.</span>
+<span class="sd"> :type gcp_conn_id: string</span>
+
+<span class="sd"> :param delegate_to: The account to impersonate, if any.</span>
+<span class="sd"> For this to work, the service account making the request must have</span>
+<span class="sd"> domain-wide delegation enabled.</span>
+<span class="sd"> :type delegate_to: string</span>
+<span class="sd"> """</span>
+
+ <span class="n">template_fields</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="s1">'_model'</span><span class="p">,</span>
+ <span class="p">]</span>
+
+ <span class="nd">@apply_defaults</span>
+ <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+ <span class="n">project_id</span><span class="p">,</span>
+ <span class="n">model</span><span class="p">,</span>
+ <span class="n">operation</span><span class="o">=</span><span class="s1">'create'</span><span class="p">,</span>
+ <span class="n">gcp_conn_id</span><span class="o">=</span><span class="s1">'google_cloud_default'</span><span class="p">,</span>
+ <span class="n">delegate_to</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="o">*</span><span class="n">args</span><span class="p">,</span>
+ <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+ <span class="nb">super</span><span class="p">(</span><span class="n">MLEngineModelOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span> <span class="o">=</span> <span class="n">project_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_model</span> <span class="o">=</span> <span class="n">model</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_operation</span> <span class="o">=</span> <span class="n">operation</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_gcp_conn_id</span> <span class="o">=</span> <span class="n">gcp_conn_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_delegate_to</span> <span class="o">=</span> <span class="n">delegate_to</span>
+
+ <span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span>
+ <span class="n">hook</span> <span class="o">=</span> <span class="n">MLEngineHook</span><span class="p">(</span>
+ <span class="n">gcp_conn_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_gcp_conn_id</span><span class="p">,</span> <span class="n">delegate_to</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_delegate_to</span><span class="p">)</span>
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_operation</span> <span class="o">==</span> <span class="s1">'create'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">hook</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model</span><span class="p">)</span>
+ <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_operation</span> <span class="o">==</span> <span class="s1">'get'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">hook</span><span class="o">.</span><span class="n">get_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model</span><span class="p">[</span><span class="s1">'name'</span><span class="p">])</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Unknown operation: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_operation</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="MLEngineVersionOperator"><a class="viewcode-back" href="../../../../integration.html#airflow.contrib.operators.mlengine_operator.MLEngineVersionOperator">[docs]</a><span class="k">class</span> <span class="nc">MLEngineVersionOperator</span><span class="p">(</span><span class="n">BaseOperator</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Operator for managing a Google Cloud ML Engine version.</span>
+
+<span class="sd"> :param project_id: The Google Cloud project name to which MLEngine</span>
+<span class="sd"> model belongs.</span>
+<span class="sd"> :type project_id: string</span>
+
+<span class="sd"> :param model_name: The name of the Google Cloud ML Engine model that the version</span>
+<span class="sd"> belongs to.</span>
+<span class="sd"> :type model_name: string</span>
+
+<span class="sd"> :param version_name: A name to use for the version being operated upon. If</span>
+<span class="sd"> not None and the `version` argument is None or does not have a value for</span>
+<span class="sd"> the `name` key, then this will be populated in the payload for the</span>
+<span class="sd"> `name` key.</span>
+<span class="sd"> :type version_name: string</span>
+
+<span class="sd"> :param version: A dictionary containing the information about the version.</span>
+<span class="sd"> If the `operation` is `create`, `version` should contain all the</span>
+<span class="sd"> information about this version such as name, and deploymentUrl.</span>
+<span class="sd"> If the `operation` is `get` or `delete`, the `version` parameter</span>
+<span class="sd"> should contain the `name` of the version.</span>
+<span class="sd"> If it is None, the only `operation` possible would be `list`.</span>
+<span class="sd"> :type version: dict</span>
+
+<span class="sd"> :param operation: The operation to perform. Available operations are:</span>
+<span class="sd"> 'create': Creates a new version in the model specified by `model_name`,</span>
+<span class="sd"> in which case the `version` parameter should contain all the</span>
+<span class="sd"> information to create that version</span>
+<span class="sd"> (e.g. `name`, `deploymentUrl`).</span>
+<span class="sd"> 'get': Gets full information of a particular version in the model</span>
+<span class="sd"> specified by `model_name`.</span>
+<span class="sd"> The name of the version should be specified in the `version`</span>
+<span class="sd"> parameter.</span>
+
+<span class="sd"> 'list': Lists all available versions of the model specified</span>
+<span class="sd"> by `model_name`.</span>
+
+<span class="sd"> 'delete': Deletes the version specified in `version` parameter from the</span>
+<span class="sd"> model specified by `model_name`).</span>
+<span class="sd"> The name of the version should be specified in the `version`</span>
+<span class="sd"> parameter.</span>
+<span class="sd"> :type operation: string</span>
+
+<span class="sd"> :param gcp_conn_id: The connection ID to use when fetching connection info.</span>
+<span class="sd"> :type gcp_conn_id: string</span>
+
+<span class="sd"> :param delegate_to: The account to impersonate, if any.</span>
+<span class="sd"> For this to work, the service account making the request must have</span>
+<span class="sd"> domain-wide delegation enabled.</span>
+<span class="sd"> :type delegate_to: string</span>
+<span class="sd"> """</span>
+
+ <span class="n">template_fields</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="s1">'_model_name'</span><span class="p">,</span>
+ <span class="s1">'_version_name'</span><span class="p">,</span>
+ <span class="s1">'_version'</span><span class="p">,</span>
+ <span class="p">]</span>
+
+ <span class="nd">@apply_defaults</span>
+ <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+ <span class="n">project_id</span><span class="p">,</span>
+ <span class="n">model_name</span><span class="p">,</span>
+ <span class="n">version_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">version</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">operation</span><span class="o">=</span><span class="s1">'create'</span><span class="p">,</span>
+ <span class="n">gcp_conn_id</span><span class="o">=</span><span class="s1">'google_cloud_default'</span><span class="p">,</span>
+ <span class="n">delegate_to</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="o">*</span><span class="n">args</span><span class="p">,</span>
+ <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+ <span class="nb">super</span><span class="p">(</span><span class="n">MLEngineVersionOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span> <span class="o">=</span> <span class="n">project_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_model_name</span> <span class="o">=</span> <span class="n">model_name</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_version_name</span> <span class="o">=</span> <span class="n">version_name</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_version</span> <span class="o">=</span> <span class="n">version</span> <span class="ow">or</span> <span class="p">{}</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_operation</span> <span class="o">=</span> <span class="n">operation</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_gcp_conn_id</span> <span class="o">=</span> <span class="n">gcp_conn_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_delegate_to</span> <span class="o">=</span> <span class="n">delegate_to</span>
+
+ <span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span>
+ <span class="k">if</span> <span class="s1">'name'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_version</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_version</span><span class="p">[</span><span class="s1">'name'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_version_name</span>
+
+ <span class="n">hook</span> <span class="o">=</span> <span class="n">MLEngineHook</span><span class="p">(</span>
+ <span class="n">gcp_conn_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_gcp_conn_id</span><span class="p">,</span> <span class="n">delegate_to</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_delegate_to</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_operation</span> <span class="o">==</span> <span class="s1">'create'</span><span class="p">:</span>
+ <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_version</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+ <span class="k">return</span> <span class="n">hook</span><span class="o">.</span><span class="n">create_version</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_name</span><span class="p">,</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_version</span><span class="p">)</span>
+ <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_operation</span> <span class="o">==</span> <span class="s1">'set_default'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">hook</span><span class="o">.</span><span class="n">set_default_version</span><span class="p">(</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_name</span><span class="p">,</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_version</span><span class="p">[</span><span class="s1">'name'</span><span class="p">])</span>
+ <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_operation</span> <span class="o">==</span> <span class="s1">'list'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">hook</span><span class="o">.</span><span class="n">list_versions</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_name</span><span class="p">)</span>
+ <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_operation</span> <span class="o">==</span> <span class="s1">'delete'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">hook</span><span class="o">.</span><span class="n">delete_version</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_name</span><span class="p">,</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_version</span><span class="p">[</span><span class="s1">'name'</span><span class="p">])</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Unknown operation: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_operation</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="MLEngineTrainingOperator"><a class="viewcode-back" href="../../../../integration.html#airflow.contrib.operators.mlengine_operator.MLEngineTrainingOperator">[docs]</a><span class="k">class</span> <span class="nc">MLEngineTrainingOperator</span><span class="p">(</span><span class="n">BaseOperator</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Operator for launching a MLEngine training job.</span>
+
+<span class="sd"> :param project_id: The Google Cloud project name within which MLEngine</span>
+<span class="sd"> training job should run. This field could be templated.</span>
+<span class="sd"> :type project_id: string</span>
+
+<span class="sd"> :param job_id: A unique templated id for the submitted Google MLEngine</span>
+<span class="sd"> training job.</span>
+<span class="sd"> :type job_id: string</span>
+
+<span class="sd"> :param package_uris: A list of package locations for MLEngine training job,</span>
+<span class="sd"> which should include the main training program + any additional</span>
+<span class="sd"> dependencies.</span>
+<span class="sd"> :type package_uris: string</span>
+
+<span class="sd"> :param training_python_module: The Python module name to run within MLEngine</span>
+<span class="sd"> training job after installing 'package_uris' packages.</span>
+<span class="sd"> :type training_python_module: string</span>
+
+<span class="sd"> :param training_args: A list of templated command line arguments to pass to</span>
+<span class="sd"> the MLEngine training program.</span>
+<span class="sd"> :type training_args: string</span>
+
+<span class="sd"> :param region: The Google Compute Engine region to run the MLEngine training</span>
+<span class="sd"> job in. This field could be templated.</span>
+<span class="sd"> :type region: string</span>
+
+<span class="sd"> :param scale_tier: Resource tier for MLEngine training job.</span>
+<span class="sd"> :type scale_tier: string</span>
+
+<span class="sd"> :param gcp_conn_id: The connection ID to use when fetching connection info.</span>
+<span class="sd"> :type gcp_conn_id: string</span>
+
+<span class="sd"> :param delegate_to: The account to impersonate, if any.</span>
+<span class="sd"> For this to work, the service account making the request must have</span>
+<span class="sd"> domain-wide delegation enabled.</span>
+<span class="sd"> :type delegate_to: string</span>
+
+<span class="sd"> :param mode: Can be one of 'DRY_RUN'/'CLOUD'. In 'DRY_RUN' mode, no real</span>
+<span class="sd"> training job will be launched, but the MLEngine training job request</span>
+<span class="sd"> will be printed out. In 'CLOUD' mode, a real MLEngine training job</span>
+<span class="sd"> creation request will be issued.</span>
+<span class="sd"> :type mode: string</span>
+<span class="sd"> """</span>
+
+ <span class="n">template_fields</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="s1">'_project_id'</span><span class="p">,</span>
+ <span class="s1">'_job_id'</span><span class="p">,</span>
+ <span class="s1">'_package_uris'</span><span class="p">,</span>
+ <span class="s1">'_training_python_module'</span><span class="p">,</span>
+ <span class="s1">'_training_args'</span><span class="p">,</span>
+ <span class="s1">'_region'</span><span class="p">,</span>
+ <span class="s1">'_scale_tier'</span><span class="p">,</span>
+ <span class="p">]</span>
+
+ <span class="nd">@apply_defaults</span>
+ <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+ <span class="n">project_id</span><span class="p">,</span>
+ <span class="n">job_id</span><span class="p">,</span>
+ <span class="n">package_uris</span><span class="p">,</span>
+ <span class="n">training_python_module</span><span class="p">,</span>
+ <span class="n">training_args</span><span class="p">,</span>
+ <span class="n">region</span><span class="p">,</span>
+ <span class="n">scale_tier</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">gcp_conn_id</span><span class="o">=</span><span class="s1">'google_cloud_default'</span><span class="p">,</span>
+ <span class="n">delegate_to</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">mode</span><span class="o">=</span><span class="s1">'PRODUCTION'</span><span class="p">,</span>
+ <span class="o">*</span><span class="n">args</span><span class="p">,</span>
+ <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+ <span class="nb">super</span><span class="p">(</span><span class="n">MLEngineTrainingOperator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span> <span class="o">=</span> <span class="n">project_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_job_id</span> <span class="o">=</span> <span class="n">job_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_package_uris</span> <span class="o">=</span> <span class="n">package_uris</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_training_python_module</span> <span class="o">=</span> <span class="n">training_python_module</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_training_args</span> <span class="o">=</span> <span class="n">training_args</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_region</span> <span class="o">=</span> <span class="n">region</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_scale_tier</span> <span class="o">=</span> <span class="n">scale_tier</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_gcp_conn_id</span> <span class="o">=</span> <span class="n">gcp_conn_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_delegate_to</span> <span class="o">=</span> <span class="n">delegate_to</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_mode</span> <span class="o">=</span> <span class="n">mode</span>
+
+ <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="s1">'Google Cloud project id is required.'</span><span class="p">)</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_job_id</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span>
+ <span class="s1">'An unique job id is required for Google MLEngine training '</span>
+ <span class="s1">'job.'</span><span class="p">)</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="n">package_uris</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span>
+ <span class="s1">'At least one python package is required for MLEngine '</span>
+ <span class="s1">'Training job.'</span><span class="p">)</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="n">training_python_module</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span>
+ <span class="s1">'Python module name to run after installing required '</span>
+ <span class="s1">'packages is required.'</span><span class="p">)</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_region</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="s1">'Google Compute Engine region is required.'</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span>
+ <span class="n">job_id</span> <span class="o">=</span> <span class="n">_normalize_mlengine_job_id</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_job_id</span><span class="p">)</span>
+ <span class="n">training_request</span> <span class="o">=</span> <span class="p">{</span>
+ <span class="s1">'jobId'</span><span class="p">:</span> <span class="n">job_id</span><span class="p">,</span>
+ <span class="s1">'trainingInput'</span><span class="p">:</span> <span class="p">{</span>
+ <span class="s1">'scaleTier'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scale_tier</span><span class="p">,</span>
+ <span class="s1">'packageUris'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_package_uris</span><span class="p">,</span>
+ <span class="s1">'pythonModule'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_python_module</span><span class="p">,</span>
+ <span class="s1">'region'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_region</span><span class="p">,</span>
+ <span class="s1">'args'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_args</span><span class="p">,</span>
+ <span class="p">}</span>
+ <span class="p">}</span>
+
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mode</span> <span class="o">==</span> <span class="s1">'DRY_RUN'</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'In dry_run mode.'</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'MLEngine Training job request is: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">training_request</span><span class="p">))</span>
+ <span class="k">return</span>
+
+ <span class="n">hook</span> <span class="o">=</span> <span class="n">MLEngineHook</span><span class="p">(</span>
+ <span class="n">gcp_conn_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_gcp_conn_id</span><span class="p">,</span> <span class="n">delegate_to</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_delegate_to</span><span class="p">)</span>
+
+ <span class="c1"># Helper method to check if the existing job's training input is the</span>
+ <span class="c1"># same as the request we get here.</span>
+ <span class="k">def</span> <span class="nf">check_existing_job</span><span class="p">(</span><span class="n">existing_job</span><span class="p">):</span>
+ <span class="k">return</span> <span class="n">existing_job</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'trainingInput'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> \
+ <span class="n">training_request</span><span class="p">[</span><span class="s1">'trainingInput'</span><span class="p">]</span>
+ <span class="k">try</span><span class="p">:</span>
+ <span class="n">finished_training_job</span> <span class="o">=</span> <span class="n">hook</span><span class="o">.</span><span class="n">create_job</span><span class="p">(</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_project_id</span><span class="p">,</span> <span class="n">training_request</span><span class="p">,</span> <span class="n">check_existing_job</span><span class="p">)</span>
+ <span class="k">except</span> <span class="n">errors</span><span class="o">.</span><span class="n">HttpError</span><span class="p">:</span>
+ <span class="k">raise</span>
+
+ <span class="k">if</span> <span class="n">finished_training_job</span><span class="p">[</span><span class="s1">'state'</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">'SUCCEEDED'</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s1">'MLEngine training job failed: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+ <span class="nb">str</span><span class="p">(</span><span class="n">finished_training_job</span><span class="p">)))</span>
+ <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">finished_training_job</span><span class="p">[</span><span class="s1">'errorMessage'</span><span class="p">])</span></div>
+</pre></div>
+
+ </div>
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+
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http://git-wip-us.apache.org/repos/asf/incubator-airflow-site/blob/28a3eb60/_modules/airflow/contrib/sensors/wasb_sensor.html
----------------------------------------------------------------------
diff --git a/_modules/airflow/contrib/sensors/wasb_sensor.html b/_modules/airflow/contrib/sensors/wasb_sensor.html
new file mode 100644
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+++ b/_modules/airflow/contrib/sensors/wasb_sensor.html
@@ -0,0 +1,327 @@
+
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
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+ <div itemprop="articleBody">
+
+ <h1>Source code for airflow.contrib.sensors.wasb_sensor</h1><div class="highlight"><pre>
+<span></span><span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the "License");</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1">#</span>
+<span class="kn">from</span> <span class="nn">airflow.contrib.hooks.wasb_hook</span> <span class="k">import</span> <span class="n">WasbHook</span>
+<span class="kn">from</span> <span class="nn">airflow.operators.sensors</span> <span class="k">import</span> <span class="n">BaseSensorOperator</span>
+<span class="kn">from</span> <span class="nn">airflow.utils.decorators</span> <span class="k">import</span> <span class="n">apply_defaults</span>
+
+
+<div class="viewcode-block" id="WasbBlobSensor"><a class="viewcode-back" href="../../../../integration.html#airflow.contrib.sensors.wasb_sensor.WasbBlobSensor">[docs]</a><span class="k">class</span> <span class="nc">WasbBlobSensor</span><span class="p">(</span><span class="n">BaseSensorOperator</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Waits for a blob to arrive on Azure Blob Storage.</span>
+
+<span class="sd"> :param container_name: Name of the container.</span>
+<span class="sd"> :type container_name: str</span>
+<span class="sd"> :param blob_name: Name of the blob.</span>
+<span class="sd"> :type blob_name: str</span>
+<span class="sd"> :param wasb_conn_id: Reference to the wasb connection.</span>
+<span class="sd"> :type wasb_conn_id: str</span>
+<span class="sd"> :param check_options: Optional keyword arguments that</span>
+<span class="sd"> `WasbHook.check_for_blob()` takes.</span>
+<span class="sd"> :type check_options: dict</span>
+<span class="sd"> """</span>
+
+ <span class="n">template_fields</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'container_name'</span><span class="p">,</span> <span class="s1">'blob_name'</span><span class="p">)</span>
+
+ <span class="nd">@apply_defaults</span>
+ <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">container_name</span><span class="p">,</span> <span class="n">blob_name</span><span class="p">,</span>
+ <span class="n">wasb_conn_id</span><span class="o">=</span><span class="s1">'wasb_default'</span><span class="p">,</span> <span class="n">check_options</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span>
+ <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+ <span class="nb">super</span><span class="p">(</span><span class="n">WasbBlobSensor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+ <span class="k">if</span> <span class="n">check_options</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="n">check_options</span> <span class="o">=</span> <span class="p">{}</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">wasb_conn_id</span> <span class="o">=</span> <span class="n">wasb_conn_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">container_name</span> <span class="o">=</span> <span class="n">container_name</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">blob_name</span> <span class="o">=</span> <span class="n">blob_name</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">check_options</span> <span class="o">=</span> <span class="n">check_options</span>
+
+ <span class="k">def</span> <span class="nf">poke</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+ <span class="s1">'Poking for blob: </span><span class="si">{self.blob_name}</span><span class="se">\n</span><span class="s1">'</span>
+ <span class="s1">'in wasb://</span><span class="si">{self.container_name}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">**</span><span class="nb">locals</span><span class="p">())</span>
+ <span class="p">)</span>
+ <span class="n">hook</span> <span class="o">=</span> <span class="n">WasbHook</span><span class="p">(</span><span class="n">wasb_conn_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">wasb_conn_id</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">hook</span><span class="o">.</span><span class="n">check_for_blob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">container_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">blob_name</span><span class="p">,</span>
+ <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">check_options</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="WasbPrefixSensor"><a class="viewcode-back" href="../../../../integration.html#airflow.contrib.sensors.wasb_sensor.WasbPrefixSensor">[docs]</a><span class="k">class</span> <span class="nc">WasbPrefixSensor</span><span class="p">(</span><span class="n">BaseSensorOperator</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Waits for blobs matching a prefix to arrive on Azure Blob Storage.</span>
+
+<span class="sd"> :param container_name: Name of the container.</span>
+<span class="sd"> :type container_name: str</span>
+<span class="sd"> :param prefix: Prefix of the blob.</span>
+<span class="sd"> :type prefix: str</span>
+<span class="sd"> :param wasb_conn_id: Reference to the wasb connection.</span>
+<span class="sd"> :type wasb_conn_id: str</span>
+<span class="sd"> :param check_options: Optional keyword arguments that</span>
+<span class="sd"> `WasbHook.check_for_prefix()` takes.</span>
+<span class="sd"> :type check_options: dict</span>
+<span class="sd"> """</span>
+
+ <span class="n">template_fields</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'container_name'</span><span class="p">,</span> <span class="s1">'prefix'</span><span class="p">)</span>
+
+ <span class="nd">@apply_defaults</span>
+ <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">container_name</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">wasb_conn_id</span><span class="o">=</span><span class="s1">'wasb_default'</span><span class="p">,</span>
+ <span class="n">check_options</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+ <span class="nb">super</span><span class="p">(</span><span class="n">WasbPrefixSensor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+ <span class="k">if</span> <span class="n">check_options</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="n">check_options</span> <span class="o">=</span> <span class="p">{}</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">wasb_conn_id</span> <span class="o">=</span> <span class="n">wasb_conn_id</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">container_name</span> <span class="o">=</span> <span class="n">container_name</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">prefix</span> <span class="o">=</span> <span class="n">prefix</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">check_options</span> <span class="o">=</span> <span class="n">check_options</span>
+
+ <span class="k">def</span> <span class="nf">poke</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+ <span class="s1">'Poking for prefix: </span><span class="si">{self.prefix}</span><span class="se">\n</span><span class="s1">'</span>
+ <span class="s1">'in wasb://</span><span class="si">{self.container_name}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">**</span><span class="nb">locals</span><span class="p">())</span>
+ <span class="p">)</span>
+ <span class="n">hook</span> <span class="o">=</span> <span class="n">WasbHook</span><span class="p">(</span><span class="n">wasb_conn_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">wasb_conn_id</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">hook</span><span class="o">.</span><span class="n">check_for_prefix</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">container_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">prefix</span><span class="p">,</span>
+ <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">check_options</span><span class="p">)</span></div>
+</pre></div>
+
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