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</div> <div role="main" class="document" itemscope itemtype="http://schema.org/Article"> <div itemprop="articleBody"> <div class="section" id="mlflow-model-registry-example"> <h1>MLflow模型注冊表實例<a class="headerlink" href="//m.eheci.com/docs/mlflow/#mlflow-model-registry-example" title=""></a></h1> <p>這個例子演示了如何使用MLflow模型注冊表來構建一個機器學習應用程序預測每日風電場的輸出功率。這個例子展示了如何:</p> <ul class="simple"> <li><p>跟蹤和日誌與MLflow模型</p></li> <li><p>模型與模型注冊登記</p></li> <li><p>描述模型,使模型版本階段過渡</p></li> <li><p>注冊模型與生產應用程序集成</p></li> <li><p>搜索和發現模型在模型中注冊表</p></li> <li><p>存檔和刪除模型</p></li> </ul> <p>這篇文章描述如何執行這些步驟使用MLflow跟蹤和MLflow模型注冊用戶界麵和api。</p> <p>的筆記本使用MLflow跟蹤和執行所有這些步驟注冊api,看到<a class="reference internal" href="//m.eheci.com/docs/mlflow/#notebook"><span class="std std-ref">模型注冊表實例筆記本</span></a>。</p> <div class="section" id="load-dataset-train-model-and-track-with-mlflow-tracking"> <h2>加載數據集、火車模型、與MLflow追蹤和跟蹤<a class="headerlink" href="//m.eheci.com/docs/mlflow/#load-dataset-train-model-and-track-with-mlflow-tracking" title=""></a></h2> <p>之前,你可以注冊一個模型在模型中注冊,你必須首先訓練和記錄<a class="reference internal" href="//m.eheci.com/docs/docs/mlflow/models.html"><span class="doc">模型</span></a>在一次<a class="reference internal" href="//m.eheci.com/docs/docs/mlflow/tracking.html"><span class="std std-ref">實驗運行</span></a>。本節展示如何加載風電場數據集,訓練模型,和日誌MLflow的訓練。</p> <div class="section" id="load-dataset"> <h3>加載數據集<a class="headerlink" href="//m.eheci.com/docs/mlflow/#load-dataset" title=""></a></h3> <p>下麵的代碼加載數據集包含天氣數據和風電場功率輸出信息在美國。數據集包含<code class="docutils literal notranslate"><span class="pre">風</span><span class="pre">方向</span></code>,<code class="docutils literal notranslate"><span class="pre">風</span><span class="pre">速度</span></code>,<code class="docutils literal notranslate"><span class="pre">空氣</span><span class="pre">溫度</span></code>功能(一次采樣每六小時<code class="docutils literal notranslate"><span class="pre">00:00</span></code>一旦在<code class="docutils literal notranslate"><span class="pre">喂飼</span></code>,一旦在<code class="docutils literal notranslate"><span class="pre">16:00時</span></code>),以及每日總輸出功率(<code class="docutils literal notranslate"><span class="pre">權力</span></code>),在未來幾年內。</p> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="kn">進口</span><span class="nn">熊貓</span><span class="k">作為</span><span class="nn">pd</span><span class="n">wind_farm_data</span><span class="o">=</span><span class="n">pd</span><span class="o">。</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">“https://github.com/dbczumar/model-registry-demo-notebook/raw/master/dataset/windfarm_data.csv”</span><span class="p">,</span><span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="k">def</span><span class="nf">get_training_data</span><span class="p">():</span><span class="n">training_data</span><span class="o">=</span><span class="n">pd</span><span class="o">。</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">wind_farm_data</span><span class="p">(</span><span class="s2">“2014-01-01”</span><span class="p">:</span><span class="s2">“2018-01-01”</span><span class="p">])</span><span class="n">X</span><span class="o">=</span><span class="n">training_data</span><span class="o">。</span><span class="n">下降</span><span class="p">(</span><span class="n">列</span><span class="o">=</span><span class="s2">“權力”</span><span class="p">)</span><span class="n">y</span><span class="o">=</span><span class="n">training_data</span><span class="p">(</span><span class="s2">“權力”</span><span class="p">]</span><span class="k">返回</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="k">def</span><span class="nf">get_validation_data</span><span class="p">():</span><span class="n">validation_data</span><span class="o">=</span><span class="n">pd</span><span class="o">。</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">wind_farm_data</span><span class="p">(</span><span class="s2">“2018-01-01”</span><span class="p">:</span><span class="s2">“2019-01-01”</span><span class="p">])</span><span class="n">X</span><span class="o">=</span><span class="n">validation_data</span><span class="o">。</span><span class="n">下降</span><span class="p">(</span><span class="n">列</span><span class="o">=</span><span class="s2">“權力”</span><span class="p">)</span><span class="n">y</span><span class="o">=</span><span class="n">validation_data</span><span class="p">(</span><span class="s2">“權力”</span><span class="p">]</span><span class="k">返回</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="k">def</span><span class="nf">get_weather_and_forecast</span><span class="p">():</span><span class="n">format_date</span><span class="o">=</span><span class="k">λ</span><span class="n">pd_date</span><span class="p">:</span><span class="n">pd_date</span><span class="o">。</span><span class="n">日期</span><span class="p">()</span><span class="o">。</span><span class="n">strftime</span><span class="p">(</span><span class="s2">“% Y - % m</span><span class="si">% d</span><span class="s2">”</span><span class="p">)</span><span class="n">今天</span><span class="o">=</span><span class="n">pd</span><span class="o">。</span><span class="n">時間戳</span><span class="p">(</span><span class="s1">“今天”</span><span class="p">)</span><span class="o">。</span><span class="n">正常化</span><span class="p">()</span><span class="n">week_ago</span><span class="o">=</span><span class="n">今天</span><span class="o">- - - - - -</span><span class="n">pd</span><span class="o">。</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">天</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="n">week_later</span><span class="o">=</span><span class="n">今天</span><span class="o">+</span><span class="n">pd</span><span class="o">。</span><span class="n">Timedelta</span><span class="p">(</span><span class="n">天</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="n">past_power_output</span><span class="o">=</span><span class="n">pd</span><span class="o">。</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">wind_farm_data</span><span class="p">)[</span><span class="n">format_date</span><span class="p">(</span><span class="n">week_ago</span><span class="p">):</span><span class="n">format_date</span><span class="p">(</span><span class="n">今天</span><span class="p">)]</span><span class="n">weather_and_forecast</span><span class="o">=</span><span class="n">pd</span><span class="o">。</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">wind_farm_data</span><span class="p">)[</span><span class="n">format_date</span><span class="p">(</span><span class="n">week_ago</span><span class="p">):</span><span class="n">format_date</span><span class="p">(</span><span class="n">week_later</span><span class="p">)]</span><span class="k">如果</span><span class="nb">len</span><span class="p">(</span><span class="n">weather_and_forecast</span><span class="p">)</span><span class="o"><</span><span class="mi">10</span><span class="p">:</span><span class="n">past_power_output</span><span class="o">=</span><span class="n">pd</span><span class="o">。</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">wind_farm_data</span><span class="p">)</span><span class="o">。</span><span class="n">iloc</span><span class="p">(</span><span class="o">- - - - - -</span><span class="mi">10</span><span class="p">:</span><span class="o">- - - - - -</span><span class="mi">5</span><span class="p">]</span><span class="n">weather_and_forecast</span><span class="o">=</span><span class="n">pd</span><span class="o">。</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">wind_farm_data</span><span class="p">)</span><span class="o">。</span><span class="n">iloc</span><span class="p">(</span><span class="o">- - - - - -</span><span class="mi">10</span><span class="p">:]</span><span class="k">返回</span><span class="n">weather_and_forecast</span><span class="o">。</span><span class="n">下降</span><span class="p">(</span><span class="n">列</span><span class="o">=</span><span class="s2">“權力”</span><span class="p">),</span><span class="n">past_power_output</span><span class="p">(</span><span class="s2">“權力”</span><span class="p">]</span></pre> </div> </div> </div> <div class="section" id="train-model"> <h3>火車模型<a class="headerlink" href="//m.eheci.com/docs/mlflow/#train-model" title=""></a></h3> <p>下麵的代碼訓練一個神經網絡使用TensorFlow Keras根據天氣預測功率輸出特性的數據集。MLflow用於追蹤模型的hyperparameters,性能指標,源代碼,和工件。</p> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="k">def</span><span class="nf">train_keras_model</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span><span class="kn">進口</span><span class="nn">tensorflow.keras</span><span class="kn">從</span><span class="nn">tensorflow.keras.models</span><span class="kn">進口</span><span class="n">順序</span><span class="kn">從</span><span class="nn">tensorflow.keras.layers</span><span class="kn">進口</span><span class="n">密集的</span><span class="n">模型</span><span class="o">=</span><span class="n">順序</span><span class="p">()</span><span class="n">模型</span><span class="o">。</span><span class="n">添加</span><span class="p">(</span><span class="n">密集的</span><span class="p">(</span><span class="mi">One hundred.</span><span class="p">,</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">X_train</span><span class="o">。</span><span class="n">形狀</span><span class="p">(</span><span class="o">- - - - - -</span><span class="mi">1</span><span class="p">),),</span><span class="n">激活</span><span class="o">=</span><span class="s2">“relu”</span><span class="p">,</span><span class="n">的名字</span><span class="o">=</span><span class="s2">“hidden_layer”</span><span class="p">))</span><span class="n">模型</span><span class="o">。</span><span class="n">添加</span><span class="p">(</span><span class="n">密集的</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="n">模型</span><span class="o">。</span><span class="n">編譯</span><span class="p">(</span><span class="n">損失</span><span class="o">=</span><span class="s2">“mse”</span><span class="p">,</span><span class="n">優化器</span><span class="o">=</span><span class="s2">“亞當”</span><span class="p">)</span><span class="n">模型</span><span class="o">。</span><span class="n">適合</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">,</span><span class="n">時代</span><span class="o">=</span><span class="mi">One hundred.</span><span class="p">,</span><span class="n">batch_size</span><span class="o">=</span><span class="mi">64年</span><span class="p">,</span><span class="n">validation_split</span><span class="o">=</span><span class="mf">。2</span><span class="p">)</span><span class="k">返回</span><span class="n">模型</span><span class="kn">進口</span><span class="nn">mlflow</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="o">=</span><span class="n">get_training_data</span><span class="p">()</span><span class="k">與</span><span class="n">mlflow</span><span class="o">。</span><span class="n">start_run</span><span class="p">():</span><span class="c1">#自動捕獲模型的參數、指標,工件,</span><span class="c1">#和源代碼的autolog()的函數</span><span class="n">mlflow</span><span class="o">。</span><span class="n">tensorflow</span><span class="o">。</span><span class="n">autolog</span><span class="p">()</span><span class="n">train_keras_model</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span><span class="n">run_id</span><span class="o">=</span><span class="n">mlflow</span><span class="o">。</span><span class="n">active_run</span><span class="p">()</span><span class="o">。</span><span class="n">信息</span><span class="o">。</span><span class="n">run_id</span></pre> </div> </div> </div> </div> <div class="section" id="register-and-manage-the-model-using-the-mlflow-ui"> <h2>注冊和管理使用MLflow UI模型<a class="headerlink" href="//m.eheci.com/docs/mlflow/#register-and-manage-the-model-using-the-mlflow-ui" title=""></a></h2> <div class="contents local topic" id="in-this-section"> <p class="topic-title first">在本節中:</p> <ul class="simple"> <li><p><a class="reference internal" href="//m.eheci.com/docs/mlflow/#create-a-new-registered-model" id="id2">創建一個新的注冊模型</a></p></li> <li><p><a class="reference internal" href="//m.eheci.com/docs/mlflow/#explore-the-model-registry-ui" id="id3">探索模型注冊界麵</a></p></li> <li><p><a class="reference internal" href="//m.eheci.com/docs/mlflow/#add-model-descriptions" id="id4">添加模型描述</a></p></li> <li><p><a class="reference internal" href="//m.eheci.com/docs/mlflow/#transition-a-model-version" id="id5">過渡模型版本</a></p></li> </ul> </div> <div class="section" id="create-a-new-registered-model"> <h3><a class="toc-backref" href="//m.eheci.com/docs/mlflow/#id2">創建一個新的注冊模型</a><a class="headerlink" href="//m.eheci.com/docs/mlflow/#create-a-new-registered-model" title=""></a></h3> <ol class="arabic"> <li><p>導航到MLflow實驗側欄點擊運行<strong>實驗</strong>圖標<img alt="實驗的圖標”src="//m.eheci.com/docs/docs/_images/experiment.png">磚的筆記本的右欄。</p> <div class="figure align-default"> <img alt="分欄”src="//m.eheci.com/docs/docs/_images/notebook-toolbar.png"> </div></li> <li><p>定位MLflow運行對應TensorFlow Keras模型訓練,和打開它MLflow UI通過單擊運行<strong>查看運行細節</strong>圖標。</p></li> <li><p>MLflow UI中,向下滾動到<strong>工件</strong>部分並單擊目錄命名<strong>模型</strong>。單擊<strong>注冊模式</strong>按鈕出現。</p> <div class="figure align-default"> <img alt="注冊模式”src="//m.eheci.com/docs/docs/_images/mlflow_ui_register_model.png"> </div></li> <li><p>選擇<strong>創建新的模型</strong>從下拉菜單中,輸入以下型號名稱:<code class="docutils literal notranslate"><span class="pre">power-forecasting-model</span></code>。</p></li> <li><p>點擊<strong>注冊</strong>。這個注冊一個新的模型<code class="docutils literal notranslate"><span class="pre">power-forecasting-model</span></code>並創建一個新的模型版本:<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>。</p> <div class="figure align-default"> <img alt="新模型版本”src="//m.eheci.com/docs/docs/_images/register_model_confirm.png"> </div><p>幾分鍾後,MLflow UI顯示一個鏈接到新的注冊模式。遵循這個鏈接打開新模型版本MLflow模型中注冊的用戶界麵。</p></li> </ol> </div> <div class="section" id="explore-the-model-registry-ui"> <h3><a class="toc-backref" href="//m.eheci.com/docs/mlflow/#id3">探索模型注冊界麵</a><a class="headerlink" href="//m.eheci.com/docs/mlflow/#explore-the-model-registry-ui" title=""></a></h3> <p>MLflow模型中的模型版本頁麵注冊用戶界麵提供了信息<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>注冊的預測模型,包括作者、創建時間,當前階段。</p> <div class="figure align-default"> <img alt="模型版本頁麵”src="//m.eheci.com/docs/docs/_images/registry_version_page.png"> </div> <p>模型版本頁麵還提供了一個<strong>源運行</strong>鏈接,打開MLflow運行用於創建UI模型MLflow來看。從MLflow運行界麵,您可以訪問<strong>源</strong>筆記本鏈接視圖的快照數據磚筆記本是用來訓練模型。</p> <div class="figure align-default"> <img alt="源運行”src="//m.eheci.com/docs/docs/_images/source_run_link.png"> </div> <div class="figure align-default"> <img alt="源的筆記本”src="//m.eheci.com/docs/docs/_images/source_notebook_link.png"> </div> <p>導航回MLflow模型注冊表,點擊<img alt="模型圖標”src="//m.eheci.com/docs/docs/_images/models-icon.png"><strong>模型</strong>在側邊欄。</p> <p>生成的MLflow模型注冊主頁中顯示所有注冊模型的列表數據磚工作空間,包括版本和階段。</p> <p>單擊<strong>power-forecasting-model</strong>鏈接打開注冊模型頁麵,該頁麵顯示的所有版本的預測模型。</p> </div> <div class="section" id="add-model-descriptions"> <h3><a class="toc-backref" href="//m.eheci.com/docs/mlflow/#id4">添加模型描述</a><a class="headerlink" href="//m.eheci.com/docs/mlflow/#add-model-descriptions" title=""></a></h3> <p>您可以添加描述模型和模型注冊版本。注冊模型描述有用的記錄信息,適用於多個模型版本(例如,一個總體概述的建模問題和數據集)。模型版本描述可用於詳細說明一個特定模式的獨特屬性版本(例如,方法和算法用於開發模型)。</p> <ol class="arabic"> <li><p>高級描述添加到注冊電力預測模型。單擊<img alt="編輯圖標”src="//m.eheci.com/docs/docs/_images/edit-icon.png">圖標,並輸入以下描述:</p> <div class="highlight-text notranslate"> <div class="highlight"> <pre><span></span>這個模型預測風電場的功率輸出基於氣象數據。天氣數據包括三個特點:風速、風向、空氣溫度。</pre> </div> </div> <div class="figure align-default"> <img alt="添加模型描述”src="//m.eheci.com/docs/docs/_images/model_description.png"> </div></li> <li><p>點擊<strong>保存</strong>。</p></li> <li><p>單擊<strong>版本1</strong>從模型注冊頁麵的鏈接導航模型版本頁麵。</p></li> <li><p>單擊<img alt="編輯圖標”src="//m.eheci.com/docs/docs/_images/edit-icon.png">圖標,並輸入以下描述:</p> <div class="highlight-text notranslate"> <div class="highlight"> <pre><span></span>該模型版本使用TensorFlow Keras。這是一個與一個隱層前饋神經網絡。</pre> </div> </div> <div class="figure align-default"> <img alt="添加模型版本描述”src="//m.eheci.com/docs/docs/_images/model_version_description.png"> </div></li> <li><p>點擊<strong>保存</strong>。</p></li> </ol> </div> <div class="section" id="transition-a-model-version"> <h3><a class="toc-backref" href="//m.eheci.com/docs/mlflow/#id5">過渡模型版本</a><a class="headerlink" href="//m.eheci.com/docs/mlflow/#transition-a-model-version" title=""></a></h3> <p>MLflow模型注冊表定義了幾個模型階段:<strong>沒有一個</strong>,<strong>暫存</strong>,<strong>生產</strong>,<code class="docutils literal notranslate"><span class="pre">存檔</span></code>。每個階段都有獨特的意義。例如,<strong>暫存</strong>同時,針對模型試驗嗎<strong>生產</strong>模型已經完成了測試或審查過程和已部署的應用程序。</p> <ol class="arabic"> <li><p>單擊<strong>階段</strong>按鈕顯示的列表可用模型階段,你可以選擇階段過渡。</p></li> <li><p>選擇<strong>過渡到- >生產</strong>並按<strong>好吧</strong>在確認窗口模型過渡到階段過渡<strong>生產</strong>。</p> <div class="figure align-default"> <img alt="過渡到生產”src="//m.eheci.com/docs/docs/_images/stage_transition_prod.png"> </div><p>模型版本轉換到後<strong>生產</strong>在UI中顯示,當前階段,和一個條目被添加到活動日誌,以反映過渡。</p> <div class="figure align-default"> <img alt="生產階段”src="//m.eheci.com/docs/docs/_images/stage_production.png"> </div> <div class="figure align-default"> <img alt="模型版本活動”src="//m.eheci.com/docs/docs/_images/activity_production.png"> </div></li> </ol> <p>MLflow模型注冊允許多個模型版本共享相同的階段。當引用一個模型階段,模型注冊表使用了最新的版本(模型版本最大的ID)。注冊模式頁麵顯示所有的版本的一個特定的模型。</p> <div class="figure align-default"> <img alt="注冊頁麵模型”src="//m.eheci.com/docs/docs/_images/model_registry_versions.png"> </div> </div> </div> <div class="section" id="register-and-manage-the-model-using-the--mlflow-api"> <h2>注冊和管理模型使用MLflow API<a class="headerlink" href="//m.eheci.com/docs/mlflow/#register-and-manage-the-model-using-the--mlflow-api" title=""></a></h2> <div class="contents local topic" id="id1"> <p class="topic-title first">在本節中:</p> <ul class="simple"> <li><p><a class="reference internal" href="//m.eheci.com/docs/mlflow/#define-the-models-name-programmatically" id="id6">通過編程方式定義模型的名字</a></p></li> <li><p><a class="reference internal" href="//m.eheci.com/docs/mlflow/#register-the-model" id="id7">注冊模式</a></p></li> <li><p><a class="reference internal" href="//m.eheci.com/docs/mlflow/#add-model-and-model-version-descriptions-using-the-api" id="id8">添加模型和模型使用API版本的描述</a></p></li> <li><p><a class="reference internal" href="//m.eheci.com/docs/mlflow/#transition-a-model-version-and-retrieve-details-using-the-api" id="id9">過渡使用API版本和檢索模型的細節</a></p></li> </ul> </div> <div class="section" id="define-the-models-name-programmatically"> <h3><a class="toc-backref" href="//m.eheci.com/docs/mlflow/#id6">通過編程方式定義模型的名字</a><a class="headerlink" href="//m.eheci.com/docs/mlflow/#define-the-models-name-programmatically" title=""></a></h3> <p>現在模型已經注冊並轉換到<strong>生產</strong>使用MLflow編程api,您可以引用它。定義注冊模型的名字如下:</p> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="n">model_name</span><span class="o">=</span><span class="s2">“power-forecasting-model”</span></pre> </div> </div> </div> <div class="section" id="register-the-model"> <h3><a class="toc-backref" href="//m.eheci.com/docs/mlflow/#id7">注冊模式</a><a class="headerlink" href="//m.eheci.com/docs/mlflow/#register-the-model" title=""></a></h3> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="n">model_name</span><span class="o">=</span><span class="n">get_model_name</span><span class="p">()</span><span class="kn">進口</span><span class="nn">mlflow</span><span class="c1">#默認路徑的MLflow autologging函數存儲TensorFlow Keras模型</span><span class="n">artifact_path</span><span class="o">=</span><span class="s2">“模型”</span><span class="n">model_uri</span><span class="o">=</span><span class="s2">”:/</span><span class="si">{run_id}</span><span class="s2">/</span><span class="si">{artifact_path}</span><span class="s2">”</span><span class="o">。</span><span class="n">格式</span><span class="p">(</span><span class="n">run_id</span><span class="o">=</span><span class="n">run_id</span><span class="p">,</span><span class="n">artifact_path</span><span class="o">=</span><span class="n">artifact_path</span><span class="p">)</span><span class="n">model_details</span><span class="o">=</span><span class="n">mlflow</span><span class="o">。</span><span class="n">register_model</span><span class="p">(</span><span class="n">model_uri</span><span class="o">=</span><span class="n">model_uri</span><span class="p">,</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_name</span><span class="p">)</span><span class="kn">進口</span><span class="nn">時間</span><span class="kn">從</span><span class="nn">mlflow.tracking.client</span><span class="kn">進口</span><span class="n">MlflowClient</span><span class="kn">從</span><span class="nn">mlflow.entities.model_registry.model_version_status</span><span class="kn">進口</span><span class="n">ModelVersionStatus</span><span class="c1">#等到模型已經準備好了</span><span class="k">def</span><span class="nf">wait_until_ready</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="n">model_version</span><span class="p">):</span><span class="n">客戶端</span><span class="o">=</span><span class="n">MlflowClient</span><span class="p">()</span><span class="k">為</span><span class="n">_</span><span class="ow">在</span><span class="nb">範圍</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span><span class="n">model_version_details</span><span class="o">=</span><span class="n">客戶端</span><span class="o">。</span><span class="n">get_model_version</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span><span class="n">版本</span><span class="o">=</span><span class="n">model_version</span><span class="p">,</span><span class="p">)</span><span class="n">狀態</span><span class="o">=</span><span class="n">ModelVersionStatus</span><span class="o">。</span><span class="n">from_string</span><span class="p">(</span><span class="n">model_version_details</span><span class="o">。</span><span class="n">狀態</span><span class="p">)</span><span class="nb">打印</span><span class="p">(</span><span class="s2">”模型狀態:</span><span class="si">% s</span><span class="s2">”</span><span class="o">%</span><span class="n">ModelVersionStatus</span><span class="o">。</span><span class="n">to_string</span><span class="p">(</span><span class="n">狀態</span><span class="p">))</span><span class="k">如果</span><span class="n">狀態</span><span class="o">= =</span><span class="n">ModelVersionStatus</span><span class="o">。</span><span class="n">準備好了</span><span class="p">:</span><span class="k">打破</span><span class="n">時間</span><span class="o">。</span><span class="n">睡眠</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="n">wait_until_ready</span><span class="p">(</span><span class="n">model_details</span><span class="o">。</span><span class="n">的名字</span><span class="p">,</span><span class="n">model_details</span><span class="o">。</span><span class="n">版本</span><span class="p">)</span></pre> </div> </div> </div> <div class="section" id="add-model-and-model-version-descriptions-using-the-api"> <h3><a class="toc-backref" href="//m.eheci.com/docs/mlflow/#id8">添加模型和模型使用API版本的描述</a><a class="headerlink" href="//m.eheci.com/docs/mlflow/#add-model-and-model-version-descriptions-using-the-api" title=""></a></h3> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="kn">從</span><span class="nn">mlflow.tracking.client</span><span class="kn">進口</span><span class="n">MlflowClient</span><span class="n">客戶端</span><span class="o">=</span><span class="n">MlflowClient</span><span class="p">()</span><span class="n">客戶端</span><span class="o">。</span><span class="n">update_registered_model</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_details</span><span class="o">。</span><span class="n">的名字</span><span class="p">,</span><span class="n">描述</span><span class="o">=</span><span class="s2">“這個模型預測風電場的功率輸出基於氣象數據。天氣數據包括三個特點:風速、風向、氣溫。”</span><span class="p">)</span><span class="n">客戶端</span><span class="o">。</span><span class="n">update_model_version</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_details</span><span class="o">。</span><span class="n">的名字</span><span class="p">,</span><span class="n">版本</span><span class="o">=</span><span class="n">model_details</span><span class="o">。</span><span class="n">版本</span><span class="p">,</span><span class="n">描述</span><span class="o">=</span><span class="s2">“這個模型版本使用TensorFlow Keras。這是一個與一個隱藏層前饋神經網絡。”</span><span class="p">)</span></pre> </div> </div> </div> <div class="section" id="transition-a-model-version-and-retrieve-details-using-the-api"> <h3><a class="toc-backref" href="//m.eheci.com/docs/mlflow/#id9">過渡使用API版本和檢索模型的細節</a><a class="headerlink" href="//m.eheci.com/docs/mlflow/#transition-a-model-version-and-retrieve-details-using-the-api" title=""></a></h3> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="n">客戶端</span><span class="o">。</span><span class="n">transition_model_version_stage</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_details</span><span class="o">。</span><span class="n">的名字</span><span class="p">,</span><span class="n">版本</span><span class="o">=</span><span class="n">model_details</span><span class="o">。</span><span class="n">版本</span><span class="p">,</span><span class="n">階段</span><span class="o">=</span><span class="s1">“生產”</span><span class="p">,</span><span class="p">)</span><span class="n">model_version_details</span><span class="o">=</span><span class="n">客戶端</span><span class="o">。</span><span class="n">get_model_version</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_details</span><span class="o">。</span><span class="n">的名字</span><span class="p">,</span><span class="n">版本</span><span class="o">=</span><span class="n">model_details</span><span class="o">。</span><span class="n">版本</span><span class="p">,</span><span class="p">)</span><span class="nb">打印</span><span class="p">(</span><span class="s2">“當前模型階段是:</span><span class="si">{階段}</span><span class="s2">’”</span><span class="o">。</span><span class="n">格式</span><span class="p">(</span><span class="n">階段</span><span class="o">=</span><span class="n">model_version_details</span><span class="o">。</span><span class="n">current_stage</span><span class="p">))</span><span class="n">latest_version_info</span><span class="o">=</span><span class="n">客戶端</span><span class="o">。</span><span class="n">get_latest_versions</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="n">階段</span><span class="o">=</span><span class="p">(</span><span class="s2">“生產”</span><span class="p">])</span><span class="n">latest_production_version</span><span class="o">=</span><span class="n">latest_version_info</span><span class="p">(</span><span class="mi">0</span><span class="p">]</span><span class="o">。</span><span class="n">版本</span><span class="nb">打印</span><span class="p">(</span><span class="s2">“在模型的最新版本</span><span class="si">% s</span><span class="s2">“是”</span><span class="si">% s</span><span class="s2">’。”</span><span class="o">%</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="n">latest_production_version</span><span class="p">))</span></pre> </div> </div> </div> </div> <div class="section" id="load-versions-of-the-registered-model-using-the-api"> <h2>負載使用API版本的注冊模型<a class="headerlink" href="//m.eheci.com/docs/mlflow/#load-versions-of-the-registered-model-using-the-api" title=""></a></h2> <p>MLflow模型組件定義函數加載模型從幾個機器學習框架。例如,<code class="docutils literal notranslate"><span class="pre">mlflow.tensorflow.load_model ()</span></code>是用於加載TensorFlow模型保存在MLflow格式,然後呢<code class="docutils literal notranslate"><span class="pre">mlflow.sklearn.load_model ()</span></code>是用於加載scikit-learn模型保存在MLflow格式。</p> <p>這些函數可以加載模型從MLflow模型注冊表。</p> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="kn">進口</span><span class="nn">mlflow.pyfunc</span><span class="n">model_version_uri</span><span class="o">=</span><span class="s2">“模型:/</span><span class="si">{model_name}</span><span class="s2">/ 1”</span><span class="o">。</span><span class="n">格式</span><span class="p">(</span><span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">)</span><span class="nb">打印</span><span class="p">(</span><span class="s2">“從URI加載注冊模型版本:</span><span class="si">{model_uri}</span><span class="s2">’”</span><span class="o">。</span><span class="n">格式</span><span class="p">(</span><span class="n">model_uri</span><span class="o">=</span><span class="n">model_version_uri</span><span class="p">))</span><span class="n">model_version_1</span><span class="o">=</span><span class="n">mlflow</span><span class="o">。</span><span class="n">pyfunc</span><span class="o">。</span><span class="n">load_model</span><span class="p">(</span><span class="n">model_version_uri</span><span class="p">)</span><span class="n">model_production_uri</span><span class="o">=</span><span class="s2">“模型:/</span><span class="si">{model_name}</span><span class="s2">/生產”</span><span class="o">。</span><span class="n">格式</span><span class="p">(</span><span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">)</span><span class="nb">打印</span><span class="p">(</span><span class="s2">“從URI加載注冊模型版本:</span><span class="si">{model_uri}</span><span class="s2">’”</span><span class="o">。</span><span class="n">格式</span><span class="p">(</span><span class="n">model_uri</span><span class="o">=</span><span class="n">model_production_uri</span><span class="p">))</span><span class="n">model_production</span><span class="o">=</span><span class="n">mlflow</span><span class="o">。</span><span class="n">pyfunc</span><span class="o">。</span><span class="n">load_model</span><span class="p">(</span><span class="n">model_production_uri</span><span class="p">)</span></pre> </div> </div> </div> <div class="section" id="forecast-power-output-with-the-production-model"> <h2>與生產模型預測輸出功率<a class="headerlink" href="//m.eheci.com/docs/mlflow/#forecast-power-output-with-the-production-model" title=""></a></h2> <p>在本節中,生產模型用於評估風電場的天氣預報數據。的<code class="docutils literal notranslate"><span class="pre">forecast_power ()</span></code>應用程序加載指定的預測模型的最新版本階段,用它來預測電力生產在未來5天。</p> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="k">def</span><span class="nf">情節</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="n">model_stage</span><span class="p">,</span><span class="n">model_version</span><span class="p">,</span><span class="n">power_predictions</span><span class="p">,</span><span class="n">past_power_output</span><span class="p">):</span><span class="kn">進口</span><span class="nn">熊貓</span><span class="k">作為</span><span class="nn">pd</span><span class="kn">進口</span><span class="nn">matplotlib.dates</span><span class="k">作為</span><span class="nn">mdat</span><span class="kn">從</span><span class="nn">matplotlib</span><span class="kn">進口</span><span class="n">pyplot</span><span class="k">作為</span><span class="n">plt</span><span class="n">指數</span><span class="o">=</span><span class="n">power_predictions</span><span class="o">。</span><span class="n">指數</span><span class="n">無花果</span><span class="o">=</span><span class="n">plt</span><span class="o">。</span><span class="n">圖</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span><span class="n">斧頭</span><span class="o">=</span><span class="n">無花果</span><span class="o">。</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111年</span><span class="p">)</span><span class="n">斧頭</span><span class="o">。</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">“日期”</span><span class="p">,</span><span class="n">大小</span><span class="o">=</span><span class="mi">20.</span><span class="p">,</span><span class="n">labelpad</span><span class="o">=</span><span class="mi">20.</span><span class="p">)</span><span class="n">斧頭</span><span class="o">。</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">“權力</span><span class="se">\ n</span><span class="s2">輸出</span><span class="se">\ n</span><span class="s2">(MW)”</span><span class="p">,</span><span class="n">大小</span><span class="o">=</span><span class="mi">20.</span><span class="p">,</span><span class="n">labelpad</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span><span class="n">旋轉</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="n">斧頭</span><span class="o">。</span><span class="n">tick_params</span><span class="p">(</span><span class="n">軸</span><span class="o">=</span><span class="s1">“兩個”</span><span class="p">,</span><span class="n">哪一個</span><span class="o">=</span><span class="s1">“主要的”</span><span class="p">,</span><span class="n">labelsize</span><span class="o">=</span><span class="mi">17</span><span class="p">)</span><span class="n">斧頭</span><span class="o">。</span><span class="n">xaxis</span><span class="o">。</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">mdat</span><span class="o">。</span><span class="n">dateformat</span><span class="p">(</span><span class="s1">' % m /</span><span class="si">% d</span><span class="s1">”</span><span class="p">))</span><span class="n">斧頭</span><span class="o">。</span><span class="n">情節</span><span class="p">(</span><span class="n">指數</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">past_power_output</span><span class="p">)),</span><span class="n">past_power_output</span><span class="p">,</span><span class="n">標簽</span><span class="o">=</span><span class="s2">“真正的”</span><span class="p">,</span><span class="n">顏色</span><span class="o">=</span><span class="s2">“紅色”</span><span class="p">,</span><span class="n">α</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="n">線寬</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="n">斧頭</span><span class="o">。</span><span class="n">情節</span><span class="p">(</span><span class="n">指數</span><span class="p">,</span><span class="n">power_predictions</span><span class="o">。</span><span class="n">擠壓</span><span class="p">(),</span><span class="s2">”——“</span><span class="p">,</span><span class="n">標簽</span><span class="o">=</span><span class="s2">“預測”</span><span class="si">% s</span><span class="s2">”</span><span class="se">\ n</span><span class="s2">在舞台的</span><span class="si">% s</span><span class="s2">”(版本</span><span class="si">% d</span><span class="s2">)”</span><span class="o">%</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="n">model_stage</span><span class="p">,</span><span class="n">model_version</span><span class="p">),</span><span class="n">顏色</span><span class="o">=</span><span class="s2">“藍色”</span><span class="p">,</span><span class="n">線寬</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span><span class="n">斧頭</span><span class="o">。</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">ymax</span><span class="o">=</span><span class="nb">馬克斯</span><span class="p">(</span><span class="mi">3500年</span><span class="p">,</span><span class="nb">int</span><span class="p">(</span><span class="nb">馬克斯</span><span class="p">(</span><span class="n">power_predictions</span><span class="o">。</span><span class="n">值</span><span class="p">)</span><span class="o">*</span><span class="mf">1.3</span><span class="p">)))</span><span class="n">斧頭</span><span class="o">。</span><span class="n">傳說</span><span class="p">(</span><span class="n">字形大小</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span><span class="n">plt</span><span class="o">。</span><span class="n">標題</span><span class="p">(</span><span class="s2">“風電場輸出功率預測”</span><span class="p">,</span><span class="n">大小</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span><span class="n">墊</span><span class="o">=</span><span class="mi">20.</span><span class="p">)</span><span class="n">plt</span><span class="o">。</span><span class="n">tight_layout</span><span class="p">()</span><span class="n">顯示</span><span class="p">(</span><span class="n">plt</span><span class="o">。</span><span class="n">顯示</span><span class="p">())</span><span class="k">def</span><span class="nf">forecast_power</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="n">model_stage</span><span class="p">):</span><span class="kn">從</span><span class="nn">mlflow.tracking.client</span><span class="kn">進口</span><span class="n">MlflowClient</span><span class="n">客戶端</span><span class="o">=</span><span class="n">MlflowClient</span><span class="p">()</span><span class="n">model_version</span><span class="o">=</span><span class="n">客戶端</span><span class="o">。</span><span class="n">get_latest_versions</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="n">階段</span><span class="o">=</span><span class="p">(</span><span class="n">model_stage</span><span class="p">))(</span><span class="mi">0</span><span class="p">]</span><span class="o">。</span><span class="n">版本</span><span class="n">model_uri</span><span class="o">=</span><span class="s2">“模型:/</span><span class="si">{model_name}</span><span class="s2">/</span><span class="si">{model_stage}</span><span class="s2">”</span><span class="o">。</span><span class="n">格式</span><span class="p">(</span><span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span><span class="n">model_stage</span><span class="o">=</span><span class="n">model_stage</span><span class="p">)</span><span class="n">模型</span><span class="o">=</span><span class="n">mlflow</span><span class="o">。</span><span class="n">pyfunc</span><span class="o">。</span><span class="n">load_model</span><span class="p">(</span><span class="n">model_uri</span><span class="p">)</span><span class="n">weather_data</span><span class="p">,</span><span class="n">past_power_output</span><span class="o">=</span><span class="n">get_weather_and_forecast</span><span class="p">()</span><span class="n">power_predictions</span><span class="o">=</span><span class="n">pd</span><span class="o">。</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">模型</span><span class="o">。</span><span class="n">預測</span><span class="p">(</span><span class="n">weather_data</span><span class="p">))</span><span class="n">power_predictions</span><span class="o">。</span><span class="n">指數</span><span class="o">=</span><span class="n">pd</span><span class="o">。</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">weather_data</span><span class="o">。</span><span class="n">指數</span><span class="p">)</span><span class="nb">打印</span><span class="p">(</span><span class="n">power_predictions</span><span class="p">)</span><span class="n">情節</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="n">model_stage</span><span class="p">,</span><span class="nb">int</span><span class="p">(</span><span class="n">model_version</span><span class="p">),</span><span class="n">power_predictions</span><span class="p">,</span><span class="n">past_power_output</span><span class="p">)</span></pre> </div> </div> </div> <div class="section" id="create-a-new-model-version"> <h2>創建一個新的模型版本<a class="headerlink" href="//m.eheci.com/docs/mlflow/#create-a-new-model-version" title=""></a></h2> <p>經典的機器學習技術也對權力的有效預測。下麵的代碼使用scikit-learn訓練隨機森林模型和寄存器MLflow模型注冊通過<code class="docutils literal notranslate"><span class="pre">mlflow.sklearn.log_model ()</span></code>函數。</p> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="kn">進口</span><span class="nn">mlflow.sklearn</span><span class="kn">從</span><span class="nn">sklearn.ensemble</span><span class="kn">進口</span><span class="n">RandomForestRegressor</span><span class="kn">從</span><span class="nn">sklearn.metrics</span><span class="kn">進口</span><span class="n">mean_squared_error</span><span class="k">與</span><span class="n">mlflow</span><span class="o">。</span><span class="n">start_run</span><span class="p">():</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">300年</span><span class="n">mlflow</span><span class="o">。</span><span class="n">log_param</span><span class="p">(</span><span class="s2">“n_estimators”</span><span class="p">,</span><span class="n">n_estimators</span><span class="p">)</span><span class="n">rand_forest</span><span class="o">=</span><span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="n">n_estimators</span><span class="p">)</span><span class="n">rand_forest</span><span class="o">。</span><span class="n">適合</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span><span class="n">val_x</span><span class="p">,</span><span class="n">val_y</span><span class="o">=</span><span class="n">get_validation_data</span><span class="p">()</span><span class="n">均方誤差</span><span class="o">=</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">rand_forest</span><span class="o">。</span><span class="n">預測</span><span class="p">(</span><span class="n">val_x</span><span class="p">),</span><span class="n">val_y</span><span class="p">)</span><span class="nb">打印</span><span class="p">(</span><span class="s2">“驗證MSE:</span><span class="si">% d</span><span class="s2">”</span><span class="o">%</span><span class="n">均方誤差</span><span class="p">)</span><span class="n">mlflow</span><span class="o">。</span><span class="n">log_metric</span><span class="p">(</span><span class="s2">“mse”</span><span class="p">,</span><span class="n">均方誤差</span><span class="p">)</span><span class="c1">#指定的“registered_model_name”參數“mlflow.sklearn.log_model ()”</span><span class="c1">#函數模型與MLflow模型注冊中心注冊。這個自動</span><span class="c1">#創建一個新的模型版本</span><span class="n">mlflow</span><span class="o">。</span><span class="n">sklearn</span><span class="o">。</span><span class="n">log_model</span><span class="p">(</span><span class="n">sk_model</span><span class="o">=</span><span class="n">rand_forest</span><span class="p">,</span><span class="n">artifact_path</span><span class="o">=</span><span class="s2">“sklearn-model”</span><span class="p">,</span><span class="n">registered_model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span><span class="p">)</span></pre> </div> </div> <div class="section" id="fetch-the-new-model-version-id-using-mlflow-model-registry-search"> <h3>獲取新的模型版本ID使用MLflow模型注冊表搜索<a class="headerlink" href="//m.eheci.com/docs/mlflow/#fetch-the-new-model-version-id-using-mlflow-model-registry-search" title=""></a></h3> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="kn">從</span><span class="nn">mlflow.tracking.client</span><span class="kn">進口</span><span class="n">MlflowClient</span><span class="n">客戶端</span><span class="o">=</span><span class="n">MlflowClient</span><span class="p">()</span><span class="n">model_version_infos</span><span class="o">=</span><span class="n">客戶端</span><span class="o">。</span><span class="n">search_model_versions</span><span class="p">(</span><span class="s2">“name = '</span><span class="si">% s</span><span class="s2">’”</span><span class="o">%</span><span class="n">model_name</span><span class="p">)</span><span class="n">new_model_version</span><span class="o">=</span><span class="nb">馬克斯</span><span class="p">([</span><span class="n">model_version_info</span><span class="o">。</span><span class="n">版本</span><span class="k">為</span><span class="n">model_version_info</span><span class="ow">在</span><span class="n">model_version_infos</span><span class="p">])</span><span class="n">wait_until_ready</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="n">new_model_version</span><span class="p">)</span></pre> </div> </div> </div> <div class="section" id="add-a-description-to-the-new-model-version"> <h3>添加一個新模型版本描述<a class="headerlink" href="//m.eheci.com/docs/mlflow/#add-a-description-to-the-new-model-version" title=""></a></h3> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="n">客戶端</span><span class="o">。</span><span class="n">update_model_version</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span><span class="n">版本</span><span class="o">=</span><span class="n">new_model_version</span><span class="p">,</span><span class="n">描述</span><span class="o">=</span><span class="s2">“這版本是一個隨機森林模型包含100決策樹在scikit-learn訓練。”</span><span class="p">)</span></pre> </div> </div> </div> <div class="section" id="transition-the-new-model-version-to-staging-and-test-the-model"> <h3>新模型版本過渡到分期和測試模型<a class="headerlink" href="//m.eheci.com/docs/mlflow/#transition-the-new-model-version-to-staging-and-test-the-model" title=""></a></h3> <p>之前將一個模型部署到生產應用程序中,通常是最佳實踐登台環境來測試它。下麵的代碼轉換新模型版本<strong>暫存</strong>並評價其性能。</p> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="n">客戶端</span><span class="o">。</span><span class="n">transition_model_version_stage</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span><span class="n">版本</span><span class="o">=</span><span class="n">new_model_version</span><span class="p">,</span><span class="n">階段</span><span class="o">=</span><span class="s2">“暫存”</span><span class="p">,</span><span class="p">)</span><span class="n">forecast_power</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="s2">“暫存”</span><span class="p">)</span></pre> </div> </div> </div> <div class="section" id="deploy-the-new-model-version-to-production"> <h3>新的模型版本部署到生產<a class="headerlink" href="//m.eheci.com/docs/mlflow/#deploy-the-new-model-version-to-production" title=""></a></h3> <p>驗證新模型版本之後執行在登台,下麵的代碼轉換模型<strong>生產</strong>並使用相同的應用程序代碼的<a class="reference internal" href="//m.eheci.com/docs/mlflow/#forecast-power-output-with-the-production-model"><span class="std std-ref">與生產模型預測輸出功率</span></a>部分生產能力預測。</p> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="n">客戶端</span><span class="o">。</span><span class="n">transition_model_version_stage</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span><span class="n">版本</span><span class="o">=</span><span class="n">new_model_version</span><span class="p">,</span><span class="n">階段</span><span class="o">=</span><span class="s2">“生產”</span><span class="p">,</span><span class="p">)</span><span class="n">forecast_power</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span><span class="s2">“生產”</span><span class="p">)</span></pre> </div> </div> <p>現在有兩個版本的模型預測模型<strong>生產</strong>階段:模型版本培訓Keras scikit-learn訓練模型和版本。</p> <div class="figure align-default"> <img alt="產品型號版本”src="//m.eheci.com/docs/docs/_images/multiple_prod_stage.png"> </div> <div class="admonition note"> <p class="admonition-title">請注意</p> <p>當引用一個模型的階段,MLflow模型模型自動注冊使用最新的生產版本。這使您可以更新您的生產模式不改變任何應用程序代碼。</p> </div> </div> </div> <div class="section" id="archive-and-delete-models"> <h2>存檔和刪除模型<a class="headerlink" href="//m.eheci.com/docs/mlflow/#archive-and-delete-models" title=""></a></h2> <p>模型版本時不再使用,可以存檔或刪除它。您還可以刪除整個注冊模型;這個刪除所有相關的模型版本。</p> <div class="section" id="archive-version-1-of-the-power-forecasting-model"> <h3>存檔<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>的預測模型<a class="headerlink" href="//m.eheci.com/docs/mlflow/#archive-version-1-of-the-power-forecasting-model" title=""></a></h3> <p>存檔<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>的預測模型,因為它不再被使用。MLflow模型中可以歸檔模式注冊UI或通過MLflow API。</p> </div> <div class="section" id="archive-version-1-in-the-mlflow-ui"> <h3>存檔<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>MLflow UI中<a class="headerlink" href="//m.eheci.com/docs/mlflow/#archive-version-1-in-the-mlflow-ui" title=""></a></h3> <p>存檔<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>的預測模型:</p> <ol class="arabic"> <li><p>打開相應的模型版本頁麵MLflow模型注冊界麵:</p> <div class="figure align-default"> <img alt="過渡到存檔”src="//m.eheci.com/docs/docs/_images/stage_transition_archived.png"> </div></li> <li><p>單擊<strong>階段</strong>按鈕,選擇<strong>過渡到- >歸檔</strong>:</p> <div class="figure align-default"> <img alt="歸檔階段”src="//m.eheci.com/docs/docs/_images/confirm_archived_transition.png"> </div></li> <li><p>新聞<strong>好吧</strong>在過渡階段確認窗口。</p> <div class="figure align-default"> <img alt="模型版本存檔”src="//m.eheci.com/docs/docs/_images/stage_archived.png"> </div></li> </ol> <div class="section" id="archive-version-1-using-the-mlflow-api"> <h4>存檔<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>使用MLflow API<a class="headerlink" href="//m.eheci.com/docs/mlflow/#archive-version-1-using-the-mlflow-api" title=""></a></h4> <p>下麵的代碼使用<code class="docutils literal notranslate"><span class="pre">MlflowClient.update_model_version ()</span></code>函數來存檔<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>的預測模型。</p> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="kn">從</span><span class="nn">mlflow.tracking.client</span><span class="kn">進口</span><span class="n">MlflowClient</span><span class="n">客戶端</span><span class="o">=</span><span class="n">MlflowClient</span><span class="p">()</span><span class="n">客戶端</span><span class="o">。</span><span class="n">transition_model_version_stage</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span><span class="n">版本</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">階段</span><span class="o">=</span><span class="s2">“存檔”</span><span class="p">,</span><span class="p">)</span></pre> </div> </div> </div> <div class="section" id="delete-version-1-of-the-power-forecasting-model"> <h4>刪除<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>的預測模型<a class="headerlink" href="//m.eheci.com/docs/mlflow/#delete-version-1-of-the-power-forecasting-model" title=""></a></h4> <p>您還可以使用MLflow UI或MLflow API刪除模型版本。</p> <div class="admonition warning"> <p class="admonition-title">警告</p> <p>模型版本刪除是永久性的和無法回複。</p> </div> <div class="section" id="delete-version-1-in-the-mlflow-ui"> <h5>刪除<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>MLflow UI中<a class="headerlink" href="//m.eheci.com/docs/mlflow/#delete-version-1-in-the-mlflow-ui" title=""></a></h5> <p>刪除<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>的預測模型:</p> <ol class="arabic"> <li><p>打開相應的模型版本頁麵MLflow模型中注冊的用戶界麵。</p> <div class="figure align-default"> <img alt="刪除模型版本”src="//m.eheci.com/docs/docs/_images/delete_version.png"> </div></li> <li><p>旁邊的下拉箭頭選擇版本標識符並單擊<strong>刪除</strong>。</p></li> </ol> </div> <div class="section" id="delete-version-1-using-the-mlflow-api"> <h5>刪除<code class="docutils literal notranslate"><span class="pre">版本</span><span class="pre">1</span></code>使用MLflow API<a class="headerlink" href="//m.eheci.com/docs/mlflow/#delete-version-1-using-the-mlflow-api" title=""></a></h5> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="n">客戶端</span><span class="o">。</span><span class="n">delete_model_version</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span><span class="n">版本</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="p">)</span></pre> </div> </div> </div> <div class="section" id="delete-the-model-using-the-mlflow-api"> <h5>刪除模型使用MLflow API<a class="headerlink" href="//m.eheci.com/docs/mlflow/#delete-the-model-using-the-mlflow-api" title=""></a></h5> <p>你必須首先轉變所有剩餘模型版本階段<strong>沒有一個</strong>或<strong>存檔</strong>。</p> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="kn">從</span><span class="nn">mlflow.tracking.client</span><span class="kn">進口</span><span class="n">MlflowClient</span><span class="n">客戶端</span><span class="o">=</span><span class="n">MlflowClient</span><span class="p">()</span><span class="n">客戶端</span><span class="o">。</span><span class="n">transition_model_version_stage</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span><span class="n">版本</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">階段</span><span class="o">=</span><span class="s2">“存檔”</span><span class="p">,</span><span class="p">)</span></pre> </div> </div> <div class="highlight-python notranslate"> <div class="highlight"> <pre><span></span><span class="n">客戶端</span><span class="o">。</span><span class="n">delete_registered_model</span><span class="p">(</span><span class="n">的名字</span><span class="o">=</span><span class="n">model_name</span><span class="p">)</span></pre> </div> </div> </div> </div> </div> </div> <div class="section" id="notebook"> <h2>筆記本<a class="headerlink" href="//m.eheci.com/docs/mlflow/#notebook" title=""></a></h2> <div class="embedded-notebook-section section" id="mlflow-model-registry-example-notebook"> <span id="mlflow-model-registry-example"></span> <h3>MLflow模型注冊表實例筆記本<a class="headerlink" href="//m.eheci.com/docs/mlflow/#mlflow-model-registry-example-notebook" title=""></a></h3> <div class="embedded-notebook"> <p class="notebook-import-help"><a 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