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data-balloon-pos="up"><svg xmlns="http://www.w3.org/2000/svg" class="icon calendar-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="calendar icon" name="calendar"><path d="M716.4 110.137c0-18.753-14.72-33.473-33.472-33.473-18.753 0-33.473 14.72-33.473 33.473v33.473h66.993v-33.473zm-334.87 0c0-18.753-14.72-33.473-33.473-33.473s-33.52 14.72-33.52 33.473v33.473h66.993v-33.473zm468.81 33.52H716.4v100.465c0 18.753-14.72 33.473-33.472 33.473a33.145 33.145 0 01-33.473-33.473V143.657H381.53v100.465c0 18.753-14.72 33.473-33.473 33.473a33.145 33.145 0 01-33.473-33.473V143.657H180.6A134.314 134.314 0 0046.66 277.595v535.756A134.314 134.314 0 00180.6 947.289h669.74a134.36 134.36 0 00133.94-133.938V277.595a134.314 134.314 0 00-133.94-133.938zm33.473 267.877H147.126a33.145 33.145 0 01-33.473-33.473c0-18.752 14.72-33.473 33.473-33.473h736.687c18.752 0 33.472 14.72 33.472 33.473a33.145 33.145 0 01-33.472 33.473z"></path></svg><span data-allow-mismatch="text">2/4/26</span><meta property="datePublished" content="2026-02-04T04:01:34.000Z"></span><!----><span class="page-reading-time-info" aria-label="Reading Time⌛" data-balloon-pos="up"><svg xmlns="http://www.w3.org/2000/svg" class="icon timer-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="timer icon" name="timer"><path d="M799.387 122.15c4.402-2.978 7.38-7.897 7.38-13.463v-1.165c0-8.933-7.38-16.312-16.312-16.312H256.33c-8.933 0-16.311 7.38-16.311 16.312v1.165c0 5.825 2.977 10.874 7.637 13.592 4.143 194.44 97.22 354.963 220.201 392.763-122.204 37.542-214.893 196.511-220.2 389.397-4.661 5.049-7.638 11.651-7.638 19.03v5.825h566.49v-5.825c0-7.379-2.849-13.981-7.509-18.9-5.049-193.016-97.867-351.985-220.2-389.527 123.24-37.67 216.446-198.453 220.588-392.892zM531.16 450.445v352.632c117.674 1.553 211.787 40.778 211.787 88.676H304.097c0-48.286 95.149-87.382 213.728-88.676V450.445c-93.077-3.107-167.901-81.297-167.901-177.093 0-8.803 6.99-15.793 15.793-15.793 8.803 0 15.794 6.99 15.794 15.793 0 80.261 63.69 145.635 142.01 145.635s142.011-65.374 142.011-145.635c0-8.803 6.99-15.793 15.794-15.793s15.793 6.99 15.793 15.793c0 95.019-73.789 172.82-165.96 177.093z"></path></svg><span>About 9 min</span><meta property="timeRequired" content="PT9M"></span><!----><!----></div><hr></div><!----><div class="" vp-content><!----><div id="markdown-content"><h1 id="ainode" tabindex="-1"><a class="header-anchor" href="#ainode"><span>AINode</span></a></h1><p>AINode is a native IoTDB node designed for registering, managing, and invoking time series-related models. It integrates industry-leading self-developed time series large models—such as the Timer series developed by Tsinghua University—and supports millisecond-level real-time inference on time series data via standard SQL statements. It enables key application scenarios including time series trend forecasting, missing value imputation, and anomaly detection.</p><p>System architecture is illustrated below:</p><figure><img src="/img/AINode-0-en.png" alt="" tabindex="0" loading="lazy"><figcaption></figcaption></figure><p>Responsibilities of the three node types are as follows:</p><ul><li><strong>ConfigNode</strong>: Manages distributed nodes and handles load balancing.</li><li><strong>DataNode</strong>: Receives and parses user SQL requests; stores time series data; performs data preprocessing computations.</li><li><strong>AINode</strong>: Manages and utilizes time series models.</li></ul><h2 id="_1-advantages-and-features" tabindex="-1"><a class="header-anchor" href="#_1-advantages-and-features"><span>1. Advantages and Features</span></a></h2><p>Compared to building a standalone machine learning service, AINode offers the following benefits:</p><ul><li><strong>Ease of Use</strong>: No Python or Java programming required. Complete model management and inference workflows are achievable via SQL statements. For example, use <code>CREATE MODEL</code> to register a model and <code>SELECT * FROM FORECAST (...)</code> for inference—simple and efficient.</li><li><strong>Eliminates Data Migration</strong>: IoTDB-native machine learning enables direct inference on data stored within IoTDB, avoiding data transfer to external ML platforms. This accelerates processing, enhances security, and reduces costs.</li></ul><figure><img src="/img/h1.png" alt="" tabindex="0" loading="lazy"><figcaption></figcaption></figure><ul><li><strong>Built-in Advanced Algorithms</strong>: Integrates state-of-the-art machine learning algorithms covering typical time series analysis tasks, empowering IoTDB with native analytical capabilities: <ul><li><strong>Time Series Forecasting</strong>: Learns patterns from historical sequences to predict future values based on past observations.</li><li><strong>Anomaly Detection for Time Series</strong>: Detects and identifies outliers in time series data to uncover abnormal behaviors.</li></ul></li></ul><h2 id="_2-core-concepts" tabindex="-1"><a class="header-anchor" href="#_2-core-concepts"><span>2. Core Concepts</span></a></h2><ul><li><strong>Model</strong>: A machine learning model that takes time series data as input and outputs analytical results or decisions. Models are the fundamental management units in AINode, supporting creation (registration), deletion, querying, and inference.</li><li><strong>Create</strong>: Loads externally designed or trained model files/algorithms into AINode for unified management and usage.</li><li><strong>Inference</strong>: Applies a registered model to perform time series analysis tasks on specified data.</li><li><strong>Built-in Capability</strong>: AINode includes native algorithms/models for common time series scenarios (e.g., forecasting and anomaly detection).</li></ul><figure><img src="/img/AINode-en.png" alt="" tabindex="0" loading="lazy"><figcaption></figcaption></figure><h2 id="_3-installation-and-deployment" tabindex="-1"><a class="header-anchor" href="#_3-installation-and-deployment"><span>3. Installation and Deployment</span></a></h2><p>Refer to the documentation <a class="route-link" href="/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_Upgrade_apache.html">AINode Deployment</a> for deployment instructions.</p><h2 id="_4-usage-guide" tabindex="-1"><a class="header-anchor" href="#_4-usage-guide"><span>4. Usage Guide</span></a></h2><p>AINode supports two core functionalities: model inference and model management (registration, viewing, deletion, loading, unloading). Details follow.</p><h3 id="_4-1-model-inference" tabindex="-1"><a class="header-anchor" href="#_4-1-model-inference"><span>4.1 Model Inference</span></a></h3><p>AINode supports forecasting inference on single target variables. Syntax, parameters, and examples are provided below.</p><ol><li><strong>SQL Syntax</strong></li></ol><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">SELECT</span><span style="color:#ABB2BF;"> * </span><span style="color:#C678DD;">FROM</span><span style="color:#ABB2BF;"> FORECAST(</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> MODEL_ID,</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> TARGETS, </span><span style="color:#7F848E;font-style:italic;">-- SQL to retrieve target variables</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> OUTPUT_START_TIME,</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> OUTPUT_LENGTH, </span></span> |
| <span class="line"><span style="color:#ABB2BF;"> OUTPUT_INTERVAL,</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> TIMECOL, </span></span> |
| <span class="line"><span style="color:#ABB2BF;"> PRESERVE_INPUT,</span></span> |
| <span class="line"><span style="color:#E06C75;"> [MODEL_OPTIONS]</span><span style="color:#ABB2BF;">?</span></span> |
| <span class="line"><span style="color:#ABB2BF;">)</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><ul><li>Built-in models require no registration. Specify <code>model_id</code> in the <code>forecast</code> function to invoke inference.</li><li>Parameter Description</li></ul><table><thead><tr><th>Parameter Name</th><th>Type</th><th>Attribute</th><th>Description</th><th>Required</th><th>Notes</th></tr></thead><tbody><tr><td>model_id</td><td>Scalar</td><td>String</td><td>Unique identifier of the forecasting model</td><td>Yes</td><td></td></tr><tr><td>targets</td><td>Table</td><td>SET SEMANTIC</td><td>Input data for target variables. IoTDB automatically sorts data by time before sending to AINode</td><td>Yes</td><td>Invalid SQL in this clause triggers query errors</td></tr><tr><td>output_start_time</td><td>Scalar</td><td>Timestamp. Default: last timestamp of targets + output_interval</td><td>Start timestamp of forecast output (issue time)</td><td>No</td><td>Must be later than the maximum timestamp in targets</td></tr><tr><td>output_length</td><td>Scalar</td><td>INT32. Default: 96</td><td>Output window size</td><td>No</td><td>Must be > 0</td></tr><tr><td>output_interval</td><td>Scalar</td><td>Interval. Default: (last_ts - first_ts) / (n - 1)</td><td>Time interval between forecast points (units: ns, us, ms, s, m, h, d, w)</td><td>No</td><td>Must be > 0</td></tr><tr><td>timecol</td><td>Scalar</td><td>String. Default: "time"</td><td>Name of the time column</td><td>No</td><td>Must exist in <code>targets</code> and be of TIMESTAMP type</td></tr><tr><td>preserve_input</td><td>Scalar</td><td>Boolean. Default: false</td><td>Whether to retain all original input rows in output</td><td>No</td><td></td></tr><tr><td>model_options</td><td>Scalar</td><td>String. Default: empty string</td><td>Model-specific key-value pairs (e.g., normalization settings); separated by ';'</td><td>No</td><td></td></tr></tbody></table><p>Notes:</p><ul><li><strong>Default Behavior</strong>: Forecasts all columns in <code>targets</code>. Currently supports INT32, INT64, FLOAT, DOUBLE types only.</li><li><strong>Input Requirements</strong>: <ul><li>Must include a time column.</li><li>Row count: Errors if below minimum; truncates to latest rows if exceeding maximum.</li></ul></li><li><strong>Output</strong>: <ul><li>Contains all target variable columns with original data types.</li><li>If <code>preserve_input=true</code>, adds an <code>is_input</code> column to mark original rows.</li></ul></li><li><strong>Timestamp Generation</strong>: <ul><li>Uses <code>OUTPUT_START_TIME</code> (optional) as forecast start point.</li><li>Uses <code>OUTPUT_INTERVAL</code> (optional, defaults to input sampling interval). Timestamp of row N: <code>OUTPUT_START_TIME + (N - 1) * OUTPUT_INTERVAL</code>.</li></ul></li></ul><ol start="2"><li><strong>Usage Example</strong></li></ol><p>Create database <code>etth</code> and table <code>eg</code>:</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">CREATE</span><span style="color:#C678DD;"> DATABASE</span><span style="color:#61AFEF;"> etth</span><span style="color:#ABB2BF;">;</span></span> |
| <span class="line"><span style="color:#C678DD;">CREATE</span><span style="color:#C678DD;"> TABLE</span><span style="color:#61AFEF;"> eg</span><span style="color:#ABB2BF;"> (hufl </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, hull </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, mufl </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, mull </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, lufl </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, lull </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD, ot </span><span style="color:#C678DD;">FLOAT</span><span style="color:#ABB2BF;"> FIELD)</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div></div></div><p>Prepare source data <a href="/img/ETTh1-tab.csv">ETTh1-tab</a></p><p>Forecast 96 future values of sensor <code>ot</code> using its latest 96 historical records:</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">IoTDB:etth</span><span style="color:#56B6C2;">></span><span style="color:#C678DD;"> SELECT</span><span style="color:#C678DD;"> Time</span><span style="color:#ABB2BF;">, HUFL, HULL, MUFL, MULL, LUFL, LULL, OT </span><span style="color:#C678DD;">FROM</span><span style="color:#ABB2BF;"> eg </span><span style="color:#C678DD;">LIMIT</span><span style="color:#D19A66;"> 96</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">-----------------------------+------+-----+-----+-----+-----+-----+------+</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#C678DD;">Time</span><span style="color:#ABB2BF;">| HUFL| HULL| MUFL| MULL| LUFL| LULL| OT|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">-----------------------------+------+-----+-----+-----+-----+-----+------+</span></span> |
| <span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">2016</span><span style="color:#ABB2BF;">-</span><span style="color:#D19A66;">07</span><span style="color:#ABB2BF;">-01T00:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">000</span><span style="color:#ABB2BF;">+</span><span style="color:#D19A66;">08</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">| </span><span style="color:#D19A66;">5</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">827</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">2</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">009</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">599</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">0</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">462</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">4</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">203</span><span style="color:#ABB2BF;">| </span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">34</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">30</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">531</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">2016</span><span style="color:#ABB2BF;">-</span><span style="color:#D19A66;">07</span><span style="color:#ABB2BF;">-01T01:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">000</span><span style="color:#ABB2BF;">+</span><span style="color:#D19A66;">08</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">| </span><span style="color:#D19A66;">5</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">693</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">2</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">076</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">492</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">0</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">426</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">4</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">142</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">371</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">27</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">787</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">2016</span><span style="color:#ABB2BF;">-</span><span style="color:#D19A66;">07</span><span style="color:#ABB2BF;">-01T02:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">000</span><span style="color:#ABB2BF;">+</span><span style="color:#D19A66;">08</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">| </span><span style="color:#D19A66;">5</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">157</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">741</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">279</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">0</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">355</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">3</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">777</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">218</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">27</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">787</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">2016</span><span style="color:#ABB2BF;">-</span><span style="color:#D19A66;">07</span><span style="color:#ABB2BF;">-01T03:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">000</span><span style="color:#ABB2BF;">+</span><span style="color:#D19A66;">08</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">| </span><span style="color:#D19A66;">5</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">09</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">942</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">279</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">0</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">391</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">3</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">807</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">279</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">25</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">044</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">......</span></span> |
| <span class="line"><span style="color:#ABB2BF;">Total </span><span style="color:#C678DD;">line</span><span style="color:#C678DD;"> number</span><span style="color:#56B6C2;"> =</span><span style="color:#D19A66;"> 96</span></span> |
| <span class="line"><span style="color:#ABB2BF;">It costs </span><span style="color:#D19A66;">0</span><span style="color:#ABB2BF;">.119s</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#ABB2BF;">IoTDB:etth</span><span style="color:#56B6C2;">></span><span style="color:#C678DD;"> SELECT</span><span style="color:#ABB2BF;"> * </span><span style="color:#C678DD;">FROM</span><span style="color:#ABB2BF;"> forecast( </span></span> |
| <span class="line"><span style="color:#ABB2BF;"> model_id </span><span style="color:#56B6C2;">=></span><span style="color:#98C379;"> 'sundial'</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> targets </span><span style="color:#56B6C2;">=></span><span style="color:#ABB2BF;"> (</span><span style="color:#C678DD;">SELECT</span><span style="color:#C678DD;"> Time</span><span style="color:#ABB2BF;">, ot </span><span style="color:#C678DD;">FROM</span><span style="color:#D19A66;"> etth</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">eg</span><span style="color:#C678DD;"> WHERE</span><span style="color:#C678DD;"> time</span><span style="color:#56B6C2;"> >=</span><span style="color:#D19A66;"> 2016</span><span style="color:#ABB2BF;">-</span><span style="color:#D19A66;">08</span><span style="color:#ABB2BF;">-07T18:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">000</span><span style="color:#ABB2BF;">+</span><span style="color:#D19A66;">08</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#C678DD;"> LIMIT</span><span style="color:#D19A66;"> 1440</span><span style="color:#ABB2BF;">) </span><span style="color:#C678DD;">ORDER BY</span><span style="color:#C678DD;"> time</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> output_length </span><span style="color:#56B6C2;">=></span><span style="color:#D19A66;"> 96</span></span> |
| <span class="line"><span style="color:#ABB2BF;">)</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">-----------------------------+---------+</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#C678DD;">time</span><span style="color:#ABB2BF;">| ot|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">-----------------------------+---------+</span></span> |
| <span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">2016</span><span style="color:#ABB2BF;">-</span><span style="color:#D19A66;">10</span><span style="color:#ABB2BF;">-06T18:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">000</span><span style="color:#ABB2BF;">+</span><span style="color:#D19A66;">08</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">20</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">781654</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">2016</span><span style="color:#ABB2BF;">-</span><span style="color:#D19A66;">10</span><span style="color:#ABB2BF;">-06T19:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">000</span><span style="color:#ABB2BF;">+</span><span style="color:#D19A66;">08</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">20</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">252121</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">2016</span><span style="color:#ABB2BF;">-</span><span style="color:#D19A66;">10</span><span style="color:#ABB2BF;">-06T20:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">000</span><span style="color:#ABB2BF;">+</span><span style="color:#D19A66;">08</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">19</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">960138</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">2016</span><span style="color:#ABB2BF;">-</span><span style="color:#D19A66;">10</span><span style="color:#ABB2BF;">-06T21:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">000</span><span style="color:#ABB2BF;">+</span><span style="color:#D19A66;">08</span><span style="color:#ABB2BF;">:</span><span style="color:#D19A66;">00</span><span style="color:#ABB2BF;">|</span><span style="color:#D19A66;">19</span><span style="color:#ABB2BF;">.</span><span style="color:#D19A66;">662334</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">......</span></span> |
| <span class="line"><span style="color:#ABB2BF;">Total </span><span style="color:#C678DD;">line</span><span style="color:#C678DD;"> number</span><span style="color:#56B6C2;"> =</span><span style="color:#D19A66;"> 96</span></span> |
| <span class="line"><span style="color:#ABB2BF;">It costs </span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">.615s</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h3 id="_4-2-registering-custom-models" tabindex="-1"><a class="header-anchor" href="#_4-2-registering-custom-models"><span>4.2 Registering Custom Models</span></a></h3><p><strong>Transformers models meeting the following criteria can be registered to AINode:</strong></p><ol><li>AINode uses Transformers v4.56.2; avoid inheriting interfaces from older versions (<4.50).</li><li>The model must inherit an AINode inference pipeline class (currently supports forecasting pipeline):<div class="language-python line-numbers-mode" data-highlighter="shiki" data-ext="python" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-python"><span class="line"><span style="color:#7F848E;font-style:italic;"># iotdb-core/ainode/iotdb/ainode/core/inference/pipeline/basic_pipeline.py</span></span> |
| <span class="line"><span style="color:#C678DD;">class</span><span style="color:#E5C07B;"> BasicPipeline</span><span style="color:#ABB2BF;">(</span><span style="color:#D19A66;">ABC</span><span style="color:#ABB2BF;">):</span></span> |
| <span class="line"><span style="color:#C678DD;"> def</span><span style="color:#56B6C2;"> __init__</span><span style="color:#ABB2BF;">(</span><span style="color:#E5C07B;font-style:italic;">self</span><span style="color:#ABB2BF;">, </span><span style="color:#D19A66;font-style:italic;">model_id</span><span style="color:#ABB2BF;">, **</span><span style="color:#D19A66;font-style:italic;">model_kwargs</span><span style="color:#ABB2BF;">):</span></span> |
| <span class="line"><span style="color:#E5C07B;"> self</span><span style="color:#ABB2BF;">.model_info </span><span style="color:#56B6C2;">=</span><span style="color:#ABB2BF;"> model_info</span></span> |
| <span class="line"><span style="color:#E5C07B;"> self</span><span style="color:#ABB2BF;">.device </span><span style="color:#56B6C2;">=</span><span style="color:#ABB2BF;"> model_kwargs.</span><span style="color:#61AFEF;">get</span><span style="color:#ABB2BF;">(</span><span style="color:#98C379;">"device"</span><span style="color:#ABB2BF;">, </span><span style="color:#98C379;">"cpu"</span><span style="color:#ABB2BF;">)</span></span> |
| <span class="line"><span style="color:#E5C07B;"> self</span><span style="color:#ABB2BF;">.model </span><span style="color:#56B6C2;">=</span><span style="color:#61AFEF;"> load_model</span><span style="color:#ABB2BF;">(model_info, </span><span style="color:#E06C75;font-style:italic;">device_map</span><span style="color:#56B6C2;">=</span><span style="color:#E5C07B;">self</span><span style="color:#ABB2BF;">.device, **model_kwargs)</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#61AFEF;"> @abstractmethod</span></span> |
| <span class="line"><span style="color:#C678DD;"> def</span><span style="color:#61AFEF;"> preprocess</span><span style="color:#ABB2BF;">(</span><span style="color:#E5C07B;font-style:italic;">self</span><span style="color:#ABB2BF;">, </span><span style="color:#D19A66;font-style:italic;">inputs</span><span style="color:#ABB2BF;">, **</span><span style="color:#D19A66;font-style:italic;">infer_kwargs</span><span style="color:#ABB2BF;">):</span></span> |
| <span class="line"><span style="color:#98C379;"> """Preprocess input data before inference, including shape validation and numerical conversion."""</span></span> |
| <span class="line"><span style="color:#C678DD;"> pass</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#61AFEF;"> @abstractmethod</span></span> |
| <span class="line"><span style="color:#C678DD;"> def</span><span style="color:#61AFEF;"> postprocess</span><span style="color:#ABB2BF;">(</span><span style="color:#E5C07B;font-style:italic;">self</span><span style="color:#ABB2BF;">, </span><span style="color:#D19A66;font-style:italic;">output</span><span style="color:#ABB2BF;">, **</span><span style="color:#D19A66;font-style:italic;">infer_kwargs</span><span style="color:#ABB2BF;">):</span></span> |
| <span class="line"><span style="color:#98C379;"> """Postprocess model outputs after inference."""</span></span> |
| <span class="line"><span style="color:#C678DD;"> pass</span></span> |
| <span class="line"></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#C678DD;">class</span><span style="color:#E5C07B;"> ForecastPipeline</span><span style="color:#ABB2BF;">(</span><span style="color:#E5C07B;">BasicPipeline</span><span style="color:#ABB2BF;">):</span></span> |
| <span class="line"><span style="color:#C678DD;"> def</span><span style="color:#56B6C2;"> __init__</span><span style="color:#ABB2BF;">(</span><span style="color:#E5C07B;font-style:italic;">self</span><span style="color:#ABB2BF;">, </span><span style="color:#D19A66;font-style:italic;">model_info</span><span style="color:#ABB2BF;">, **</span><span style="color:#D19A66;font-style:italic;">model_kwargs</span><span style="color:#ABB2BF;">):</span></span> |
| <span class="line"><span style="color:#56B6C2;"> super</span><span style="color:#ABB2BF;">().</span><span style="color:#56B6C2;">__init__</span><span style="color:#ABB2BF;">(model_info, </span><span style="color:#E06C75;font-style:italic;">model_kwargs</span><span style="color:#56B6C2;">=</span><span style="color:#ABB2BF;">model_kwargs)</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#C678DD;"> def</span><span style="color:#61AFEF;"> preprocess</span><span style="color:#ABB2BF;">(</span><span style="color:#E5C07B;font-style:italic;">self</span><span style="color:#ABB2BF;">, </span><span style="color:#D19A66;font-style:italic;">inputs</span><span style="color:#ABB2BF;">: list[dict[</span><span style="color:#56B6C2;">str</span><span style="color:#ABB2BF;">, dict[</span><span style="color:#56B6C2;">str</span><span style="color:#ABB2BF;">, torch.Tensor] </span><span style="color:#56B6C2;">|</span><span style="color:#ABB2BF;"> torch.Tensor]], **</span><span style="color:#D19A66;font-style:italic;">infer_kwargs</span><span style="color:#ABB2BF;">):</span></span> |
| <span class="line"><span style="color:#98C379;"> """</span></span> |
| <span class="line"><span style="color:#98C379;"> Preprocess and validate input data before model inference.</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#98C379;"> Args:</span></span> |
| <span class="line"><span style="color:#98C379;"> inputs (list[dict]):</span></span> |
| <span class="line"><span style="color:#98C379;"> Input data as list of dicts containing:</span></span> |
| <span class="line"><span style="color:#98C379;"> - 'targets': Tensor of shape (input_length,) or (target_count, input_length).</span></span> |
| <span class="line"><span style="color:#98C379;"> - 'past_covariates': Optional dict of tensors, each of shape (input_length,).</span></span> |
| <span class="line"><span style="color:#98C379;"> - 'future_covariates': Optional dict of tensors, each of shape (input_length,).</span></span> |
| <span class="line"><span style="color:#98C379;"> infer_kwargs (dict, optional): Additional inference parameters, e.g.:</span></span> |
| <span class="line"><span style="color:#98C379;"> - `output_length` (int): Required if 'future_covariates' provided for validation.</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#98C379;"> Raises:</span></span> |
| <span class="line"><span style="color:#98C379;"> ValueError: For invalid input formats (missing keys, invalid tensor shapes).</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#98C379;"> Returns:</span></span> |
| <span class="line"><span style="color:#98C379;"> Preprocessed and validated input ready for model inference.</span></span> |
| <span class="line"><span style="color:#98C379;"> """</span></span> |
| <span class="line"><span style="color:#C678DD;"> pass</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#C678DD;"> def</span><span style="color:#61AFEF;"> forecast</span><span style="color:#ABB2BF;">(</span><span style="color:#E5C07B;font-style:italic;">self</span><span style="color:#ABB2BF;">, </span><span style="color:#D19A66;font-style:italic;">inputs</span><span style="color:#ABB2BF;">, **</span><span style="color:#D19A66;font-style:italic;">infer_kwargs</span><span style="color:#ABB2BF;">):</span></span> |
| <span class="line"><span style="color:#98C379;"> """</span></span> |
| <span class="line"><span style="color:#98C379;"> Perform forecasting on given inputs.</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#98C379;"> Parameters:</span></span> |
| <span class="line"><span style="color:#98C379;"> inputs: Input data for forecasting (type/structure model-specific).</span></span> |
| <span class="line"><span style="color:#98C379;"> **infer_kwargs: Additional parameters, e.g.:</span></span> |
| <span class="line"><span style="color:#98C379;"> - `output_length` (int): Number of future time points to generate.</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#98C379;"> Returns:</span></span> |
| <span class="line"><span style="color:#98C379;"> Forecast output (format model-specific).</span></span> |
| <span class="line"><span style="color:#98C379;"> """</span></span> |
| <span class="line"><span style="color:#C678DD;"> pass</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#C678DD;"> def</span><span style="color:#61AFEF;"> postprocess</span><span style="color:#ABB2BF;">(</span><span style="color:#E5C07B;font-style:italic;">self</span><span style="color:#ABB2BF;">, </span><span style="color:#D19A66;font-style:italic;">outputs</span><span style="color:#ABB2BF;">: list[torch.Tensor], **</span><span style="color:#D19A66;font-style:italic;">infer_kwargs</span><span style="color:#ABB2BF;">) -> list[torch.Tensor]:</span></span> |
| <span class="line"><span style="color:#98C379;"> """</span></span> |
| <span class="line"><span style="color:#98C379;"> Postprocess model outputs, validate shapes, and ensure expected dimensions.</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#98C379;"> Args:</span></span> |
| <span class="line"><span style="color:#98C379;"> outputs: List of 2D tensors, each of shape `[target_count, output_length]`.</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#98C379;"> Raises:</span></span> |
| <span class="line"><span style="color:#98C379;"> InferenceModelInternalException: For invalid output tensor shapes.</span></span> |
| <span class="line"><span style="color:#98C379;"> ValueError: For incorrect output format.</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#98C379;"> Returns:</span></span> |
| <span class="line"><span style="color:#98C379;"> List of postprocessed 2D tensors.</span></span> |
| <span class="line"><span style="color:#98C379;"> """</span></span> |
| <span class="line"><span style="color:#C678DD;"> pass</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div></li><li>Modify <code>config.json</code> to include:<div class="language-json line-numbers-mode" data-highlighter="shiki" data-ext="json" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-json"><span class="line"><span style="color:#ABB2BF;">{</span></span> |
| <span class="line"><span style="color:#E06C75;"> "auto_map"</span><span style="color:#ABB2BF;">: {</span></span> |
| <span class="line"><span style="color:#E06C75;"> "AutoConfig"</span><span style="color:#ABB2BF;">: </span><span style="color:#98C379;">"config.Chronos2CoreConfig"</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#E06C75;"> "AutoModelForCausalLM"</span><span style="color:#ABB2BF;">: </span><span style="color:#98C379;">"model.Chronos2Model"</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> },</span></span> |
| <span class="line"><span style="color:#E06C75;"> "pipeline_cls"</span><span style="color:#ABB2BF;">: </span><span style="color:#98C379;">"pipeline_chronos2.Chronos2Pipeline"</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#E06C75;"> "model_type"</span><span style="color:#ABB2BF;">: </span><span style="color:#98C379;">"custom_t5"</span></span> |
| <span class="line"><span style="color:#ABB2BF;">}</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><ul><li>Must specify model Config and model classes via <code>auto_map</code>.</li><li>Must implement and specify the inference pipeline class.</li><li><code>model_type</code> serves as a unique identifier. Must not conflict with existing model types (for both <code>builtin</code> and <code>user_defined</code> models).</li></ul></li><li>Ensure the model directory contains: <ul><li><code>config.json</code> (model configuration)</li><li><code>model.safetensors</code> (model weights)</li><li>Other <code>.py</code> files (model code)</li><li><em>Note: Filenames for config and weights are fixed and non-customizable.</em></li></ul></li></ol><p><strong>SQL Syntax for Registration:</strong></p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">CREATE</span><span style="color:#ABB2BF;"> MODEL </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">model_id</span><span style="color:#56B6C2;">></span><span style="color:#C678DD;"> USING</span><span style="color:#ABB2BF;"> URI </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">uri</span><span style="color:#56B6C2;">></span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><p><strong>Parameter Details:</strong></p><ul><li><strong>model_id</strong>: Unique identifier for the custom model (case-sensitive): <ul><li>Allowed characters: <code>[0-9a-zA-Z_]</code> (alphanumeric and underscore; cannot start with digit/underscore)</li><li>Length: 2–64 characters</li></ul></li><li><strong>uri</strong>: Local URI path containing model code and weights.</li></ul><p><strong>Registration Example:</strong><br> Upload a custom Transformers model from local path. AINode copies the folder to the <code>user_defined</code> directory.</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">CREATE</span><span style="color:#ABB2BF;"> MODEL chronos2 </span><span style="color:#C678DD;">USING</span><span style="color:#ABB2BF;"> URI </span><span style="color:#98C379;">'file:///path/to/chronos2'</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><p>Registration proceeds asynchronously. Check status via <code>SHOW MODELS</code> (see Section 4.3). After successful registration, invoke inference via standard queries.</p><h3 id="_4-3-viewing-models" tabindex="-1"><a class="header-anchor" href="#_4-3-viewing-models"><span>4.3 Viewing Models</span></a></h3><p>Registered models can be queried using:</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">SHOW MODELS</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><p>Filter by specific model:</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">SHOW MODELS </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">model_id</span><span style="color:#56B6C2;">></span><span style="color:#7F848E;font-style:italic;"> -- Display specific model only</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><p>Result columns:</p><table><thead><tr><th><strong>ModelId</strong></th><th><strong>ModelType</strong></th><th><strong>Category</strong></th><th><strong>State</strong></th></tr></thead><tbody><tr><td>Model ID</td><td>Model Type</td><td>Category</td><td>State</td></tr></tbody></table><p>State transition diagram:</p><figure><img src="/img/ainode-upgrade-state-apache-en.png" alt="" tabindex="0" loading="lazy"><figcaption></figcaption></figure><p>State Flow Explanation:</p><ol><li>After AINode startup, <code>SHOW MODELS</code> displays only <strong>system built-in (BUILTIN)</strong> models.</li><li>User-uploaded models are marked as <strong>user-defined (USER_DEFINED)</strong>. AINode attempts to parse <code>ModelType</code> from config; displays empty if parsing fails.</li><li>Time series large model weights (built-in) are not bundled with AINode. Download occurs on startup: <ul><li><code>ACTIVATING</code> during download → <code>ACTIVE</code> on success → <code>INACTIVE</code> on failure.</li></ul></li></ol><p><strong>Viewing Example:</strong></p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">IoTDB</span><span style="color:#56B6C2;">></span><span style="color:#ABB2BF;"> SHOW MODELS</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">---------------------+--------------+--------------+-------------+</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| ModelId| ModelType| Category| </span><span style="color:#C678DD;">State</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">---------------------+--------------+--------------+-------------+</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| arima| sktime| builtin| active|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| holtwinters| sktime| builtin| active|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">|exponential_smoothing| sktime| builtin| active|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| naive_forecaster| sktime| builtin| active|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| stl_forecaster| sktime| builtin| active|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| gaussian_hmm| sktime| builtin| active|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| gmm_hmm| sktime| builtin| active|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| stray| sktime| builtin| active|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| custom| | user_defined| active|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| timer_xl| timer| builtin| activating|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| sundial| sundial| builtin| active|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| chronos2| t5| builtin| inactive|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">---------------------+--------------+--------------+-------------+</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p><strong>Built-in Traditional Time Series Models:</strong></p><table><thead><tr><th>Model Name</th><th>Core Concept</th><th>Applicable Scenarios</th><th>Key Features</th></tr></thead><tbody><tr><td><strong>ARIMA</strong> (Autoregressive Integrated Moving Average)</td><td>Combines AR, differencing (I), and MA for stationary or differenced series</td><td>Univariate forecasting (stock prices, sales, economics)</td><td>1. For linear trends with weak seasonality2. Requires (p,d,q) tuning3. Sensitive to missing values</td></tr><tr><td><strong>Holt-Winters</strong> (Triple Exponential Smoothing)</td><td>Exponential smoothing with level, trend, and seasonal components</td><td>Data with clear trend & seasonality (monthly sales, power demand)</td><td>1. Handles additive/multiplicative seasonality2. Weights recent data higher3. Simple implementation</td></tr><tr><td><strong>Exponential Smoothing</strong></td><td>Weighted average of history with exponentially decaying weights</td><td>Trending but non-seasonal data (short-term demand)</td><td>1. Few parameters, simple computation2. Suitable for stable/slow-changing series3. Extensible to double/triple smoothing</td></tr><tr><td><strong>Naive Forecaster</strong></td><td>Uses last observation as next prediction (simplest baseline)</td><td>Benchmarking or data with no clear pattern</td><td>1. No training needed2. Sensitive to sudden changes3. Seasonal variant uses prior season value</td></tr><tr><td><strong>STL Forecaster</strong></td><td>Decomposes series into trend, seasonal, residual; forecasts components</td><td>Complex seasonality/trends (climate, traffic)</td><td>1. Handles non-fixed seasonality2. Robust to outliers3. Components can use other models</td></tr><tr><td><strong>Gaussian HMM</strong></td><td>Hidden states generate observations; each state follows Gaussian distribution</td><td>State sequence prediction/classification (speech, finance)</td><td>1. Models temporal state transitions2. Observations independent per state3. Requires state count</td></tr><tr><td><strong>GMM HMM</strong></td><td>Extends Gaussian HMM; each state uses Gaussian Mixture Model</td><td>Multi-modal observation scenarios (motion recognition, biosignals)</td><td>1. More flexible than single Gaussian2. Higher complexity3. Requires GMM component count</td></tr><tr><td><strong>STRAY</strong> (Search for Outliers using Random Projection and Adaptive Thresholding)</td><td>Uses SVD to detect anomalies in high-dimensional time series</td><td>High-dimensional anomaly detection (sensor networks, IT monitoring)</td><td>1. No distribution assumption2. Handles high dimensions3. Sensitive to global anomalies</td></tr></tbody></table><p><strong>Built-in Time Series Large Models:</strong></p><table><thead><tr><th>Model Name</th><th>Core Concept</th><th>Applicable Scenarios</th><th>Key Features</th></tr></thead><tbody><tr><td><strong>Timer-XL</strong></td><td>Long-context time series large model pretrained on massive industrial data</td><td>Complex industrial forecasting requiring ultra-long history (energy, aerospace, transport)</td><td>1. Supports input of tens of thousands of time points2. Covers non-stationary, multivariate, and covariate scenarios3. Pretrained on trillion-scale high-quality industrial IoT data</td></tr><tr><td><strong>Timer-Sundial</strong></td><td>Generative foundation model with "Transformer + TimeFlow" architecture</td><td>Zero-shot forecasting requiring uncertainty quantification (finance, supply chain, renewable energy)</td><td>1. Strong zero-shot generalization; supports point & probabilistic forecasting2. Flexible analysis of any prediction distribution statistic3. Innovative flow-matching architecture for efficient non-deterministic sample generation</td></tr><tr><td><strong>Chronos-2</strong></td><td>Universal time series foundation model based on discrete tokenization</td><td>Rapid zero-shot univariate forecasting; scenarios enhanced by covariates (promotions, weather)</td><td>1. Powerful zero-shot probabilistic forecasting2. Unified multi-variable & covariate modeling (strict input requirements): a. Future covariate names ⊆ historical covariate names b. Each historical covariate length = target length c. Each future covariate length = prediction length3. Efficient encoder-only structure balancing performance and speed</td></tr></tbody></table><h3 id="_4-4-deleting-models" tabindex="-1"><a class="header-anchor" href="#_4-4-deleting-models"><span>4.4 Deleting Models</span></a></h3><p>Registered models can be deleted via SQL. AINode removes the corresponding folder under <code>user_defined</code>. Syntax:</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">DROP</span><span style="color:#ABB2BF;"> MODEL </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">model_id</span><span style="color:#56B6C2;">></span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><ul><li>Specify a successfully registered <code>model_id</code>.</li><li>Deletion is asynchronous; model enters <code>DROPPING</code> state (unavailable for inference during this phase).</li><li><strong>Built-in models cannot be deleted.</strong></li></ul><h3 id="_4-5-loading-unloading-models" tabindex="-1"><a class="header-anchor" href="#_4-5-loading-unloading-models"><span>4.5 Loading/Unloading Models</span></a></h3><p>AINode supports two loading strategies:</p><ul><li><strong>On-Demand Loading</strong>: Load model temporarily during inference, then release resources. Suitable for testing or low-load scenarios.</li><li><strong>Persistent Loading</strong>: Keep model instances resident in CPU memory or GPU VRAM to support high-concurrency inference. Users specify load/unload targets via SQL; AINode auto-manages instance counts. Current loaded status is queryable.</li></ul><p>Details below:</p><ol><li><p><strong>Configuration Parameters</strong><br> Edit these settings to control persistent loading behavior:</p><div class="language-properties line-numbers-mode" data-highlighter="shiki" data-ext="properties" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-properties"><span class="line"><span style="color:#7F848E;font-style:italic;"># Ratio of total device memory/VRAM usable by AINode for inference</span></span> |
| <span class="line"><span style="color:#7F848E;font-style:italic;"># Datatype: Float</span></span> |
| <span class="line"><span style="color:#C678DD;">ain_inference_memory_usage_ratio</span><span style="color:#ABB2BF;">=</span><span style="color:#98C379;">0.4</span></span> |
| <span class="line"></span> |
| <span class="line"><span style="color:#7F848E;font-style:italic;"># Memory overhead ratio per loaded model instance (model_size * this_value)</span></span> |
| <span class="line"><span style="color:#7F848E;font-style:italic;"># Datatype: Float</span></span> |
| <span class="line"><span style="color:#C678DD;">ain_inference_extra_memory_ratio</span><span style="color:#ABB2BF;">=</span><span style="color:#98C379;">1.2</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div></li><li><p><strong>List Available Devices</strong></p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">SHOW AI_DEVICES</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><p>Example:</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">IoTDB</span><span style="color:#56B6C2;">></span><span style="color:#ABB2BF;"> SHOW AI_DEVICES</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">-------------+</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| DeviceId|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">-------------+</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| cpu|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#D19A66;">0</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">-------------+</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div></li><li><p><strong>Load Model</strong><br> Manually load model; system auto-balances instance count based on resources:</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">LOAD</span><span style="color:#ABB2BF;"> MODEL </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">existing_model_id</span><span style="color:#56B6C2;">></span><span style="color:#C678DD;"> TO</span><span style="color:#ABB2BF;"> DEVICES </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">device_id</span><span style="color:#56B6C2;">></span><span style="color:#ABB2BF;">(, </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">device_id</span><span style="color:#56B6C2;">></span><span style="color:#ABB2BF;">)*</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><p>Parameters:</p><ul><li><code>existing_model_id</code>: Model ID (current version supports <code>timer_xl</code> and <code>sundial</code> only)</li><li><code>device_id</code>: <ul><li><code>cpu</code>: Load into server memory</li><li><code>gpu_id</code>: Load into specified GPU(s), e.g., <code>'0,1'</code> for GPUs 0 and 1<br> Example:</li></ul></li></ul><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">LOAD</span><span style="color:#ABB2BF;"> MODEL sundial </span><span style="color:#C678DD;">TO</span><span style="color:#ABB2BF;"> DEVICES </span><span style="color:#98C379;">'cpu,0,1'</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div></li><li><p><strong>Unload Model</strong><br> Unload all instances of a model; system reallocates freed resources:</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">UNLOAD</span><span style="color:#ABB2BF;"> MODEL </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">existing_model_id</span><span style="color:#56B6C2;">></span><span style="color:#C678DD;"> FROM</span><span style="color:#ABB2BF;"> DEVICES </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">device_id</span><span style="color:#56B6C2;">></span><span style="color:#ABB2BF;">(, </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">device_id</span><span style="color:#56B6C2;">></span><span style="color:#ABB2BF;">)*</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div><p>Parameters same as <code>LOAD MODEL</code>.<br> Example:</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#C678DD;">UNLOAD</span><span style="color:#ABB2BF;"> MODEL sundial </span><span style="color:#C678DD;">FROM</span><span style="color:#ABB2BF;"> DEVICES </span><span style="color:#98C379;">'cpu,0,1'</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div></div></div></li><li><p><strong>View Loaded Models</strong></p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">SHOW LOADED MODELS</span></span> |
| <span class="line"><span style="color:#ABB2BF;">SHOW LOADED MODELS </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">device_id</span><span style="color:#56B6C2;">></span><span style="color:#ABB2BF;">(, </span><span style="color:#56B6C2;"><</span><span style="color:#ABB2BF;">device_id</span><span style="color:#56B6C2;">></span><span style="color:#ABB2BF;">)* </span><span style="color:#7F848E;font-style:italic;">-- Filter by device</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div></div></div><p>Example (sundial loaded on CPU, GPU 0, GPU 1):</p><div class="language-sql line-numbers-mode" data-highlighter="shiki" data-ext="sql" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-sql"><span class="line"><span style="color:#ABB2BF;">IoTDB</span><span style="color:#56B6C2;">></span><span style="color:#ABB2BF;"> SHOW LOADED MODELS</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">-------------+--------------+------------------+</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| DeviceId| ModelId| </span><span style="color:#56B6C2;">Count</span><span style="color:#ABB2BF;">(instances)|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">-------------+--------------+------------------+</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| cpu| sundial| </span><span style="color:#D19A66;">4</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#D19A66;">0</span><span style="color:#ABB2BF;">| sundial| </span><span style="color:#D19A66;">6</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">| </span><span style="color:#D19A66;">1</span><span style="color:#ABB2BF;">| sundial| </span><span style="color:#D19A66;">6</span><span style="color:#ABB2BF;">|</span></span> |
| <span class="line"><span style="color:#ABB2BF;">+</span><span style="color:#7F848E;font-style:italic;">-------------+--------------+------------------+</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><ul><li><code>DeviceId</code>: Device identifier</li><li><code>ModelId</code>: Loaded model ID</li><li><code>Count(instances)</code>: Number of model instances per device (auto-assigned by system)</li></ul></li></ol><h3 id="_4-6-time-series-large-models-overview" tabindex="-1"><a class="header-anchor" href="#_4-6-time-series-large-models-overview"><span>4.6 Time Series Large Models Overview</span></a></h3><p>AINode supports multiple time series large models. For detailed introduction and deployment guidance, refer to <a class="route-link" href="/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model_Upgrade_apache.html">Time Series Large Models</a>.</p><h2 id="_5-permission-management" tabindex="-1"><a class="header-anchor" href="#_5-permission-management"><span>5. Permission Management</span></a></h2><p>AINode leverages IoTDB's native authentication system. Users require <code>USE_MODEL</code> permission to manage models. For inference, users must have access permissions to source time series referenced in the input SQL.</p><table><thead><tr><th><strong>Permission Name</strong></th><th><strong>Scope</strong></th><th><strong>Admin User (default ROOT)</strong></th><th><strong>Regular User</strong></th></tr></thead><tbody><tr><td>USE_MODEL</td><td>CREATE MODEL / SHOW MODELS / DROP MODEL</td><td>√</td><td>√</td></tr><tr><td>READ_SCHEMA & READ_DATA</td><td>FORECAST</td><td>√</td><td>√</td></tr></tbody></table><h2 id="_6-contributing-open-source-time-series-large-models-to-iotdb-ainode" tabindex="-1"><a class="header-anchor" href="#_6-contributing-open-source-time-series-large-models-to-iotdb-ainode"><span>6. Contributing Open Source Time Series Large Models to IoTDB-AINode</span></a></h2><p>To add a new built-in custom model to AINode (using Chronos2 as example):</p><ul><li><p><strong>Initiate Discussion</strong><br> Email <a href="mailto:dev@iotdb.apache.org" target="_blank" rel="noopener noreferrer">dev@iotdb.apache.org</a> or open an issue in the main repository.</p></li><li><p><strong>Submit Pull Request to Main Branch</strong></p><ol><li>Verify the model's open-source license and declare it appropriately in the IoTDB repository.</li><li>Create a new package for the model under <code>iotdb-core/ainode/iotdb/ainode/core/model/</code>: <ul><li>Include model configuration class</li><li>Include model class for inference execution</li><li>Include class inheriting AINode's inference pipeline</li></ul><div class="language-bash line-numbers-mode" data-highlighter="shiki" data-ext="bash" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-bash"><span class="line"><span style="color:#61AFEF;">root@rootMacBook-Pro</span><span style="color:#98C379;"> model</span><span style="color:#98C379;"> %</span><span style="color:#98C379;"> eza</span><span style="color:#D19A66;"> --tree</span><span style="color:#D19A66;"> --level=2</span><span style="color:#98C379;"> .</span></span> |
| <span class="line"><span style="color:#56B6C2;">.</span></span> |
| <span class="line"><span style="color:#61AFEF;">├──</span><span style="color:#98C379;"> chronos2</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> __init__.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> base.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> chronos_bolt.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> config.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> dataset.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> layers.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> model.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> pipeline_chronos2.py</span><span style="color:#7F848E;font-style:italic;"> # Inherits AINode inference pipeline</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> └──</span><span style="color:#98C379;"> utils.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">├──</span><span style="color:#98C379;"> sktime</span></span> |
| <span class="line"><span style="color:#61AFEF;">├──</span><span style="color:#98C379;"> sundial</span></span> |
| <span class="line"><span style="color:#61AFEF;">└──</span><span style="color:#98C379;"> timer_xl</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div></li><li>Add model metadata in <code>iotdb-core/ainode/iotdb/ainode/core/model/model_info.py</code>:<div class="language-python line-numbers-mode" data-highlighter="shiki" data-ext="python" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-python"><span class="line"><span style="color:#D19A66;">BUILTIN_HF_TRANSFORMERS_MODEL_MAP</span><span style="color:#56B6C2;"> =</span><span style="color:#ABB2BF;"> {</span></span> |
| <span class="line"><span style="color:#98C379;"> "chronos2"</span><span style="color:#ABB2BF;">: </span><span style="color:#61AFEF;">ModelInfo</span><span style="color:#ABB2BF;">(</span></span> |
| <span class="line"><span style="color:#E06C75;font-style:italic;"> model_id</span><span style="color:#56B6C2;">=</span><span style="color:#98C379;">"chronos2"</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#E06C75;font-style:italic;"> category</span><span style="color:#56B6C2;">=</span><span style="color:#ABB2BF;">ModelCategory.</span><span style="color:#D19A66;">BUILTIN</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#E06C75;font-style:italic;"> state</span><span style="color:#56B6C2;">=</span><span style="color:#ABB2BF;">ModelStates.</span><span style="color:#D19A66;">INACTIVE</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#E06C75;font-style:italic;"> model_type</span><span style="color:#56B6C2;">=</span><span style="color:#98C379;">"t5"</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#E06C75;font-style:italic;"> pipeline_cls</span><span style="color:#56B6C2;">=</span><span style="color:#98C379;">"pipeline_chronos2.Chronos2Pipeline"</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#E06C75;font-style:italic;"> repo_id</span><span style="color:#56B6C2;">=</span><span style="color:#98C379;">"amazon/chronos-2"</span><span style="color:#ABB2BF;">, </span><span style="color:#7F848E;font-style:italic;"># [Optional] HuggingFace weights repo</span></span> |
| <span class="line"><span style="color:#E06C75;font-style:italic;"> auto_map</span><span style="color:#56B6C2;">=</span><span style="color:#ABB2BF;">{</span></span> |
| <span class="line"><span style="color:#98C379;"> "AutoConfig"</span><span style="color:#ABB2BF;">: </span><span style="color:#98C379;">"config.Chronos2CoreConfig"</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#98C379;"> "AutoModelForCausalLM"</span><span style="color:#ABB2BF;">: </span><span style="color:#98C379;">"model.Chronos2Model"</span><span style="color:#ABB2BF;">,</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> },</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> ),</span></span> |
| <span class="line"><span style="color:#ABB2BF;">}</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div></li><li>Add model entry in <code>integration-test/src/test/java/org/apache/iotdb/ainode/utils/AINodeTestUtils.java</code>:<div class="language-java line-numbers-mode" data-highlighter="shiki" data-ext="java" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-java"><span class="line"><span style="color:#C678DD;">public</span><span style="color:#C678DD;"> static</span><span style="color:#C678DD;"> final</span><span style="color:#E5C07B;"> Map</span><span style="color:#ABB2BF;"><</span><span style="color:#E5C07B;">String</span><span style="color:#ABB2BF;">,</span><span style="color:#E5C07B;"> FakeModelInfo</span><span style="color:#ABB2BF;">></span><span style="color:#E06C75;"> BUILTIN_LTSM_MAP </span><span style="color:#56B6C2;">=</span></span> |
| <span class="line"><span style="color:#E5C07B;"> Stream</span><span style="color:#ABB2BF;">.</span><span style="color:#61AFEF;">of</span><span style="color:#ABB2BF;">(</span></span> |
| <span class="line"><span style="color:#C678DD;"> new</span><span style="color:#E5C07B;"> AbstractMap</span><span style="color:#ABB2BF;">.</span><span style="color:#E5C07B;">SimpleEntry</span><span style="color:#ABB2BF;"><>(</span></span> |
| <span class="line"><span style="color:#98C379;"> "sundial"</span><span style="color:#ABB2BF;">, </span><span style="color:#C678DD;">new</span><span style="color:#61AFEF;"> FakeModelInfo</span><span style="color:#ABB2BF;">(</span><span style="color:#98C379;">"sundial"</span><span style="color:#ABB2BF;">, </span><span style="color:#98C379;">"sundial"</span><span style="color:#ABB2BF;">, </span><span style="color:#98C379;">"builtin"</span><span style="color:#ABB2BF;">, </span><span style="color:#98C379;">"active"</span><span style="color:#ABB2BF;">)),</span></span> |
| <span class="line"><span style="color:#C678DD;"> new</span><span style="color:#E5C07B;"> AbstractMap</span><span style="color:#ABB2BF;">.</span><span style="color:#E5C07B;">SimpleEntry</span><span style="color:#ABB2BF;"><>(</span></span> |
| <span class="line"><span style="color:#98C379;"> "timer_xl"</span><span style="color:#ABB2BF;">, </span><span style="color:#C678DD;">new</span><span style="color:#61AFEF;"> FakeModelInfo</span><span style="color:#ABB2BF;">(</span><span style="color:#98C379;">"timer_xl"</span><span style="color:#ABB2BF;">, </span><span style="color:#98C379;">"timer"</span><span style="color:#ABB2BF;">, </span><span style="color:#98C379;">"builtin"</span><span style="color:#ABB2BF;">, </span><span style="color:#98C379;">"active"</span><span style="color:#ABB2BF;">)),</span></span> |
| <span class="line"><span style="color:#C678DD;"> new</span><span style="color:#E5C07B;"> AbstractMap</span><span style="color:#ABB2BF;">.</span><span style="color:#E5C07B;">SimpleEntry</span><span style="color:#ABB2BF;"><>(</span></span> |
| <span class="line"><span style="color:#98C379;"> "chronos2"</span><span style="color:#ABB2BF;">, </span><span style="color:#C678DD;">new</span><span style="color:#61AFEF;"> FakeModelInfo</span><span style="color:#ABB2BF;">(</span><span style="color:#98C379;">"chronos2"</span><span style="color:#ABB2BF;">, </span><span style="color:#98C379;">"t5"</span><span style="color:#ABB2BF;">, </span><span style="color:#98C379;">"builtin"</span><span style="color:#ABB2BF;">, </span><span style="color:#98C379;">"active"</span><span style="color:#ABB2BF;">)))</span></span> |
| <span class="line"><span style="color:#ABB2BF;"> .</span><span style="color:#61AFEF;">collect</span><span style="color:#ABB2BF;">(</span><span style="color:#E5C07B;">Collectors</span><span style="color:#ABB2BF;">.</span><span style="color:#61AFEF;">toMap</span><span style="color:#ABB2BF;">(</span><span style="color:#E5C07B;">Map</span><span style="color:#ABB2BF;">.</span><span style="color:#E5C07B;">Entry</span><span style="color:#C678DD;">::</span><span style="color:#ABB2BF;">getKey, </span><span style="color:#E5C07B;">Map</span><span style="color:#ABB2BF;">.</span><span style="color:#E5C07B;">Entry</span><span style="color:#C678DD;">::</span><span style="color:#ABB2BF;">getValue));</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div></li><li>Submit PR; ensure CI tests pass before merging.</li></ol></li><li><p><strong>Packaging and Deployment</strong></p><ol><li>Build AINode to generate installation package:<div class="language-bash line-numbers-mode" data-highlighter="shiki" data-ext="bash" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-bash"><span class="line"><span style="color:#7F848E;font-style:italic;"># Build IoTDB and AINode together</span></span> |
| <span class="line"><span style="color:#61AFEF;">mvn</span><span style="color:#98C379;"> clean</span><span style="color:#98C379;"> package</span><span style="color:#D19A66;"> -pl</span><span style="color:#98C379;"> distribution</span><span style="color:#D19A66;"> -P</span><span style="color:#98C379;"> with-ainode</span><span style="color:#D19A66;"> -am</span><span style="color:#D19A66;"> -DskipTests</span></span> |
| <span class="line"><span style="color:#7F848E;font-style:italic;"># Build AINode only</span></span> |
| <span class="line"><span style="color:#61AFEF;">mvn</span><span style="color:#98C379;"> clean</span><span style="color:#98C379;"> package</span><span style="color:#D19A66;"> -pl</span><span style="color:#98C379;"> iotdb-core/ainode</span><span style="color:#D19A66;"> -P</span><span style="color:#98C379;"> with-ainode</span><span style="color:#D19A66;"> -am</span><span style="color:#D19A66;"> -DskipTests</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div></li><li>Ensure model weights are accessible at runtime: <ul><li>If weights are fetchable from HuggingFace, confirm <code>repo_id</code> is set.</li><li>Otherwise, manually place weights in <code>data/ainode/models/builtin/<model_id></code> before/during startup.</li></ul><div class="language-bash line-numbers-mode" data-highlighter="shiki" data-ext="bash" style="background-color:#282c34;color:#abb2bf;"><pre class="shiki one-dark-pro vp-code"><code class="language-bash"><span class="line"><span style="color:#61AFEF;">root@rootMacBook-Pro</span><span style="color:#98C379;"> models</span><span style="color:#98C379;"> %</span><span style="color:#98C379;"> eza</span><span style="color:#D19A66;"> --tree</span><span style="color:#D19A66;"> --level=3</span><span style="color:#98C379;"> .</span></span> |
| <span class="line"><span style="color:#56B6C2;">.</span></span> |
| <span class="line"><span style="color:#61AFEF;">├──</span><span style="color:#98C379;"> __init__.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">├──</span><span style="color:#98C379;"> builtin</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> __init__.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> chronos2</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> │</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> __init__.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> │</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> config.json</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> │</span><span style="color:#98C379;"> └──</span><span style="color:#98C379;"> model.safetensors</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> sundial</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> │</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> __init__.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> │</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> config.json</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> │</span><span style="color:#98C379;"> └──</span><span style="color:#98C379;"> model.safetensors</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> └──</span><span style="color:#98C379;"> timer_xl</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> __init__.py</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> ├──</span><span style="color:#98C379;"> config.json</span></span> |
| <span class="line"><span style="color:#61AFEF;">│</span><span style="color:#98C379;"> └──</span><span style="color:#98C379;"> model.safetensors</span></span> |
| <span class="line"><span style="color:#61AFEF;">└──</span><span style="color:#98C379;"> user_defined</span></span></code></pre><div class="line-numbers" aria-hidden="true" style="counter-reset:line-number 0;"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div></li></ol></li><li><p><strong>Complete Example</strong><br> Reference implementation: <a href="https://github.com/apache/iotdb/pull/16903" target="_blank" rel="noopener noreferrer">https://github.com/apache/iotdb/pull/16903</a></p></li></ul></div><!----><!----><!----></div><footer class="vp-page-meta"><div class="vp-meta-item edit-link"><a class="auto-link external-link vp-meta-label" href="https://github.com/apache/iotdb-docs/edit/main/src/UserGuide/latest-Table/AI-capability/AINode_Upgrade_apache.md" aria-label="Found Error? Edit this page on GitHub" rel="noopener noreferrer" target="_blank"><!--[--><svg xmlns="http://www.w3.org/2000/svg" class="icon edit-icon" viewBox="0 0 1024 1024" fill="currentColor" aria-label="edit icon" name="edit"><path d="M430.818 653.65a60.46 60.46 0 0 1-50.96-93.281l71.69-114.012 7.773-10.365L816.038 80.138A60.46 60.46 0 0 1 859.225 62a60.46 60.46 0 0 1 43.186 18.138l43.186 43.186a60.46 60.46 0 0 1 0 86.373L588.879 565.55l-8.637 8.637-117.466 68.234a60.46 60.46 0 0 1-31.958 11.229z"></path><path d="M728.802 962H252.891A190.883 190.883 0 0 1 62.008 771.98V296.934a190.883 190.883 0 0 1 190.883-192.61h267.754a60.46 60.46 0 0 1 0 120.92H252.891a69.962 69.962 0 0 0-69.098 69.099V771.98a69.962 69.962 0 0 0 69.098 69.098h475.911A69.962 69.962 0 0 0 797.9 771.98V503.363a60.46 60.46 0 1 1 120.922 0V771.98A190.883 190.883 0 0 1 728.802 962z"></path></svg><!--]-->Found Error? Edit this page on GitHub<!----></a></div><div class="vp-meta-item git-info"><div class="update-time"><span class="vp-meta-label">Last Updated: </span><time class="vp-meta-info" datetime="2026-02-04T04:01:34.000Z" data-allow-mismatch>2/4/26, 4:01 AM</time></div><!----></div></footer><nav class="vp-page-nav"><a class="route-link auto-link prev" href="/UserGuide/latest-Table/AI-capability/AINode_apache.html" aria-label="AINode(V2.0.5/6)"><div class="hint"><span class="arrow start"></span>Prev</div><div class="link"><!---->AINode(V2.0.5/6)</div></a><a class="route-link auto-link next" href="/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.html" aria-label="TimeSeries Large Model(V2.0.5/6)"><div class="hint">Next<span class="arrow end"></span></div><div class="link">TimeSeries Large Model(V2.0.5/6)<!----></div></a></nav><!----><!----><!--]--></main><!--]--><footer class="site-footer"><span id="doc-version" style="display:none;">latest-Table</span><p class="copyright-text">Copyright © 2026 The Apache Software Foundation. |
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