[dolphinscheduler-website] branch asf-site updated: Automated deployment: c0b4ce862ca262e3a3d0636f552230dbbd850bb9

Tue, 20 Sep 2022 22:34:15 -0700 

This is an automated email from the ASF dual-hosted git repository.

github-bot pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/dolphinscheduler-website.git


The following commit(s) were added to refs/heads/asf-site by this push:
     new 86ab69438 Automated deployment: 
c0b4ce862ca262e3a3d0636f552230dbbd850bb9
86ab69438 is described below

commit 86ab69438315968e4aae8683765aaf8d2f3d9658
Author: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
AuthorDate: Wed Sep 21 05:33:38 2022 +0000

    Automated deployment: c0b4ce862ca262e3a3d0636f552230dbbd850bb9
---
 .../dev/user_doc/guide/resource/configuration.html |  15 +++++-
 .../dev/user_doc/guide/resource/configuration.json |   2 +-
 en-us/docs/dev/user_doc/guide/task/mlflow.html     |  40 ++++++---------
 en-us/docs/dev/user_doc/guide/task/mlflow.json     |   2 +-
 img/tasks/demo/mlflow-models-docker-compose.png    | Bin 24671 -> 0 bytes
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 python/3.0.1/.doctrees/tutorial.doctree            | Bin 57717 -> 57717 bytes
 python/dev/.buildinfo                              |   2 +-
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 python/dev/.doctrees/tasks/pytorch.doctree         | Bin 50802 -> 50802 bytes
 python/dev/.doctrees/tasks/sagemaker.doctree       | Bin 25925 -> 25925 bytes
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 python/dev/.doctrees/tutorial.doctree              | Bin 75682 -> 75682 bytes
 .../_modules/pydolphinscheduler/tasks/mlflow.html  |  15 ++----
 python/dev/api.html                                |   8 ++-
 python/dev/genindex.html                           |   2 -
 python/dev/objects.inv                             | Bin 9122 -> 9114 bytes
 python/dev/searchindex.js                          |   2 +-
 python/dev/tasks/mlflow.html                       |  54 +++++----------------
 .../dev/user_doc/guide/resource/configuration.html |  15 +++++-
 .../dev/user_doc/guide/resource/configuration.json |   2 +-
 zh-cn/docs/dev/user_doc/guide/task/mlflow.html     |  39 ++++++---------
 zh-cn/docs/dev/user_doc/guide/task/mlflow.json     |   2 +-
 144 files changed, 87 insertions(+), 121 deletions(-)

diff --git a/en-us/docs/dev/user_doc/guide/resource/configuration.html 
b/en-us/docs/dev/user_doc/guide/resource/configuration.html
index ef46e60dd..e703f16c8 100644
--- a/en-us/docs/dev/user_doc/guide/resource/configuration.html
+++ b/en-us/docs/dev/user_doc/guide/resource/configuration.html
@@ -53,9 +53,9 @@ If you deploy DolphinScheduler in <code>Standalone</code> 
mode, you only need to
 # resource view suffixs</span>
 <span 
class="hljs-comment">#resource.view.suffixs=txt,log,sh,bat,conf,cfg,py,java,sql,xml,hql,properties,json,yml,yaml,ini,js</span>
 <span class="hljs-comment">
-# resource storage type: HDFS, S3, NONE</span>
+# resource storage type: HDFS, S3, OSS, NONE</span>
 <span class="hljs-meta">resource.storage.type</span>=<span 
class="hljs-string">NONE</span>
-<span class="hljs-comment"># resource store on HDFS/S3 path, resource file 
will store to this base path, self configuration, please make sure the 
directory exists on hdfs and have read write permissions. 
&quot;/dolphinscheduler&quot; is recommended</span>
+<span class="hljs-comment"># resource store on HDFS/S3/OSS path, resource file 
will store to this base path, self configuration, please make sure the 
directory exists on hdfs and have read write permissions. 
&quot;/dolphinscheduler&quot; is recommended</span>
 <span class="hljs-meta">resource.storage.upload.base.path</span>=<span 
class="hljs-string">/tmp/dolphinscheduler</span>
 <span class="hljs-comment">
 # The AWS access key. if resource.storage.type=S3 or use EMR-Task, This 
configuration is required</span>
@@ -69,6 +69,17 @@ If you deploy DolphinScheduler in <code>Standalone</code> 
mode, you only need to
 <span class="hljs-comment"># You need to set this parameter when private cloud 
s3. If S3 uses public cloud, you only need to set resource.aws.region or set to 
the endpoint of a public cloud such as S3.cn-north-1.amazonaws.com.cn</span>
 <span class="hljs-meta">resource.aws.s3.endpoint</span>=<span 
class="hljs-string">http://localhost:9000</span>
 <span class="hljs-comment">
+# alibaba cloud access key id, required if you set resource.storage.type=OSS 
</span>
+<span class="hljs-meta">resource.alibaba.cloud.access.key.id</span>=<span 
class="hljs-string">&lt;your-access-key-id&gt;</span>
+<span class="hljs-comment"># alibaba cloud access key secret, required if you 
set resource.storage.type=OSS</span>
+<span class="hljs-meta">resource.alibaba.cloud.access.key.secret</span>=<span 
class="hljs-string">&lt;your-access-key-secret&gt;</span>
+<span class="hljs-comment"># alibaba cloud region, required if you set 
resource.storage.type=OSS</span>
+<span class="hljs-meta">resource.alibaba.cloud.region</span>=<span 
class="hljs-string">cn-hangzhou</span>
+<span class="hljs-comment"># oss bucket name, required if you set 
resource.storage.type=OSS</span>
+<span class="hljs-meta">resource.alibaba.cloud.oss.bucket.name</span>=<span 
class="hljs-string">dolphinscheduler</span>
+<span class="hljs-comment"># oss bucket endpoint, required if you set 
resource.storage.type=OSS</span>
+<span class="hljs-meta">resource.alibaba.cloud.oss.endpoint</span>=<span 
class="hljs-string">https://oss-cn-hangzhou.aliyuncs.com</span>
+<span class="hljs-comment">
 # if resource.storage.type=HDFS, the user must have the permission to create 
directories under the HDFS root path</span>
 <span class="hljs-meta">resource.hdfs.root.user</span>=<span 
class="hljs-string">hdfs</span>
 <span class="hljs-comment"># if resource.storage.type=S3, the value like: 
s3a://dolphinscheduler; if resource.storage.type=HDFS and namenode HA is 
enabled, you need to copy core-site.xml and hdfs-site.xml to conf dir</span>
diff --git a/en-us/docs/dev/user_doc/guide/resource/configuration.json 
b/en-us/docs/dev/user_doc/guide/resource/configuration.json
index eb0ce17c8..6e9c8e391 100644
--- a/en-us/docs/dev/user_doc/guide/resource/configuration.json
+++ b/en-us/docs/dev/user_doc/guide/resource/configuration.json
@@ -1,6 +1,6 @@
 {
   "filename": "configuration.md",
-  "__html": "<h1>Resource Center Configuration</h1>\n<ul>\n<li>You could use 
<code>Resource Center</code> to upload text files, UDFs and other task-related 
files.</li>\n<li>You could configure <code>Resource Center</code> to use 
distributed file system like <a 
href=\"https://hadoop.apache.org/docs/r2.7.0/\";>Hadoop</a> (2.6+), <a 
href=\"https://github.com/minio/minio\";>MinIO</a> cluster or remote storage 
products like <a href=\"https://aws.amazon.com/s3/\";>AWS S3</a>, <a 
href=\"https://ww [...]
+  "__html": "<h1>Resource Center Configuration</h1>\n<ul>\n<li>You could use 
<code>Resource Center</code> to upload text files, UDFs and other task-related 
files.</li>\n<li>You could configure <code>Resource Center</code> to use 
distributed file system like <a 
href=\"https://hadoop.apache.org/docs/r2.7.0/\";>Hadoop</a> (2.6+), <a 
href=\"https://github.com/minio/minio\";>MinIO</a> cluster or remote storage 
products like <a href=\"https://aws.amazon.com/s3/\";>AWS S3</a>, <a 
href=\"https://ww [...]
   "link": "/dist/en-us/docs/dev/user_doc/guide/resource/configuration.html",
   "meta": {}
 }
\ No newline at end of file
diff --git a/en-us/docs/dev/user_doc/guide/task/mlflow.html 
b/en-us/docs/dev/user_doc/guide/task/mlflow.html
index 6324807cc..ec6e530fd 100644
--- a/en-us/docs/dev/user_doc/guide/task/mlflow.html
+++ b/en-us/docs/dev/user_doc/guide/task/mlflow.html
@@ -33,7 +33,6 @@ reproducibility, deployment, and a central model registry.</p>
 <ul>
 <li>MLFLOW: Use <code>MLflow models serve</code> to deploy a model service</li>
 <li>Docker: Run the container after packaging the docker image</li>
-<li>Docker Compose: Use docker compose to run the container, it will replace 
the docker run above</li>
 </ul>
 </li>
 </ul>
@@ -185,31 +184,17 @@ reproducibility, deployment, and a central model 
registry.</p>
 <p><img src="/img/tasks/demo/mlflow-models-mlflow.png" 
alt="mlflow-models-mlflow"></p>
 <h4>Docker</h4>
 <p><img src="/img/tasks/demo/mlflow-models-docker.png" 
alt="mlflow-models-docker"></p>
-<h4>DOCKER COMPOSE</h4>
-<p><img src="/img/tasks/demo/mlflow-models-docker-compose.png" 
alt="mlflow-models-docker-compose"></p>
-<table>
-<thead>
-<tr>
-<th><strong>Parameter</strong></th>
-<th><strong>Description</strong></th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Max Cpu Limit</td>
-<td>For example, <code>1.0</code> or <code>0.5</code>, the same as docker 
compose.</td>
-</tr>
-<tr>
-<td>Max Memory Limit</td>
-<td>For example <code>1G</code> or <code>500M</code>, the same as docker 
compose.</td>
-</tr>
-</tbody>
-</table>
 <h2>Environment to Prepare</h2>
 <h3>Conda Environment</h3>
-<p>You need to enter the admin account to configure a conda environment 
variable（Please
-install <a href="https://docs.continuum.io/anaconda/install/";>anaconda</a>
-or <a 
href="https://docs.conda.io/en/latest/miniconda.html#installing";>miniconda</a> 
in advance).</p>
+<p>Please install <a 
href="https://docs.continuum.io/anaconda/install/";>anaconda</a> or <a 
href="https://docs.conda.io/en/latest/miniconda.html#installing";>miniconda</a> 
in advance.</p>
+<p><strong>Method A:</strong></p>
+<p>Config anaconda environment in 
<code>/dolphinscheduler/conf/env/dolphinscheduler_env.sh</code>.</p>
+<p>Add the following content to the file:</p>
+<pre><code class="language-bash"><span class="hljs-comment"># config anaconda 
environment</span>
+<span class="hljs-built_in">export</span> PATH=/opt/anaconda3/bin:<span 
class="hljs-variable">$PATH</span>
+</code></pre>
+<p><strong>Method B:</strong></p>
+<p>You need to enter the admin account to configure a conda environment 
variable.</p>
 <p><img src="/img/tasks/demo/mlflow-conda-env.png" alt="mlflow-conda-env"></p>
 <p>Note During the configuration task, select the conda environment created 
above. Otherwise, the program cannot find the
 Conda environment.</p>
@@ -224,6 +209,13 @@ mlflow server -h 0.0.0.0 -p 5000 --serve-artifacts 
--backend-store-uri sqlite://
 <p>After running, an MLflow service is started.</p>
 <p>After this, you can visit the MLflow service 
(<code>http://localhost:5000</code>) page to view the experiments and 
models.</p>
 <p><img src="/img/tasks/demo/mlflow-server.png" alt="mlflow-server"></p>
+<h3>Preset Algorithm Repository Configuration</h3>
+<p>If you can't access github, you can modify the following fields in the 
<code>commom.properties</code> configuration file to replace the github address 
with an accessible address.</p>
+<pre><code class="language-yaml"><span class="hljs-comment"># mlflow task 
plugin preset repository</span>
+<span 
class="hljs-string">ml.mlflow.preset_repository=https://github.com/apache/dolphinscheduler-mlflow</span>
+<span class="hljs-comment"># mlflow task plugin preset repository 
version</span>
+<span 
class="hljs-string">ml.mlflow.preset_repository_version=&quot;main&quot;</span>
+</code></pre>
 </div></section><footer class="footer-container"><div 
class="footer-body"><div><h3>About us</h3><h4>Do you need feedback? Please 
contact us through the following ways.</h4></div><div 
class="contact-container"><ul><li><a 
href="https://join.slack.com/t/asf-dolphinscheduler/shared_invite/zt-1e36toy4n-5n9U2R__FDM05R~MJFFVBg";><img
 class="img-base" src="/img/slack.png"/><img class="img-change" 
src="/img/slack-selected.png"/><p>Slack</p></a></li><li><a 
href="/en-us/docs/latest/user_doc/contribu [...]
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diff --git a/en-us/docs/dev/user_doc/guide/task/mlflow.json 
b/en-us/docs/dev/user_doc/guide/task/mlflow.json
index 5adef14a0..c7f57d0a6 100644
--- a/en-us/docs/dev/user_doc/guide/task/mlflow.json
+++ b/en-us/docs/dev/user_doc/guide/task/mlflow.json
@@ -1,6 +1,6 @@
 {
   "filename": "mlflow.md",
-  "__html": "<h1>MLflow Node</h1>\n<h2>Overview</h2>\n<p><a 
href=\"https://mlflow.org\";>MLflow</a> is an excellent open source platform to 
manage the ML lifecycle, including experimentation,\nreproducibility, 
deployment, and a central model registry.</p>\n<p>MLflow task plugin used to 
execute MLflow tasks, Currently contains MLflow Projects and MLflow Models. 
(Model Registry will soon be rewarded for support)</p>\n<ul>\n<li>MLflow 
Projects: Package data science code in a format to reprod [...]
+  "__html": "<h1>MLflow Node</h1>\n<h2>Overview</h2>\n<p><a 
href=\"https://mlflow.org\";>MLflow</a> is an excellent open source platform to 
manage the ML lifecycle, including experimentation,\nreproducibility, 
deployment, and a central model registry.</p>\n<p>MLflow task plugin used to 
execute MLflow tasks, Currently contains MLflow Projects and MLflow Models. 
(Model Registry will soon be rewarded for support)</p>\n<ul>\n<li>MLflow 
Projects: Package data science code in a format to reprod [...]
   "link": "/dist/en-us/docs/dev/user_doc/guide/task/mlflow.html",
   "meta": {}
 }
\ No newline at end of file
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index 76c4e94ca..d03ed28de 100644
--- a/python/dev/_modules/pydolphinscheduler/tasks/mlflow.html
+++ b/python/dev/_modules/pydolphinscheduler/tasks/mlflow.html
@@ -121,8 +121,7 @@
     <span class="sd">&quot;&quot;&quot;MLflow deploy 
type.&quot;&quot;&quot;</span>
 
     <span class="n">MLFLOW</span> <span class="o">=</span> <span 
class="s2">&quot;MLFLOW&quot;</span>
-    <span class="n">DOCKER</span> <span class="o">=</span> <span 
class="s2">&quot;DOCKER&quot;</span>
-    <span class="n">DOCKER_COMPOSE</span> <span class="o">=</span> <span 
class="s2">&quot;DOCKER COMPOSE&quot;</span></div>
+    <span class="n">DOCKER</span> <span class="o">=</span> <span 
class="s2">&quot;DOCKER&quot;</span></div>
 
 
 <span class="n">DEFAULT_MLFLOW_TRACKING_URI</span> <span class="o">=</span> 
<span class="s2">&quot;http://127.0.0.1:5000&quot;</span>
@@ -162,10 +161,8 @@
 <span class="sd">    :param model_uri: Model-URI of MLflow , support 
models:/&lt;model_name&gt;/suffix format and runs:/ format.</span>
 <span class="sd">        See 
https://mlflow.org/docs/latest/tracking.html#artifact-stores</span>
 <span class="sd">    :param mlflow_tracking_uri: MLflow tracking server uri, 
default is http://127.0.0.1:5000</span>
-<span class="sd">    :param deploy_mode: MLflow deploy mode, support MLFLOW, 
DOCKER, DOCKER COMPOSE, default is DOCKER</span>
+<span class="sd">    :param deploy_mode: MLflow deploy mode, support MLFLOW, 
DOCKER, default is DOCKER</span>
 <span class="sd">    :param port: deploy port, default is 7000</span>
-<span class="sd">    :param cpu_limit: cpu limit, default is 1.0</span>
-<span class="sd">    :param memory_limit: memory limit, default is 500M</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="n">mlflow_task_type</span> <span class="o">=</span> <span 
class="n">MLflowTaskType</span><span class="o">.</span><span 
class="n">MLFLOW_MODELS</span>
@@ -174,8 +171,6 @@
         <span class="s2">&quot;deploy_type&quot;</span><span class="p">,</span>
         <span class="s2">&quot;deploy_model_key&quot;</span><span 
class="p">,</span>
         <span class="s2">&quot;deploy_port&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;cpu_limit&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;memory_limit&quot;</span><span 
class="p">,</span>
     <span class="p">}</span>
 
     <span class="k">def</span> <span class="fm">__init__</span><span 
class="p">(</span>
@@ -185,8 +180,6 @@
         <span class="n">mlflow_tracking_uri</span><span class="p">:</span> 
<span class="n">Optional</span><span class="p">[</span><span 
class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span 
class="n">DEFAULT_MLFLOW_TRACKING_URI</span><span class="p">,</span>
         <span class="n">deploy_mode</span><span class="p">:</span> <span 
class="n">Optional</span><span class="p">[</span><span 
class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span 
class="n">MLflowDeployType</span><span class="o">.</span><span 
class="n">DOCKER</span><span class="p">,</span>
         <span class="n">port</span><span class="p">:</span> <span 
class="n">Optional</span><span class="p">[</span><span 
class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span 
class="mi">7000</span><span class="p">,</span>
-        <span class="n">cpu_limit</span><span class="p">:</span> <span 
class="n">Optional</span><span class="p">[</span><span 
class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span 
class="mf">1.0</span><span class="p">,</span>
-        <span class="n">memory_limit</span><span class="p">:</span> <span 
class="n">Optional</span><span class="p">[</span><span 
class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span 
class="s2">&quot;500M&quot;</span><span class="p">,</span>
         <span class="o">*</span><span class="n">args</span><span 
class="p">,</span>
         <span class="o">**</span><span class="n">kwargs</span>
     <span class="p">):</span>
@@ -194,9 +187,7 @@
         <span class="nb">super</span><span class="p">()</span><span 
class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span 
class="n">name</span><span class="p">,</span> <span 
class="n">mlflow_tracking_uri</span><span class="p">,</span> <span 
class="o">*</span><span class="n">args</span><span class="p">,</span> <span 
class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span 
class="n">deploy_type</span> <span class="o">=</span> <span 
class="n">deploy_mode</span><span class="o">.</span><span 
class="n">upper</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span 
class="n">deploy_model_key</span> <span class="o">=</span> <span 
class="n">model_uri</span>
-        <span class="bp">self</span><span class="o">.</span><span 
class="n">deploy_port</span> <span class="o">=</span> <span 
class="n">port</span>
-        <span class="bp">self</span><span class="o">.</span><span 
class="n">cpu_limit</span> <span class="o">=</span> <span 
class="n">cpu_limit</span>
-        <span class="bp">self</span><span class="o">.</span><span 
class="n">memory_limit</span> <span class="o">=</span> <span 
class="n">memory_limit</span></div>
+        <span class="bp">self</span><span class="o">.</span><span 
class="n">deploy_port</span> <span class="o">=</span> <span 
class="n">port</span></div>
 
 
 <div class="viewcode-block" id="MLFlowProjectsCustom"><a class="viewcode-back" 
href="../../../tasks/mlflow.html#pydolphinscheduler.tasks.MLFlowProjectsCustom">[docs]</a><span
 class="k">class</span> <span class="nc">MLFlowProjectsCustom</span><span 
class="p">(</span><span class="n">BaseMLflow</span><span class="p">):</span>
diff --git a/python/dev/api.html b/python/dev/api.html
index aecd7850d..e1da7b013 100644
--- a/python/dev/api.html
+++ b/python/dev/api.html
@@ -2916,7 +2916,7 @@ process_definition.resource_plugin.</p>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="pydolphinscheduler.tasks.MLflowModels">
-<em class="property"><span class="pre">class</span><span class="w"> 
</span></em><span class="sig-prename descclassname"><span 
class="pre">pydolphinscheduler.tasks.</span></span><span class="sig-name 
descname"><span class="pre">MLflowModels</span></span><span 
class="sig-paren">(</span><em class="sig-param"><span class="n"><span 
class="pre">name</span></span><span class="p"><span 
class="pre">:</span></span><span class="w"> </span><span class="n"><span 
class="pre">str</span></span></em>, <e [...]
+<em class="property"><span class="pre">class</span><span class="w"> 
</span></em><span class="sig-prename descclassname"><span 
class="pre">pydolphinscheduler.tasks.</span></span><span class="sig-name 
descname"><span class="pre">MLflowModels</span></span><span 
class="sig-paren">(</span><em class="sig-param"><span class="n"><span 
class="pre">name</span></span><span class="p"><span 
class="pre">:</span></span><span class="w"> </span><span class="n"><span 
class="pre">str</span></span></em>, <e [...]
 <dd><p>Bases: <a class="reference internal" 
href="tasks/mlflow.html#pydolphinscheduler.tasks.mlflow.BaseMLflow" 
title="pydolphinscheduler.tasks.mlflow.BaseMLflow"><code class="xref py 
py-class docutils literal notranslate"><span 
class="pre">BaseMLflow</span></code></a></p>
 <p>Task MLflow models object, declare behavior for MLflow models task to 
dolphinscheduler.</p>
 <p>Deploy machine learning models in diverse serving environments.</p>
@@ -2927,10 +2927,8 @@ process_definition.resource_plugin.</p>
 <li><p><strong>model_uri</strong> – Model-URI of MLflow , support 
models:/&lt;model_name&gt;/suffix format and runs:/ format.
 See https://mlflow.org/docs/latest/tracking.html#artifact-stores</p></li>
 <li><p><strong>mlflow_tracking_uri</strong> – MLflow tracking server uri, 
default is <a class="reference external" 
href="http://127.0.0.1:5000";>http://127.0.0.1:5000</a></p></li>
-<li><p><strong>deploy_mode</strong> – MLflow deploy mode, support MLFLOW, 
DOCKER, DOCKER COMPOSE, default is DOCKER</p></li>
+<li><p><strong>deploy_mode</strong> – MLflow deploy mode, support MLFLOW, 
DOCKER, default is DOCKER</p></li>
 <li><p><strong>port</strong> – deploy port, default is 7000</p></li>
-<li><p><strong>cpu_limit</strong> – cpu limit, default is 1.0</p></li>
-<li><p><strong>memory_limit</strong> – memory limit, default is 500M</p></li>
 </ul>
 </dd>
 </dl>
@@ -3011,7 +3009,7 @@ process_definition.resource_plugin.</p>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" 
id="pydolphinscheduler.tasks.MLflowModels._child_task_mlflow_attr">
-<span class="sig-name descname"><span 
class="pre">_child_task_mlflow_attr</span></span><em class="property"><span 
class="w"> </span><span class="p"><span class="pre">=</span></span><span 
class="w"> </span><span class="pre">{'cpu_limit',</span> <span 
class="pre">'deploy_model_key',</span> <span class="pre">'deploy_port',</span> 
<span class="pre">'deploy_type',</span> <span 
class="pre">'memory_limit'}</span></em><a class="headerlink" 
href="#pydolphinscheduler.tasks.MLflowModels._child_task [...]
+<span class="sig-name descname"><span 
class="pre">_child_task_mlflow_attr</span></span><em class="property"><span 
class="w"> </span><span class="p"><span class="pre">=</span></span><span 
class="w"> </span><span class="pre">{'deploy_model_key',</span> <span 
class="pre">'deploy_port',</span> <span 
class="pre">'deploy_type'}</span></em><a class="headerlink" 
href="#pydolphinscheduler.tasks.MLflowModels._child_task_mlflow_attr" 
title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
diff --git a/python/dev/genindex.html b/python/dev/genindex.html
index 3e77b55ab..3c141354d 100644
--- a/python/dev/genindex.html
+++ b/python/dev/genindex.html
@@ -1221,8 +1221,6 @@
       <li><a 
href="api.html#pydolphinscheduler.constants.Delimiter.DIRECTION">DIRECTION 
(pydolphinscheduler.constants.Delimiter attribute)</a>
 </li>
       <li><a 
href="tasks/mlflow.html#pydolphinscheduler.tasks.mlflow.MLflowDeployType.DOCKER">DOCKER
 (pydolphinscheduler.tasks.mlflow.MLflowDeployType attribute)</a>
-</li>
-      <li><a 
href="tasks/mlflow.html#pydolphinscheduler.tasks.mlflow.MLflowDeployType.DOCKER_COMPOSE">DOCKER_COMPOSE
 (pydolphinscheduler.tasks.mlflow.MLflowDeployType attribute)</a>
 </li>
       <li><a 
href="tasks/dvc.html#pydolphinscheduler.tasks.dvc.DvcTaskType.DOWNLOAD">DOWNLOAD
 (pydolphinscheduler.tasks.dvc.DvcTaskType attribute)</a>
 </li>
diff --git a/python/dev/objects.inv b/python/dev/objects.inv
index 82d2d7b69..855a26360 100644
Binary files a/python/dev/objects.inv and b/python/dev/objects.inv differ
diff --git a/python/dev/searchindex.js b/python/dev/searchindex.js
index f04852b3f..ef736bfa8 100644
--- a/python/dev/searchindex.js
+++ b/python/dev/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["api", "cli", "concept", "config", 
"howto/index", "howto/remote-submit", "index", "resources_plugin/develop", 
"resources_plugin/index", "resources_plugin/local", 
"resources_plugin/resource-plugin", "start", "tasks/condition", "tasks/datax", 
"tasks/dependent", "tasks/dvc", "tasks/flink", "tasks/func_wrap", "tasks/http", 
"tasks/index", "tasks/map_reduce", "tasks/mlflow", "tasks/openmldb", 
"tasks/procedure", "tasks/python", "tasks/pytorch", "tasks/sagemaker", " [...]
\ No newline at end of file
+Search.setIndex({"docnames": ["api", "cli", "concept", "config", 
"howto/index", "howto/remote-submit", "index", "resources_plugin/develop", 
"resources_plugin/index", "resources_plugin/local", 
"resources_plugin/resource-plugin", "start", "tasks/condition", "tasks/datax", 
"tasks/dependent", "tasks/dvc", "tasks/flink", "tasks/func_wrap", "tasks/http", 
"tasks/index", "tasks/map_reduce", "tasks/mlflow", "tasks/openmldb", 
"tasks/procedure", "tasks/python", "tasks/pytorch", "tasks/sagemaker", " [...]
\ No newline at end of file
diff --git a/python/dev/tasks/mlflow.html b/python/dev/tasks/mlflow.html
index ed97dbde9..e583068ad 100644
--- a/python/dev/tasks/mlflow.html
+++ b/python/dev/tasks/mlflow.html
@@ -138,17 +138,6 @@
         <span class="n">experiment_name</span><span class="o">=</span><span 
class="s2">&quot;xgboost&quot;</span><span class="p">,</span>
     <span class="p">)</span>
 
-    <span class="c1"># Using MLFLOW to deploy model from custom mlflow 
project</span>
-    <span class="n">deploy_mlflow</span> <span class="o">=</span> <span 
class="n">MLflowModels</span><span class="p">(</span>
-        <span class="n">name</span><span class="o">=</span><span 
class="s2">&quot;deploy_mlflow&quot;</span><span class="p">,</span>
-        <span class="n">model_uri</span><span class="o">=</span><span 
class="s2">&quot;models:/xgboost_native/Production&quot;</span><span 
class="p">,</span>
-        <span class="n">mlflow_tracking_uri</span><span 
class="o">=</span><span class="n">mlflow_tracking_uri</span><span 
class="p">,</span>
-        <span class="n">deploy_mode</span><span class="o">=</span><span 
class="n">MLflowDeployType</span><span class="o">.</span><span 
class="n">MLFLOW</span><span class="p">,</span>
-        <span class="n">port</span><span class="o">=</span><span 
class="mi">7001</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <span class="n">train_custom</span> <span class="o">&gt;&gt;</span> <span 
class="n">deploy_mlflow</span>
-
     <span class="c1"># run automl to train model</span>
     <span class="n">train_automl</span> <span class="o">=</span> <span 
class="n">MLFlowProjectsAutoML</span><span class="p">(</span>
         <span class="n">name</span><span class="o">=</span><span 
class="s2">&quot;train_automl&quot;</span><span class="p">,</span>
@@ -183,16 +172,16 @@
         <span class="n">search_params</span><span class="o">=</span><span 
class="s2">&quot;max_depth=[5, 10];n_estimators=[100, 200]&quot;</span><span 
class="p">,</span>
     <span class="p">)</span>
 
-    <span class="c1"># Using DOCKER COMPOSE to deploy model from 
train_basic_algorithm</span>
-    <span class="n">deploy_docker_compose</span> <span class="o">=</span> 
<span class="n">MLflowModels</span><span class="p">(</span>
-        <span class="n">name</span><span class="o">=</span><span 
class="s2">&quot;deploy_docker_compose&quot;</span><span class="p">,</span>
+    <span class="c1"># Using MLFLOW to deploy model from training lightgbm 
project</span>
+    <span class="n">deploy_mlflow</span> <span class="o">=</span> <span 
class="n">MLflowModels</span><span class="p">(</span>
+        <span class="n">name</span><span class="o">=</span><span 
class="s2">&quot;deploy_mlflow&quot;</span><span class="p">,</span>
         <span class="n">model_uri</span><span class="o">=</span><span 
class="s2">&quot;models:/iris_B/Production&quot;</span><span class="p">,</span>
         <span class="n">mlflow_tracking_uri</span><span 
class="o">=</span><span class="n">mlflow_tracking_uri</span><span 
class="p">,</span>
-        <span class="n">deploy_mode</span><span class="o">=</span><span 
class="n">MLflowDeployType</span><span class="o">.</span><span 
class="n">DOCKER_COMPOSE</span><span class="p">,</span>
-        <span class="n">port</span><span class="o">=</span><span 
class="mi">7003</span><span class="p">,</span>
+        <span class="n">deploy_mode</span><span class="o">=</span><span 
class="n">MLflowDeployType</span><span class="o">.</span><span 
class="n">MLFLOW</span><span class="p">,</span>
+        <span class="n">port</span><span class="o">=</span><span 
class="mi">7001</span><span class="p">,</span>
     <span class="p">)</span>
 
-    <span class="n">train_basic_algorithm</span> <span 
class="o">&gt;&gt;</span> <span class="n">deploy_docker_compose</span>
+    <span class="n">train_basic_algorithm</span> <span 
class="o">&gt;&gt;</span> <span class="n">deploy_mlflow</span>
 
     <span class="n">pd</span><span class="o">.</span><span 
class="n">submit</span><span class="p">()</span>
 
@@ -408,11 +397,6 @@ for example <a class="reference external" 
href="https://github.com/mlflow/mlflow
 <span class="sig-name descname"><span class="pre">DOCKER</span></span><em 
class="property"><span class="w"> </span><span class="p"><span 
class="pre">=</span></span><span class="w"> </span><span 
class="pre">'DOCKER'</span></em><a class="headerlink" 
href="#pydolphinscheduler.tasks.mlflow.MLflowDeployType.DOCKER" 
title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
-<dl class="py attribute">
-<dt class="sig sig-object py" 
id="pydolphinscheduler.tasks.mlflow.MLflowDeployType.DOCKER_COMPOSE">
-<span class="sig-name descname"><span 
class="pre">DOCKER_COMPOSE</span></span><em class="property"><span class="w"> 
</span><span class="p"><span class="pre">=</span></span><span class="w"> 
</span><span class="pre">'DOCKER</span> <span 
class="pre">COMPOSE'</span></em><a class="headerlink" 
href="#pydolphinscheduler.tasks.mlflow.MLflowDeployType.DOCKER_COMPOSE" 
title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
-
 <dl class="py attribute">
 <dt class="sig sig-object py" 
id="pydolphinscheduler.tasks.mlflow.MLflowDeployType.MLFLOW">
 <span class="sig-name descname"><span class="pre">MLFLOW</span></span><em 
class="property"><span class="w"> </span><span class="p"><span 
class="pre">=</span></span><span class="w"> </span><span 
class="pre">'MLFLOW'</span></em><a class="headerlink" 
href="#pydolphinscheduler.tasks.mlflow.MLflowDeployType.MLFLOW" 
title="Permalink to this definition"></a></dt>
@@ -444,7 +428,7 @@ for example <a class="reference external" 
href="https://github.com/mlflow/mlflow
 
 <dl class="py class">
 <dt class="sig sig-object py" 
id="pydolphinscheduler.tasks.mlflow.MLflowModels">
-<em class="property"><span class="pre">class</span><span class="w"> 
</span></em><span class="sig-prename descclassname"><span 
class="pre">pydolphinscheduler.tasks.mlflow.</span></span><span class="sig-name 
descname"><span class="pre">MLflowModels</span></span><span 
class="sig-paren">(</span><em class="sig-param"><span class="n"><span 
class="pre">name</span></span><span class="p"><span 
class="pre">:</span></span><span class="w"> </span><span class="n"><span 
class="pre">str</span></span></ [...]
+<em class="property"><span class="pre">class</span><span class="w"> 
</span></em><span class="sig-prename descclassname"><span 
class="pre">pydolphinscheduler.tasks.mlflow.</span></span><span class="sig-name 
descname"><span class="pre">MLflowModels</span></span><span 
class="sig-paren">(</span><em class="sig-param"><span class="n"><span 
class="pre">name</span></span><span class="p"><span 
class="pre">:</span></span><span class="w"> </span><span class="n"><span 
class="pre">str</span></span></ [...]
 <dd><p>Bases: <a class="reference internal" 
href="#pydolphinscheduler.tasks.mlflow.BaseMLflow" 
title="pydolphinscheduler.tasks.mlflow.BaseMLflow"><code class="xref py 
py-class docutils literal notranslate"><span 
class="pre">BaseMLflow</span></code></a></p>
 <p>Task MLflow models object, declare behavior for MLflow models task to 
dolphinscheduler.</p>
 <p>Deploy machine learning models in diverse serving environments.</p>
@@ -455,16 +439,14 @@ for example <a class="reference external" 
href="https://github.com/mlflow/mlflow
 <li><p><strong>model_uri</strong> – Model-URI of MLflow , support 
models:/&lt;model_name&gt;/suffix format and runs:/ format.
 See https://mlflow.org/docs/latest/tracking.html#artifact-stores</p></li>
 <li><p><strong>mlflow_tracking_uri</strong> – MLflow tracking server uri, 
default is <a class="reference external" 
href="http://127.0.0.1:5000";>http://127.0.0.1:5000</a></p></li>
-<li><p><strong>deploy_mode</strong> – MLflow deploy mode, support MLFLOW, 
DOCKER, DOCKER COMPOSE, default is DOCKER</p></li>
+<li><p><strong>deploy_mode</strong> – MLflow deploy mode, support MLFLOW, 
DOCKER, default is DOCKER</p></li>
 <li><p><strong>port</strong> – deploy port, default is 7000</p></li>
-<li><p><strong>cpu_limit</strong> – cpu limit, default is 1.0</p></li>
-<li><p><strong>memory_limit</strong> – memory limit, default is 500M</p></li>
 </ul>
 </dd>
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" 
id="pydolphinscheduler.tasks.mlflow.MLflowModels._child_task_mlflow_attr">
-<span class="sig-name descname"><span 
class="pre">_child_task_mlflow_attr</span></span><em class="property"><span 
class="w"> </span><span class="p"><span class="pre">=</span></span><span 
class="w"> </span><span class="pre">{'cpu_limit',</span> <span 
class="pre">'deploy_model_key',</span> <span class="pre">'deploy_port',</span> 
<span class="pre">'deploy_type',</span> <span 
class="pre">'memory_limit'}</span></em><a class="headerlink" 
href="#pydolphinscheduler.tasks.mlflow.MLflowModels._chi [...]
+<span class="sig-name descname"><span 
class="pre">_child_task_mlflow_attr</span></span><em class="property"><span 
class="w"> </span><span class="p"><span class="pre">=</span></span><span 
class="w"> </span><span class="pre">{'deploy_model_key',</span> <span 
class="pre">'deploy_port',</span> <span 
class="pre">'deploy_type'}</span></em><a class="headerlink" 
href="#pydolphinscheduler.tasks.mlflow.MLflowModels._child_task_mlflow_attr" 
title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -525,15 +507,6 @@ See 
https://mlflow.org/docs/latest/tracking.html#artifact-stores</p></li>
 <span class="w">    </span><span class="nt">parameters</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">-P learning_rate=0.2 -P colsample_bytree=0.8 -P 
subsample=0.9</span><span class="w"></span>
 <span class="w">    </span><span class="nt">experiment_name</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">xgboost</span><span class="w"></span>
 
-
-<span class="w">  </span><span class="p p-Indicator">-</span><span class="w"> 
</span><span class="nt">name</span><span class="p">:</span><span class="w"> 
</span><span class="l l-Scalar l-Scalar-Plain">deploy_mlflow</span><span 
class="w"></span>
-<span class="w">    </span><span class="nt">deps</span><span 
class="p">:</span><span class="w"> </span><span class="p 
p-Indicator">[</span><span class="nv">train_xgboost_native</span><span class="p 
p-Indicator">]</span><span class="w"></span>
-<span class="w">    </span><span class="nt">task_type</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">MLflowModels</span><span class="w"> </span>
-<span class="w">    </span><span class="nt">model_uri</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">models:/xgboost_native/Production</span><span class="w"></span>
-<span class="w">    </span><span class="nt">mlflow_tracking_uri</span><span 
class="p">:</span><span class="w"> </span><span 
class="nv">*mlflow_tracking_uri</span><span class="w"></span>
-<span class="w">    </span><span class="nt">deploy_mode</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">MLFLOW</span><span class="w"></span>
-<span class="w">    </span><span class="nt">port</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">7001</span><span class="w"></span>
-
 <span class="w">  </span><span class="p p-Indicator">-</span><span class="w"> 
</span><span class="nt">name</span><span class="p">:</span><span class="w"> 
</span><span class="l l-Scalar l-Scalar-Plain">train_automl</span><span 
class="w"></span>
 <span class="w">    </span><span class="nt">task_type</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">MLFlowProjectsAutoML</span><span class="w"> </span>
 <span class="w">    </span><span class="nt">mlflow_tracking_uri</span><span 
class="p">:</span><span class="w"> </span><span 
class="nv">*mlflow_tracking_uri</span><span class="w"></span>
@@ -561,14 +534,13 @@ See 
https://mlflow.org/docs/latest/tracking.html#artifact-stores</p></li>
 <span class="w">    </span><span class="nt">data_path</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">/data/examples/iris</span><span class="w"></span>
 <span class="w">    </span><span class="nt">search_params</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">max_depth=[5, 10];n_estimators=[100, 200]</span><span 
class="w"></span>
 
-
-<span class="w">  </span><span class="p p-Indicator">-</span><span class="w"> 
</span><span class="nt">name</span><span class="p">:</span><span class="w"> 
</span><span class="l l-Scalar 
l-Scalar-Plain">deploy_docker_compose</span><span class="w"></span>
-<span class="w">    </span><span class="nt">task_type</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">MLflowModels</span><span class="w"> </span>
+<span class="w">  </span><span class="p p-Indicator">-</span><span class="w"> 
</span><span class="nt">name</span><span class="p">:</span><span class="w"> 
</span><span class="l l-Scalar l-Scalar-Plain">deploy_mlflow</span><span 
class="w"></span>
 <span class="w">    </span><span class="nt">deps</span><span 
class="p">:</span><span class="w"> </span><span class="p 
p-Indicator">[</span><span class="nv">train_basic_algorithm</span><span 
class="p p-Indicator">]</span><span class="w"></span>
+<span class="w">    </span><span class="nt">task_type</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">MLflowModels</span><span class="w"></span>
 <span class="w">    </span><span class="nt">model_uri</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">models:/iris_B/Production</span><span class="w"></span>
 <span class="w">    </span><span class="nt">mlflow_tracking_uri</span><span 
class="p">:</span><span class="w"> </span><span 
class="nv">*mlflow_tracking_uri</span><span class="w"></span>
-<span class="w">    </span><span class="nt">deploy_mode</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">DOCKER COMPOSE</span><span class="w"></span>
-<span class="w">    </span><span class="nt">port</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">7003</span><span class="w"></span>
+<span class="w">    </span><span class="nt">deploy_mode</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">MLFLOW</span><span class="w"></span>
+<span class="w">    </span><span class="nt">port</span><span 
class="p">:</span><span class="w"> </span><span class="l l-Scalar 
l-Scalar-Plain">7001</span><span class="w"></span>
 </pre></div>
 </div>
 </section>
diff --git a/zh-cn/docs/dev/user_doc/guide/resource/configuration.html 
b/zh-cn/docs/dev/user_doc/guide/resource/configuration.html
index 0116fbc5d..3260b9659 100644
--- a/zh-cn/docs/dev/user_doc/guide/resource/configuration.html
+++ b/zh-cn/docs/dev/user_doc/guide/resource/configuration.html
@@ -52,10 +52,10 @@
 # user data local directory path, please make sure the directory exists and 
have read write permissions</span>
 <span class="hljs-meta">data.basedir.path</span>=<span 
class="hljs-string">/tmp/dolphinscheduler</span>
 <span class="hljs-comment">
-# resource storage type: HDFS, S3, NONE</span>
+# resource storage type: HDFS, S3, OSS, NONE</span>
 <span class="hljs-meta">resource.storage.type</span>=<span 
class="hljs-string">HDFS</span>
 <span class="hljs-comment">
-# resource store on HDFS/S3 path, resource file will store to this hadoop hdfs 
path, self configuration,</span>
+# resource store on HDFS/S3/OSS path, resource file will store to this hadoop 
hdfs path, self configuration,</span>
 <span class="hljs-comment"># please make sure the directory exists on hdfs and 
have read write permissions. &quot;/dolphinscheduler&quot; is recommended</span>
 <span class="hljs-meta">resource.storage.upload.base.path</span>=<span 
class="hljs-string">/tmp/dolphinscheduler</span>
 <span class="hljs-comment">
@@ -70,6 +70,17 @@
 <span class="hljs-comment"># You need to set this parameter when private cloud 
s3. If S3 uses public cloud, you only need to set resource.aws.region or set to 
the endpoint of a public cloud such as S3.cn-north-1.amazonaws.com.cn</span>
 <span class="hljs-meta">resource.aws.s3.endpoint</span>=<span 
class="hljs-string">http://localhost:9000</span>
 <span class="hljs-comment">
+# alibaba cloud access key id, required if you set resource.storage.type=OSS 
</span>
+<span class="hljs-meta">resource.alibaba.cloud.access.key.id</span>=<span 
class="hljs-string">&lt;your-access-key-id&gt;</span>
+<span class="hljs-comment"># alibaba cloud access key secret, required if you 
set resource.storage.type=OSS</span>
+<span class="hljs-meta">resource.alibaba.cloud.access.key.secret</span>=<span 
class="hljs-string">&lt;your-access-key-secret&gt;</span>
+<span class="hljs-comment"># alibaba cloud region, required if you set 
resource.storage.type=OSS</span>
+<span class="hljs-meta">resource.alibaba.cloud.region</span>=<span 
class="hljs-string">cn-hangzhou</span>
+<span class="hljs-comment"># oss bucket name, required if you set 
resource.storage.type=OSS</span>
+<span class="hljs-meta">resource.alibaba.cloud.oss.bucket.name</span>=<span 
class="hljs-string">dolphinscheduler</span>
+<span class="hljs-comment"># oss bucket endpoint, required if you set 
resource.storage.type=OSS</span>
+<span class="hljs-meta">resource.alibaba.cloud.oss.endpoint</span>=<span 
class="hljs-string">https://oss-cn-hangzhou.aliyuncs.com</span>
+<span class="hljs-comment">
 # if resource.storage.type=HDFS, the user must have the permission to create 
directories under the HDFS root path</span>
 <span class="hljs-meta">resource.hdfs.root.user</span>=<span 
class="hljs-string">root</span>
 <span class="hljs-comment"># if resource.storage.type=S3, the value like: 
s3a://dolphinscheduler;</span>
diff --git a/zh-cn/docs/dev/user_doc/guide/resource/configuration.json 
b/zh-cn/docs/dev/user_doc/guide/resource/configuration.json
index 40d1e2906..4dd924208 100644
--- a/zh-cn/docs/dev/user_doc/guide/resource/configuration.json
+++ b/zh-cn/docs/dev/user_doc/guide/resource/configuration.json
@@ -1,6 +1,6 @@
 {
   "filename": "configuration.md",
-  "__html": "<h1>资源中心配置详情</h1>\n<ul>\n<li>资源中心通常用于上传文件、UDF 
函数，以及任务组管理等操作。</li>\n<li>资源中心可以对接分布式的文件存储系统，如<a 
href=\"https://hadoop.apache.org/docs/r2.7.0/\";>Hadoop</a>（2.6+）或者<a 
href=\"https://github.com/minio/minio\";>MinIO</a>集群，也可以对接远端的对象存储，如<a 
href=\"https://aws.amazon.com/s3/\";>AWS S3</a>或者<a 
href=\"https://www.aliyun.com/product/oss\";>阿里云 
OSS</a>等。</li>\n<li>资源中心也可以直接对接本地文件系统。在单机模式下，您无需依赖<code>Hadoop</code>或<code>S3</code>一类的外部存储系统，可以方便地对接本地文件系统进行体验。</li>\n<li>除此之外，对于集群模式下的部署，您可以通过使用<
 [...]
+  "__html": "<h1>资源中心配置详情</h1>\n<ul>\n<li>资源中心通常用于上传文件、UDF 
函数，以及任务组管理等操作。</li>\n<li>资源中心可以对接分布式的文件存储系统，如<a 
href=\"https://hadoop.apache.org/docs/r2.7.0/\";>Hadoop</a>（2.6+）或者<a 
href=\"https://github.com/minio/minio\";>MinIO</a>集群，也可以对接远端的对象存储，如<a 
href=\"https://aws.amazon.com/s3/\";>AWS S3</a>或者<a 
href=\"https://www.aliyun.com/product/oss\";>阿里云 
OSS</a>等。</li>\n<li>资源中心也可以直接对接本地文件系统。在单机模式下，您无需依赖<code>Hadoop</code>或<code>S3</code>一类的外部存储系统，可以方便地对接本地文件系统进行体验。</li>\n<li>除此之外，对于集群模式下的部署，您可以通过使用<
 [...]
   "link": "/dist/zh-cn/docs/dev/user_doc/guide/resource/configuration.html",
   "meta": {}
 }
\ No newline at end of file
diff --git a/zh-cn/docs/dev/user_doc/guide/task/mlflow.html 
b/zh-cn/docs/dev/user_doc/guide/task/mlflow.html
index 01adce9e4..f238f4315 100644
--- a/zh-cn/docs/dev/user_doc/guide/task/mlflow.html
+++ b/zh-cn/docs/dev/user_doc/guide/task/mlflow.html
@@ -32,7 +32,6 @@
 <ul>
 <li>MLFLOW: 直接使用 <code>mlflow models serve</code> 部署模型。</li>
 <li>Docker: 打包 DOCKER 镜像后部署模型。</li>
-<li>Docker Compose: 使用Docker Compose 部署模型，将会取代上面的Docker部署。</li>
 </ul>
 </li>
 </ul>
@@ -177,30 +176,17 @@
 <p><img src="/img/tasks/demo/mlflow-models-mlflow.png" 
alt="mlflow-models-mlflow"></p>
 <h4>Docker</h4>
 <p><img src="/img/tasks/demo/mlflow-models-docker.png" 
alt="mlflow-models-docker"></p>
-<h4>DOCKER COMPOSE</h4>
-<p><img src="/img/tasks/demo/mlflow-models-docker-compose.png" 
alt="mlflow-models-docker-compose"></p>
-<table>
-<thead>
-<tr>
-<th><strong>任务参数</strong></th>
-<th><strong>描述</strong></th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>最大CPU限制</td>
-<td>如 <code>1.0</code> 或者 <code>0.5</code>，与 docker compose 一致</td>
-</tr>
-<tr>
-<td>最大内存限制</td>
-<td>如 <code>1G</code> 或者 <code>500M</code>，与 docker compose 一致</td>
-</tr>
-</tbody>
-</table>
 <h2>环境准备</h2>
 <h3>conda 环境配置</h3>
-<p>你需要进入admin账户配置一个conda环境变量（请提前<a 
href="https://docs.continuum.io/anaconda/install/";>安装anaconda</a>
-或者<a 
href="https://docs.conda.io/en/latest/miniconda.html#installing";>安装miniconda</a>
 )。</p>
+<p>请提前<a href="https://docs.continuum.io/anaconda/install/";>安装anaconda</a> 
或者<a 
href="https://docs.conda.io/en/latest/miniconda.html#installing";>安装miniconda</a></p>
+<p><strong>方法A：</strong></p>
+<p>配置文件：/dolphinscheduler/conf/env/dolphinscheduler_env.sh。</p>
+<p>在文件最后添加内容</p>
+<pre><code># 配置你的conda环境路径
+export PATH=/opt/anaconda3/bin:$PATH
+</code></pre>
+<p><strong>方法B：</strong></p>
+<p>你需要进入admin账户配置一个conda环境变量。</p>
 <p><img src="/img/tasks/demo/mlflow-conda-env.png" alt="mlflow-conda-env"></p>
 <p>后续注意配置任务时，环境选择上面创建的conda环境，否则程序会找不到conda环境。</p>
 <p><img src="/img/tasks/demo/mlflow-set-conda-env.png" 
alt="mlflow-set-conda-env"></p>
@@ -214,6 +200,13 @@ mlflow server -h 0.0.0.0 -p 5000 --serve-artifacts 
--backend-store-uri sqlite://
 <p>运行后会启动一个MLflow服务。</p>
 <p>可以通过访问 MLflow service (<code>http://localhost:5000</code>) 页面查看实验与模型。</p>
 <p><img src="/img/tasks/demo/mlflow-server.png" alt="mlflow-server"></p>
+<h3>内置算法仓库配置</h3>
+<p>如果遇到github无法访问的情况，可以修改<code>commom.properties</code>配置文件的以下字段，将github地址替换能访问的地址。</p>
+<pre><code class="language-yaml"><span class="hljs-comment"># mlflow task 
plugin preset repository</span>
+<span 
class="hljs-string">ml.mlflow.preset_repository=https://github.com/apache/dolphinscheduler-mlflow</span>
+<span class="hljs-comment"># mlflow task plugin preset repository 
version</span>
+<span 
class="hljs-string">ml.mlflow.preset_repository_version=&quot;main&quot;</span>
+</code></pre>
 </div></section><footer class="footer-container"><div 
class="footer-body"><div><h3>联系我们</h3><h4>有问题需要反馈？请通过以下方式联系我们。</h4></div><div 
class="contact-container"><ul><li><a 
href="https://join.slack.com/t/asf-dolphinscheduler/shared_invite/zt-1e36toy4n-5n9U2R__FDM05R~MJFFVBg";><img
 class="img-base" src="/img/slack.png"/><img class="img-change" 
src="/img/slack-selected.png"/><p>Slack</p></a></li><li><a 
href="/zh-cn/docs/latest/user_doc/contribute/join/subscribe.html"><img 
class="img-base" src=" [...]
   <script src="/asset/js/react/react-with-addons.min.js"></script>
   <script src="/asset/js/react/react-dom.min.js"></script>
diff --git a/zh-cn/docs/dev/user_doc/guide/task/mlflow.json 
b/zh-cn/docs/dev/user_doc/guide/task/mlflow.json
index 3d6656a7e..42800a4c4 100644
--- a/zh-cn/docs/dev/user_doc/guide/task/mlflow.json
+++ b/zh-cn/docs/dev/user_doc/guide/task/mlflow.json
@@ -1,6 +1,6 @@
 {
   "filename": "mlflow.md",
-  "__html": "<h1>MLflow节点</h1>\n<h2>综述</h2>\n<p><a 
href=\"https://mlflow.org\";>MLflow</a> 是一个MLops领域一个优秀的开源项目， 
用于管理机器学习的生命周期，包括实验、可再现性、部署和中心模型注册。</p>\n<p>MLflow 组件用于执行 MLflow 任务，目前包含Mlflow 
Projects，和MLflow Models。（Model Registry将在不就的将来支持）。</p>\n<ul>\n<li>MLflow 
Projects: 将代码打包，并可以运行到任务的平台上。</li>\n<li>MLflow Models: 
在不同的服务环境中部署机器学习模型。</li>\n<li>Model Registry: 在一个中央存储库中存储、注释、发现和管理模型 
(你也可以在你的MLflow project 里面自行注册模型)。</li>\n</ul>\n<p>目前 Mlflow 
组件支持的和即将支持的内容如下中：</p>\n<ul>\n<li>MLflow Project [...]
+  "__html": "<h1>MLflow节点</h1>\n<h2>综述</h2>\n<p><a 
href=\"https://mlflow.org\";>MLflow</a> 是一个MLops领域一个优秀的开源项目， 
用于管理机器学习的生命周期，包括实验、可再现性、部署和中心模型注册。</p>\n<p>MLflow 组件用于执行 MLflow 任务，目前包含Mlflow 
Projects，和MLflow Models。（Model Registry将在不就的将来支持）。</p>\n<ul>\n<li>MLflow 
Projects: 将代码打包，并可以运行到任务的平台上。</li>\n<li>MLflow Models: 
在不同的服务环境中部署机器学习模型。</li>\n<li>Model Registry: 在一个中央存储库中存储、注释、发现和管理模型 
(你也可以在你的MLflow project 里面自行注册模型)。</li>\n</ul>\n<p>目前 Mlflow 
组件支持的和即将支持的内容如下中：</p>\n<ul>\n<li>MLflow Project [...]
   "link": "/dist/zh-cn/docs/dev/user_doc/guide/task/mlflow.html",
   "meta": {}
 }
\ No newline at end of file

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