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new 8bb4c428242 Publishing website 2023/04/18 22:17:00 at commit 1c63a02
8bb4c428242 is described below
commit 8bb4c42824273e9e4740f8b5859ac59ef0b23b19
Author: jenkins <bui...@apache.org>
AuthorDate: Tue Apr 18 22:17:01 2023 +0000
Publishing website 2023/04/18 22:17:00 at commit 1c63a02
---
website/generated-content/documentation/index.xml | 2 +-
website/generated-content/documentation/ml/about-ml/index.html | 2 +-
.../documentation/sdks/python-machine-learning/index.html | 2 +-
website/generated-content/sitemap.xml | 2 +-
4 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/website/generated-content/documentation/index.xml
b/website/generated-content/documentation/index.xml
index 39a0289a1fe..5defb0ce992 100644
--- a/website/generated-content/documentation/index.xml
+++ b/website/generated-content/documentation/index.xml
@@ -106,7 +106,7 @@ that illustrates running Scikit-learn models with Apache
Beam.</p>
<li>Use tensorflow 2.7 or later.</li>
<li>Pass the path of the model to the TensorFlow
<code>ModelHandler</code> by using
<code>model_uri=&lt;path_to_trained_model&gt;</code>.</li>
<li>Alternatively, you can pass the path to saved weights of the trained
model, a function to build the model using
<code>create_model_fn=&lt;function&gt;</code>, and set the
<code>model_type=ModelType.SAVED_WEIGHTS</code>.
-See <a
href="https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow_with_tensorflowhub.ipynb";>this
notebook</a> that illustrates running Tensorflow models with Built-in model
handlers.</li>
+See <a
href="https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb";>this
notebook</a> that illustrates running Tensorflow models with Built-in model
handlers.</li>
</ul>
</li>
<li>Using <code>tfx_bsl</code>.
diff --git a/website/generated-content/documentation/ml/about-ml/index.html
b/website/generated-content/documentation/ml/about-ml/index.html
index 8e4e61e1f6a..e92768efd5a 100644
--- a/website/generated-content/documentation/ml/about-ml/index.html
+++ b/website/generated-content/documentation/ml/about-ml/index.html
@@ -29,7 +29,7 @@ that illustrates running PyTorch models with Apache
Beam.</p><h4 id=scikit-learn
<code>model_uri=<path_to_pickled_file></code> and <code>model_file_type:
<ModelFileType></code>, where you can specify
<code>ModelFileType.PICKLE</code> or <code>ModelFileType.JOBLIB</code>,
depending on how the model was serialized.</li></ol><p>See <a
href=https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_sklearn.ipynb>this
notebook</a>
that illustrates running Scikit-learn models with Apache Beam.</p><h4
id=tensorflow>TensorFlow</h4><p>To use TensorFlow with the RunInference API,
you have two options:</p><ol><li>Use the built-in TensorFlow Model Handlers in
Apache Beam SDK - <code>TFModelHandlerNumpy</code> and
<code>TFModelHandlerTensor</code>.<ul><li>Depending on the type of input for
your model, use <code>TFModelHandlerNumpy</code> for <code>numpy</code> input
and <code>TFModelHandlerTensor</code> for <code>tf.Tenso [...]
-See <a
href=https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow_with_tensorflowhub.ipynb>this
notebook</a> that illustrates running Tensorflow models with Built-in model
handlers.</li></ul></li><li>Using <code>tfx_bsl</code>.<ul><li>Use this
approach if your model input is of type <code>tf.Example</code>.</li><li>Use
<code>tfx_bsl</code> version 1.10.0 or later.</li><li>Create a model handler
using <code>tfx_bsl.public.beam.run_inference.CreateM [...]
+See <a
href=https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb>this
notebook</a> that illustrates running Tensorflow models with Built-in model
handlers.</li></ul></li><li>Using <code>tfx_bsl</code>.<ul><li>Use this
approach if your model input is of type <code>tf.Example</code>.</li><li>Use
<code>tfx_bsl</code> version 1.10.0 or later.</li><li>Create a model handler
using <code>tfx_bsl.public.beam.run_inference.CreateModelHandler()</code [...]
See <a
href=https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb>this
notebook</a>
that illustrates running TensorFlow models with Apache Beam and
tfx-bsl.</li></ul></li></ol><h2 id=automatic-model-refresh>Automatic model
refresh</h2><p>To automatically update the model being used with the
RunInference <code>PTransform</code> without stopping the pipeline, pass a <a
href=https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelMetadata><code>ModelMetadata</code></a>
side input <code>PCollection</code> to the R [...]
an update to the side input. This could happen with global windowed side
inputs with data driven triggers, such as <code>AfterCount</code> and
<code>AfterProcessingTime</code>. Until the side input is updated, emit the
default or initial model ID that is used to pass the respective
<code>ModelHandler</code> as a side input.</p><h2 id=custom-inference>Custom
Inference</h2><p>The RunInference API doesn’t currently support making
remote inference calls using, for example, the Natural [...]
diff --git
a/website/generated-content/documentation/sdks/python-machine-learning/index.html
b/website/generated-content/documentation/sdks/python-machine-learning/index.html
index 04aa8a31a89..8b7811f281a 100644
---
a/website/generated-content/documentation/sdks/python-machine-learning/index.html
+++
b/website/generated-content/documentation/sdks/python-machine-learning/index.html
@@ -39,7 +39,7 @@ that illustrates running PyTorch models with Apache
Beam.</p><h3 id=scikit-learn
<code>model_uri=<path_to_pickled_file></code> and <code>model_file_type:
<ModelFileType></code>, where you can specify
<code>ModelFileType.PICKLE</code> or <code>ModelFileType.JOBLIB</code>,
depending on how the model was serialized.</li></ol><p>See <a
href=https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_sklearn.ipynb>this
notebook</a>
that illustrates running Scikit-learn models with Apache Beam.</p><h3
id=tensorflow>TensorFlow</h3><p>To use TensorFlow with the RunInference API,
you have two options:</p><ol><li>Use the built-in TensorFlow Model Handlers in
Apache Beam SDK - <code>TFModelHandlerNumpy</code> and
<code>TFModelHandlerTensor</code>.<ul><li>Depending on the type of input for
your model, use <code>TFModelHandlerNumpy</code> for <code>numpy</code> input
and <code>TFModelHandlerTensor</code> for <code>tf.Tenso [...]
-See <a
href=https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow_with_tensorflowhub.ipynb>this
notebook</a> that illustrates running Tensorflow models with Built-in model
handlers.</li></ul></li><li>Using <code>tfx_bsl</code>.<ul><li>Use this
approach if your model input is of type <code>tf.Example</code>.</li><li>Use
<code>tfx_bsl</code> version 1.10.0 or later.</li><li>Create a model handler
using <code>tfx_bsl.public.beam.run_inference.CreateM [...]
+See <a
href=https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb>this
notebook</a> that illustrates running Tensorflow models with Built-in model
handlers.</li></ul></li><li>Using <code>tfx_bsl</code>.<ul><li>Use this
approach if your model input is of type <code>tf.Example</code>.</li><li>Use
<code>tfx_bsl</code> version 1.10.0 or later.</li><li>Create a model handler
using <code>tfx_bsl.public.beam.run_inference.CreateModelHandler()</code [...]
See <a
href=https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb>this
notebook</a>
that illustrates running TensorFlow models with Apache Beam and
tfx-bsl.</li></ul></li></ol><h2 id=use-custom-models>Use custom
models</h2><p>If you would like to use a model that isn’t specified by
one of the supported frameworks, the RunInference API is designed flexibly to
allow you to use any custom machine learning models.
You only need to create your own <code>ModelHandler</code> or
<code>KeyedModelHandler</code> with logic to load your model and use it to run
the inference.</p><p>A simple example can be found in <a
href=https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_custom_inference.ipynb>this
notebook</a>.
diff --git a/website/generated-content/sitemap.xml
b/website/generated-content/sitemap.xml
index 9103d943b91..a4979c10191 100644
--- a/website/generated-content/sitemap.xml
+++ b/website/generated-content/sitemap.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><urlset
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9";
xmlns:xhtml="http://www.w3.org/1999/xhtml";><url><loc>/blog/beam-2.46.0/</loc><lastmod>2023-04-18T11:41:46-04:00</lastmod></url><url><loc>/categories/blog/</loc><lastmod>2023-04-18T11:41:46-04:00</lastmod></url><url><loc>/blog/</loc><lastmod>2023-04-18T11:41:46-04:00</lastmod></url><url><loc>/categories/</loc><lastmod>2023-04-18T11:41:46-04:00</lastmod></url><url><loc>/catego
[...]
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><urlset
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9";
xmlns:xhtml="http://www.w3.org/1999/xhtml";><url><loc>/blog/beam-2.46.0/</loc><lastmod>2023-04-18T16:44:37-04:00</lastmod></url><url><loc>/categories/blog/</loc><lastmod>2023-04-18T16:44:37-04:00</lastmod></url><url><loc>/blog/</loc><lastmod>2023-04-18T16:44:37-04:00</lastmod></url><url><loc>/categories/</loc><lastmod>2023-04-18T16:44:37-04:00</lastmod></url><url><loc>/catego
[...]
\ No newline at end of file
