Revert "GITBOOK-117: No subject" (#1275)

montanalow · web-flow · commit 24ff2d215265 · 2024-01-05T13:16:03.000-07:00
diff --git a/pgml-cms/docs/SUMMARY.md b/pgml-cms/docs/SUMMARY.md
@@ -36,7 +36,7 @@
     * [pgml.tune()](introduction/apis/sql-extensions/pgml.tune.md)
   * [Client SDKs](introduction/apis/client-sdks/README.md)
     * [Overview](introduction/apis/client-sdks/getting-started.md)
-    * [Collections](../../pgml-docs/docs/guides/sdks/collections.md)
+    * [Collections](introduction/apis/client-sdks/collections.md)
     * [Pipelines](introduction/apis/client-sdks/pipelines.md)
     * [Search](introduction/apis/client-sdks/search.md)
     * [Tutorials](introduction/apis/client-sdks/tutorials/README.md)
diff --git a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md
@@ -26,11 +26,11 @@ pgml.deploy(
 
 There are 3 different deployment strategies available:
 
-| Strategy      | Description                                                                                                           |
-| ------------- | --------------------------------------------------------------------------------------------------------------------- |
-| `most_recent` | The most recently trained model for this project is immediately deployed, regardless of metrics.                      |
-| `best_score`  | The model that achieved the best key metric score is immediately deployed.                                            |
-| `rollback`    | The model that was last deployed for this project is immediately redeployed, overriding the currently deployed model. |
+| Strategy      | Description                                                                                      |
+| ------------- |--------------------------------------------------------------------------------------------------|
+| `most_recent` | The most recently trained model for this project is immediately deployed, regardless of metrics. |
+| `best_score`  | The model that achieved the best key metric score is immediately deployed.                       |
+| `rollback`    | The model that was deployed before to the current one is deployed.                               |
 
 The default deployment behavior allows any algorithm to qualify. It's automatically used during training, but can be manually executed as well:
 
@@ -40,11 +40,12 @@ The default deployment behavior allows any algorithm to qualify. It's automatica
 
 #### SQL
 
-<pre class="language-sql"><code class="lang-sql"><strong>SELECT * FROM pgml.deploy(
-</strong>    'Handwritten Digit Image Classifier',
+```sql
+SELECT * FROM pgml.deploy(
+   'Handwritten Digit Image Classifier',
     strategy => 'best_score'
 );
-</code></pre>
+```
 
 #### Output
 
@@ -121,3 +122,22 @@ SELECT * FROM pgml.deploy(
  Handwritten Digit Image Classifier | rollback | xgboost
 (1 row)
 ```
+
+### Specific Model IDs
+
+In the case you need to deploy an exact model that is not the `most_recent` or `best_score`, you may deploy a model by id. Model id's can be found in the `pgml.models` table. 
+
+#### SQL
+
+```sql
+SELECT * FROM pgml.deploy(12);
+```
+
+#### Output
+
+```sql
+              project               | strategy | algorithm
+------------------------------------+----------+-----------
+ Handwritten Digit Image Classifier | specific | xgboost
+(1 row)
+```
diff --git a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.train/data-pre-processing.md b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.train/data-pre-processing.md
@@ -25,11 +25,11 @@ In this example:
 
 There are 3 steps to preprocessing data:
 
-* [Encoding](data-pre-processing.md#ordinal-encoding) categorical values into quantitative values
-* [Imputing](data-pre-processing.md#imputing-missing-values) NULL values to some quantitative value
-* [Scaling](data-pre-processing.md#scaling-values) quantitative values across all variables to similar ranges
+* [Encoding](../../../../../../pgml-dashboard/content/docs/training/preprocessing.md#categorical-encodings) categorical values into quantitative values
+* [Imputing](../../../../../../pgml-dashboard/content/docs/training/preprocessing.md#imputing-missing-values) NULL values to some quantitative value
+* [Scaling](../../../../../../pgml-dashboard/content/docs/training/preprocessing.md#scaling-values) quantitative values across all variables to similar ranges
 
-These preprocessing steps may be specified on a per-column basis to the [train()](./) function. By default, PostgresML does minimal preprocessing on training data, and will raise an error during analysis if NULL values are encountered without a preprocessor. All types other than `TEXT` are treated as quantitative variables and cast to floating point representations before passing them to the underlying algorithm implementations.
+These preprocessing steps may be specified on a per-column basis to the [train()](../../../../../../docs/training/overview/) function. By default, PostgresML does minimal preprocessing on training data, and will raise an error during analysis if NULL values are encountered without a preprocessor. All types other than `TEXT` are treated as quantitative variables and cast to floating point representations before passing them to the underlying algorithm implementations.
 
 ```sql
 SELECT pgml.train(
diff --git a/pgml-cms/docs/resources/developer-docs/contributing.md b/pgml-cms/docs/resources/developer-docs/contributing.md
@@ -67,7 +67,7 @@ Once there, you can initialize `pgrx` and get going:
 #### Pgrx command line and environments
 
 ```commandline
-cargo install cargo-pgrx --version "0.9.8" --locked && \
+cargo install cargo-pgrx --version "0.11.2" --locked && \
 cargo pgrx init # This will take a few minutes
 ```
 
diff --git a/pgml-cms/docs/resources/developer-docs/installation.md b/pgml-cms/docs/resources/developer-docs/installation.md
@@ -36,7 +36,7 @@ brew bundle
 PostgresML is written in Rust, so you'll need to install the latest compiler from [rust-lang.org](https://rust-lang.org). Additionally, we use the Rust PostgreSQL extension framework `pgrx`, which requires some initialization steps:
 
 ```bash
-cargo install cargo-pgrx --version 0.9.8 && \
+cargo install cargo-pgrx --version 0.11.2 && \
 cargo pgrx init
 ```
 
@@ -63,8 +63,7 @@ To install the necessary Python packages into a virtual environment, use the `vi
 ```bash
 virtualenv pgml-venv && \
 source pgml-venv/bin/activate && \
-pip install -r requirements.txt && \
-pip install -r requirements-xformers.txt --no-dependencies
+pip install -r requirements.txt
 ```
 {% endtab %}
 
@@ -146,7 +145,7 @@ pgml_test=# SELECT pgml.version();
 We like and use pgvector a lot, as documented in our blog posts and examples, to store and search embeddings. You can install pgvector from source pretty easily:
 
 ```bash
-git clone --branch v0.4.4 https://github.com/pgvector/pgvector && \
+git clone --branch v0.5.0 https://github.com/pgvector/pgvector && \
 cd pgvector && \
 echo "trusted = true" >> vector.control && \
 make && \
@@ -288,7 +287,7 @@ We use the `pgrx` Postgres Rust extension framework, which comes with its own in
 
 ```bash
 cd pgml-extension && \
-cargo install cargo-pgrx --version 0.9.8 && \
+cargo install cargo-pgrx --version 0.11.2 && \
 cargo pgrx init
 ```
 
diff --git a/pgml-docs/docs/guides/sdks/collections.md b/pgml-docs/docs/guides/sdks/collections.md
