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Jan 5, 2024
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Revert "GITBOOK-117: No subject" #1275

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2 changes: 1 addition & 1 deletion pgml-cms/docs/SUMMARY.md
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Expand Up @@ -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)
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36 changes: 28 additions & 8 deletions pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md
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Expand Up @@ -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:

Expand All @@ -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

Expand Down Expand Up @@ -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)
```
8 changes: 4 additions & 4 deletions pgml-cms/docs/introduction/apis/sql-extensions/pgml.train/data-pre-processing.md
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Expand Up @@ -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(
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2 changes: 1 addition & 1 deletion pgml-cms/docs/resources/developer-docs/contributing.md
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Expand Up @@ -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
```

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9 changes: 4 additions & 5 deletions pgml-cms/docs/resources/developer-docs/installation.md
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Expand Up @@ -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
```

Expand All @@ -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 %}

Expand Down Expand Up @@ -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 && \
Expand Down Expand Up @@ -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
```

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