PostgresML installation consists of three parts: PostgreSQL database, Postgres extension for machine learning and a dashboard app. The extension provides all the machine learning functionality and can be used independently using any SQL IDE. The dashboard app provides an easy to use interface for writing SQL notebooks, performing and tracking ML experiments and ML models.

If you want to check out the functionality without the hassle of Docker,[sign up for a free PostgresML account](https://postgresml.org/signup). You'll get a free database in seconds, with access to GPUs and state of the art LLMs.

For more details, take a look at our[Quick Start with Docker](https://postgresml.org/docs/guides/setup/quick_start_with_docker) documentation.

If you want to check out the functionality without the hassle of Docker,[sign up for a free PostgresML account](https://postgresml.org/signup). You'll get a free database in seconds, with access to GPUs and state of the art LLMs.

For more details, take a look at our[Quick Start with Docker](https://postgresml.org/docs/guides/developer-docs/quick-start-with-docker) documentation.

- On local installation, go to dashboard app at`http://localhost:8000/` to use SQL notebooks.

- On the cloud console click on the**Dashboard** button to connect to your instance with a SQL notebook, or connect directly with tools listed below.

- On local installation, go to dashboard app at`http://localhost:8000/` to use SQL notebooks.

comrak ="0.17"

chrono ="0.4"

csv-async ="1"

convert_case ="0.6"

dotenv ="0.15"

env_logger ="0.10"

itertools ="0.10"

parking_lot ="0.12"

lazy_static ="1.4"

log ="0.4"

markdown ="1.0.0-alpha.13"

num-traits ="0.2"

once_cell ="1.18"

rand ="0.8"

tantivy ="0.19"

time ="0.3"

tokio = {version ="1",features = ["full"] }

url ="2.4"

yaml-rust ="0.4"

zoomies = {git="https://github.com/HyperparamAI/zoomies.git",branch="master" }

pgvector = {version ="0.2.2",features = ["sqlx","postgres" ] }

PostgresML provides a dashboard with analytical views of the training data and model performance, as well as integrated notebooks for rapid iteration. It is primarily written in Rust using[Rocket](https://rocket.rs/) as a lightweight web framework and[SQLx](https://github.com/launchbadge/sqlx) to interact with the database.

Please see the[quick start instructions](https://postgresml.org/user_guides/setup/quick_start_with_docker/) for general information on installing or deploying PostgresML. A[developer guide](https://postgresml.org/developer_guide/overview/) is also available for those who would like to contribute.

Please see the[quick start instructions](https://postgresml.org/docs/guides/getting-started/sign-up) for general information on installing or deploying PostgresML. A[developer guide](https://postgresml.org/developer_guide/overview/) is also available for those who would like to contribute.

PostgresML comes with a web app to provide visibility into models and datasets in your database. If you're running[our Docker container](/docs/guides/setup/quick_start_with_docker/), you can view it running on[http://localhost:8000/](http://localhost:8000/).

PostgresML comes with a web app to provide visibility into models and datasets in your database. If you're running[our Docker container](/docs/guides/developer-docs/quick-start-with-docker), you can view it running on[http://localhost:8000/](http://localhost:8000/).

If you're using our <ahref="/docs/guides/setup/quick_start_with_docker">Docker</a> stack, you can import the data there:</p>

If you're using our <ahref="/docs/guides/developer-docs/quick-start-with-docker">Docker</a> stack, you can import the data there:</p>

!!! tip

If you're just looking to try PostgresML without installing it on your system, take a look at our[Quick Start with Docker](/docs/guides/setup/quick_start_with_docker) guide.

If you're just looking to try PostgresML without installing it on your system, take a look at our[Quick Start with Docker](/docs/guides/developer-docs/quick-start-with-docker) guide.

!!!

asyncfndoc_handler<'a>(path:PathBuf,cluster:&Cluster) ->Result<ResponseOk,Status>{

let guides =vec![

NavLink::new("Setup").children(vec![

NavLink::new("Installation").children(vec![

NavLink::new("v2").href("/docs/guides/setup/v2/installation"),

NavLink::new("Upgrade from v1.0 to v2.0")

.href("/docs/guides/setup/v2/upgrade-from-v1"),

NavLink::new("v1").href("/docs/guides/setup/installation"),

NavLink::new("Quick Start with Docker")

.href("/docs/guides/setup/quick_start_with_docker"),

NavLink::new("Distributed Training").href("/docs/guides/setup/distributed_training"),

NavLink::new("GPU Support").href("/docs/guides/setup/gpu_support"),

NavLink::new("Developer Setup").href("/docs/guides/setup/developers"),

NavLink::new("Training").children(vec![

NavLink::new("Overview").href("/docs/guides/training/overview"),

NavLink::new("Algorithm Selection").href("/docs/guides/training/algorithm_selection"),

NavLink::new("Hyperparameter Search")

.href("/docs/guides/training/hyperparameter_search"),

NavLink::new("Preprocessing Data").href("/docs/guides/training/preprocessing"),

NavLink::new("Joint Optimization").href("/docs/guides/training/joint_optimization"),

NavLink::new("Predictions").children(vec![

NavLink::new("Overview").href("/docs/guides/predictions/overview"),

NavLink::new("Deployments").href("/docs/guides/predictions/deployments"),

NavLink::new("Batch Predictions").href("/docs/guides/predictions/batch"),

NavLink::new("Transformers").children(vec![

NavLink::new("Setup").href("/docs/guides/transformers/setup"),

NavLink::new("Pre-trained Models").href("/docs/guides/transformers/pre_trained_models"),

NavLink::new("Fine Tuning").href("/docs/guides/transformers/fine_tuning"),

NavLink::new("Embeddings").href("/docs/guides/transformers/embeddings"),

NavLink::new("Vector Operations").children(vec![

NavLink::new("Overview").href("/docs/guides/vector_operations/overview")

NavLink::new("Dashboard").href("/docs/guides/dashboard/overview"),

NavLink::new("Schema").children(vec![

NavLink::new("Models").href("/docs/guides/schema/models"),

NavLink::new("Snapshots").href("/docs/guides/schema/snapshots"),

NavLink::new("Projects").href("/docs/guides/schema/projects"),

NavLink::new("Deployments").href("/docs/guides/schema/deployments"),

render(cluster,&path, guides,"Guides",&Path::new("docs")).await

use rocket::fs::NamedFile;

pubasyncfngitbook_assets(path:PathBuf) ->Option<NamedFile>{

let path =PathBuf::from(&config::docs_dir())

.join(path);

NamedFile::open(path).await.ok()

asyncfndoc_handler(path:PathBuf,cluster:&Cluster) ->Result<ResponseOk,Status>{

let root =PathBuf::from("docs/guides/");

let index_path =PathBuf::from(&config::docs_dir())

.join(&root)

let contents = tokio::fs::read_to_string(&index_path).await.expect(

index_path

let mdast =::markdown::to_mdast(&contents,&::markdown::ParseOptions::default())

let guides = markdown::parse_summary_into_nav_links(&mdast)

render(cluster,&path, guides,"Guides",&Path::new("docs"),&config::docs_dir()).await

&config::blogs_dir(),

) ->Result<ResponseOk,Status>{

let url = path.clone();

let path =Path::new(&config::content_dir())

let path =Path::new(&content)

.join(folder)

.join(&(path.to_str().unwrap().to_string() +".md"));

info!("path: {:?}", path);

let contents =match tokio::fs::read_to_string(&path).await{

Ok(contents) => contents,

pubfnroutes() ->Vec<Route>{

routes![doc_handler, blog_handler, search]

routes![gitbook_assets,doc_handler, blog_handler, search]

<divclass="collapse navbar-collapse"id="navbarSupportedContent">

<ulclass="navbar-nav flex-grow-1 gap-4 me-auto mb-4 mb-lg-0">

<liclass="nav-item d-flex align-items-center">

<aclass="nav-link p-0"href="/docs/guides/setup/quick_start_with_docker">Docs</a>

<aclass="nav-link p-0"href="/docs/guides/developer-docs/quick-start-with-docker">Docs</a>

<% if !standalone_dashboard { %>