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Each `Base` object has an `id` (a unique hash) as it does in the other SDKs. This field is only populated if the `Base` has been previously serialized. If you *really* need the hash, you can get it using the `get_id()` method. Be aware that this call will fully serialize the object to create the `id` if the `id` is not populated! By default, the hash will be generated without decomposing the object. However, you can pass `decompose=True` as an argument if you want the decomposed `id`. | [
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Note that all typed attributes of a class **must be initialized with a non-mutable default value** or a value that you will only change by reassignment. If you'd like mutable defaults, you should write an `__init__` method for the class to set these (see the `Block` example in the first section). | [
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class AlternativeLine(Base, speckle_type="Line_Two"):
"""
The `speckle_type` is automatically populated by the class name.
You can override this behaviour as demonstrated here.
"""
a: Point = None
b: Point = None | [
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You can also mark typed attributes as detachable or chunkable by updating the internal `_detachable` set or `_chunkable` dict with the provided helper methods. | [
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# properties are also picked up and serialised as you'd expect
@property
def origin(self):
return self._origin | [
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@origin.setter
def origin(self, value: Point):
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The `BaseObjectSerializer` is used behind the scenes in the `operations` for decomposing and serializing `Base` objects so they can be sent/received to the server. You probably won't ever need to use it directly. However, if you want, you can use it to get the id (hash) and a serializable object representation of the decomposed `Base`. You can learn more about the Speckle `Base` object [here](/dev/base) and the decomposition API [here](/dev/decomposition). | [
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] |
In this tutorial, we'll be working with Speckle data and using it to create a super simple dashboard. We'll be receiving geometry from a stream, updating the data, and using it to do some calculations and simple plots using Plotly and Dash. | [
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We will assume you have general knowledge of Python and Speckle. If anything trips you up, have a look back at the [Python examples](/dev/py-examples) or the [Speckle Concepts](/user/concepts). | [
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If you want to follow along with the code, the repo for this project can be found [here](https://github.com/izzylys/demo-specklepy). | [
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] |
If you've already been through the [Python examples](/dev/py-examples), you'll already know how to receive objects from the server. As a refresher, you'll need to create a `SpeckleClient` which serves as your entry point to the API. We then authenticate this client using a token from a local account. If you haven't used the [Manager](/user/manager) to add a local account, you can go to `your-server.com/profile` and create a Personal Access Token to use here. We'll then use this client to get the commit we're interested in. | [
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In this case, we're going to be looking at this twisty building Alan generated in Grasshopper and send to Speckle using the [Grasshopper Connector](/user/grasshopper). It has 10 levels split into separate objects each containing fields for facades, columns, banisters, and floor slabs. | [
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The snippet below shows how you would authenticate the client, get the commit we're interested in, and use a server transport to receive the commit object. | [
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] |
```py
from specklepy.api import operations
from specklepy.api.client import SpeckleClient
from specklepy.api.credentials import get_default_account
from specklepy.transports.server import ServerTransport | [
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] |
Now that we've received this building from Grasshopper, let's make some modifications. The data has already been structured nicely into a list of `Base` objects each representing a level. Each level has attributes containing that level's facade, columns, banister, and floor slab. | [
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Let's say we want to do some estimated embodied carbon calculations. However, currently no material information has been added to the elements in the stream. We can add this ourselves and update the stream for the rest of the team. | [
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To do this, let's create classes for each material and add these materials to a "material" attribute on each object within the stream. Obviously, this is a bit of an over the top example. It would be more efficient to just add a string attribute to each object that indicates the material, then look up the material properties from a database when running the calculations. However, I want to have some fun here and show you some of the cool things you can do with the `Base` class! | [
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Let's start with defining our materials as `Base` subclasses. This ensures they'll be serialised correctly and that they'll be picked up by the `Base` type registry. We can then create a mapping for each element type name to the `speckle_type` of the corresponding material class. The `speckle_type` is prepopulated by the class and defaults to the class name. | [
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] |
```py
# density (kg/m^3) and embodied carbon (kg CO^2/kg) estimates
# from https://www.greenspec.co.uk/building-design/embodied-energy/
class Concrete(Base):
density: str = 2400
embodied_carbon = 0.159 | [
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MATERIALS_MAPPING = {
"@floorSlab": "Concrete",
"@banister": "Glass",
"@facade": "Glass",
"@columns": "Steel",
}
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We can now write a function that iterates through all the members of a level and adds a material if it exists in our mapping. | [
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] |
```py
def add_materials_data(level: Base) -> Base:
# first, get all the attribute names
names = level.get_member_names()
# then iterate through them to check if they exist in our mapping
for name in names:
if name not in MATERIALS_MAPPING.keys():
break
# if they do, use this class method to get the class and init it
material = Base.get_registered_type(MATERIALS_MAPPING[name])()
# now we can add a `@material` attribute dynamically to each object.
# note that we're making it detachable with the `@`
prop = level[name]
if isinstance(prop, Base):
prop["@material"] = material
elif isinstance(prop, list):
for item in prop:
item["@material"] = material
return level
``` | [
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] |
Note that we've added a _detached_ dynamic attribute called `@material` to each element we are interested in. We've marked it as detachable (prepended with an `@`) so we don't store hundreds of copies of the same materials classes in this stream. Each instance of each materials class in our case is the same, so we only need to store each once. However, we want to reference them within multiple elements. Using a detachable attribute is the solution! You get a reference for the correct material object in each element, but you only store unique objects (in our case, one instance of each material class) in the stream. | [
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We can now use the `add_materials_data()` method we wrote to update all the levels in the stream and send these updated levels back. Let's add on to what we've already written. | [
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To send our updated building, we'll need to create a parent `Base` and place our list of nested levels inside this parent. To keep things consistent, we'll be adding them to a field called `data`. | [
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We'll then use `operations.send()` to send this object to the stream, then use `client.commit.create()` to commit our changes to the stream. If we want to send to a new branch, we can do so by first creating one using `client.branch.create(stream_id, name, description)`. The snippet below shows this full process. | [
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If we go back to the web to look at the stream, you'll see your newest commit. The geometry should look the same since this hasn't been modified, but stepping into the data will show you the new material attribute. As you can see in the gif below, the id of the `Concrete` object is the same on different floor slabs since the attribute is detached and the objects are identical. | [
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We've now seen how we can receive, work with, and send data from Python just like you can with any of the other SDKs and connectors. Great, now we can use that data to make something cool! Let's plot the data using [plotly](https://plotly.com/python/) and display it on a simple page using [dash](https://dash.plotly.com/layout). I won't go into too much detail on using the libraries themselves, but most of the plotting code has just been modified from the boilerplate and examples in their docs. | [
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To start, let's extract some existing data from the objects and plot them. Each geometry object we're interested in has a `Vertices` attribute containing a list of points. Let's plot the vertices of the floor slabs and columns on a 3D scatter plot. To do this, we'll need to construct a pandas data frame with the values we are interested in by iterating through the levels. | [
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] |
def construct_points_df(levels: List[Base]):
# initialise a pandas data frame
df_vertices = pd.DataFrame(columns=("x", "y", "z", "element")) | [
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vertices = []
for level in levels:
# add column vertices
columns = level["@columns"]
for column in columns:
points = column.Vertices
for p in points:
vertices.append({"x": p.x, "y": p.y, "z": p.z, "element": "columns"})
# add floor slab vertices
floorslab = level["@floorSlab"]
points = floorslab.Vertices
for p in points:
vertices.append({"x": p.x, "y": p.y, "z": p.z, "element": "floorSlab"}) | [
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Now we can just pass that data frame into the plotly express `scatter_3d` method and `show()` the figure. | [
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fig = px.scatter_3d(
df_vertices,
x="x",
y="y",
z="z",
color="element",
opacity=0.7,
title="Element Vertices (m)",
) | [
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Plotly plots have interactivity built in, so this is all the code we need to produce this 3D scatter plot. | [
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] |
Next, let's make use of the material properties we added to each of the stream objects. Let's calculate the mass and embodied carbon for each element type in one level and construct a data frame with the results. | [
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] |
```py
def construct_carbon_df(level: Base):
data = {"element": [], "volume": [], "mass": [], "embodied carbon": []} | [
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] |
# get the attributes on the level object
names = level.get_dynamic_member_names()
# iterate through and find the elements with a `volume` attribute
for name in names:
prop = level[name]
if isinstance(prop, Base):
if not hasattr(prop, "volume"):
break
# if it has a volume, use the material attribute to calculated
# the embodied carbon
data["volume"].append(prop.volume)
data["mass"].append(data["volume"][-1] * prop["@material"].density)
data["embodied carbon"].append(
data["mass"][-1] * prop["@material"].embodied_carbon
)
elif isinstance(prop, list):
if not hasattr(prop[0], "volume"):
break
data["volume"].append(sum(p.volume for p in prop))
data["mass"].append(data["volume"][-1] * prop[0]["@material"].density)
data["embodied carbon"].append(
data["mass"][-1] * prop[0]["@material"].embodied_carbon
)
data["element"].append(name[1:]) # removing the prepending `@` | [
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Using this data frame, we can use the plotly express `pie()` and `bar()` methods to create some lovely plots. | [
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] |
```py
# take the first level from our levels list and construct a data frame
df_carbon = construct_carbon_df(levels[0]) | [
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figures["volumes"] = px.pie(
df_carbon,
values="volume",
names="element",
color="element",
title="Volumes of Elements Per Floor (m3)",
) | [
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figures["carbon bar"] = px.bar(
df_carbon,
x="element",
y="embodied carbon",
color="element",
title="Embodied Carbon Per Floor (kgC02)",
) | [
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] |
figures["carbon pie"] = px.pie(
df_carbon,
values="embodied carbon",
names="element",
color="element",
title="Embodied Carbon Per Floor (kgC02)",
)
``` | [
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The last thing we're going to do is add the figures to a dash app. To do this, we are simply taking the boilerplate [dash layout](https://dash.plotly.com/layout), replacing the sample figure with our own figures, and adding some custom css in `/assets/style.css`. | [
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```py
import dash
import dash_core_components as dcc
import dash_html_components as html
from demo_specklepy.speckle_data import get_figures | [
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app.layout = html.Div(
children=[
html.Div(
children=[
html.H1(children="speckle-py example", className="header-title"),
html.P(
children="""
Speckle is the open source data platform for AEC.
We free your data from proprietary file formats so you can own
and access your data wherever you need it - including Python!
""",
className="header-description",
),
],
className="header",
),
html.Div(
children=[
html.Div(
children=[
dcc.Graph(id="example-3d-scatter", figure=figures["vertices"])
],
className="card",
),
html.Div(
children=[
dcc.Graph(id="volume-pie-chart", figure=figures["volumes"])
],
className="card",
),
html.Div(
children=[
dcc.Graph(id="carbon-bar-graph", figure=figures["carbon bar"])
],
className="card",
),
html.Div(
children=[
dcc.Graph(id="carbon-pie-chart", figure=figures["carbon pie"])
],
className="card",
),
],
className="wrapper",
),
],
) | [
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Now all that's left is to run `app.py` and head to [`http://127.0.0.1:8050/`](http://127.0.0.1:8050/) to see your amazing plots. | [
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] |
And voila - you've created the basis for a dashboard powered by your Speckle data! This is just a simple start, and we'd love to see the interesting things you build with Speckle. If you have any questions or want to share what you've been hacking, start a conversation on our [forum](https://speckle.community/) | [
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For reference, all the code for this project can be found [here](https://github.com/izzylys/demo-specklepy).
# Introduction | [
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The Python SDK can be found in our [speckle-py repo](https://github.com/specklesystems/speckle-py), its readme contains instructions on how to build it.
It's compatible with Python 3.6+. | [
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1. a `SpeckleClient` which allows you to interact with the server API
2. `operations` and `transports` for sending and receiving large objects
3. a `Base` object and accompanying serializer for creating and customising your own Speckle objects | [
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It may be helpful to know where the local accounts and object cache dbs are stored. Depending on on your OS, you can find the dbs at: | [
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] |
- Windows: `APPDATA` or `<USER>\AppData\Roaming\Speckle`
- Linux: `$XDG_DATA_HOME` or by default `~/.local/share/Speckle`
- Mac: `~/.config/Speckle`
# QuickStart | [
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] |
In a rush? Don't feel like reading through a lot of documentation? This guide will get you up and running with a local dev server and have you sending data to Speckle ASAP. Feel free to dip into the [User Guide](/user/) or [Dev Docs](/dev/speckle-sharp/) for more detailed explanations where you need them. | [
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Before you can spin up a local dev server, you'll need to install the following: | [
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Speckle Manager is our desktop application for managing local accounts and connectors. You will need to install it before you can use the Speckle connectors. Get the latest version [here](https://speckle.systems/download/). | [
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The Manager allows you to add and remove Speckle accounts to your local environment. Simply head to the "Accounts" page to add your first account. You'll need to enter a server URL (which can just be `http://localhost:3000`) then follow the instructions to log in/register and authorise the Manager. From Manager, you'll also be able to set a Default Account which is the account any desktop connectors will use if you don't specify an account. | [
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The Manager also has a "Connectors" page where you can view, install, update, and uninstall our official desktop connectors. | [
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[Speckle Web](/dev/web) is a monorepo containing the three web components of Speckle: the Server, the Frontend, and the Viewer. To get started, clone the repo from [here](https://github.com/specklesystems/speckle-server). | [
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There are a few setup steps to go through after a fresh clone. | [
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2. Bootstrap to the Viewer with the Frontend and install packages | [
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4. Copy and fill in an `.env` file in `packages/server` based on the `.env-example` | [
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Once you've gone through the initial setup, you're just three steps away from spinning up your dev server and exploring the frontend. | [
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] |
1. Start local instances of both Postgres and Redis
2. In `packages/server`, run `npm run dev`
3. In `packages/frontend`, run `npm run dev` | [
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Tada ✨ You should now see the frontend at `localhost:3000`! You can explore the API using the GraphQL Playground at `localhost:3000/graphql`. | [
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[Speckle-Sharp](/dev/speckle-sharp/) is the home of all thing Speckle C# including the .NET SDK and the desktop connectors. The connectors use our default interoperability kit [Objects](/dev/speckle-sharp/objects): a .NET object model containing geometry and AEC element base classes. It works great out of the box, but can be forked and extended by you if you need extended capabilities. | [
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The repo also contains the [DesktopUI](https://github.com/specklesystems/speckle-sharp/tree/master/DesktopUI): a WPF application which you can implement to build your own connectors! | [
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Currently, we have four connectors within Speckle Sharp: Dynamo, Grasshopper, Revit, and Rhino. The connectors are what free your data from their source applications and allow you to access them from wherever you need them. They provide simple and intuitive user interfaces for selecting, sending, and receiving data to and from your Server. | [
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] |
You can install release builds of the connectors from the [Speckle Manager](#manager) or you can debug them locally by cloning [the repo](https://github.com/specklesystems/speckle-sharp). Instructions for building and debugging each of the connectors can be found in the [Dev Docs](/dev/speckle-sharp/connectors). | [
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That's all, folks! Your lighting fast introduction to Speckle is now complete. There's plenty more to see in the Dev Docs, so have a browse if you want more information. If you have any questions, feedback, or ideas, join us on [the forum](https://speckle.community/) and start a discussion!
# Introduction | [
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Speckle server, frontend and viewer are hosted in our [speckle-server](https://github.com/specklesystems/speckle-server) repo. | [
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To set up a local Speckle server for development, check out the Readme files in the git repo for a quick start, or the [Local Setup](/dev/server-local-dev) page for detailed instructions. | [
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] |
To deploy a Speckle server on a non-production cloud environment, checkout the guide for deploying to [Kubernetes](/dev/server-setup-k8s) or for deploying to Docker with [Docker Compose](/dev/server-manualsetup). | [
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Our Speckle Server offers both a [GraphQL API](/dev/server-graphql-api) and a [REST API](/dev/server-rest-api), for most of your queries the GQL API is probably best suited because of its flexibility and ease of use.
When working with uploading and downloading objects, the REST API is preferred.
# Database backup, upgrade, and restore | [
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We recommend backing up your database regularly, and especially prior to upgrading Speckle. Backing up a database is often a requirement of a database upgrade, migrating your database between servers, or recovering from an incident. | [
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] |
Speckle's preferred method of backing up and restoring data in a Postgres database is with [pgAdmin](https://www.pgadmin.org/). This guide assumes you are running Speckle via Docker-Compose, based on [our guide](./server-manualsetup.md). | [
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1. Docker should be [installed and running](https://docs.docker.com/get-docker/).
1. We expect you to have followed the instructions for [manual installation](./server-manualsetup.md) | [
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] |
1. Determine if pgAdmin is already installed and running. From your terminal window, request the list of running processes from Docker. Full details of this command can be found in [Docker's documentation](https://docs.docker.com/engine/reference/commandline/ps/). | [
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] |
```shell
docker ps --filter name='pgadmin'
``` | [
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1. If pgAdmin is already running, you should see something like the following output. If so, you can move on to the next section. | [
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```shell
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
04fb5cfc43e9 dpage/pgadmin4 "/entrypoint.sh" 8 days ago Up 53 minutes 443/tcp, 127.0.0.1:16543->80/tcp speckle-server-pgadmin-1
``` | [
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1. Save the following Docker-Compose configuration in a file named `docker-compose-pgadmin.yml` | [
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```yaml
version: '3'
services:
pgadmin:
image: dpage/pgadmin4
restart: always
environment:
PGADMIN_DEFAULT_EMAIL: 'admin@localhost.com'
PGADMIN_DEFAULT_PASSWORD: 'admin'
volumes:
- pgadmin-data:/var/lib/pgadmin
ports:
- '127.0.0.1:16543:80' | [
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] |
```shell
docker compose --file docker-compose-pgadmin.yml up --detach
``` | [
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] |
```shell
docker ps --filter name='pgadmin'
``` | [
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1. Find the port that pgAdmin is being served on using [Docker's `ps` command](https://docs.docker.com/engine/reference/commandline/ps/). | [
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