# Quickstart

```note:: If you haven't launched Hydrosphere.io platform, you can learn how to do it here - https://hydrosphere.io/serving-docs/latest/install/index.html. 
```

## Installation

Install the latest release of hydrosdk via pip
```
pip install hydrosdk
```
or you can install latest version from git by running
```
pip install git+git://github.com/Hydrospheredata/hydro-serving-sdk.git
```

## Using hydrosdk

To use hydrosdk, you must first import it connect to your Hydrosphere.io platform by 
creating a [Cluster](hydrosdk/hydrosdk.cluster) object.


```python
from hydrosdk.cluster import Cluster

# Provide your cluster address here
cluster = Cluster("my-cluster")
```

Now that you have established a connection to Hydrosphere platform via Cluster object, you can make manage your cluster.
 The following lists all model versions deployed to your platform and prints their information:
 
 ```python
from hydrosdk.modelversion import ModelVersion

# Print out model names and versions
for modelversion in ModelVersion.list(cluster=cluster):
    print(modelversion)
```

It's also easy to send data to your deployed models.
For example, the following loads a csv and sends all rows to your deployed model through predictor object,
 which is designed to make inference for your data via GRPC API.
 
```python
from hydrosdk.cluster import Cluster
from hydrosdk.application import Application
from grpc import ssl_channel_credentials
import pandas as pd

cluster = Cluster("http-cluster-address",
                  grpc_address="grpc-cluster-address", ssl=True,
                  grpc_credentials=ssl_channel_credentials())

app = Application.find_by_name(cluster, "application-name")
predictor = app.predictor()

df = pd.read_csv("path/to/data.csv")
for row in df.itertuples(index=False):
    predictor.predict(row._asdict())
```

Other topics such as ModelVersion, metrics and uploading training data will be covered in more detail in the following sections,
 so don't worry if you do not completely understand the examples.