Hydrosphere CLI, orhs, is a command-line interface designed to work with the Hydrosphere platform.
Source code: https://github.com/Hydrospheredata/hydro-serving-cli PyPI: https://pypi.org/project/hs/
Use pip to install hs:
Check the installation:
hs clusterThis command lets you operate cluster instances. A cluster points to your Hydrosphere instance. You can use this command to work with different Hydrosphere instances.
See hs cluster --help for more information.
hs uploadThis command lets you upload models to the Hydrosphere platform. During the upload, hs looks for a serving.yaml file in the current directory. This file must contain a definition of the model ().
See hs upload --help for more information.
hs applyThis command is an extended version of the hs upload command, which also allows you to operate applications and host selector resources.
See hs apply --help for more information.
hs profileThis command lets you upload your training data to build profiles.
$ hs profile push - upload training data to compute its profiles.
$ hs profile status - show profiling status for a given model.
See hs profile --help for more information.
hs appThis command provides information about available applications.
$ hs app list - list all existing applications.
$ hs app rm - remove a certain application.
See hs app --help - for more information.
hs modelThis command provides information about available models.
$ hs model list - list all existing models.
$ hs model rm - remove a certain model.
See hs model --help for more information.
pip install hshs --versionThe Hydrosphere platform can be installed in the following orchestrator's:
To install Hydrosphere using docker-compose, you should have the following prerequisites installed on your machine.
Download the latest $2.4.3$ release from the :
Unpack the tar ball:
Set up an environment:
Clone the serving repository:
Set up an environment:
To check the installation, open . By default, Hydrosphere UI is available at port 80.
To install Hydrosphere on the Kubernetes cluster you should have the following prerequisites fulfilled.
PV support on the underlying infrastructure (if persistence is required)
Docker registry with pull/push access (if the built-in one is not used)
Add the Hydrosphere charts repository:
Install the chart from repo to the cluster:
Clone the repository:
Build dependencies:
Install the chart:
After the chart has been installed, you have to expose the ui component outside of the cluster. For the sake of simplicity, we will just port-forward it locally.
To check the installation, open .
export HYDROSPHERE_RELEASE=released_version
wget -O hydro-serving-${HYDROSPHERE_RELEASE}.tar.gz https://github.com/Hydrospheredata/hydro-serving/archive/${HYDROSPHERE_RELEASE}.tar.gztar -xvf hydro-serving-${HYDROSPHERE_RELEASE}.tar.gzcd hydro-serving-${HYDROSPHERE_RELEASE}
docker-compose upgit clone https://github.com/Hydrospheredata/hydro-servingcd hydro-serving
docker-compose up -dhelm repo add hydrosphere https://hydrospheredata.github.io/hydro-serving/helmhelm install --name serving --namespace hydrosphere hydrosphere/servinggit clone https://github.com/Hydrospheredata/hydro-serving.git
cd hydro-serving/helmhelm dependency build servinghelm install --namespace hydrosphere servingkubectl port-forward -n hydrosphere svc/serving-ui 8080:9090Python SDK offers a simple and convenient way of integrating a user's workflow scripts with Hydrosphere API.
Source code: https://github.com/Hydrospheredata/hydro-serving-sdk PyPI: https://pypi.org/project/hydrosdk/
You can learn more about it in its documentation here.
You can use pip to install hydrosdk
pip install hydrosdkYou can access the locally deployed Hydrosphere platform from previous by running the following code:
from hydrosdk import Cluster, Application
import pandas as pd
cluster = Cluster("http://localhost", grpc_address="localhost:9090")
app = Application.find(cluster, "my-model")
predictor = app.predictor()
df = pd.read_csv("path/to/data.csv")
for row in df.itertuples(index=False):
predictor.predict(row)