@datarobot/pulumi-datarobot NPM

DataRobot Resource Provider

The DataRobot Resource Provider lets you manage DataRobot resources. The provider is built on terraform-provider-datarobot. To use this package, please install the Pulumi CLI first.

This package is available for several languages/platforms:

To use from Python, install using pip:

pip install pulumi_datarobot

To use from JavaScript or TypeScript in Node.js, install using either npm:

npm install @datarobot/pulumi-datarobot

or yarn:

yarn add @datarobot/pulumi-datarobot

go get github.com/datarobot-community/pulumi-datarobot/sdk/go/...

To use from .NET, install using dotnet add package:

dotnet add package DataRobotPulumi.Datarobot

No install necessary, just run pulumi up.

The following configuration points are available for the DataRobot provider:

datarobot:apikey (environment: DATAROBOT_API_TOKEN) - the API key for DataRobot
datarobot:endpoint (environment: DATAROBOT_ENDPOINT) - the endpoint for DataRobot

Keep the following items in mind if running in an air-gapped environment:

Run pulumi login --local to store state files on your local filesystem, instead of the default Pulumi Cloud. Pulumi binaries are available here.
Set DATAROBOT_ENDPOINT: https://{datarobot.example.com}/api/v2 (replacing {datarobot.example.com} with your specific deployment endpoint)
For Python, the pulumi and pulumi-datarobot packages must be installed in the air-gapped system.
Example using pip wheel:
1. create a directory where you want to store package wheels.
```
mkdir folder_containing_wheel
```
2. Now install wheels of the python library you want to install
```
pip wheel pulumi-datarobot -w folder_containing_wheel
```
  This will store all your required dependent wheels of the pulumi-datarobot package in the folder. you can check it with doing ls -ltr`.
3. Now, you can make a tar file of this folder.
```
tar cf folder_containing_wheel.tar folder_containing_wheel/
```
  and you can transfer it to your air-gapped system.
  Now untar the folder.
```
tar xf folder_containing_wheel.tar
cd folder_containing_wheel/
```
  now install wheels from the folder.
```
pip install * -f ./ --no-index 
```