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hangfei authored Oct 12, 2022
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"\n",
"First step is to provision required cloud resources if you want to use Feathr. Feathr provides a python based client to interact with cloud resources.\n",
"\n",
"Please follow the steps [here](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html) to provision required cloud resources. Due to the complexity of the possible cloud environment, it is almost impossible to create a script that works for all the use cases. Because of this, [azure_resource_provision.sh](https://github.com/linkedin/feathr/blob/main/docs/how-to-guides/azure_resource_provision.sh) is a full end to end command line to create all the required resources, and you can tailor the script as needed, while [the companion documentation](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-cli.html) can be used as a complete guide for using that shell script. \n",
"Please follow the steps [here](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html) to provision required cloud resources. Due to the complexity of the possible cloud environment, it is almost impossible to create a script that works for all the use cases. Because of this, [azure_resource_provision.sh](https://github.com/feathr-ai/feathr/blob/main/docs/how-to-guides/azure_resource_provision.sh) is a full end to end command line to create all the required resources, and you can tailor the script as needed, while [the companion documentation](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-cli.html) can be used as a complete guide for using that shell script. \n",
"\n",
"\n",
"![Architecture](https://github.com/linkedin/feathr/blob/main/docs/images/architecture.png?raw=true)"
"![Architecture](https://github.com/feathr-ai/feathr/blob/main/docs/images/architecture.png?raw=true)"
]
},
{
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"\n",
"In the first step (Provision cloud resources), you should have provisioned all the required cloud resources. If you use Feathr CLI to create a workspace, you should have a folder with a file called `feathr_config.yaml` in it with all the required configurations. Otherwise, update the configuration below.\n",
"\n",
"The code below will write this configuration string to a temporary location and load it to Feathr. Please still refer to [feathr_config.yaml](https://github.com/linkedin/feathr/blob/main/feathr_project/feathrcli/data/feathr_user_workspace/feathr_config.yaml) and use that as the source of truth. It should also have more explanations on the meaning of each variable."
"The code below will write this configuration string to a temporary location and load it to Feathr. Please still refer to [feathr_config.yaml](https://github.com/feathr-ai/feathr/blob/main/feathr_project/feathrcli/data/feathr_user_workspace/feathr_config.yaml) and use that as the source of truth. It should also have more explanations on the meaning of each variable."
]
},
{
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"source": [
"import tempfile\n",
"yaml_config = \"\"\"\n",
"# Please refer to https://github.com/linkedin/feathr/blob/main/feathr_project/feathrcli/data/feathr_user_workspace/feathr_config.yaml for explanations on the meaning of each field.\n",
"# Please refer to https://github.com/feathr-ai/feathr/blob/main/feathr_project/feathrcli/data/feathr_user_workspace/feathr_config.yaml for explanations on the meaning of each field.\n",
"api_version: 1\n",
"project_config:\n",
" project_name: 'feathr_getting_started'\n",
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"source": [
"## Prerequisite: Setup necessary environment variables (Skip this step if using the above Quick Start Template)\n",
"\n",
"You should setup the environment variables in order to run this sample. More environment variables can be set by referring to [feathr_config.yaml](https://github.com/linkedin/feathr/blob/main/feathr_project/feathrcli/data/feathr_user_workspace/feathr_config.yaml) and use that as the source of truth. It also has more explanations on the meaning of each variable.\n",
"You should setup the environment variables in order to run this sample. More environment variables can be set by referring to [feathr_config.yaml](https://github.com/feathr-ai/feathr/blob/main/feathr_project/feathrcli/data/feathr_user_workspace/feathr_config.yaml) and use that as the source of truth. It also has more explanations on the meaning of each variable.\n",
"\n",
"To run this notebook, for Azure users, you need REDIS_PASSWORD.\n",
"To run this notebook, for Databricks useres, you need DATABRICKS_WORKSPACE_TOKEN_VALUE and REDIS_PASSWORD."
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" After a bit of data exploration, we want to create a training dataset like this:\n",
"\n",
" \n",
"![Feature Flow](https://github.com/linkedin/feathr/blob/main/docs/images/product_recommendation.jpg?raw=true)"
"![Feature Flow](https://github.com/feathr-ai/feathr/blob/main/docs/images/product_recommendation.jpg?raw=true)"
]
},
{
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"1. Feature source: what source data that this feature is based on\n",
"2. Transformation: what transformation is used to transform the source data into feature. Transformation can be optional when you just want to take a column out from the source data.\n",
"\n",
"(For more details on feature definition, please refer to the [Feathr Feature Definition Guide](https://github.com/linkedin/feathr/blob/main/docs/concepts/feature-definition.md))"
"(For more details on feature definition, please refer to the [Feathr Feature Definition Guide](https://github.com/feathr-ai/feathr/blob/main/docs/concepts/feature-definition.md))"
]
},
{
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"To create a training dataset using Feathr, we need to provide a feature join settings to specify\n",
"what features and how these features should be joined to the observation data. \n",
"\n",
"(To learn more on this topic, please refer to [Point-in-time Correctness](https://github.com/linkedin/feathr/blob/main/docs/concepts/point-in-time-join.md))"
"(To learn more on this topic, please refer to [Point-in-time Correctness](https://github.com/feathr-ai/feathr/blob/main/docs/concepts/point-in-time-join.md))"
]
},
{
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"![img](../images/online_inference.jpg)\n",
"\n",
"\n",
"In this section, we will focus on materialize features to online store. For materialization to offline store, you can check out our [user guide](https://github.com/linkedin/feathr/blob/main/docs/concepts/materializing-features.md#materializing-features-to-offline-store).\n",
"In this section, we will focus on materialize features to online store. For materialization to offline store, you can check out our [user guide](https://github.com/feathr-ai/feathr/blob/main/docs/concepts/materializing-features.md#materializing-features-to-offline-store).\n",
"\n",
"We can push the computed features to the online store like below:"
]
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