# Zeblok Python SDK
Zeblok-sdk will help integrate your MLOps workflows with the Zeblok Ai-MicroCloud platform.
## Components of the Python SDK
### Auth
- API Authentication
### Resources
- Plan
- Namespace
### Managed Service
- Microservice
- Orchestration-Addons
- Ai-API
- Ai-Pipeline
- DataLake
- DataSet
# Interface
<h3> Authentication </h3>
- API Authentication: `APIAuth(app_url, api_access_key,api_access_secret)`
<h3> Plans </h3>
- Get all plans: `get_all()`
- Get a plan by id: `get_by_id(plan_id)`
- Validate a plan by id: `validate_id(plan_id)`
- Get filtered details of a plan: `get_filtered_details(plan_id, fields_req)`
<h3> Namespaces </h3>
- Get all namespaces: `get_all()`
- Get a namespace by id: `get_by_id(namespace_id)`
- Validate a namespace by id: `validate_id(namespace_id)`
<h3> MicroServices </h3>
- Get all microservices: `get_all()`
- Get a microservice by id: `get_by_id(plan_id)`
- Validate a microservice by id: `validate_id(microservice_id)`
- Spawn a microservices by
id: `spawn(display_name, microservice_id, plan_id, microservice_name, namespace_id, envs, ports, args, command)`
<h3> Orchestration-Addons </h3>
- Get all orchestrations: `get_all()`
- Get an orchestration by id: `get_by_id(orchestration_id)`
- Validate an orchestration by id: `validate_id(orchestration_id)`
- Spawn an orchestrations by
id: `spawn(orchestration_id, plan_id, namespace_id, orchestration_name, min_workers, max_workers)`
<h3> AI-APIs </h3>
- Get all AI-APIs with `deployed` state: `get_all(state=deployed)`
- Get all AI-APIs with `ready` state: `get_all(state=ready)`
- Validate an AI-API: `validate(image_name, state)`
- Create and Spawn an AI-API: `create_and_spawn(ai_api_name, model_folder_path, plan_id, namespace_id, ai_api_type)`
- Create an AI-API: `create(ai_api_name, model_folder_path, ai_api_type)`
- Spawn an AI-API: `spawn(image_name, namespace_id, plan_id)`
<h3> AI-Pipelines </h3>
- Get all AI-Pipelines with `created` state: `get_all(state=created)`
- Get all AI-Pipelines with `ready` state: `get_all(state=ready)`
- Validate an AI-Pipeline: `validate(image_name, state)`
- Create and Spawn an
AI-API: `create_and_spawn(ai_pipeline_name, ai_pipeline_folder_path, caas_plan_id, ai_pipeline_plan_id, namespace_id)`
- Create an
AI-Pipeline: `create(ai_pipeline_name, ai_pipeline_folder_path, caas_plan_id, ai_pipeline_plan_id, namespace_id)`
- Spawn an AI-Pipeline: `spawn(ai_pipeline_plan_id, namespace_id, ai_pipeline_image_name)`
<h3> DataLake </h3>
- Check if a bucket exists: `bucket_exists()`
- Check if an object exists: `object_exists(object_name)`
- Upload an object: `upload_object(local_file_pathname, object_name)`
- Download an object: `download_object(object_name, local_dir, filename)`
- Get object PreSigned URL: `get_presigned_url(object_name)`
- Upload a folder: `upload_folder(folder_path)`
<h3> DataSet </h3>
- Get all DataSets: `get_all()`
- Get Dataset information by name: `get_by_name(dataset_name)`
- Create a DataSet: `create_dataset(dataset_name, dataset_description)`
- Upload files in a DataSet: `upload_dataset(dataset_id, filepaths)`
## Usage
Checkout the detailed usage of the SDK at [Usage.md](Usage.md#usage)
# Contact Zeblok
| **Team** | **Email** |
|--------------------|--------------------|
| **Technical Team** | support@zeblok.com |
| **Sales Team** | sales@zeblok.com |
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"description": "# Zeblok Python SDK\r\n\r\nZeblok-sdk will help integrate your MLOps workflows with the Zeblok Ai-MicroCloud platform.\r\n\r\n## Components of the Python SDK\r\n\r\n### Auth\r\n\r\n- API Authentication\r\n\r\n### Resources\r\n\r\n- Plan\r\n- Namespace\r\n\r\n### Managed Service\r\n\r\n- Microservice\r\n- Orchestration-Addons\r\n- Ai-API\r\n- Ai-Pipeline\r\n- DataLake\r\n- DataSet\r\n\r\n# Interface\r\n\r\n<h3> Authentication </h3>\r\n\r\n- API Authentication: `APIAuth(app_url, api_access_key,api_access_secret)`\r\n\r\n<h3> Plans </h3>\r\n\r\n- Get all plans: `get_all()`\r\n- Get a plan by id: `get_by_id(plan_id)`\r\n- Validate a plan by id: `validate_id(plan_id)`\r\n- Get filtered details of a plan: `get_filtered_details(plan_id, fields_req)`\r\n\r\n<h3> Namespaces </h3>\r\n\r\n- Get all namespaces: `get_all()`\r\n- Get a namespace by id: `get_by_id(namespace_id)`\r\n- Validate a namespace by id: `validate_id(namespace_id)`\r\n\r\n<h3> MicroServices </h3>\r\n\r\n- Get all microservices: `get_all()`\r\n- Get a microservice by id: `get_by_id(plan_id)`\r\n- Validate a microservice by id: `validate_id(microservice_id)`\r\n- Spawn a microservices by\r\n id: `spawn(display_name, microservice_id, plan_id, microservice_name, namespace_id, envs, ports, args, command)`\r\n\r\n<h3> Orchestration-Addons </h3>\r\n\r\n- Get all orchestrations: `get_all()`\r\n- Get an orchestration by id: `get_by_id(orchestration_id)`\r\n- Validate an orchestration by id: `validate_id(orchestration_id)`\r\n- Spawn an orchestrations by\r\n id: `spawn(orchestration_id, plan_id, namespace_id, orchestration_name, min_workers, max_workers)`\r\n\r\n<h3> AI-APIs </h3>\r\n\r\n- Get all AI-APIs with `deployed` state: `get_all(state=deployed)`\r\n- Get all AI-APIs with `ready` state: `get_all(state=ready)`\r\n- Validate an AI-API: `validate(image_name, state)`\r\n- Create and Spawn an AI-API: `create_and_spawn(ai_api_name, model_folder_path, plan_id, namespace_id, ai_api_type)`\r\n- Create an AI-API: `create(ai_api_name, model_folder_path, ai_api_type)`\r\n- Spawn an AI-API: `spawn(image_name, namespace_id, plan_id)`\r\n\r\n<h3> AI-Pipelines </h3>\r\n\r\n- Get all AI-Pipelines with `created` state: `get_all(state=created)`\r\n- Get all AI-Pipelines with `ready` state: `get_all(state=ready)`\r\n- Validate an AI-Pipeline: `validate(image_name, state)`\r\n- Create and Spawn an\r\n AI-API: `create_and_spawn(ai_pipeline_name, ai_pipeline_folder_path, caas_plan_id, ai_pipeline_plan_id, namespace_id)`\r\n- Create an\r\n AI-Pipeline: `create(ai_pipeline_name, ai_pipeline_folder_path, caas_plan_id, ai_pipeline_plan_id, namespace_id)`\r\n- Spawn an AI-Pipeline: `spawn(ai_pipeline_plan_id, namespace_id, ai_pipeline_image_name)`\r\n\r\n<h3> DataLake </h3>\r\n\r\n- Check if a bucket exists: `bucket_exists()`\r\n- Check if an object exists: `object_exists(object_name)`\r\n- Upload an object: `upload_object(local_file_pathname, object_name)`\r\n- Download an object: `download_object(object_name, local_dir, filename)`\r\n- Get object PreSigned URL: `get_presigned_url(object_name)`\r\n- Upload a folder: `upload_folder(folder_path)`\r\n\r\n<h3> DataSet </h3>\r\n\r\n- Get all DataSets: `get_all()`\r\n- Get Dataset information by name: `get_by_name(dataset_name)`\r\n- Create a DataSet: `create_dataset(dataset_name, dataset_description)`\r\n- Upload files in a DataSet: `upload_dataset(dataset_id, filepaths)`\r\n\r\n## Usage\r\n\r\nCheckout the detailed usage of the SDK at [Usage.md](Usage.md#usage)\r\n\r\n# Contact Zeblok\r\n\r\n| **Team** | **Email** |\r\n|--------------------|--------------------|\r\n| **Technical Team** | support@zeblok.com |\r\n| **Sales Team** | sales@zeblok.com |\r\n\r\n",
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