| Name | prefect-azure JSON |
| Version |
0.3.8
JSON |
| download |
| home_page | None |
| Summary | Prefect integrations with Microsoft Azure services |
| upload_time | 2024-04-24 14:12:08 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | Apache License 2.0 |
| keywords |
prefect
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# prefect-azure
<p align="center">
<a href="https://pypi.python.org/pypi/prefect-azure/" alt="PyPI version">
<img alt="PyPI" src="https://img.shields.io/pypi/v/prefect-azure?color=26272B&labelColor=090422"></a>
<a href="https://pepy.tech/badge/prefect-azure/" alt="Downloads">
<img src="https://img.shields.io/pypi/dm/prefect-azure?color=26272B&labelColor=090422" /></a>
</p>
`prefect-azure` is a collection of Prefect integrations for orchestration workflows with Azure.
## Getting Started
### Installation
Install `prefect-azure` with `pip`
```bash
pip install prefect-azure
```
To use Blob Storage:
```bash
pip install "prefect-azure[blob_storage]"
```
To use Cosmos DB:
```bash
pip install "prefect-azure[cosmos_db]"
```
To use ML Datastore:
```bash
pip install "prefect-azure[ml_datastore]"
```
## Examples
### Download a blob
```python
from prefect import flow
from prefect_azure import AzureBlobStorageCredentials
from prefect_azure.blob_storage import blob_storage_download
@flow
def example_blob_storage_download_flow():
connection_string = "connection_string"
blob_storage_credentials = AzureBlobStorageCredentials(
connection_string=connection_string,
)
data = blob_storage_download(
blob="prefect.txt",
container="prefect",
azure_credentials=blob_storage_credentials,
)
return data
example_blob_storage_download_flow()
```
Use `with_options` to customize options on any existing task or flow:
```python
custom_blob_storage_download_flow = example_blob_storage_download_flow.with_options(
name="My custom task name",
retries=2,
retry_delay_seconds=10,
)
```
### Run a command on an Azure container instance
```python
from prefect import flow
from prefect_azure import AzureContainerInstanceCredentials
from prefect_azure.container_instance import AzureContainerInstanceJob
@flow
def container_instance_job_flow():
aci_credentials = AzureContainerInstanceCredentials.load("MY_BLOCK_NAME")
container_instance_job = AzureContainerInstanceJob(
aci_credentials=aci_credentials,
resource_group_name="azure_resource_group.example.name",
subscription_id="<MY_AZURE_SUBSCRIPTION_ID>",
command=["echo", "hello world"],
)
return container_instance_job.run()
```
### Use Azure Container Instance as infrastructure
If we have `a_flow_module.py`:
```python
from prefect import flow, get_run_logger
@flow
def log_hello_flow(name="Marvin"):
logger = get_run_logger()
logger.info(f"{name} said hello!")
if __name__ == "__main__":
log_hello_flow()
```
We can run that flow using an Azure Container Instance, but first create the infrastructure block:
```python
from prefect_azure import AzureContainerInstanceCredentials
from prefect_azure.container_instance import AzureContainerInstanceJob
container_instance_job = AzureContainerInstanceJob(
aci_credentials=AzureContainerInstanceCredentials.load("MY_BLOCK_NAME"),
resource_group_name="azure_resource_group.example.name",
subscription_id="<MY_AZURE_SUBSCRIPTION_ID>",
)
container_instance_job.save("aci-dev")
```
Then, create the deployment either on the UI or through the CLI:
```bash
prefect deployment build a_flow_module.py:log_hello_flow --name aci-dev -ib container-instance-job/aci-dev
```
Visit [Prefect Deployments](https://docs.prefect.io/tutorials/deployments/) for more information about deployments.
## Azure Container Instance Worker
The Azure Container Instance worker is an excellent way to run
your workflows on Azure.
To get started, create an Azure Container Instances typed work pool:
```
prefect work-pool create -t azure-container-instance my-aci-work-pool
```
Then, run a worker that pulls jobs from the work pool:
```
prefect worker start -n my-aci-worker -p my-aci-work-pool
```
The worker should automatically read the work pool's type and start an
Azure Container Instance worker.
Raw data
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"description": "# prefect-azure\n\n<p align=\"center\">\n <a href=\"https://pypi.python.org/pypi/prefect-azure/\" alt=\"PyPI version\">\n <img alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/prefect-azure?color=26272B&labelColor=090422\"></a>\n <a href=\"https://pepy.tech/badge/prefect-azure/\" alt=\"Downloads\">\n <img src=\"https://img.shields.io/pypi/dm/prefect-azure?color=26272B&labelColor=090422\" /></a>\n</p>\n\n`prefect-azure` is a collection of Prefect integrations for orchestration workflows with Azure.\n\n## Getting Started\n\n### Installation\n\nInstall `prefect-azure` with `pip`\n\n```bash\npip install prefect-azure\n```\n\nTo use Blob Storage:\n```bash\npip install \"prefect-azure[blob_storage]\"\n```\n\nTo use Cosmos DB:\n```bash\npip install \"prefect-azure[cosmos_db]\"\n```\n\nTo use ML Datastore:\n```bash\npip install \"prefect-azure[ml_datastore]\"\n```\n\n## Examples\n\n### Download a blob\n\n```python\nfrom prefect import flow\n\nfrom prefect_azure import AzureBlobStorageCredentials\nfrom prefect_azure.blob_storage import blob_storage_download\n\n@flow\ndef example_blob_storage_download_flow():\n connection_string = \"connection_string\"\n blob_storage_credentials = AzureBlobStorageCredentials(\n connection_string=connection_string,\n )\n data = blob_storage_download(\n blob=\"prefect.txt\",\n container=\"prefect\",\n azure_credentials=blob_storage_credentials,\n )\n return data\n\nexample_blob_storage_download_flow()\n```\n\nUse `with_options` to customize options on any existing task or flow:\n```python\ncustom_blob_storage_download_flow = example_blob_storage_download_flow.with_options(\n name=\"My custom task name\",\n retries=2,\n retry_delay_seconds=10,\n)\n```\n\n### Run a command on an Azure container instance\n\n```python\nfrom prefect import flow\nfrom prefect_azure import AzureContainerInstanceCredentials\nfrom prefect_azure.container_instance import AzureContainerInstanceJob\n\n\n@flow\ndef container_instance_job_flow():\n aci_credentials = AzureContainerInstanceCredentials.load(\"MY_BLOCK_NAME\")\n container_instance_job = AzureContainerInstanceJob(\n aci_credentials=aci_credentials,\n resource_group_name=\"azure_resource_group.example.name\",\n subscription_id=\"<MY_AZURE_SUBSCRIPTION_ID>\",\n command=[\"echo\", \"hello world\"],\n )\n return container_instance_job.run()\n```\n\n### Use Azure Container Instance as infrastructure\n\nIf we have `a_flow_module.py`:\n\n```python\nfrom prefect import flow, get_run_logger\n\n@flow\ndef log_hello_flow(name=\"Marvin\"):\n logger = get_run_logger()\n logger.info(f\"{name} said hello!\")\n\nif __name__ == \"__main__\":\n log_hello_flow()\n```\n\nWe can run that flow using an Azure Container Instance, but first create the infrastructure block:\n\n```python\nfrom prefect_azure import AzureContainerInstanceCredentials\nfrom prefect_azure.container_instance import AzureContainerInstanceJob\n\ncontainer_instance_job = AzureContainerInstanceJob(\n aci_credentials=AzureContainerInstanceCredentials.load(\"MY_BLOCK_NAME\"),\n resource_group_name=\"azure_resource_group.example.name\",\n subscription_id=\"<MY_AZURE_SUBSCRIPTION_ID>\",\n)\ncontainer_instance_job.save(\"aci-dev\")\n```\n\nThen, create the deployment either on the UI or through the CLI:\n```bash\nprefect deployment build a_flow_module.py:log_hello_flow --name aci-dev -ib container-instance-job/aci-dev\n```\n\nVisit [Prefect Deployments](https://docs.prefect.io/tutorials/deployments/) for more information about deployments.\n\n## Azure Container Instance Worker\nThe Azure Container Instance worker is an excellent way to run \nyour workflows on Azure. \n\nTo get started, create an Azure Container Instances typed work pool:\n```\nprefect work-pool create -t azure-container-instance my-aci-work-pool\n```\n\nThen, run a worker that pulls jobs from the work pool:\n```\nprefect worker start -n my-aci-worker -p my-aci-work-pool\n```\n\nThe worker should automatically read the work pool's type and start an \nAzure Container Instance worker.\n",
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