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112 lines
3.0 KiB
Markdown
112 lines
3.0 KiB
Markdown
# Create run with compute session
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:::{admonition} Experimental feature
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This is an experimental feature, and may change at any time. Learn [more](../../how-to-guides/faq.md#stable-vs-experimental).
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:::
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A prompt flow compute session provides computing resources that are required for the application to run, including a Docker image that contains all necessary dependency packages. This reliable and scalable compute session environment enables prompt flow to efficiently execute its tasks and functions for a seamless user experience.
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If you're a new user, we recommend that you use the compute session (preview). You can easily customize the environment by adding packages in the requirements.txt file in flow.dag.yaml in the flow folder.
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## Create a run with compute session
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::::{tab-set}
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:::{tab-item} CLI
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:sync: CLI
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```bash
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pfazure run create --flow path/to/flow --data path/to/data --stream
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```
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:::
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:::{tab-item} SDK
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:sync: SDK
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```python
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from promptflow.azure import PFClient
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pf = PFClient(
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credential=credential,
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subscription_id="<SUBSCRIPTION_ID>", # this will look like xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
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resource_group_name="<RESOURCE_GROUP>",
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workspace_name="<AML_WORKSPACE_NAME>",
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)
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pf.run(
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flow=flow,
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data=data,
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)
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```
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:::
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::::
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## Specify pip requirements for compute session
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If `requirements.txt` exists in the same folder with `flow.dag.yaml`.
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The dependencies in it will be automatically installed for compute session.
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You can also specify which requirements file to use in `flow.dag.yaml` like this:
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```yaml
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$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
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environment:
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python_requirements_txt: path/to/requirement/file
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...
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```
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Reference [Flow YAML Schema](../../reference/flow-yaml-schema-reference.md) for details.
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## Customize compute session
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In compute session case, you can also specify the instance type, if you don't specify the instance type, Azure Machine Learning chooses an instance type (VM size) based on factors like quota, cost, performance and disk size, learn more about [serverless compute](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-serverless-compute).
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```yaml
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$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json
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flow: <path_to_flow>
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data: <path_to_flow>/data.jsonl
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column_mapping:
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url: ${data.url}
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# define instance type only work for compute session.
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resources:
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instance_type: <instance_type>
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```
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::::{tab-set}
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:::{tab-item} CLI
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:sync: CLI
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```bash
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pfazure run create --file run.yml
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```
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:::
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:::{tab-item} SDK
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:sync: SDK
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```python
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from promptflow.client import load_run
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run = load_run(source="run.yml")
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pf = PFClient(
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credential=credential,
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subscription_id="<SUBSCRIPTION_ID>", # this will look like xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
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resource_group_name="<RESOURCE_GROUP>",
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workspace_name="<AML_WORKSPACE_NAME>",
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)
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pf.runs.create_or_update(
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run=run
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)
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```
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:::
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::::
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## Next steps
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- Try the example [here](https://github.com/microsoft/promptflow/blob/main/examples/tutorials/run-management/cloud-run-management.ipynb).
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