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Updated step operator docs #2908

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Merged
merged 5 commits into from
Aug 7, 2024
Merged

Updated step operator docs #2908

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64c4523
Updated step operator docs
htahir1 Aug 6, 2024
2e2fe76
Added some docs
htahir1 Aug 6, 2024
6797ecb
Fixed analytics opt out command
htahir1 Aug 7, 2024
a716e4f
Fixed PR comments
htahir1 Aug 7, 2024
7983b60
Merge branch 'develop' into doc/fixstepdocs
htahir1 Aug 7, 2024
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16 changes: 8 additions & 8 deletions docs/book/component-guide/step-operators/vertex.md
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Expand Up @@ -85,7 +85,7 @@ Once you added the step operator to your active stack, you can use it to execute
from zenml import step


@step(step_operator= <NAME>)
@step(step_operator=<NAME>)
def trainer(...) -> ...:
"""Train a model."""
# This step will be executed in Vertex.
Expand Down Expand Up @@ -115,13 +115,13 @@ For additional configuration of the Vertex step operator, you can pass `VertexSt
from zenml import step
from zenml.integrations.gcp.flavors.vertex_step_operator_flavor import VertexStepOperatorSettings

@step(step_operator= <NAME>, settings={"step_operator.vertex": VertexStepOperatorSettings(
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Not sure we should fix that to a name here, people might think they can just copy-paste. the settings key is always fixed which is why we can hard code it here.

accelerator_type = "NVIDIA_TESLA_T4" # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType
accelerator_count = 1
machine_type = "n1-standard-2" # see https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types
disk_type = "pd-ssd" # see https://cloud.google.com/vertex-ai/docs/training/configure-storage#disk-types
disk_size_gb = 100 # see https://cloud.google.com/vertex-ai/docs/training/configure-storage#disk-size
)})
@step(step_operator=<STEP_OPERATOR_NAME>, settings={"step_operator.vertex": VertexStepOperatorSettings(
accelerator_type= "NVIDIA_TESLA_T4", # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType
accelerator_count = 1,
machine_type = "n1-standard-2", # see https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types
disk_type = "pd-ssd", # see https://cloud.google.com/vertex-ai/docs/training/configure-storage#disk-types
disk_size_gb = 100, # see https://cloud.google.com/vertex-ai/docs/training/configure-storage#disk-size
)})
def trainer(...) -> ...:
"""Train a model."""
# This step will be executed in Vertex.
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5 changes: 3 additions & 2 deletions docs/book/getting-started/zenml-pro/system-architectures.md
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Expand Up @@ -43,11 +43,12 @@ varying scenarios described below.
![Scenario 1: Full SaaS deployment](../../.gitbook/assets/cloud_architecture_scenario_1.png)


In this scenario, all services are hosted on infrastructure hosted by the ZenML Team.
In this scenario, all services are hosted on infrastructure hosted by the ZenML Team,
except the MLOps stack components.
Customer secrets and credentials required to access customer infrastructure are
stored and managed by the ZenML Pro Control Plane.

On our infrastructure for ZenML Pro SaaS only ML _metadata_ (e.g. pipeline and
On the ZenML Pro infrastructure, only ML _metadata_ (e.g. pipeline and
model tracking and versioning information) is stored. All the actual ML data
artifacts (e.g. data produced or consumed by pipeline steps, logs and
visualizations, models) are stored on the customer cloud. This can be set up
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2 changes: 1 addition & 1 deletion docs/book/reference/global-settings.md
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Expand Up @@ -92,7 +92,7 @@ In addition to the above, you may also find the following files and folders unde
In order to help us better understand how the community uses ZenML, the pip package reports **anonymized** usage statistics. You can always opt out by using the CLI command:

```bash
zenml config analytics opt-out
zenml analytics opt-out
```

#### Why does ZenML collect analytics? <a href="#motivation" id="motivation"></a>
Expand Down