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Added MLflow permissions #1013
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Added MLflow permissions #1013
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## MLflow Experiment usage Valid [permission levels](https://docs.databricks.com/security/access-control/workspace-acl.html#mlflow-experiment-permissions-1) for [databricks_mlflow_experiment](mlflow_experiment.md) are: `CAN_READ`, `CAN_EDIT`, and `CAN_MANAGE`. ```hcl data "databricks_current_user" "me" {} resource "databricks_mlflow_experiment" "this" { name = "${data.databricks_current_user.me.home}/Sample" artifact_location = "dbfs:/tmp/my-experiment" description = "My MLflow experiment description" } resource "databricks_group" "auto" { display_name = "Automation" } resource "databricks_group" "eng" { display_name = "Engineering" } resource "databricks_permissions" "experiment_usage" { experiment_id = databricks_mlflow_experiment.this.id access_control { group_name = "users" permission_level = "CAN_READ" } access_control { group_name = databricks_group.auto.display_name permission_level = "CAN_MANAGE" } access_control { group_name = databricks_group.eng.display_name permission_level = "CAN_EDIT" } } ``` ## MLflow Model usage Valid [permission levels](https://docs.databricks.com/security/access-control/workspace-acl.html#mlflow-model-permissions-1) for [databricks_mlflow_model](mlflow_model.md) are: `CAN_READ`, `CAN_EDIT`, `CAN_MANAGE_STAGING_VERSIONS`, `CAN_MANAGE_PRODUCTION_VERSIONS`, and `CAN_MANAGE`. ```hcl resource "databricks_mlflow_model" "this" { name = "SomePredictions" } resource "databricks_group" "auto" { display_name = "Automation" } resource "databricks_group" "eng" { display_name = "Engineering" } resource "databricks_permissions" "model_usage" { registered_model_id = databricks_mlflow_model.this.registered_model_id access_control { group_name = "users" permission_level = "CAN_READ" } access_control { group_name = databricks_group.auto.display_name permission_level = "CAN_MANAGE_PRODUCTION_VERSIONS" } access_control { group_name = databricks_group.eng.display_name permission_level = "CAN_MANAGE_STAGING_VERSIONS" } } ``` Fixes #1012
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Codecov Report
@@ Coverage Diff @@
## master #1013 +/- ##
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- Coverage 90.65% 90.64% -0.01%
==========================================
Files 106 106
Lines 9256 9259 +3
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+ Hits 8391 8393 +2
- Misses 513 514 +1
Partials 352 352
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* Added support for `databricks_permissions` for `databricks_mlflow_experiment` and `databricks_mlflow_model` ([#1013](#1013)). * Added `Using XXX auth` explanation to HTTP 403 errors, which should help troubleshooting misconfigured authentication or provider aliasing. Example error message now looks like: *cannot create group: /2.0/preview/scim/v2/Groups is only accessible by admins. Using databricks-cli auth: host=https://XXX.cloud.databricks.com/, token=`***REDACTED***`, profile=demo.* All sensitive configuration parameters (`token`, `password`, and `azure_client_secret`) are redacted and replaced with `***REDACTED***` ([#821](#821)). * Improved documentation with regards to public subnets in AWS quick start ([#1005](#1005)). * Added `databricks_mount` code genration for [exporter](https://registry.terraform.io/providers/databrickslabs/databricks/latest/docs/guides/experimental-exporter) tooling ([#1006](#1006)). * Increase dependency check frequency ([#1007](#1007)). * Added experimental resources.
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## MLflow Experiment usage Valid [permission levels](https://docs.databricks.com/security/access-control/workspace-acl.html#mlflow-experiment-permissions-1) for [databricks_mlflow_experiment](mlflow_experiment.md) are: `CAN_READ`, `CAN_EDIT`, and `CAN_MANAGE`. ```hcl data "databricks_current_user" "me" {} resource "databricks_mlflow_experiment" "this" { name = "${data.databricks_current_user.me.home}/Sample" artifact_location = "dbfs:/tmp/my-experiment" description = "My MLflow experiment description" } resource "databricks_group" "auto" { display_name = "Automation" } resource "databricks_group" "eng" { display_name = "Engineering" } resource "databricks_permissions" "experiment_usage" { experiment_id = databricks_mlflow_experiment.this.id access_control { group_name = "users" permission_level = "CAN_READ" } access_control { group_name = databricks_group.auto.display_name permission_level = "CAN_MANAGE" } access_control { group_name = databricks_group.eng.display_name permission_level = "CAN_EDIT" } } ``` ## MLflow Model usage Valid [permission levels](https://docs.databricks.com/security/access-control/workspace-acl.html#mlflow-model-permissions-1) for [databricks_mlflow_model](mlflow_model.md) are: `CAN_READ`, `CAN_EDIT`, `CAN_MANAGE_STAGING_VERSIONS`, `CAN_MANAGE_PRODUCTION_VERSIONS`, and `CAN_MANAGE`. ```hcl resource "databricks_mlflow_model" "this" { name = "SomePredictions" } resource "databricks_group" "auto" { display_name = "Automation" } resource "databricks_group" "eng" { display_name = "Engineering" } resource "databricks_permissions" "model_usage" { registered_model_id = databricks_mlflow_model.this.registered_model_id access_control { group_name = "users" permission_level = "CAN_READ" } access_control { group_name = databricks_group.auto.display_name permission_level = "CAN_MANAGE_PRODUCTION_VERSIONS" } access_control { group_name = databricks_group.eng.display_name permission_level = "CAN_MANAGE_STAGING_VERSIONS" } } ``` Fixes databricks#1012
michael-berk
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Feb 15, 2023
* Added support for `databricks_permissions` for `databricks_mlflow_experiment` and `databricks_mlflow_model` ([databricks#1013](databricks#1013)). * Added `Using XXX auth` explanation to HTTP 403 errors, which should help troubleshooting misconfigured authentication or provider aliasing. Example error message now looks like: *cannot create group: /2.0/preview/scim/v2/Groups is only accessible by admins. Using databricks-cli auth: host=https://XXX.cloud.databricks.com/, token=`***REDACTED***`, profile=demo.* All sensitive configuration parameters (`token`, `password`, and `azure_client_secret`) are redacted and replaced with `***REDACTED***` ([databricks#821](databricks#821)). * Improved documentation with regards to public subnets in AWS quick start ([databricks#1005](databricks#1005)). * Added `databricks_mount` code genration for [exporter](https://registry.terraform.io/providers/databrickslabs/databricks/latest/docs/guides/experimental-exporter) tooling ([databricks#1006](databricks#1006)). * Increase dependency check frequency ([databricks#1007](databricks#1007)). * Added experimental resources.
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MLflow Experiment usage
Valid permission levels for databricks_mlflow_experiment are:
CAN_READ
,CAN_EDIT
, andCAN_MANAGE
.MLflow Model usage
Valid permission levels for databricks_mlflow_model are:
CAN_READ
,CAN_EDIT
,CAN_MANAGE_STAGING_VERSIONS
,CAN_MANAGE_PRODUCTION_VERSIONS
, andCAN_MANAGE
.Fixes #1012