Fix doc links (#152)

template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/README.md.tmpl

@@ -189,7 +189,7 @@ Otherwise, e.g. if iterating on ML code for a new project, follow the steps belo
 * Install the `dbx` CLI via `pip install --upgrade dbx`
 * Run `databricks configure --profile {{ .input_project_name }}-dev --token --host <your-dev-workspace-url>`, passing the URL of your dev workspace.
   This should prompt you to enter an API token
-* [Create a personal access token]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "dev-tools/auth.html#personal-access-tokens-for-users")) }})
+* [Create a personal access token]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "dev-tools/auth/pat.html")) }})
   in your dev workspace and paste it into the prompt from the previous step
 * From within the root directory of the current project, use the [dbx sync](https://dbx.readthedocs.io/en/latest/guides/python/devloop/mixed/#using-dbx-sync-repo-for-local-to-repo-synchronization) tool to copy code files from your local machine into the Repo by running
   `dbx sync repo --profile {{ .input_project_name }}-dev --source . --dest-repo your-repo-name`, where `your-repo-name` should be the last segment of the full repo name (`/Repos/username/your-repo-name`)

template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/notebooks/ModelDeployment.py.tmpl

@@ -16,7 +16,7 @@
 #  * model_uri (required)  - URI of the model to deploy. Must be in the format "models:/<name>/<version-id>", as described in
 #                            https://www.mlflow.org/docs/latest/model-registry.html#fetching-an-mlflow-model-from-the-model-registry
 #                            This parameter is read as a task value
-#                            ({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "dev-tools/databricks-utils.html#get-command-dbutilsjobstaskvaluesget")) }}),
+#                            ({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "dev-tools/databricks-utils.html")) }}),
 #                            rather than as a notebook widget. That is, we assume a preceding task (the Train.py
 #                            notebook) has set a task value with key "model_uri".
 ##################################################################################

template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/README.md.tmpl

@@ -79,7 +79,7 @@ To set up the databricks CLI using a Databricks personal access token, take the
 
 1. Follow [databricks CLI]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "dev-tools/cli/databricks-cli.html")) }}) to download and set up the databricks CLI locally.
 2. Complete the `TODO` in `{{template `project_name_alphanumeric_underscore` .}}/databricks.yml` to add the dev workspace URI under `targets.dev.workspace.host`.
-3. [Create a personal access token]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "dev-tools/auth.html#personal-access-tokens-for-users")) }})
+3. [Create a personal access token]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "dev-tools/auth/pat.html")) }})
   in your dev workspace and copy it.
 4. Set an env variable `DATABRICKS_TOKEN` with your Databricks personal access token in your terminal. For example, run `export DATABRICKS_TOKEN=dapi12345` if the access token is dapi12345.
 5. You can now use the databricks CLI to validate and deploy ML resource configurations to the dev workspace.

template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/README.md.tmpl

@@ -66,7 +66,7 @@ Otherwise, e.g. if iterating on ML code for a new project, follow the steps belo
 * Install the `dbx` CLI via `pip install --upgrade dbx`
 * Run `databricks configure --profile {{ .input_project_name }}-dev --token --host <your-dev-workspace-url>`, passing the URL of your dev workspace.
   This should prompt you to enter an API token
-* [Create a personal access token]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "dev-tools/auth.html#personal-access-tokens-for-users")) }})
+* [Create a personal access token]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "dev-tools/auth/pat.html")) }})
   in your dev workspace and paste it into the prompt from the previous step
 * From within the root directory of the current project, use the [dbx sync](https://dbx.readthedocs.io/en/latest/guides/python/devloop/mixed/#using-dbx-sync-repo-for-local-to-repo-synchronization) tool to copy code files from your local machine into the Repo by running
   `dbx sync repo --profile {{ .input_project_name }}-dev --source . --dest-repo your-repo-name`, where `your-repo-name` should be the last segment of the full repo name (`/Repos/username/your-repo-name`)

template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/utils.py.tmpl

@@ -9,7 +9,7 @@ def get_deployed_model_stage_for_env(env):
     """
     # For a registered model version to be served, it needs to be in either the Staging or Production
     # model registry stage
-    # ({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "applications/machine-learning/manage-model-lifecycle/index.html#transition-a-model-stage")) }}).
+    # ({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "machine-learning/manage-model-lifecycle/workspace-model-registry.html")) }}).
     # For models in dev and staging environments, we deploy the model to the "Staging" stage, and in prod we deploy to the
     # "Production" stage
     _MODEL_STAGE_FOR_ENV = {