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WalkthroughReplaced Alauda AI Model Serving Operator references with Knative Operator/Knative Serving across installation and overview docs; renamed operator package/env identifiers; updated installation, verification, YAML examples, networking guidance (Kourier default), and added comprehensive fine-tuning installation and template guidance. Changes
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Actionable comments posted: 2
🤖 Fix all issues with AI agents
In `@docs/en/installation/ai-cluster.mdx`:
- Around line 233-255: Fix the grammar typo in the sentence "Install Knative CE
Operator window will popup." located under the "### 1. Installing the Knative CE
Operator" section by changing "popup" to the two-word form "pop up" so the
sentence reads "Install Knative CE Operator window will pop up."; update any
identical occurrences of "popup" in nearby sentences (e.g., the "Install Knative
CE Operator" window lines) to the correct two-word form.
- Around line 269-329: In the "Creating Knative Serving Instance" section update
the numbered step that currently reads "7.Click **Create**." to include a space
after the period so it reads "7. Click **Create**." — locate the step under the
Steps list in the KnativeServing procedure (the line containing "7.Click
**Create**.") and insert the missing space.
🧹 Nitpick comments (1)
docs/en/installation/ai-cluster.mdx (1)
112-120: Reduce repetitive “is the” phrasing in callouts.Small wording tweak improves readability.
♻️ Suggested wording
-6. `${KNATIVE_CE_OPERATOR_PKG_NAME}` is the path to the Knative CE Operator package tarball. -7. `${REGISTRY_ADDRESS}` is the address of the external registry. -8. `${REGISTRY_USERNAME}` is the username of the external registry. -9. `${REGISTRY_PASSWORD}` is the password of the external registry. +6. `${KNATIVE_CE_OPERATOR_PKG_NAME}`: path to the Knative CE Operator package tarball. +7. `${REGISTRY_ADDRESS}`: external registry address. +8. `${REGISTRY_USERNAME}`: external registry username. +9. `${REGISTRY_PASSWORD}`: external registry password.
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Actionable comments posted: 1
🤖 Fix all issues with AI agents
In `@docs/en/installation/ai-cluster.mdx`:
- Around line 238-240: Clarify whether the istio-system namespace is provisioned
by the core/base deployment or not: update the text around the mentions of
"istio-system" and the Knative installation note (references: "istio-system",
"Knative CE Operator", "Kourier") to state explicitly whether the base cluster
still creates istio-system for legacy certificate storage, or, if Istio is not
installed, specify the alternative namespace users should use for storing custom
certificates (and change the guidance at lines that reference storing certs in
istio-system to point to that alternative). Ensure a single clear sentence near
the Knative note indicates if istio-system is present by default or only present
when Istio is installed and give the recommended namespace to use for custom
certs when using Kourier.
🧹 Nitpick comments (1)
docs/en/installation/ai-cluster.mdx (1)
287-314: Add a short security caution for enabling host-level podspec features.The YAML enables hostNetwork/hostIPC and other podspec features, which materially expands workload privileges. A brief warning helps admins understand the security implications and scope it to trusted workloads/namespaces.
💡 Suggested doc addition
@@ 6. Replace the content with the following YAML: 7. Click **Create**. + :::warning + The features below enable host-level podspec capabilities (e.g., hostNetwork/hostIPC). Use only for trusted workloads/namespaces and follow your cluster security policies. + ::: + ```yaml apiVersion: operator.knative.dev/v1beta1 kind: KnativeServing
|
/test-pass |
Signed-off-by: Yuan Fang <yuanfang@alauda.io>
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Actionable comments posted: 2
🧹 Nitpick comments (5)
docs/en/installation/fine-tuning.mdx (2)
19-26: Consider adding placeholder annotations for clarity.The
violet pushcommand has hardcoded example values. Consider adding# [!code callout]annotations (consistent with ai-cluster.mdx) to highlight values that need replacement.✏️ Suggested improvement
```bash -# Note: replace platform address, username, password, and cluster name accordingly. -violet push --platform-address="https://192.168.171.123" \ ---platform-username="admin@cpaas.io" \ ---platform-password="platform-password" \ ---clusters=g1-c1-gpu \ -your-downloaded-package-file.tgz +# Note: replace platform address, username, password, and cluster name accordingly. +violet push --platform-address="https://192.168.171.123" \ # [!code callout] +--platform-username="admin@cpaas.io" \ # [!code callout] +--platform-password="platform-password" \ # [!code callout] +--clusters=g1-c1-gpu \ # [!code callout] +your-downloaded-package-file.tgz # [!code callout]</details> <details> <summary>🤖 Prompt for AI Agents</summary>Verify each finding against the current code and only fix it if needed.
In
@docs/en/installation/fine-tuning.mdxaround lines 19 - 26, Update the
documented example for the violet push command to mark values that must be
replaced by callers: add the inline placeholder annotation # [!code callout] to
the --platform-address value, the --platform-username flag value, the
--platform-password flag value, the --clusters flag value, and the trailing
package filename so readers know which tokens to substitute; locate the example
containing the violet push line and its flags (theviolet pushcommand and the
flags--platform-address,--platform-username,--platform-password,
--clusters, and the package filename) and append the comment annotation to
each of those lines.</details> --- `93-95`: **Consider documenting how to find the latest template image tag.** The command uses a specific image tag (`20251119-g6a584922`) which will become outdated. Consider adding guidance on how users can find the latest available tag, or mention that users should check Docker Hub for the most recent version. <details> <summary>🤖 Prompt for AI Agents</summary>Verify each finding against the current code and only fix it if needed.
In
@docs/en/installation/fine-tuning.mdxaround lines 93 - 95, The docs
currently hardcode the image tag "20251119-g6a584922" in the nerdctl command;
update the fine-tuning instructions to either (a) recommend using the image's
"latest" tag (e.g., alaudadockerhub/training-templates:latest) or (b) add a
short note telling users how to find the most recent tag on Docker Hub (search
the alaudadockerhub/training-templates repository and pick the newest tag), and
update the command example to show a placeholder like
alaudadockerhub/training-templates: so readers know to verify the
tag before running the nerdctl command.</details> </blockquote></details> <details> <summary>docs/en/installation/ai-cluster.mdx (3)</summary><blockquote> `29-33`: **Naming inconsistency: "Knative Operator" vs "Knative CE Operator".** The documentation uses "Knative Operator" in lines 29-31, but the download package name references "knative-operator.ALL.v1.x.x-yymmdd.tgz" and later sections (e.g., Line 236, 281) refer to "Knative CE Operator". Consider using a consistent name throughout the document, or clarify the distinction between "Knative Operator" (generic) and "Knative CE Operator" (the specific product name). <details> <summary>🤖 Prompt for AI Agents</summary>Verify each finding against the current code and only fix it if needed.
In
@docs/en/installation/ai-cluster.mdxaround lines 29 - 33, The doc uses
inconsistent names: "Knative Operator" and "Knative CE Operator" and the package
name "knative-operator.ALL.v1.x.x-yymmdd.tgz"; update the text to use a single,
consistent product name (either change "Knative Operator" to "Knative CE
Operator" everywhere or explicitly state that "Knative CE Operator" is the
specific product name while "Knative Operator" is the generic term), and update
the package reference and other occurrences (e.g., the string
"knative-operator.ALL.v1.x.x-yymmdd.tgz" and all mentions of "Knative
Operator"/"Knative CE Operator") so the naming is consistent or the distinction
is clarified.</details> --- `117-117`: **Naming inconsistency in callout.** Line 117 references "Knative CE Operator" while the variable name `KNATIVE_OPERATOR_PKG_NAME` and other references use "Knative Operator". Consider aligning the terminology. <details> <summary>✏️ Suggested fix</summary> ```diff -6. `${KNATIVE_OPERATOR_PKG_NAME}` is the path to the Knative CE Operator package tarball. +6. `${KNATIVE_OPERATOR_PKG_NAME}` is the path to the Knative Operator package tarball.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@docs/en/installation/ai-cluster.mdx` at line 117, Update the callout text to match the variable name KNATIVE_OPERATOR_PKG_NAME by replacing "Knative CE Operator" with "Knative Operator" (e.g., "is the path to the Knative Operator package tarball") so terminology is consistent with other references to KNATIVE_OPERATOR_PKG_NAME and surrounding documentation.
296-298: Consider adding a callout for the domain placeholder.The YAML example uses
example.comas the domain value. While Line 321 explains theprivate-registryplaceholder, there's no callout explaining that users should replaceexample.comwith their actual domain. This could lead to configuration errors.✏️ Suggested addition to callouts
<Callouts> 1. `private-registry` is a placeholder for your private registry address. You can find this in the **Administrator** view, then click **Clusters**, select `your cluster`, and check the **Private Registry** value in the **Basic Info** section. +2. `example.com` is a placeholder for your serving domain. Replace it with the domain you configured in the AmlCluster **Domain** field. </Callouts>🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@docs/en/installation/ai-cluster.mdx` around lines 296 - 298, Add a brief callout next to the YAML example clarifying that the domain placeholder must be replaced with the user's actual domain; specifically reference the "domain:" key and the "example.com" value shown in the snippet so readers understand that "example.com" is not a literal value to keep (similar to the existing note for "private-registry"), and place the callout near the "registries-skipping-tag-resolving" / "domain:" block for visibility.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@docs/en/installation/fine-tuning.mdx`:
- Line 68: Replace the hard-coded GitLab URL value for the YAML key
gitlabBaseUrl with a generic placeholder to avoid exposing or suggesting a real
instance; locate the gitlabBaseUrl entry in the YAML example and change its
value to something like https://your-gitlab-instance.example.com (or a clearly
marked placeholder) and ensure surrounding docs/text reflect it's a placeholder.
- Around line 102-108: The table row for the template
training-object-detection-ultralytics is missing a Runtime Image; update that
row in docs/en/installation/fine-tuning.mdx to include the runtime image column
(e.g., use the same Yolov5 runtime used above:
[alaudadockerhub/yolov5-runtime:v0.1.0](https://hub.docker.com/r/alaudadockerhub/yolov5-runtime)
or replace with the correct ultralytics runtime image if different) so the row
has a complete five-column entry.
---
Nitpick comments:
In `@docs/en/installation/ai-cluster.mdx`:
- Around line 29-33: The doc uses inconsistent names: "Knative Operator" and
"Knative CE Operator" and the package name
"knative-operator.ALL.v1.x.x-yymmdd.tgz"; update the text to use a single,
consistent product name (either change "Knative Operator" to "Knative CE
Operator" everywhere or explicitly state that "Knative CE Operator" is the
specific product name while "Knative Operator" is the generic term), and update
the package reference and other occurrences (e.g., the string
"knative-operator.ALL.v1.x.x-yymmdd.tgz" and all mentions of "Knative
Operator"/"Knative CE Operator") so the naming is consistent or the distinction
is clarified.
- Line 117: Update the callout text to match the variable name
KNATIVE_OPERATOR_PKG_NAME by replacing "Knative CE Operator" with "Knative
Operator" (e.g., "is the path to the Knative Operator package tarball") so
terminology is consistent with other references to KNATIVE_OPERATOR_PKG_NAME and
surrounding documentation.
- Around line 296-298: Add a brief callout next to the YAML example clarifying
that the domain placeholder must be replaced with the user's actual domain;
specifically reference the "domain:" key and the "example.com" value shown in
the snippet so readers understand that "example.com" is not a literal value to
keep (similar to the existing note for "private-registry"), and place the
callout near the "registries-skipping-tag-resolving" / "domain:" block for
visibility.
In `@docs/en/installation/fine-tuning.mdx`:
- Around line 19-26: Update the documented example for the violet push command
to mark values that must be replaced by callers: add the inline placeholder
annotation # [!code callout] to the --platform-address value, the
--platform-username flag value, the --platform-password flag value, the
--clusters flag value, and the trailing package filename so readers know which
tokens to substitute; locate the example containing the violet push line and its
flags (the `violet push` command and the flags `--platform-address`,
`--platform-username`, `--platform-password`, `--clusters`, and the package
filename) and append the comment annotation to each of those lines.
- Around line 93-95: The docs currently hardcode the image tag
"20251119-g6a584922" in the nerdctl command; update the fine-tuning instructions
to either (a) recommend using the image's "latest" tag (e.g.,
alaudadockerhub/training-templates:latest) or (b) add a short note telling users
how to find the most recent tag on Docker Hub (search the
alaudadockerhub/training-templates repository and pick the newest tag), and
update the command example to show a placeholder like
alaudadockerhub/training-templates:<latest-tag> so readers know to verify the
tag before running the nerdctl command.
ℹ️ Review info
Configuration used: Organization UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (3)
docs/en/installation/ai-cluster.mdxdocs/en/installation/fine-tuning.mdxdocs/en/overview/architecture.mdx
🚧 Files skipped from review as they are similar to previous changes (1)
- docs/en/overview/architecture.mdx
docs/en/installation/fine-tuning.mdx
Outdated
| gitlabAdminTokenSecretRef: | ||
| name: aml-gitlab-admin-token | ||
| namespace: cpaas-system | ||
| gitlabBaseUrl: https://aml-gitlab.alaudatech.net |
There was a problem hiding this comment.
Consider using a placeholder for gitlabBaseUrl.
The YAML example contains what appears to be a real GitLab URL (https://aml-gitlab.alaudatech.net). Consider using a placeholder like https://your-gitlab-instance.example.com to avoid confusion or unintended connections.
✏️ Suggested fix
- gitlabBaseUrl: https://aml-gitlab.alaudatech.net
+ gitlabBaseUrl: https://your-gitlab-instance.example.com # [!code callout]🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@docs/en/installation/fine-tuning.mdx` at line 68, Replace the hard-coded
GitLab URL value for the YAML key gitlabBaseUrl with a generic placeholder to
avoid exposing or suggesting a real instance; locate the gitlabBaseUrl entry in
the YAML example and change its value to something like
https://your-gitlab-instance.example.com (or a clearly marked placeholder) and
ensure surrounding docs/text reflect it's a placeholder.
docs/en/installation/fine-tuning.mdx
Outdated
Comment on lines
102
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108
| | Template | Task Type | Supported Models | Use Cases | Runtime Image | | ||
| |---------------------------------------|-------------------------|------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------| | ||
| | finetune-object-detection | Object Detection | yolov5 (Community PyTorch version) | Suitable for high-density, real-time object localization and classification in images. Applicable in industrial quality inspection, logistics inventory, urban security, smart retail, and agricultural monitoring for millisecond-level anomaly detection and counting statistics. | [alaudadockerhub/yolov5-runtime:v0.1.0](https://hub.docker.com/r/alaudadockerhub/yolov5-runtime) | | ||
| | finetune-time-series-forecasting | Time Series Forecasting | AWS Chronos-Bolt-Small (AutoGluon wrapped) | Zero-shot pre-trained large time series model that outputs multi-step probabilistic forecasts from historical sequences in retail, energy, finance, etc., without feature engineering, enabling minute-level deployment. | [alaudadockerhub/autogluon-chronos-rt:v1.4.0-0](https://hub.docker.com/r/alaudadockerhub/autogluon-chronos-rt) | | ||
| | finetune-image-classification-vit | Image Classification | Google ViT series | Mainly used for various computer vision tasks such as image classification, object detection, and image segmentation. | [alaudadockerhub/llm-trainer:v1.4.4](https://hub.docker.com/r/alaudadockerhub/llm-trainer) | | ||
| | finetune-text-generation-llamafactory | Text Generation | GPT-4o / Llama series (OpenAI / Meta versions) | Used for generating text, code, dialogues, and multimodal content, such as chat AI, content creation, code assistance, and personalized recommendation systems. | [alaudadockerhub/llamafactory-runtime:v1.5.1](https://hub.docker.com/r/alaudadockerhub/llamafactory-runtime) | | ||
| | training-object-detection-ultralytics | Object Detection | yolov5 (Community PyTorch version) | Suitable for high-density, real-time object localization and classification in images. Applicable in industrial quality inspection, logistics inventory, urban security, smart retail, and agricultural monitoring for millisecond-level anomaly detection and counting statistics. | |
There was a problem hiding this comment.
Missing Runtime Image for training-object-detection-ultralytics template.
The table row for training-object-detection-ultralytics is missing the Runtime Image value. This could confuse users trying to set up this template.
✏️ Suggested fix
-| training-object-detection-ultralytics | Object Detection | yolov5 (Community PyTorch version) | Suitable for high-density, real-time object localization and classification in images. Applicable in industrial quality inspection, logistics inventory, urban security, smart retail, and agricultural monitoring for millisecond-level anomaly detection and counting statistics. |
+| training-object-detection-ultralytics | Object Detection | yolov5 (Community PyTorch version) | Suitable for high-density, real-time object localization and classification in images. Applicable in industrial quality inspection, logistics inventory, urban security, smart retail, and agricultural monitoring for millisecond-level anomaly detection and counting statistics. | [alaudadockerhub/yolov5-runtime:v0.1.0](https://hub.docker.com/r/alaudadockerhub/yolov5-runtime) |Or if a different image is required, please specify the correct one.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| | Template | Task Type | Supported Models | Use Cases | Runtime Image | | |
| |---------------------------------------|-------------------------|------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------| | |
| | finetune-object-detection | Object Detection | yolov5 (Community PyTorch version) | Suitable for high-density, real-time object localization and classification in images. Applicable in industrial quality inspection, logistics inventory, urban security, smart retail, and agricultural monitoring for millisecond-level anomaly detection and counting statistics. | [alaudadockerhub/yolov5-runtime:v0.1.0](https://hub.docker.com/r/alaudadockerhub/yolov5-runtime) | | |
| | finetune-time-series-forecasting | Time Series Forecasting | AWS Chronos-Bolt-Small (AutoGluon wrapped) | Zero-shot pre-trained large time series model that outputs multi-step probabilistic forecasts from historical sequences in retail, energy, finance, etc., without feature engineering, enabling minute-level deployment. | [alaudadockerhub/autogluon-chronos-rt:v1.4.0-0](https://hub.docker.com/r/alaudadockerhub/autogluon-chronos-rt) | | |
| | finetune-image-classification-vit | Image Classification | Google ViT series | Mainly used for various computer vision tasks such as image classification, object detection, and image segmentation. | [alaudadockerhub/llm-trainer:v1.4.4](https://hub.docker.com/r/alaudadockerhub/llm-trainer) | | |
| | finetune-text-generation-llamafactory | Text Generation | GPT-4o / Llama series (OpenAI / Meta versions) | Used for generating text, code, dialogues, and multimodal content, such as chat AI, content creation, code assistance, and personalized recommendation systems. | [alaudadockerhub/llamafactory-runtime:v1.5.1](https://hub.docker.com/r/alaudadockerhub/llamafactory-runtime) | | |
| | training-object-detection-ultralytics | Object Detection | yolov5 (Community PyTorch version) | Suitable for high-density, real-time object localization and classification in images. Applicable in industrial quality inspection, logistics inventory, urban security, smart retail, and agricultural monitoring for millisecond-level anomaly detection and counting statistics. | | |
| | Template | Task Type | Supported Models | Use Cases | Runtime Image | | |
| |---------------------------------------|-------------------------|------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------| | |
| | finetune-object-detection | Object Detection | yolov5 (Community PyTorch version) | Suitable for high-density, real-time object localization and classification in images. Applicable in industrial quality inspection, logistics inventory, urban security, smart retail, and agricultural monitoring for millisecond-level anomaly detection and counting statistics. | [alaudadockerhub/yolov5-runtime:v0.1.0](https://hub.docker.com/r/alaudadockerhub/yolov5-runtime) | | |
| | finetune-time-series-forecasting | Time Series Forecasting | AWS Chronos-Bolt-Small (AutoGluon wrapped) | Zero-shot pre-trained large time series model that outputs multi-step probabilistic forecasts from historical sequences in retail, energy, finance, etc., without feature engineering, enabling minute-level deployment. | [alaudadockerhub/autogluon-chronos-rt:v1.4.0-0](https://hub.docker.com/r/alaudadockerhub/autogluon-chronos-rt) | | |
| | finetune-image-classification-vit | Image Classification | Google ViT series | Mainly used for various computer vision tasks such as image classification, object detection, and image segmentation. | [alaudadockerhub/llm-trainer:v1.4.4](https://hub.docker.com/r/alaudadockerhub/llm-trainer) | | |
| | finetune-text-generation-llamafactory | Text Generation | GPT-4o / Llama series (OpenAI / Meta versions) | Used for generating text, code, dialogues, and multimodal content, such as chat AI, content creation, code assistance, and personalized recommendation systems. | [alaudadockerhub/llamafactory-runtime:v1.5.1](https://hub.docker.com/r/alaudadockerhub/llamafactory-runtime) | | |
| | training-object-detection-ultralytics | Object Detection | yolov5 (Community PyTorch version) | Suitable for high-density, real-time object localization and classification in images. Applicable in industrial quality inspection, logistics inventory, urban security, smart retail, and agricultural monitoring for millisecond-level anomaly detection and counting statistics. | [alaudadockerhub/yolov5-runtime:v0.1.0](https://hub.docker.com/r/alaudadockerhub/yolov5-runtime) | |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@docs/en/installation/fine-tuning.mdx` around lines 102 - 108, The table row
for the template training-object-detection-ultralytics is missing a Runtime
Image; update that row in docs/en/installation/fine-tuning.mdx to include the
runtime image column (e.g., use the same Yolov5 runtime used above:
[alaudadockerhub/yolov5-runtime:v0.1.0](https://hub.docker.com/r/alaudadockerhub/yolov5-runtime)
or replace with the correct ultralytics runtime image if different) so the row
has a complete five-column entry.
|
/test-pass |
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Adopt Knative CE Operator(supported by Alauda Middleware team) for system integration
Summary by CodeRabbit