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Summary of ChangesHello @yoursanonymous, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the documentation by introducing a comprehensive 'Advanced Concepts Tutorial Series.' This new series provides in-depth, end-to-end guides for running production-grade batch workloads on Kubernetes using Volcano, covering complex scenarios like distributed machine learning, big data processing, GPU resource management, multi-tenancy, and integration with workflow engines like Argo Workflows. The aim is to bridge the gap between basic examples and real-world production configurations, offering practical solutions to common engineering challenges. Highlights
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Code Review
This pull request adds a comprehensive tutorial series for advanced Volcano concepts, which is a great addition to the documentation. The tutorials cover important real-world scenarios like distributed training, big data processing, and multi-tenancy. My review focuses on ensuring the correctness of the examples and maintaining documentation quality. I've found a few issues in the example code snippets that would prevent them from running correctly, such as an invalid TF_CONFIG generation in the TensorFlow tutorial and an incorrect successCondition in the Argo Workflows tutorial. I've also pointed out some minor inconsistencies, like incorrect pod names in commands and the use of backslashes in .gitignore. Additionally, several new markdown files are missing a final newline character. Addressing these points will improve the quality and usability of the new tutorials.
I am having trouble creating individual review comments. Click here to see my feedback.
content/en/docs/tutorial-argo-workflows.md (80)
The successCondition for a Volcano Job appears to be incorrect. The job status is typically found in status.state.phase. The expression should check this field to correctly determine if the job has completed.
successCondition: status.state.phase == 'Completed' # Wait for the Job to finish
content/en/docs/tutorial-gpu-scheduling.md (95)
The pod name in the example kubectl logs command seems incorrect. Based on the job manifest, the task name is gpu-task, so the pod name should be gpu-sharing-tutorial-gpu-task-0, not gpu-sharing-tutorial-completion-task-0.
kubectl logs gpu-sharing-tutorial-gpu-task-0
content/en/docs/tutorial-tensorflow.md (71-74)
The generated TF_CONFIG JSON is invalid because the host strings in the ps and worker arrays are not quoted. This will cause the TensorFlow application to fail when parsing the configuration. The shell script should be updated to wrap each host:port combination in double quotes.
PS_HOSTS=$(cat /etc/volcano/ps.host | sed 's/.*/"&:2222"/' | tr '\n' ',' | sed 's/,$//');
WORKER_HOSTS=$(cat /etc/volcano/worker.host | sed 's/.*/"&:2222"/' | tr '\n' ',' | sed 's/,$//');
export TF_CONFIG="{\"cluster\":{\"ps\":[${PS_HOSTS}],\"worker\":[${WORKER_HOSTS}]},\"task\":{\"type\":\"ps\",\"index\":${VK_TASK_INDEX}},\"environment\":\"cloud\"}";
python /var/tf_dist_mnist/dist_mnist.py
content/en/docs/tutorial-tensorflow.md (93-96)
The generated TF_CONFIG JSON is invalid because the host strings in the ps and worker arrays are not quoted. This will cause the TensorFlow application to fail when parsing the configuration. The shell script should be updated to wrap each host:port combination in double quotes.
PS_HOSTS=$(cat /etc/volcano/ps.host | sed 's/.*/"&:2222"/' | tr '\n' ',' | sed 's/,$//');
WORKER_HOSTS=$(cat /etc/volcano/worker.host | sed 's/.*/"&:2222"/' | tr '\n' ',' | sed 's/,$//');
export TF_CONFIG="{\"cluster\":{\"ps\":[${PS_HOSTS}],\"worker\":[${WORKER_HOSTS}]},\"task\":{\"type\":\"worker\",\"index\":${VK_TASK_INDEX}},\"environment\":\"cloud\"}";
python /var/tf_dist_mnist/dist_mnist.py
.gitignore (9)
For cross-platform compatibility in .gitignore files, it's best practice to use forward slashes (/) as path separators instead of backslashes (\).
.github/instructions/codacy.instructions.md
content/en/docs/advanced-tutorials.md (36)
The file is missing a final newline. It's a good practice to end files with a newline character.
content/en/docs/tutorial-argo-workflows.md (134)
The file is missing a final newline. It's a good practice to end files with a newline character.
content/en/docs/tutorial-gpu-scheduling.md (113)
The file is missing a final newline. It's a good practice to end files with a newline character.
content/en/docs/tutorial-multi-tenancy.md (145)
The file is missing a final newline. It's a good practice to end files with a newline character.
content/en/docs/tutorial-spark.md (122)
The file is missing a final newline. It's a good practice to end files with a newline character.
content/en/docs/tutorial-tensorflow.md (152)
The file is missing a final newline. It's a good practice to end files with a newline character.
content/en/docs/tutorials.md (369)
The file is missing a final newline. It's a good practice to end files with a newline character.
Signed-off-by: vinayak sharma <vinayaks0111@gmail.com>
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@JesseStutler , I have fixed the CI issues and validated the manifests . I also executed the Quickstart tutorial on a live cluster, and it worked as expected end-to-end. Please let me know if any additional checks are needed. |
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/kind documentation
This pull request introduces a new, comprehensive tutorial series focused on real-world production scenarios. It addresses the gap between basic examples and the complex end-to-end configurations required for production environments.