Update links in MaxText RTD documentation

PiperOrigin-RevId: 951149730

Author: SurbhiJainUSC

.github/workflows/docs_link_check.yml

@@ -64,7 +64,7 @@ jobs:
           echo "exit_code=$?" >> $GITHUB_OUTPUT
 
       - name: Prepare issue body
-        if: steps.linkcheck.outcome == 'failure'
+        if: steps.linkcheck.outcome == 'failure' && github.event_name == 'schedule'
         run: |
           DATE=$(date +%Y-%m-%d)
           echo "ISSUE_DATE=$DATE" >> $GITHUB_ENV
@@ -92,7 +92,7 @@ jobs:
           echo 'Please review and fix the broken links in the documentation.' >> issue-body.md
 
       - name: Create issue for broken links
-        if: steps.linkcheck.outcome == 'failure'
+        if: steps.linkcheck.outcome == 'failure' && github.event_name == 'schedule'
         uses: peter-evans/create-issue-from-file@fca9117c27cdc29c6c4db3b86c48e4115a786710  # v6.0.0
         with:
           title: Documentation Link Check Failed - ${{ env.ISSUE_DATE }}

docs/guides/checkpointing_solutions/multi_tier_checkpointing.md

@@ -32,16 +32,16 @@ Multi-tier checkpointing stores checkpoints across multiple tiers of storage:
 ## Assumptions
 
 - **GKE Environment**: A **Google Kubernetes Engine (GKE)** cluster must be used. GCE infrastructure solutions like QueuedResources are not supported.
-- **Multi-Tier Checkpointing Enabled on GKE cluster level**: The Multi-Tier Checkpointing feature must be enabled and configured on your GKE cluster. This involves setting up the necessary CSI drivers and configurations as outlined in the [Google Cloud Checkpointing Documentation](https://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/training/multi-tier-checkpointing).
-- **Multi-Slice Workload**: The training job must be a [multi-slice environment](https://cloud.google.com/kubernetes-engine/docs/how-to/tpu-multislice), meaning it utilizes more than one node pool.
+- **Multi-Tier Checkpointing Enabled on GKE cluster level**: The Multi-Tier Checkpointing feature must be enabled and configured on your GKE cluster. This involves setting up the necessary CSI drivers and configurations as outlined in the [Google Cloud Checkpointing Documentation](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/training/multi-tier-checkpointing).
+- **Multi-Slice Workload**: The training job must be a [multi-slice environment](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/tpu-multislice), meaning it utilizes more than one node pool.
 - **Orbax Checkpointer**: The [Orbax library](https://orbax.readthedocs.io) must be used for checkpointing in your training script.
-- **Ramdisk Mounted via Jobset**: Each workload pod must have a [ramdisk directory mounted by Jobset](https://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/training/multi-tier-checkpointing#update-jobset) using the Multi-Tier Checkpointing CSI driver. This provides a high-speed, in-memory storage location for checkpoints.
-- **Supported TPU types**: [v4](https://cloud.google.com/tpu/docs/v4), [v5e](https://cloud.google.com/tpu/docs/v5e), [v5p](https://cloud.google.com/tpu/docs/v5p), and [v6e](https://cloud.google.com/tpu/docs/v6e)
-- **Cluster version**: Gke cluster version needs to be later than [1.32.3-gke.1170000](https://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/training/multi-tier-checkpointing#existing-cluster).
+- **Ramdisk Mounted via Jobset**: Each workload pod must have a [ramdisk directory mounted by Jobset](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/training/multi-tier-checkpointing#update-jobset) using the Multi-Tier Checkpointing CSI driver. This provides a high-speed, in-memory storage location for checkpoints.
+- **Supported TPU types**: [v4](https://docs.cloud.google.com/tpu/docs/v4), [v5e](https://docs.cloud.google.com/tpu/docs/v5e), [v5p](https://docs.cloud.google.com/tpu/docs/v5p), and [v6e](https://docs.cloud.google.com/tpu/docs/v6e)
+- **Cluster version**: Gke cluster version needs to be later than [1.32.3-gke.1170000](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/training/multi-tier-checkpointing#existing-cluster).
 
 ## Cluster creation using XPK
 
-To run workloads with Multi-Tier Checkpointing (MTC), you need a Google Kubernetes Engine (GKE) cluster with the necessary drivers and features enabled. You can create a properly configured cluster using the **XPK** or by setting it up manually with `gcloud` commands following [Google Cloud Checkpointing Documentation](https://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/training/multi-tier-checkpointing).
+To run workloads with Multi-Tier Checkpointing (MTC), you need a Google Kubernetes Engine (GKE) cluster with the necessary drivers and features enabled. You can create a properly configured cluster using the **XPK** or by setting it up manually with `gcloud` commands following [Google Cloud Checkpointing Documentation](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/training/multi-tier-checkpointing).
 
 The [xpk script](https://github.com/AI-Hypercomputer/xpk/blob/main/xpk.py) provides a streamlined way to create a GKE cluster with all the required MTC settings. The key flags used are:
 

docs/guides/data_input_pipeline/data_input_grain.md

@@ -15,7 +15,7 @@
 
 ## Why is determinism important for a data input pipeline?
 
-Determinism in a data input pipeline means that the same input data always results in the same sequence of batches at each step. This is typically achieved by setting a fixed [shuffle seed](https://google-grain.readthedocs.io/en/stable/grain.dataset.html#grain.MapDataset) during pipeline initialization. In an ideal scenario, where training runs uninterrupted, this determinism is straightforward (deterministic without preemption). However, real-world distributed training environments often face preemptions due to maintenance, hardware failures, or resource constraints. When a preempted training run resumes, the data input pipeline is re-initialized. If the same shuffle seed is used, the pipeline restarts from the beginning, potentially re-training the model on initial data. Conversely, a new seed produces a different batch sequence, making it difficult to track which data has been seen and how often each example is used for training. This lack of control can impact model performance and reproducibility.
+Determinism in a data input pipeline means that the same input data always results in the same sequence of batches at each step. This is typically achieved by setting a fixed [shuffle seed](https://google-grain.readthedocs.io/en/stable/tutorials/dataset_basic_tutorial.html#mapdataset) during pipeline initialization. In an ideal scenario, where training runs uninterrupted, this determinism is straightforward (deterministic without preemption). However, real-world distributed training environments often face preemptions due to maintenance, hardware failures, or resource constraints. When a preempted training run resumes, the data input pipeline is re-initialized. If the same shuffle seed is used, the pipeline restarts from the beginning, potentially re-training the model on initial data. Conversely, a new seed produces a different batch sequence, making it difficult to track which data has been seen and how often each example is used for training. This lack of control can impact model performance and reproducibility.
 
 ## How Grain achieves determinism
 

docs/guides/monitoring_and_debugging/use_vertex_ai_tensorboard.md

@@ -22,14 +22,14 @@ MaxText supports automatic upload of logs collected in a directory to a Tensorbo
 
 ## What is Vertex AI Tensorboard and Vertex AI Experiment
 
-Vertex AI Tensorboard is a fully managed and enterprise-ready version of open-source Tensorboard. To learn more about Vertex AI Tensorboard, visit [this](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-introduction). Vertex AI Experiment is a tool that helps to track and analyze an experiment run on Vertex AI Tensorboard. To learn more about Vertex AI Experiments, visit [this](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments).
+Vertex AI Tensorboard is a fully managed and enterprise-ready version of open-source Tensorboard. To learn more about Vertex AI Tensorboard, visit [this](https://docs.cloud.google.com/vertex-ai/docs/experiments/tensorboard-introduction). Vertex AI Experiment is a tool that helps to track and analyze an experiment run on Vertex AI Tensorboard. To learn more about Vertex AI Experiments, visit [this](https://docs.cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments).
 
 You can use a single Vertex AI Tensorboard instance to track and compare metrics from multiple Vertex AI Experiments. While you can view metrics from multiple Vertex AI Experiments within a single Tensorboard instance, the underlying log data for each experiment remains separate.
 
 ## Prerequisites
 
-- Enable [Vertex AI API](https://cloud.google.com/vertex-ai/docs/start/cloud-environment#set_up_a_project) in your Google Cloud console.
-- Assign [Vertex AI User IAM role](https://cloud.google.com/vertex-ai/docs/general/access-control#aiplatform.user) to the service account used by the TPU VMs. This is required to create and access the Vertex AI Tensorboard in Google Cloud console. If you are using XPK for MaxText, the necessary Vertex AI User IAM role will be automatically assigned to your node pools by XPK – no need to assign it manually.
+- Enable [Vertex AI API](https://docs.cloud.google.com/vertex-ai/docs/start/cloud-environment#set_up_a_project) in your Google Cloud console.
+- Assign [Vertex AI User IAM role](https://docs.cloud.google.com/vertex-ai/docs/general/access-control#aiplatform.user) to the service account used by the TPU VMs. This is required to create and access the Vertex AI Tensorboard in Google Cloud console. If you are using XPK for MaxText, the necessary Vertex AI User IAM role will be automatically assigned to your node pools by XPK – no need to assign it manually.
 
 ## Upload logs to Vertex AI Tensorboard
 
@@ -47,7 +47,7 @@ The above configuration will upload logs in `config.tensorboard_dir` to Vertex T
 
 **Scenario 2: Running MaxText on GCE**
 
-Set `use_vertex_tensorboard=True` to upload logs in `config.tensorboard_dir` to a Tensorboard instance in Vertex AI. You can manually create a Tensorboard instance named `<config.vertex_tensorboard_project>-tb-instance` and an Experiment named `config.run_name` in Vertex AI on Google Cloud console. Otherwise, MaxText will create those resources for you when `use_vertex_tensorboard=True`. Note that Vertex AI is available in only [these](https://cloud.google.com/vertex-ai/docs/general/locations#available-regions) regions.
+Set `use_vertex_tensorboard=True` to upload logs in `config.tensorboard_dir` to a Tensorboard instance in Vertex AI. You can manually create a Tensorboard instance named `<config.vertex_tensorboard_project>-tb-instance` and an Experiment named `config.run_name` in Vertex AI on Google Cloud console. Otherwise, MaxText will create those resources for you when `use_vertex_tensorboard=True`. Note that Vertex AI is available in only [these](https://docs.cloud.google.com/vertex-ai/docs/general/locations#available-regions) regions.
 
 **Scenario 2.1: Configuration to upload logs to Vertex AI Tensorboard**
 

docs/run_maxtext/run_maxtext_elastic_training.md

@@ -42,7 +42,7 @@ This demo shows recovery via *checkpoint restore* on a fixed mesh: when a slice
 
 This guide assumes you already have a **Pathways-enabled GKE cluster** created with `xpk`, and a MaxText Docker image in your Artifact Registry. If you don't:
 
-1. **Install XPK and create a Pathways GKE cluster.** Follow [Running MaxText with XPK](run_maxtext_via_xpk.md) and the [Pathways & XPK cluster guide](https://cloud.google.com/ai-hypercomputer/docs/workloads/pathways-on-cloud/create-gke-cluster#xpk). Cluster creation and management is out of scope for this page.
+1. **Install XPK and create a Pathways GKE cluster.** Follow [Running MaxText with XPK](run_maxtext_via_xpk.md) and the [Pathways & XPK cluster guide](https://docs.cloud.google.com/ai-hypercomputer/docs/workloads/pathways-on-cloud/create-gke-cluster#xpk). Cluster creation and management is out of scope for this page.
 2. **Build and upload the MaxText Docker image.** See [Build MaxText](../build_maxtext.md).
 
 ```{note}