fix

Author: Shuwen-Fang

docs/run_maxtext/run_maxtext_via_xpk.md

@@ -133,13 +133,13 @@ Ensure Docker is configured for sudoless use before running the build script. Fo
    - **For TPUs:**
 
      ```
-     bash dependencies/scripts/docker_build_dependency_image.sh DEVICE=tpu MODE=stable
+     bash src/dependencies/scripts/docker_build_dependency_image.sh DEVICE=tpu MODE=stable
      ```
 
    - **For GPUs:**
 
      ```
-     bash dependencies/scripts/docker_build_dependency_image.sh DEVICE=gpu MODE=stable
+     bash src/dependencies/scripts/docker_build_dependency_image.sh DEVICE=gpu MODE=stable
      ```
 
 ---
@@ -165,8 +165,8 @@ This guide focuses on submitting workloads to an existing cluster. Cluster creat
 2. **Configure gcloud CLI**
 
    ```
-   gcloud config set project ${PROJECT_ID}
-   gcloud config set compute/zone ${ZONE}
+   gcloud config set project ${PROJECT_ID?}
+   gcloud config set compute/zone ${ZONE?}
    ```
 
 ### A Note on multi-slice and multi-node runs
@@ -180,24 +180,24 @@ For instance, to run a job across **four TPU slices**, you would change `--num-s
 
      ```
      xpk workload create\
-       --cluster ${CLUSTER_NAME}\
+       --cluster ${CLUSTER_NAME?}\
        --workload ${USER}-tpu-job\
        --base-docker-image maxtext_base_image\
        --tpu-type v5litepod-256\
        --num-slices 1\
-       --command "python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=${USER}-tpu-job base_output_directory=${BASE_OUTPUT_DIR} dataset_path=${DATASET_PATH} steps=100"
+       --command "python3 -m maxtext.trainers.pre_train.train run_name=${USER}-tpu-job base_output_directory=${BASE_OUTPUT_DIR?} dataset_path=${DATASET_PATH?} steps=100"
      ```
 
    - **On your GPU cluster:**
 
      ```
      xpk workload create\
-       --cluster ${CLUSTER_NAME}\
+       --cluster ${CLUSTER_NAME?}\
        --workload ${USER}-gpu-job\
        --base-docker-image maxtext_base_image\
        --device-type h100-80gb-8\
        --num-nodes 2\
-       --command "python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=${USER}-gpu-job base_output_directory=${BASE_OUTPUT_DIR} dataset_path=${DATASET_PATH} steps=100"
+       --command "python3 -m maxtext.trainers.pre_train.train run_name=${USER}-gpu-job base_output_directory=${BASE_OUTPUT_DIR?} dataset_path=${DATASET_PATH?} steps=100"
      ```
 
 ---
@@ -233,7 +233,7 @@ The AOT artifact must be included in your Docker image. The `docker_upload_runne
 ```bash
 export CLOUD_IMAGE_NAME="${USER}-maxtext-aot-runner"
 
-bash dependencies/scripts/docker_upload_runner.sh CLOUD_IMAGE_NAME=${CLOUD_IMAGE_NAME}
+bash src/dependencies/scripts/docker_upload_runner.sh CLOUD_IMAGE_NAME=${CLOUD_IMAGE_NAME}
 ```
 
 ### Step 3: Create the XPK workload with the AOT artifact
@@ -276,13 +276,13 @@ Your job will now start faster by skipping the JAX compilation step on the clust
 - **List your jobs:**
 
   ```
-  xpk workload list --cluster ${CLUSTER_NAME}
+  xpk workload list --cluster ${CLUSTER_NAME?}
   ```
 
 - **Analyze output:** Checkpoints and other artifacts will be saved to the Google Cloud Storage bucket you specified in `BASE_OUTPUT_DIR`.
 
 - **Delete a job:**
 
   ```
-  xpk workload delete --cluster ${CLUSTER_NAME} --workload <your-workload-name>
+  xpk workload delete --cluster ${CLUSTER_NAME?} --workload <your-workload-name>
   ```