Create and run a job that uses storage volumes

This document explains how to create and run a Batch job that uses one or more external storage volumes. External storage options include new or existing persistent disk, new local SSDs, existing Cloud Storage buckets, and an existing network file system (NFS) such as a Filestore file share.

Regardless of whether you add external storage volumes, each Compute Engine VM for a job has a boot disk, which provides storage for the job's operating system (OS) image and instructions. For information about configuring the boot disk for a job, see VM OS environment overview instead.

Before you begin

Create a job that uses storage volumes

Optionally, a job can use one or more of each of the following types of external storage volumes. For more information about all of the types of storage volumes and the differences and restrictions for each, see the documentation for Compute Engine VM storage options.

You can allow a job to use each storage volume by including it in your job's definition and specifying its mount path (mountPath) in your runnables. To learn how to create a job that uses storage volumes, see one or more of the following sections:

Use a persistent disk

A job that uses persistent disks has the following restrictions:

  • All persistent disks: Review the restrictions for all persistent disks.

  • New versus existing persistent disks: Each persistent disk in a job can be either new (defined in and created with the job) or existing (already created in your project and specified in the job). To use a persistent disk, it needs to be formatted and mounted to the job's VMs, which must be in the same location as the persistent disk. Batch mounts any persistent disks that you include in a job and formats any new persistent disks, but you must format and unmount any existing persistent disks that you want a job to use.

    The supported location options, format options, and mount options vary between new and existing persistent disks as described in the following table:

    New persistent disks Existing persistent disks
    Format options

    The persistent disk is automatically formatted with an ext4 file system.

    You must format the persistent disk to use an ext4 file system before using it for a job.

    Mount options

    All options are supported.

    All options except writing are supported. This is due to restrictions of multi-writer mode.

    You must detatch the persistent disk from any VMs that it is attached to before using it for a job.
    Location options

    You can only create zonal persistent disks.

    You can select any location for your job. The persistent disks get created in the zone your project runs in.

    You can select zonal and regional persistent disks.


    You must set the job's location (or, if specified, just the job's allowed locations) to only locations that contain all of the job's persistent disks. For example, for a zonal persistent disk, the job's location must be the disk's zone; for a regional persistent disk, the job's location must be either the disk's region or, if specifying zones, one or both of the specific zones where the regional persistent disk is located.

  • Instance templates: If you want to use a VM instance template while creating this job, you must attach any persistent disk(s) for this job in the instance template. Otherwise, if you don't want to use an instance template, you must attach any persistent disk(s) directly in the job definition.

You can create a job that uses a persistent disk using the Google Cloud console, gcloud CLI, Batch API, Go, Java, Node.js, Python, or C++.

Console

Using the Google Cloud console, the following example creates a job that runs a script to read a file from an existing zonal persistent disk that is located in the us-central1-a zone. The example script assumes that the job has an existing zonal persistent disk that contains a text file named example.txt in the root directory.

Optional: Create an example zonal persistent disk

If you want to create a zonal persistent disk that you can use to run the example script, do the following before creating your job:

  1. Attach a new, blank persistent named example-disk to a Linux VM in the us-central1-a zone and then run commands on the VM to format and mount the disk. For instructions, see Add a persistent disk to your VM.

    Do not disconnect from the VM yet.

  2. To create example.txt on the persistent disk, run the following commands on the VM:

    1. To change the current working directory to the root directory of the persistent disk, type the following command:

      cd VM_MOUNT_PATH

      Replace VM_MOUNT_PATH with the path to the directory where the persistent disk was mounted to this VM in the previous step—for example, /mnt/disks/example-disk.

    2. Press Enter.

    3. To create and define a file named example.txt, type the following command:

      cat > example.txt

    4. Press Enter.

    5. Type the contents of the file. For example, type Hello world!.

    6. To save the file, press Ctrl+D (or Command+D on macOS).

    When you're done, you can disconnect from the VM.

  3. Detach the persistent disk from the VM.

    • If you don't need the VM anymore, you can delete the VM, which automatically detatches the persistent disk.

    • Otherwise, detach the persistent disk. For instructions, see Detaching and reattaching boot disks and detach the example-disk persistent disk instead of the VM's boot disk.

Create a job that uses the existing zonal persistent disk

To create a job that uses existing zonal persistent disks using the Google Cloud console, do the following:

  1. In the Google Cloud console, go to the Job list page.

    Go to Job list

  2. Click Create. The Create batch job page opens. In the left pane, the Job details page is selected.

  3. Configure the Job details page:

    1. Optional: In the Job name field, customize the job name.

      For example, enter example-disk-job.

    2. Configure the Task details section:

      1. In the New runnable window, add at least one script or container for this job to run.

        For example, to run a script that prints the contents of a file that is named example.txt and located in the root directory of the persistent disk that this job uses, do the following:

        1. Select the Script checkbox. A text box appears.

        2. In the text box, enter the following script:

          echo "Here is the content of the example.txt file in the persistent disk."
cat MOUNT_PATH/example.txt

          Replace MOUNT_PATH with the path to where you plan to mount the persistent disk to the VMs for this job—for example, /mnt/disks/example-disk.

        3. Click Done.

      2. In the Task count field, enter the number of tasks for this job.

        For example, enter 1 (default).

      3. In the Parallelism field, enter the number of tasks to run concurrently.

        For example, enter 1 (default).

  4. Configure the Resource specifications page:

    1. In the left pane, click Resource specifications. The Resource specifications page opens.

    2. Select the location for this job. To use an existing zonal persistent disk, a job's VMs must be located in the same zone.

      1. In the Region field, select a region.

        For example, to use the example zonal persistent disk, select us-central1 (Iowa) (default).

      2. In the Zone field, select a zone.

        For example, select us-central1-a (Iowa).

  5. Configure the Additional configurations page:

    1. In the left pane, click Additional configurations. The Additional configurations page opens.

    2. For each existing zonal persistent disk that you want to mount to this job, do the following:

      1. In the Storage volume section, click Add new volume. The New volume window appears.

      2. In the New volume window, do the following:

        1. In the Volume type section, select Persistent disk (default).

        2. In the Disk list, select an existing zonal persistent disk that you want to mount to this job. The disk must be located in the same zone as this job.

          For example, select the existing zonal persistent disk that you prepared, which is located in the us-central1-a zone and contains the file example.txt.

        3. Optional: If you want to rename this zonal persistent disk, do the following:

          1. Select Customize the device name.

          2. In the Device name field, enter the new name for your disk.

        4. In the Mount path field, enter the mount path (MOUNT_PATH) for this persistent disk:

          For example, enter the following:

          /mnt/disks/EXISTING_PERSISTENT_DISK_NAME

          Replace EXISTING_PERSISTENT_DISK_NAME with the name of the disk. If you renamed the zonal persistent disk, use the new name.

          For example, replace EXISTING_PERSISTENT_DISK_NAME with example-disk.

        5. Click Done.

  6. Optional: Configure the other fields for this job.

  7. Optional: To review the job configuration, in the left pane, click Preview.

  8. Click Create.

The Job details page displays the job that you created.

gcloud

Using the gcloud CLI, the following example creates a job that attaches and mounts an existing persistent disk and a new persistent disk. The job has 3 tasks that each run a script to create a file in the new persistent disk named output_task_TASK_INDEX.txt where TASK_INDEX is the index of each task: 0, 1, and 2.

To create a job that uses persistent disks using the gcloud CLI, use the gcloud batch jobs submit command. In the job's JSON configuration file, specify the persistent disks in the instances field and mount the persistent disk in the volumes field.

  1. Create a JSON file.

    • If you are not using an instance template for this job, create a JSON file with the following contents:

      {
    "allocationPolicy": {
        "instances": [
            {
                "policy": {
                    "disks": [
                        {
                            "deviceName": "EXISTING_PERSISTENT_DISK_NAME",
                            "existingDisk": "projects/PROJECT_ID/EXISTING_PERSISTENT_DISK_LOCATION/disks/EXISTING_PERSISTENT_DISK_NAME"
                        },
                        {
                            "newDisk": {
                                "sizeGb": NEW_PERSISTENT_DISK_SIZE,
                                "type": "NEW_PERSISTENT_DISK_TYPE"
                            },
                            "deviceName": "NEW_PERSISTENT_DISK_NAME"
                        }
                    ]
                }
            }
        ],
        "location": {
            "allowedLocations": [
                "EXISTING_PERSISTENT_DISK_LOCATION"
            ]
        }
    },
    "taskGroups": [
        {
            "taskSpec": {
                "runnables": [
                    {
                        "script": {
                            "text": "echo Hello world from task ${BATCH_TASK_INDEX}. >> /mnt/disks/NEW_PERSISTENT_DISK_NAME/output_task_${BATCH_TASK_INDEX}.txt"
                        }
                    }
                ],
                "volumes": [
                    {
                        "deviceName": "NEW_PERSISTENT_DISK_NAME",
                        "mountPath": "/mnt/disks/NEW_PERSISTENT_DISK_NAME",
                        "mountOptions": "rw,async"
                    },
                    {

                        "deviceName": "EXISTING_PERSISTENT_DISK_NAME",
                        "mountPath": "/mnt/disks/EXISTING_PERSISTENT_DISK_NAME"
                    }
                ]
            },
            "taskCount":3
        }
    ],
    "logsPolicy": {
        "destination": "CLOUD_LOGGING"
    }
}

      Replace the following:

      • PROJECT_ID: the project ID of your project.
      • EXISTING_PERSISTENT_DISK_NAME: the name of an existing persistent disk.
      • EXISTING_PERSISTENT_DISK_LOCATION: the location of an existing persistent disk. For each existing zonal persistent disk, the job's location must be the disk's zone; for each existing regional persistent disk, the job's location must be either the disk's region or, if specifying zones, one or both of the specific zones where the regional persistent disk is located. If you are not specifying any existing persistent disks, you can select any location. Learn more about the allowedLocations field.
      • NEW_PERSISTENT_DISK_SIZE: the size of the new persistent disk in GB. The allowed sizes depend on the type of persistent disk, but the minimum is often 10 GB (10) and the maximum is often 64 TB (64000).
      • NEW_PERSISTENT_DISK_TYPE: the disk type of the new persistent disk, either pd-standard, pd-balanced, pd-ssd, or pd-extreme. For Batch jobs, the default is pd-balanced.
      • NEW_PERSISTENT_DISK_NAME: the name of the new persistent disk.

    • If you are using a VM instance template for this job, create a JSON file as shown previously, except replace the instances field with the following:

      "instances": [
    {
        "instanceTemplate": "INSTANCE_TEMPLATE_NAME"
    }
],

      where INSTANCE_TEMPLATE_NAME is the name of the instance template for this job. For a job that uses persistent disks, this instance template must define and attach the persistent disks that you want the job to use. For this example, the template must define and attach a new persistent disk named NEW_PERSISTENT_DISK_NAME and and attach an existing persistent disk named EXISTING_PERSISTENT_DISK_NAME.

  2. Run the following command:

    gcloud batch jobs submit JOB_NAME \
  --location LOCATION \
  --config JSON_CONFIGURATION_FILE

    Replace the following:

    • JOB_NAME: the name of the job.

    • LOCATION: the location of the job.

    • JSON_CONFIGURATION_FILE: the path for a JSON file with the job's configuration details.

API

Using the Batch API, the following example creates a job that attaches and mounts an existing persistent disk and a new persistent disk. The job has 3 tasks that each run a script to create a file in the new persistent disk named output_task_TASK_INDEX.txt where TASK_INDEX is the index of each task: 0, 1, and 2.

To create a job that uses persistent disks using the Batch API, use the jobs.create method. In the request, specify the persistent disks in the instances field and mount the persistent disk in the volumes field.

  • If you are not using an instance template for this job, make the following request:

    POST https://batch.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/jobs?job_id=JOB_NAME

{
    "allocationPolicy": {
        "instances": [
            {
                "policy": {
                    "disks": [
                        {
                            "deviceName": "EXISTING_PERSISTENT_DISK_NAME",
                            "existingDisk": "projects/PROJECT_ID/EXISTING_PERSISTENT_DISK_LOCATION/disks/EXISTING_PERSISTENT_DISK_NAME"
                        },
                        {
                            "newDisk": {
                                "sizeGb": NEW_PERSISTENT_DISK_SIZE,
                                "type": "NEW_PERSISTENT_DISK_TYPE"
                            },
                            "deviceName": "NEW_PERSISTENT_DISK_NAME"
                        }
                    ]
                }
            }
        ],
        "location": {
            "allowedLocations": [
                "EXISTING_PERSISTENT_DISK_LOCATION"
            ]
        }
    },
    "taskGroups": [
        {
            "taskSpec": {
                "runnables": [
                    {
                        "script": {
                            "text": "echo Hello world from task ${BATCH_TASK_INDEX}. >> /mnt/disks/NEW_PERSISTENT_DISK_NAME/output_task_${BATCH_TASK_INDEX}.txt"
                        }
                    }
                ],
                "volumes": [
                    {
                        "deviceName": "NEW_PERSISTENT_DISK_NAME",
                        "mountPath": "/mnt/disks/NEW_PERSISTENT_DISK_NAME",
                        "mountOptions": "rw,async"
                    },
                    {

                        "deviceName": "EXISTING_PERSISTENT_DISK_NAME",
                        "mountPath": "/mnt/disks/EXISTING_PERSISTENT_DISK_NAME"
                    }
                ]
            },
            "taskCount":3
        }
    ],
    "logsPolicy": {
        "destination": "CLOUD_LOGGING"
    }
}

    Replace the following:

    • PROJECT_ID: the project ID of your project.
    • LOCATION: the location of the job.
    • JOB_NAME: the name of the job.
    • EXISTING_PERSISTENT_DISK_NAME: the name of an existing persistent disk.
    • EXISTING_PERSISTENT_DISK_LOCATION: the location of an existing persistent disk. For each existing zonal persistent disk, the job's location must be the disk's zone; for each existing regional persistent disk, the job's location must be either the disk's region or, if specifying zones, one or both of the specific zones where the regional persistent disk is located. If you are not specifying any existing persistent disks, you can select any location. Learn more about the allowedLocations field.
    • NEW_PERSISTENT_DISK_SIZE: the size of the new persistent disk in GB. The allowed sizes depend on the type of persistent disk, but the minimum is often 10 GB (10) and the maximum is often 64 TB (64000).
    • NEW_PERSISTENT_DISK_TYPE: the disk type of the new persistent disk, either pd-standard, pd-balanced, pd-ssd, or pd-extreme. For Batch jobs, the default is pd-balanced.
    • NEW_PERSISTENT_DISK_NAME: the name of the new persistent disk.

  • If you are using a VM instance template for this job, create a JSON file as shown previously, except replace the instances field with the following:

    "instances": [
    {
        "instanceTemplate": "INSTANCE_TEMPLATE_NAME"
    }
],
...

    Where INSTANCE_TEMPLATE_NAME is the name of the instance template for this job. For a job that uses persistent disks, this instance template must define and attach the persistent disks that you want the job to use. For this example, the template must define and attach a new persistent disk named NEW_PERSISTENT_DISK_NAME and and attach an existing persistent disk named EXISTING_PERSISTENT_DISK_NAME.

Go

To create a Batch job that uses new or existing persistent disks using the Cloud Client Libraries for Go, use the CreateJob function and include the following:

For a code sample of a similar use case, see Use a Cloud Storage bucket.

Java

To create a Batch job that uses new or existing persistent disks using the Cloud Client Libraries for Java, use the CreateJobRequest class and include the following:

For a code sample of a similar use case, see Use a Cloud Storage bucket.

Node.js

To create a Batch job that uses new or existing persistent disks using the Cloud Client Libraries for Node.js, use the createJob method and include the following:

For a code sample of a similar use case, see Use a Cloud Storage bucket.

Python

To create a Batch job that uses new or existing persistent disks using the Cloud Client Libraries for Python, use the CreateJob function and include the following:

  • To attach persistent disks to the VMs for a job, include one of the following:
  • To mount the persistent disks to the job, use the Volume class with the device_name attribute and mount_path attribute. For new persistent disks, also use the mount_options attribute to enable writing.

For a code sample of a similar use case, see Use a Cloud Storage bucket.

C++

To create a Batch job that uses new or existing persistent disks using the Cloud Client Libraries for C++, use the CreateJob function and include the following:

  • To attach persistent disks to the VMs for a job, include one of the following:
    • If you are not using a VM instance template for this job, use the set_remote_path method.
    • If you are using a VM instance template for this job, use the set_instance_template method.
  • To mount the persistent disks to the job, use the volumes field with the deviceName and mountPath fields. For new persistent disks, also use the mountOptions field to enable writing.

For a code sample of a similar use case, see Use a Cloud Storage bucket.

Use a local SSD

A job that uses local SSDs has the following restrictions:

You can create a job that uses a local SSD using the gcloud CLI or Batch API. The following example describes how to create a job that creates, attaches, and mounts a local SSD. The job also has 3 tasks that each run a script to create a file in the local SSD named output_task_TASK_INDEX.txt where TASK_INDEX is the index of each task: 0, 1, and 2.

gcloud

To create a job that uses local SSDs using the gcloud CLI, use the gcloud batch jobs submit command. In the job's JSON configuration file, create and attach the local SSDs in the instances field and mount the local SSDs in the volumes field.

  1. Create a JSON file.

    • If you are not using an instance template for this job, create a JSON file with the following contents:

      {
    "allocationPolicy": {
        "instances": [
            {
                "policy": {
                    "machineType": MACHINE_TYPE,
                    "disks": [
                        {
                            "newDisk": {
                                "sizeGb": LOCAL_SSD_SIZE,
                                "type": "local-ssd"
                            },
                            "deviceName": "LOCAL_SSD_NAME"
                        }
                    ]
                }
            }
        ]
    },
    "taskGroups": [
        {
            "taskSpec": {
                "runnables": [
                    {
                        "script": {
                            "text": "echo Hello world from task ${BATCH_TASK_INDEX}. >> /mnt/disks/LOCAL_SSD_NAME/output_task_${BATCH_TASK_INDEX}.txt"
                        }
                    }
                ],
                "volumes": [
                    {
                        "deviceName": "LOCAL_SSD_NAME",
                        "mountPath": "/mnt/disks/LOCAL_SSD_NAME",
                        "mountOptions": "rw,async"
                    }
                ]
            },
            "taskCount":3
        }
    ],
    "logsPolicy": {
        "destination": "CLOUD_LOGGING"
    }
}

      Replace the following:

      • MACHINE_TYPE: the machine type, which can be predefined or custom, of the job's VMs. The allowed number of local SSDs depends on the machine type for your job's VMs.
      • LOCAL_SSD_NAME: the name of a local SSD created for this job.
      • LOCAL_SSD_SIZE: the size of all the local SSDs in GB. Each local SSD is 375 GB, so this value must be a multiple of 375 GB. For example, for 2 local SSDs, set this value to 750 GB.

    • If you are using a VM instance template for this job, create a JSON file as shown previously, except replace the instances field with the following:

      "instances": [
    {
        "instanceTemplate": "INSTANCE_TEMPLATE_NAME"
    }
],

      where INSTANCE_TEMPLATE_NAME is the name of the instance template for this job. For a job that uses local SSDs, this instance template must define and attach the local SSDs that you want the job to use. For this example, the template must define and attach a local SSD named LOCAL_SSD_NAME.

  2. Run the following command:

    gcloud batch jobs submit JOB_NAME \
  --location LOCATION \
  --config JSON_CONFIGURATION_FILE

    Replace the following:

    • JOB_NAME: the name of the job.
    • LOCATION: the location of the job.
    • JSON_CONFIGURATION_FILE: the path for a JSON file with the job's configuration details.

API

To create a job that uses local SSDs using the Batch API, use the jobs.create method. In the request, create and attach the local SSDs in the instances field and mount the local SSDs in the volumes field.

  • If you are not using an instance template for this job, make the following request:

    POST https://batch.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/jobs?job_id=JOB_NAME

{
    "allocationPolicy": {
        "instances": [
            {
                "policy": {
                    "machineType": MACHINE_TYPE,
                    "disks": [
                        {
                            "newDisk": {
                                "sizeGb": LOCAL_SSD_SIZE,
                                "type": "local-ssd"
                            },
                            "deviceName": "LOCAL_SSD_NAME"
                        }
                    ]
                }
            }
        ]
    },
    "taskGroups": [
        {
            "taskSpec": {
                "runnables": [
                    {
                        "script": {
                            "text": "echo Hello world from task ${BATCH_TASK_INDEX}. >> /mnt/disks/LOCAL_SSD_NAME/output_task_${BATCH_TASK_INDEX}.txt"
                        }
                    }
                ],
                "volumes": [
                    {
                        "deviceName": "LOCAL_SSD_NAME",
                        "mountPath": "/mnt/disks/LOCAL_SSD_NAME",
                        "mountOptions": "rw,async"
                    }
                ]
            },
            "taskCount":3
        }
    ],
    "logsPolicy": {
        "destination": "CLOUD_LOGGING"
    }
}

    Replace the following:

    • PROJECT_ID: the project ID of your project.
    • LOCATION: the location of the job.
    • JOB_NAME: the name of the job.
    • MACHINE_TYPE: the machine type, which can be predefined or custom, of the job's VMs. The allowed number of local SSDs depends on the machine type for your job's VMs.
    • LOCAL_SSD_NAME: the name of a local SSD created for this job.
    • LOCAL_SSD_SIZE: the size of all the local SSDs in GB. Each local SSD is 375 GB, so this value must be a multiple of 375 GB. For example, for 2 local SSDs, set this value to 750 GB.

  • If you are using a VM instance template for this job, create a JSON file as shown previously, except replace the instances field with the following:

    "instances": [
    {
        "instanceTemplate": "INSTANCE_TEMPLATE_NAME"
    }
],
...

    Where INSTANCE_TEMPLATE_NAME is the name of the instance template for this job. For a job that uses local SSDs, this instance template must define and attach the local SSDs that you want the job to use. For this example, the template must define and attach a local SSD named LOCAL_SSD_NAME.

Use a Cloud Storage bucket

To create a job that uses an existing Cloud Storage bucket, select one of the following methods:

  • Recommended: Mount a bucket directly to your job's VMs by specifying the bucket in the job's definition, as shown in this section. When the job runs, the bucket is automatically mounted to the VMs for your job using Cloud Storage FUSE.
  • Create a job with tasks that directly access a Cloud Storage bucket by using the gsutil command-line tool or client libraries for the Cloud Storage API. To learn how to access a Cloud Storage bucket directly from a VM, see the Compute Engine documentation for Writing and reading data from Cloud Storage buckets.

Before you create a job that uses a bucket, create a bucket or identify an existing bucket. For more information, see Create buckets and List buckets.

You can create a job that uses a Cloud Storage bucket using the Google Cloud console, gcloud CLI, Batch API, Go, Java, Node.js, Python, or C++.

The following example describes how to create a job that mounts a Cloud Storage bucket. The job also has 3 tasks that each run a script to create a file in the bucket named output_task_TASK_INDEX.txt where TASK_INDEX is the index of each task: 0, 1, and 2.

Console

To create a job that uses a Cloud Storage bucket using the Google Cloud console, do the following:

  1. In the Google Cloud console, go to the Job list page.

    Go to Job list

  2. Click Create. The Create batch job page opens. In the left pane, the Job details page is selected.

  3. Configure the Job details page:

    1. Optional: In the Job name field, customize the job name.

      For example, enter example-bucket-job.

    2. Configure the Task details section:

      1. In the New runnable window, add at least one script or container for this job to run.

        For example, do the following:

        1. Select the Script checkbox. A text box appears.

        2. In the text box, enter the following script:

          echo Hello world from task ${BATCH_TASK_INDEX}. >> MOUNT_PATH/output_task_${BATCH_TASK_INDEX}.txt

          Replace MOUNT_PATH with the mount path that this job's runnables use to access an existing Cloud Storage bucket. The path must start with /mnt/disks/ followed by a directory or path that you choose. For example, if you want to represent this bucket with a directory named my-bucket, set the mount path to /mnt/disks/my-bucket.

        3. Click Done.

      2. In the Task count field, enter the number of tasks for this job.

        For example, enter 3.

      3. In the Parallelism field, enter the number of tasks to run concurrently.

        For example, enter 1 (default).

  4. Configure the Additional configurations page:

    1. In the left pane, click Additional configurations. The Additional configurations page opens.

    2. For each Cloud Storage bucket that you want to mount to this job, do the following:

      1. In the Storage volume section, click Add new volume. The New volume window appears.

      2. In the New volume window, do the following:

        1. In the Volume type section, select Cloud Storage bucket.

        2. In the Storage Bucket name field, enter the name of an existing bucket.

          For example, enter the bucket you specified in this job's runnable.

        3. In the Mount path field, enter the mount path of the bucket (MOUNT_PATH), which you specified in the runnable.

        4. Click Done.

  5. Optional: Configure the other fields for this job.

  6. Optional: To review the job configuration, in the left pane, click Preview.

  7. Click Create.

The Job details page displays the job that you created.

gcloud

To create a job that uses a Cloud Storage bucket using the gcloud CLI, use the gcloud batch jobs submit command. In the job's JSON configuration file, mount the bucket in the volumes field.

For example, to create a job that outputs files to a Cloud Storage:

  1. Create a JSON file with the following contents:

    {
    "taskGroups": [
        {
            "taskSpec": {
                "runnables": [
                    {
                        "script": {
                            "text": "echo Hello world from task ${BATCH_TASK_INDEX}. >> MOUNT_PATH/output_task_${BATCH_TASK_INDEX}.txt"
                        }
                    }
                ],
                "volumes": [
                    {
                        "gcs": {
                            "remotePath": "BUCKET_PATH"
                        },
                        "mountPath": "MOUNT_PATH"
                    }
                ]
            },
            "taskCount": 3
        }
    ],
    "logsPolicy": {
        "destination": "CLOUD_LOGGING"
    }
}

    Replace the following:

    • BUCKET_PATH: the path of the bucket directory that you want this job to access, which must start with the name of the bucket. For example, for a bucket named BUCKET_NAME, the path BUCKET_NAME represents the root directory of the bucket and the path BUCKET_NAME/subdirectory represents the subdirectory subdirectory.
    • MOUNT_PATH: the mount path that the job's runnables use to access this bucket. The path must start with /mnt/disks/ followed by a directory or path that you choose. For example, if you want to represent this bucket with a directory named my-bucket, set the mount path to /mnt/disks/my-bucket.

  2. Run the following command:

    gcloud batch jobs submit JOB_NAME \
  --location LOCATION \
  --config JSON_CONFIGURATION_FILE

    Replace the following:

    • JOB_NAME: the name of the job.
    • LOCATION: the location of the job.
    • JSON_CONFIGURATION_FILE: the path for a JSON file with the job's configuration details.

API

To create a job that uses a Cloud Storage bucket using the Batch API, use the jobs.create method and mount the bucket in the volumes field.

POST https://batch.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/jobs?job_id=JOB_NAME

{
    "taskGroups": [
        {
            "taskSpec": {
                "runnables": [
                    {
                        "script": {
                            "text": "echo Hello world from task ${BATCH_TASK_INDEX}. >> MOUNT_PATH/output_task_${BATCH_TASK_INDEX}.txt"
                        }
                    }
                ],
                "volumes": [
                    {
                        "gcs": {
                            "remotePath": "BUCKET_PATH"
                        },
                        "mountPath": "MOUNT_PATH"
                    }
                ]
            },
            "taskCount": 3
        }
    ],
    "logsPolicy": {
            "destination": "CLOUD_LOGGING"
    }
}

Replace the following:

  • PROJECT_ID: the project ID of your project.
  • LOCATION: the location of the job.
  • JOB_NAME: the name of the job.
  • BUCKET_PATH: the path of the bucket directory that you want this job to access, which must start with the name of the bucket. For example, for a bucket named BUCKET_NAME, the path BUCKET_NAME represents the root directory of the bucket and the path BUCKET_NAME/subdirectory represents the subdirectory subdirectory.
  • MOUNT_PATH: the mount path that the job's runnables use to access this bucket. The path must start with /mnt/disks/ followed by a directory or path that you choose. For example, if you want to represent this bucket with a directory named my-bucket, set the mount path to /mnt/disks/my-bucket.

Go

Go

For more information, see the Batch Go API reference documentation.

To authenticate to Batch, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

import (
	"context"
	"fmt"
	"io"

	batch "cloud.google.com/go/batch/apiv1"
	batchpb "google.golang.org/genproto/googleapis/cloud/batch/v1"
	durationpb "google.golang.org/protobuf/types/known/durationpb"
)

// Creates and runs a job that executes the specified script
func createScriptJobWithBucket(w io.Writer, projectID, region, jobName, bucketName string) error {
	// projectID := "your_project_id"
	// region := "us-central1"
	// jobName := "some-job"
	// jobName := "some-bucket"

	ctx := context.Background()
	batchClient, err := batch.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %w", err)
	}
	defer batchClient.Close()

	// Define what will be done as part of the job.
	command := &batchpb.Runnable_Script_Text{
		Text: "echo Hello world from task ${BATCH_TASK_INDEX}. >> /mnt/share/output_task_${BATCH_TASK_INDEX}.txt",
	}

	// Specify the Google Cloud Storage bucket to mount
	volume := &batchpb.Volume{
		Source: &batchpb.Volume_Gcs{
			Gcs: &batchpb.GCS{
				RemotePath: bucketName,
			},
		},
		MountPath:    "/mnt/share",
		MountOptions: []string{},
	}

	// We can specify what resources are requested by each task.
	resources := &batchpb.ComputeResource{
		// CpuMilli is milliseconds per cpu-second. This means the task requires 50% of a single CPUs.
		CpuMilli:  500,
		MemoryMib: 16,
	}

	taskSpec := &batchpb.TaskSpec{
		Runnables: []*batchpb.Runnable{{
			Executable: &batchpb.Runnable_Script_{
				Script: &batchpb.Runnable_Script{Command: command},
			},
		}},
		ComputeResource: resources,
		MaxRunDuration: &durationpb.Duration{
			Seconds: 3600,
		},
		MaxRetryCount: 2,
		Volumes:       []*batchpb.Volume{volume},
	}

	// Tasks are grouped inside a job using TaskGroups.
	taskGroups := []*batchpb.TaskGroup{
		{
			TaskCount: 4,
			TaskSpec:  taskSpec,
		},
	}

	// Policies are used to define on what kind of virtual machines the tasks will run on.
	// In this case, we tell the system to use "e2-standard-4" machine type.
	// Read more about machine types here: https://cloud.google.com/compute/docs/machine-types
	allocationPolicy := &batchpb.AllocationPolicy{
		Instances: []*batchpb.AllocationPolicy_InstancePolicyOrTemplate{{
			PolicyTemplate: &batchpb.AllocationPolicy_InstancePolicyOrTemplate_Policy{
				Policy: &batchpb.AllocationPolicy_InstancePolicy{
					MachineType: "e2-standard-4",
				},
			},
		}},
	}

	// We use Cloud Logging as it's an out of the box available option
	logsPolicy := &batchpb.LogsPolicy{
		Destination: batchpb.LogsPolicy_CLOUD_LOGGING,
	}

	jobLabels := map[string]string{"env": "testing", "type": "script"}

	// The job's parent is the region in which the job will run
	parent := fmt.Sprintf("projects/%s/locations/%s", projectID, region)

	job := batchpb.Job{
		TaskGroups:       taskGroups,
		AllocationPolicy: allocationPolicy,
		Labels:           jobLabels,
		LogsPolicy:       logsPolicy,
	}

	req := &batchpb.CreateJobRequest{
		Parent: parent,
		JobId:  jobName,
		Job:    &job,
	}

	created_job, err := batchClient.CreateJob(ctx, req)
	if err != nil {
		return fmt.Errorf("unable to create job: %w", err)
	}

	fmt.Fprintf(w, "Job created: %v\n", created_job)

	return nil
}

Java

Java

For more information, see the Batch Java API reference documentation.

To authenticate to Batch, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

import com.google.cloud.batch.v1.AllocationPolicy;
import com.google.cloud.batch.v1.AllocationPolicy.InstancePolicy;
import com.google.cloud.batch.v1.AllocationPolicy.InstancePolicyOrTemplate;
import com.google.cloud.batch.v1.BatchServiceClient;
import com.google.cloud.batch.v1.ComputeResource;
import com.google.cloud.batch.v1.CreateJobRequest;
import com.google.cloud.batch.v1.GCS;
import com.google.cloud.batch.v1.Job;
import com.google.cloud.batch.v1.LogsPolicy;
import com.google.cloud.batch.v1.LogsPolicy.Destination;
import com.google.cloud.batch.v1.Runnable;
import com.google.cloud.batch.v1.Runnable.Script;
import com.google.cloud.batch.v1.TaskGroup;
import com.google.cloud.batch.v1.TaskSpec;
import com.google.cloud.batch.v1.Volume;
import com.google.protobuf.Duration;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class CreateWithMountedBucket {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // TODO(developer): Replace these variables before running the sample.
    // Project ID or project number of the Cloud project you want to use.
    String projectId = "YOUR_PROJECT_ID";

    // Name of the region you want to use to run the job. Regions that are
    // available for Batch are listed on: https://cloud.google.com/batch/docs/get-started#locations
    String region = "europe-central2";

    // The name of the job that will be created.
    // It needs to be unique for each project and region pair.
    String jobName = "JOB_NAME";

    // Name of the bucket to be mounted for your Job.
    String bucketName = "BUCKET_NAME";

    createScriptJobWithBucket(projectId, region, jobName, bucketName);
  }

  // This method shows how to create a sample Batch Job that will run
  // a simple command on Cloud Compute instances.
  public static void createScriptJobWithBucket(String projectId, String region, String jobName,
      String bucketName)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the `batchServiceClient.close()` method on the client to safely
    // clean up any remaining background resources.
    try (BatchServiceClient batchServiceClient = BatchServiceClient.create()) {

      // Define what will be done as part of the job.
      Runnable runnable =
          Runnable.newBuilder()
              .setScript(
                  Script.newBuilder()
                      .setText(
                          "echo Hello world from task ${BATCH_TASK_INDEX}. >> "
                              + "/mnt/share/output_task_${BATCH_TASK_INDEX}.txt")
                      // You can also run a script from a file. Just remember, that needs to be a
                      // script that's already on the VM that will be running the job.
                      // Using setText() and setPath() is mutually exclusive.
                      // .setPath("/tmp/test.sh")
                      .build())
              .build();

      Volume volume = Volume.newBuilder()
          .setGcs(GCS.newBuilder()
              .setRemotePath(bucketName)
              .build())
          .setMountPath("/mnt/share")
          .build();

      // We can specify what resources are requested by each task.
      ComputeResource computeResource =
          ComputeResource.newBuilder()
              // In milliseconds per cpu-second. This means the task requires 50% of a single CPUs.
              .setCpuMilli(500)
              // In MiB.
              .setMemoryMib(16)
              .build();

      TaskSpec task =
          TaskSpec.newBuilder()
              // Jobs can be divided into tasks. In this case, we have only one task.
              .addRunnables(runnable)
              .addVolumes(volume)
              .setComputeResource(computeResource)
              .setMaxRetryCount(2)
              .setMaxRunDuration(Duration.newBuilder().setSeconds(3600).build())
              .build();

      // Tasks are grouped inside a job using TaskGroups.
      // Currently, it's possible to have only one task group.
      TaskGroup taskGroup = TaskGroup.newBuilder().setTaskCount(4).setTaskSpec(task).build();

      // Policies are used to define on what kind of virtual machines the tasks will run on.
      // In this case, we tell the system to use "e2-standard-4" machine type.
      // Read more about machine types here: https://cloud.google.com/compute/docs/machine-types
      InstancePolicy instancePolicy =
          InstancePolicy.newBuilder().setMachineType("e2-standard-4").build();

      AllocationPolicy allocationPolicy =
          AllocationPolicy.newBuilder()
              .addInstances(InstancePolicyOrTemplate.newBuilder().setPolicy(instancePolicy).build())
              .build();

      Job job =
          Job.newBuilder()
              .addTaskGroups(taskGroup)
              .setAllocationPolicy(allocationPolicy)
              .putLabels("env", "testing")
              .putLabels("type", "script")
              .putLabels("mount", "bucket")
              // We use Cloud Logging as it's an out of the box available option.
              .setLogsPolicy(
                  LogsPolicy.newBuilder().setDestination(Destination.CLOUD_LOGGING).build())
              .build();

      CreateJobRequest createJobRequest =
          CreateJobRequest.newBuilder()
              // The job's parent is the region in which the job will run.
              .setParent(String.format("projects/%s/locations/%s", projectId, region))
              .setJob(job)
              .setJobId(jobName)
              .build();

      Job result =
          batchServiceClient
              .createJobCallable()
              .futureCall(createJobRequest)
              .get(5, TimeUnit.MINUTES);

      System.out.printf("Successfully created the job: %s", result.getName());
    }
  }
}

Node.js

Node.js

For more information, see the Batch Node.js API reference documentation.

To authenticate to Batch, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

/**
 * TODO(developer): Uncomment and replace these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
/**
 * The region you want to the job to run in. The regions that support Batch are listed here:
 * https://cloud.google.com/batch/docs/get-started#locations
 */
// const region = 'us-central-1';
/**
 * The name of the job that will be created.
 * It needs to be unique for each project and region pair.
 */
// const jobName = 'YOUR_JOB_NAME';
/**
 * The name of the bucket to be mounted.
 */
// const bucketName = 'YOUR_BUCKET_NAME';

// Imports the Batch library
const batchLib = require('@google-cloud/batch');
const batch = batchLib.protos.google.cloud.batch.v1;

// Instantiates a client
const batchClient = new batchLib.v1.BatchServiceClient();

// Define what will be done as part of the job.
const task = new batch.TaskSpec();
const runnable = new batch.Runnable();
runnable.script = new batch.Runnable.Script();
runnable.script.text =
  'echo Hello world from task ${BATCH_TASK_INDEX}. >> /mnt/share/output_task_${BATCH_TASK_INDEX}.txt';
// You can also run a script from a file. Just remember, that needs to be a script that's
// already on the VM that will be running the job. Using runnable.script.text and runnable.script.path is mutually
// exclusive.
// runnable.script.path = '/tmp/test.sh'
task.runnables = [runnable];

const gcsBucket = new batch.GCS();
gcsBucket.remotePath = bucketName;
const gcsVolume = new batch.Volume();
gcsVolume.gcs = gcsBucket;
gcsVolume.mountPath = '/mnt/share';
task.volumes = [gcsVolume];

// We can specify what resources are requested by each task.
const resources = new batch.ComputeResource();
resources.cpuMilli = 2000; // in milliseconds per cpu-second. This means the task requires 2 whole CPUs.
resources.memoryMib = 16;
task.computeResource = resources;

task.maxRetryCount = 2;
task.maxRunDuration = {seconds: 3600};

// Tasks are grouped inside a job using TaskGroups.
const group = new batch.TaskGroup();
group.taskCount = 4;
group.taskSpec = task;

// Policies are used to define on what kind of virtual machines the tasks will run on.
// In this case, we tell the system to use "e2-standard-4" machine type.
// Read more about machine types here: https://cloud.google.com/compute/docs/machine-types
const allocationPolicy = new batch.AllocationPolicy();
const policy = new batch.AllocationPolicy.InstancePolicy();
policy.machineType = 'e2-standard-4';
const instances = new batch.AllocationPolicy.InstancePolicyOrTemplate();
instances.policy = policy;
allocationPolicy.instances = [instances];

const job = new batch.Job();
job.name = jobName;
job.taskGroups = [group];
job.allocationPolicy = allocationPolicy;
job.labels = {env: 'testing', type: 'script'};
// We use Cloud Logging as it's an option available out of the box
job.logsPolicy = new batch.LogsPolicy();
job.logsPolicy.destination = batch.LogsPolicy.Destination.CLOUD_LOGGING;

// The job's parent is the project and region in which the job will run
const parent = `projects/${projectId}/locations/${region}`;

async function callCreateJob() {
  // Construct request
  const request = {
    parent,
    jobId: jobName,
    job,
  };

  // Run request
  const response = await batchClient.createJob(request);
  console.log(response);
}

callCreateJob();

Python

Python

For more information, see the Batch Python API reference documentation.

To authenticate to Batch, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

from google.cloud import batch_v1


def create_script_job_with_bucket(
    project_id: str, region: str, job_name: str, bucket_name: str
) -> batch_v1.Job:
    """
    This method shows how to create a sample Batch Job that will run
    a simple command on Cloud Compute instances.

    Args:
        project_id: project ID or project number of the Cloud project you want to use.
        region: name of the region you want to use to run the job. Regions that are
            available for Batch are listed on: https://cloud.google.com/batch/docs/get-started#locations
        job_name: the name of the job that will be created.
            It needs to be unique for each project and region pair.
        bucket_name: name of the bucket to be mounted for your Job.

    Returns:
        A job object representing the job created.
    """
    client = batch_v1.BatchServiceClient()

    # Define what will be done as part of the job.
    task = batch_v1.TaskSpec()
    runnable = batch_v1.Runnable()
    runnable.script = batch_v1.Runnable.Script()
    runnable.script.text = "echo Hello world from task ${BATCH_TASK_INDEX}. >> /mnt/share/output_task_${BATCH_TASK_INDEX}.txt"
    task.runnables = [runnable]

    gcs_bucket = batch_v1.GCS()
    gcs_bucket.remote_path = bucket_name
    gcs_volume = batch_v1.Volume()
    gcs_volume.gcs = gcs_bucket
    gcs_volume.mount_path = "/mnt/share"
    task.volumes = [gcs_volume]

    # We can specify what resources are requested by each task.
    resources = batch_v1.ComputeResource()
    resources.cpu_milli = 500  # in milliseconds per cpu-second. This means the task requires 50% of a single CPUs.
    resources.memory_mib = 16
    task.compute_resource = resources

    task.max_retry_count = 2
    task.max_run_duration = "3600s"

    # Tasks are grouped inside a job using TaskGroups.
    # Currently, it's possible to have only one task group.
    group = batch_v1.TaskGroup()
    group.task_count = 4
    group.task_spec = task

    # Policies are used to define on what kind of virtual machines the tasks will run on.
    # In this case, we tell the system to use "e2-standard-4" machine type.
    # Read more about machine types here: https://cloud.google.com/compute/docs/machine-types
    allocation_policy = batch_v1.AllocationPolicy()
    policy = batch_v1.AllocationPolicy.InstancePolicy()
    policy.machine_type = "e2-standard-4"
    instances = batch_v1.AllocationPolicy.InstancePolicyOrTemplate()
    instances.policy = policy
    allocation_policy.instances = [instances]

    job = batch_v1.Job()
    job.task_groups = [group]
    job.allocation_policy = allocation_policy
    job.labels = {"env": "testing", "type": "script", "mount": "bucket"}
    # We use Cloud Logging as it's an out of the box available option
    job.logs_policy = batch_v1.LogsPolicy()
    job.logs_policy.destination = batch_v1.LogsPolicy.Destination.CLOUD_LOGGING

    create_request = batch_v1.CreateJobRequest()
    create_request.job = job
    create_request.job_id = job_name
    # The job's parent is the region in which the job will run
    create_request.parent = f"projects/{project_id}/locations/{region}"

    return client.create_job(create_request)

C++

C++

For more information, see the Batch C++ API reference documentation.

To authenticate to Batch, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

#include "google/cloud/batch/v1/batch_client.h"

  [](std::string const& project_id, std::string const& location_id,
     std::string const& job_id, std::string const& bucket_name) {
    // Initialize the request; start with the fields that depend on the sample
    // input.
    google::cloud::batch::v1::CreateJobRequest request;
    request.set_parent("projects/" + project_id + "/locations/" + location_id);
    request.set_job_id(job_id);
    // Most of the job description is fixed in this example; use a string to
    // initialize it, and then override the GCS remote path.
    auto constexpr kText = R"pb(
      task_groups {
        task_count: 4
        task_spec {
          compute_resource { cpu_milli: 500 memory_mib: 16 }
          max_retry_count: 2
          max_run_duration { seconds: 3600 }
          runnables {
            script {
              text: "echo Hello world from task ${BATCH_TASK_INDEX}. >> /mnt/share/output_task_${BATCH_TASK_INDEX}.txt"
            }
          }
          volumes { mount_path: "/mnt/share" }
        }
      }
      allocation_policy {
        instances {
          policy { machine_type: "e2-standard-4" provisioning_model: STANDARD }
        }
      }
      labels { key: "env" value: "testing" }
      labels { key: "type" value: "script" }
      logs_policy { destination: CLOUD_LOGGING }
    )pb";
    auto* job = request.mutable_job();
    if (!google::protobuf::TextFormat::ParseFromString(kText, job)) {
      throw std::runtime_error("Error parsing Job description");
    }
    job->mutable_task_groups(0)
        ->mutable_task_spec()
        ->mutable_volumes(0)
        ->mutable_gcs()
        ->set_remote_path(bucket_name);
    // Create a client and issue the request.
    auto client = google::cloud::batch_v1::BatchServiceClient(
        google::cloud::batch_v1::MakeBatchServiceConnection());
    auto response = client.CreateJob(request);
    if (!response) throw std::move(response).status();
    std::cout << "Job : " << response->DebugString() << "\n";
  }

Use a network file system

You can create a job that uses an existing network file system (NFS), such as a Filestore file share, using the Google Cloud console, gcloud CLI or Batch API.

Before creating a job that uses a NFS, make sure that your network's firewall is properly configured to allow traffic between your job's VMs and the NFS. For more information, see Configuring firewall rules for Filestore.

This following example describes how to create a job that specifies and mounts a NFS. The job also has 3 tasks that each run a script to create a file in the NFS named output_task_TASK_INDEX.txt where TASK_INDEX is the index of each task: 0, 1, and 2.

Console

To create a job that uses a NFS using the Google Cloud console, do the following:

  1. In the Google Cloud console, go to the Job list page.

    Go to Job list

  2. Click Create. The Create batch job page opens. In the left pane, the Job details page is selected.

  3. Configure the Job details page:

    1. Optional: In the Job name field, customize the job name.

      For example, enter example-nfs-job.

    2. Configure the Task details section:

      1. In the New runnable window, add at least one script or container for this job to run.

        For example, do the following:

        1. Select the Script checkbox. A text box appears.

        2. In the text box, enter the following script:

          echo Hello world from task ${BATCH_TASK_INDEX}. >> MOUNT_PATH/output_task_${BATCH_TASK_INDEX}.txt

          Replace MOUNT_PATH with the mount path that the job's runnable use to access this NFS. The path must start with /mnt/disks/ followed by a directory or path that you choose. For example, if you want to represent this NFS with a directory named my-nfs, set the mount path to /mnt/disks/my-nfs.

        3. Click Done.

      2. In the Task count field, enter the number of tasks for this job.

        For example, enter 3.

      3. In the Parallelism field, enter the number of tasks to run concurrently.

        For example, enter 1 (default).

  4. Configure the Additional configurations page:

    1. In the left pane, click Additional configurations. The Additional configurations page opens.

    2. For each Cloud Storage bucket that you want to mount to this job, do the following:

      1. In the Storage volume section, click Add new volume. The New volume window appears.

      2. In the New volume window, do the following:

        1. In the Volume type section, select Network file system.

        2. In the File server field, enter the IP address of the server where the NFS you specified in this job's runnable is located.

          For example, if your NFS is a Filestore file share, then specify the IP address of the Filestore instance, which you can get by describing the Filestore instance.

        3. In the Remote path field, enter a path that can access the NFS you specified in the previous step.

          The path of the NFS directory must start with a / followed by the root directory of the NFS.

        4. In the Mount path field, enter the mount path to the NFS (MOUNT_PATH), which you specified in the previous step.

    3. Click Done.

  5. Optional: Configure the other fields for this job.

  6. Optional: To review the job configuration, in the left pane, click Preview.

  7. Click Create.

The Job details page displays the job that you created.

gcloud

To create a job that uses a NFS using the gcloud CLI, use the gcloud batch jobs submit command. In the job's JSON configuration file, mount the NFS in the volumes field.

  1. Create a JSON file with the following contents:

    {
    "taskGroups": [
        {
            "taskSpec": {
                "runnables": [
                    {
                        "script": {
                            "text": "echo Hello world from task ${BATCH_TASK_INDEX}. >> MOUNT_PATH/output_task_${BATCH_TASK_INDEX}.txt"
                        }
                    }
                ],
                "volumes": [
                    {
                        "nfs": {
                            "server": "NFS_IP_ADDRESS",
                            "remotePath": "NFS_PATH"
                        },
                        "mountPath": "MOUNT_PATH"
                    }
                ]
            },
            "taskCount": 3
        }
    ],
    "logsPolicy": {
        "destination": "CLOUD_LOGGING"
    }
}

    Replace the following:

    • NFS_IP_ADDRESS: the IP address of the NFS. For example, if your NFS is a Filestore file share, then specify the IP address of the Filestore instance, which you can get by describing the Filestore instance.
    • NFS_PATH: the path of the NFS directory that you want this job to access, which must start with a / followed by the root directory of the NFS. For example, for a Filestore file share named FILE_SHARE_NAME, the path /FILE_SHARE_NAME represents the root directory of the file share and the path /FILE_SHARE_NAME/subdirectory represents the subdirectory subdirectory.
    • MOUNT_PATH: the mount path that the job's runnables use to access this NFS. The path must start with /mnt/disks/ followed by a directory or path that you choose. For example, if you want to represent this NFS with a directory named my-nfs, set the mount path to /mnt/disks/my-nfs.

  2. Run the following command:

    gcloud batch jobs submit JOB_NAME \
  --location LOCATION \
  --config JSON_CONFIGURATION_FILE

    Replace the following:

    • JOB_NAME: the name of the job.
    • LOCATION: the location of the job.
    • JSON_CONFIGURATION_FILE: the path for a JSON file with the job's configuration details.

API

To create a job that uses a NFS using the Batch API, use the jobs.create method and mount the NFS in the volumes field.

POST https://batch.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/jobs?job_id=JOB_NAME

   {
    "taskGroups": [
        {
            "taskSpec": {
                "runnables": [
                    {
                        "script": {
                            "text": "echo Hello world from task ${BATCH_TASK_INDEX}. >> MOUNT_PATH/output_task_${BATCH_TASK_INDEX}.txt"
                        }
                    }
                ],
                "volumes": [
                    {
                        "nfs": {
                            "server": "NFS_IP_ADDRESS",
                            "remotePath": "NFS_PATH"
                        },
                        "mountPath": "MOUNT_PATH"
                    }
                ]
            },
            "taskCount": 3
        }
    ],
    "logsPolicy": {
        "destination": "CLOUD_LOGGING"
    }
}

Replace the following:

  • PROJECT_ID: the project ID of your project.
  • LOCATION: the location of the job.
  • JOB_NAME: the name of the job.
  • NFS_IP_ADDRESS: the IP address of the Network File System. For example, if your NFS is a Filestore file share, then specify the IP address of the Filestore instance, which you can get by describing the Filestore instance.
  • NFS_PATH: the path of the NFS directory that you want this job to access, which must start with a / followed by the root directory of the NFS. For example, for a Filestore file share named FILE_SHARE_NAME, the path /FILE_SHARE_NAME represents the root directory of the file share and the path /FILE_SHARE_NAME/subdirectory represents a subdirectory.
  • MOUNT_PATH: the mount path that the job's runnables use to access this NFS. The path must start with /mnt/disks/ followed by a directory or path that you choose. For example, if you want to represent this NFS with a directory named my-nfs, set the mount path to /mnt/disks/my-nfs.

What's next