Configure queue for job attachments Submit with job attachments How job attachments are stored Next steps

Submit jobs with job attachments in Deadline Cloud

Many farms use shared filesystems to share files between the hosts that submit jobs and those that run jobs. For example, in the previous simple_file_job example, the local filesystem is shared between the AWS CloudShell terminal windows, which run in tab one where you submit the job, and tab two where you run the worker agent.

A shared filesystem is advantageous when the submitter workstation and the worker hosts are on the same local area network. If you store your data on premises near the workstations that access it, then using a cloud-based farm means you have to share your filesystems over a high-latency VPN or synchronize your filesystems in the cloud. Neither of these options are easy to set up or operate.

AWS Deadline Cloud offers a simple solution with job attachments, which are similar to email attachments. With job attachments, you attach data to your job. Then, Deadline Cloud handles the details of transferring and storing your job data in Amazon Simple Storage Service (Amazon S3) buckets.

Content creation workflows are often iterative, meaning a user submits jobs with a small subset of modified files. Because Amazon S3 buckets store job attachments in a content-addressable storage, the name of each object is based on the hash of the object's data and the contents of a directory tree are stored in a manifest file format attached to a job.

Before you can follow the procedures in this section, you must complete the following:

To run jobs with job attachments, complete the following steps.

Topics

Add a job attachments configuration to your queue
Submit simple_file_job with job attachments
Understanding how job attachments are stored in Amazon S3
Next steps

Add a job attachments configuration to your queue

To enable job attachments in your queue, add a job attachments configuration to the queue resource in your account.

To add a job attachments configuration to your queue

Choose your first CloudShell tab, then enter one of the following commands to use an Amazon S3 bucket for job attachments.
- If you don't have an existing private Amazon S3 bucket, you can create and use a new S3 bucket.
```
DEV_FARM_BUCKET=$(echo $DEV_FARM_NAME \
    | tr '[:upper:]' '[:lower:]')-$(xxd -l 16 -p /dev/urandom)
if [ "$AWS_REGION" == "us-east-1" ]; then LOCATION_CONSTRAINT=
else LOCATION_CONSTRAINT="--create-bucket-configuration \
    LocationConstraint=${AWS_REGION}"
fi
aws s3api create-bucket \
    $LOCATION_CONSTRAINT \
    --acl private \
    --bucket ${DEV_FARM_BUCKET}
```
- If you already have a private Amazon S3 bucket, you can use it by replacing MY_BUCKET_NAME with the name of your bucket.
```
DEV_FARM_BUCKET=MY_BUCKET_NAME
```
After you create or choose your Amazon S3 bucket, add the bucket name to ~/.bashrc to make the bucket available for other terminal sessions.
```
echo "DEV_FARM_BUCKET=$DEV_FARM_BUCKET" >> ~/.bashrc
```

Create an AWS Identity and Access Management (IAM) role for the queue.


aws iam create-role --role-name "${DEV_FARM_NAME}QueueRole" \
    --assume-role-policy-document \
        '{
            "Version": "2012-10-17",
            "Statement": [
                {
                    "Effect": "Allow",
                    "Principal": {
                        "Service": "credentials.deadline.amazonaws.com"
                    },
                    "Action": "sts:AssumeRole"
                }
            ]
        }'
aws iam put-role-policy \
    --role-name "${DEV_FARM_NAME}QueueRole" \
    --policy-name S3BucketsAccess \
    --policy-document \
            '{
                "Version": "2012-10-17",
                "Statement": [
                {
                    "Action": [
                        "s3:GetObject*",
                        "s3:GetBucket*",
                        "s3:List*",
                        "s3:DeleteObject*",
                        "s3:PutObject",
                        "s3:PutObjectLegalHold",
                        "s3:PutObjectRetention",
                        "s3:PutObjectTagging",
                        "s3:PutObjectVersionTagging",
                        "s3:Abort*"
                    ],
                    "Resource": [
                        "arn:aws:s3:::'$DEV_FARM_BUCKET'",
                        "arn:aws:s3:::'$DEV_FARM_BUCKET'/*"
                    ],
                    "Effect": "Allow"
                }
            ]
            }'

Update your queue to include the job attachments settings and the IAM role.


QUEUE_ROLE_ARN="arn:aws:iam::$(aws sts get-caller-identity \
        --query "Account" --output text):role/${DEV_FARM_NAME}QueueRole"
aws deadline update-queue \
    --farm-id $DEV_FARM_ID \
    --queue-id $DEV_QUEUE_ID \
    --role-arn $QUEUE_ROLE_ARN \
    --job-attachment-settings \
        '{
            "s3BucketName": "'$DEV_FARM_BUCKET'",
            "rootPrefix": "JobAttachments"
        }'

Confirm that you updated your queue.


deadline queue get

Output such as the following is shown:


...
jobAttachmentSettings:
  s3BucketName: DEV_FARM_BUCKET
  rootPrefix: JobAttachments
roleArn: arn:aws:iam::ACCOUNT_NUMBER:role/DeveloperFarmQueueRole
...

Submit simple_file_job with job attachments

When you use job attachments, job bundles must give Deadline Cloud enough information to determine the job's data flow, such as using PATH parameters. In the case of the simple_file_job, you edited the template.yaml file to tell Deadline Cloud that the data flow is in the input file and output file.

After you've added the job attachments configuration to your queue, you can submit the simple_file_job sample with job attachments. After you do this, you can view the logging and job output to confirm that the simple_file_job with job attachments is working.

To submit the simple_file_job job bundle with job attachments

Choose your first CloudShell tab, then open the JobBundle-Samples directory.


cd ~/deadline-cloud-samples/job_bundles/

Submit simple_file_job to the queue. When prompted to confirm the upload, enter y.


deadline bundle submit simple_file_job \
    -p InFile=simple_job/template.yaml \
    -p OutFile=hash-jobattachments.txt

To view the job attachments data transfer session log output, choose your second CloudShell tab.


JOB_ID=$(deadline config get defaults.job_id)
SESSION_ID=$(aws deadline list-sessions \
        --farm-id $DEV_FARM_ID \
        --queue-id $DEV_QUEUE_ID \
        --job-id $JOB_ID \
        --query "sessions[0].sessionId" \
        --output text)
cat ~/demoenv-logs/$DEV_QUEUE_ID/$SESSION_ID.log

List the session actions that were run within the session.


aws deadline list-session-actions \
    --farm-id $DEV_FARM_ID \
    --queue-id $DEV_QUEUE_ID \
    --job-id $JOB_ID \
    --session-id $SESSION_ID

Output such as the following is shown:


{
    "sessionactions": [
        {
            "sessionActionId": "sessionaction-123-0",
            "status": "SUCCEEDED",
            "startedAt": "<timestamp>",
            "endedAt": "<timestamp>",
            "progressPercent": 100.0,
            "definition": {
                "syncInputJobAttachments": {}
            }
        },
        {
            "sessionActionId": "sessionaction-123-1",
            "status": "SUCCEEDED",
            "startedAt": "<timestamp>",
            "endedAt": "<timestamp>",
            "progressPercent": 100.0,
            "definition": {
                "taskRun": {
                    "taskId": "task-abc-0",
                    "stepId": "step-def"
                }
            }
        }
    ]
}

The first session action downloaded the input job attachments, while the second action runs the task like in previous steps and then uploaded the output job attachments.

List the output directory.
```
ls *.txt
```
Output such as hash.txt is shown, but hash-jobattachments.txt doesn't exist.
Download the output from the most recent job.
```
deadline job download-output
```

View the output of the downloaded file.


cat hash-jobattachments.txt

Output such as the following is shown:


eaa2df5d34b54be5ac34c56a24a8c237b8487231a607eaf530a04d76b89c9cd3  /tmp/openjd/session-123/assetroot-abc/simple_job/template.yaml

Understanding how job attachments are stored in Amazon S3

You can use the AWS Command Line Interface (AWS CLI) to upload or download data for job attachments, which are stored in Amazon S3 buckets. Understanding how Deadline Cloud stores job attachments on Amazon S3 will help when you develop workloads and pipeline integrations.

To inspect how Deadline Cloud job attachments are stored in Amazon S3

Choose your first CloudShell tab, then open the job bundle samples directory.
```
cd ~/deadline-cloud-samples/job_bundles/
```

Inspect the job properties.


deadline job get

Output such as the following is shown:


parameters:
  Message:
    string: Welcome to AWS Deadline Cloud!
  InFile:
    path: /home/cloudshell-user/deadline-cloud-samples/job_bundles/simple_job/template.yaml
  OutFile:
    path: /home/cloudshell-user/deadline-cloud-samples/job_bundles/hash-jobattachments.txt
attachments:
  manifests:
  - rootPath: /home/cloudshell-user/deadline-cloud-samples/job_bundles/
    rootPathFormat: posix
    outputRelativeDirectories:
    - .
    inputManifestPath: farm-3040c59a5b9943d58052c29d907a645d/queue-cde9977c9f4d4018a1d85f3e6c1a4e6e/Inputs/f46af01ca8904cd8b514586671c79303/0d69cd94523ba617c731f29c019d16e8_input.xxh128
    inputManifestHash: f95ef91b5dab1fc1341b75637fe987ee
  fileSystem: COPIED

The attachments field contains a list of manifest structures that describe input and output data paths that the job uses when it runs. Look at rootPath to see the local directory path on the machine that submitted the job. To view the Amazon S3 object suffix that contains a manifest file, review the inputManifestFile. The manifest file contains metadata for a directory tree snapshot of the job's input data.

Pretty-print the Amazon S3 manifest object to see the input directory structure for the job.


MANIFEST_SUFFIX=$(aws deadline get-job \
     --farm-id $DEV_FARM_ID \
     --queue-id $DEV_QUEUE_ID \
     --job-id $JOB_ID \
     --query "attachments.manifests[0].inputManifestPath" \
     --output text)
 aws s3 cp s3://$DEV_FARM_BUCKET/JobAttachments/Manifests/$MANIFEST_SUFFIX - | jq .

Output such as the following is shown:


{
     "hashAlg": "xxh128",
     "manifestVersion": "2023-03-03",
     "paths": [
     {
         "hash": "2ec297b04c59c4741ed97ac8fb83080c",
         "mtime": 1698186190000000,
         "path": "simple_job/template.yaml",
         "size": 445
     }
     ],
     "totalSize": 445
 }

Construct the Amazon S3 prefix that holds manifests for the output job attachments and list the object under it.


SESSION_ACTION=$(aws deadline list-session-actions \
    --farm-id $DEV_FARM_ID \
    --queue-id $DEV_QUEUE_ID \
    --job-id $JOB_ID \
    --session-id $SESSION_ID \
    --query "sessionActions[?definition.taskRun != null] | [0]")
STEP_ID=$(echo $SESSION_ACTION | jq -r .definition.taskRun.stepId)
TASK_ID=$(echo $SESSION_ACTION | jq -r .definition.taskRun.taskId)
TASK_OUTPUT_PREFIX=JobAttachments/Manifests/$DEV_FARM_ID/$DEV_QUEUE_ID/$JOB_ID/$STEP_ID/$TASK_ID/
aws s3api list-objects-v2 --bucket $DEV_FARM_BUCKET --prefix $TASK_OUTPUT_PREFIX

The output job attachments are not directly referenced from the job resource but are instead placed in an Amazon S3 bucket based on farm resource IDs.

Get the newest manifest object key for the specific session action id, then pretty-print the manifest objects.


SESSION_ACTION_ID=$(echo $SESSION_ACTION | jq -r .sessionActionId)
 MANIFEST_KEY=$(aws s3api list-objects-v2 \
     --bucket $DEV_FARM_BUCKET \
     --prefix $TASK_OUTPUT_PREFIX \
     --query "Contents[*].Key" --output text \
     | grep $SESSION_ACTION_ID \
     | sort | tail -1)
 MANIFEST_OBJECT=$(aws s3 cp s3://$DEV_FARM_BUCKET/$MANIFEST_KEY -)
 echo $MANIFEST_OBJECT | jq .

You'll see properties of the file hash-jobattachments.txt in the output such as the following:


{
     "hashAlg": "xxh128",
     "manifestVersion": "2023-03-03",
     "paths": [
     {
         "hash": "f60b8e7d0fabf7214ba0b6822e82e08b",
         "mtime": 1698785252554950,
         "path": "hash-jobattachments.txt",
         "size": 182
     }
     ],
     "totalSize": 182
 }

Your job will only have a single manifest object per task run, but in general it is possible to have more of objects per task run.

View content-addressible Amazon S3 storage output under the Data prefix.


 FILE_HASH=$(echo $MANIFEST_OBJECT | jq -r .paths[0].hash)
 FILE_PATH=$(echo $MANIFEST_OBJECT | jq -r .paths[0].path)
 aws s3 cp s3://$DEV_FARM_BUCKET/JobAttachments/Data/$FILE_HASH -

Output such as the following is shown:


eaa2df5d34b54be5ac34c56a24a8c237b8487231a607eaf530a04d76b89c9cd3  /tmp/openjd/session-123/assetroot-abc/simple_job/template.yaml

Next steps

After learning how to submit jobs with attachments using the Deadline Cloud CLI, you can explore:

Submit with Deadline Cloud to learn how to run jobs using an OpenJD bundle on your worker hosts.
Add a service-managed fleet to your developer farm in Deadline Cloud to run your jobs on hosts managed by Deadline Cloud.
Clean up your farm resources in Deadline Cloud to shut down the resources that you used for this tutorial.

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