Example job definitions - Amazon Batch
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Example job definitions

The following example job definitions illustrate how to use common patterns such as environment variables, parameter substitution, and volume mounts.

Use environment variables

The following example job definition uses environment variables to specify a file type and Amazon S3 URL. This particular example is from the Creating a Simple "Fetch & Run" Amazon Batch Job compute blog post. The fetch_and_run.sh script that's described in the blog post uses these environment variables to download the myjob.sh script from S3 and declare its file type.

Even though the command and environment variables are hardcoded into the job definition in this example, you can specify command and environment variable overrides to make the job definition more versatile.

{ "jobDefinitionName": "fetch_and_run", "type": "container", "containerProperties": { "image": "123456789012.dkr.ecr.us-east-1.amazonaws.com/fetch_and_run", "resourceRequirements": [ { "type": "MEMORY", "value": "2000" }, { "type": "VCPU", "value": "2" } ], "command": [ "myjob.sh", "60" ], "jobRoleArn": "arn:aws-cn:iam::123456789012:role/AWSBatchS3ReadOnly", "environment": [ { "name": "BATCH_FILE_S3_URL", "value": "s3://my-batch-scripts/myjob.sh" }, { "name": "BATCH_FILE_TYPE", "value": "script" } ], "user": "nobody" } }

Using parameter substitution

The following example job definition illustrates how to allow for parameter substitution and to set default values.

The Ref:: declarations in the command section are used to set placeholders for parameter substitution. When you submit a job with this job definition, you specify the parameter overrides to fill in those values, such as the inputfile and outputfile. The parameters section that follows sets a default for codec, but you can override that parameter as needed.

For more information, see Parameters.

{ "jobDefinitionName": "ffmpeg_parameters", "type": "container", "parameters": {"codec": "mp4"}, "containerProperties": { "image": "my_repo/ffmpeg", "resourceRequirements": [ { "type": "MEMORY", "value": "2000" }, { "type": "VCPU", "value": "2" } ], "command": [ "ffmpeg", "-i", "Ref::inputfile", "-c", "Ref::codec", "-o", "Ref::outputfile" ], "jobRoleArn": "arn:aws-cn:iam::123456789012:role/ECSTask-S3FullAccess", "user": "nobody" } }

Test GPU functionality

The following example job definition tests if the GPU workload AMI described in Using a GPU workload AMI is configured properly. This example job definition runs the TensorFlow deep MNIST classifier example from GitHub.

{ "containerProperties": { "image": "tensorflow/tensorflow:1.8.0-devel-gpu", "resourceRequirements": [ { "type": "MEMORY", "value": "32000" }, { "type": "VCPU", "value": "8" } ], "command": [ "sh", "-c", "cd /tensorflow/tensorflow/examples/tutorials/mnist; python mnist_deep.py" ] }, "type": "container", "jobDefinitionName": "tensorflow_mnist_deep" }

You can create a file with the preceding JSON text called tensorflow_mnist_deep.json and then register an Amazon Batch job definition with the following command:

aws batch register-job-definition --cli-input-json file://tensorflow_mnist_deep.json

Multi-node parallel job

The following example job definition illustrates a multi-node parallel job. For more information, see Building a tightly coupled molecular dynamics workflow with multi-node parallel jobs in Amazon Batch in the Amazon Compute blog.

{ "jobDefinitionName": "gromacs-jobdef", "jobDefinitionArn": "arn:aws-cn:batch:us-east-2:123456789012:job-definition/gromacs-jobdef:1", "revision": 6, "status": "ACTIVE", "type": "multinode", "parameters": {}, "nodeProperties": { "numNodes": 2, "mainNode": 0, "nodeRangeProperties": [ { "targetNodes": "0:1", "container": { "image": "123456789012.dkr.ecr.us-east-2.amazonaws.com/gromacs_mpi:latest", "resourceRequirements": [ { "type": "MEMORY", "value": "24000" }, { "type": "VCPU", "value": "8" } ], "command": [], "jobRoleArn": "arn:aws-cn:iam::123456789012:role/ecsTaskExecutionRole", "ulimits": [], "instanceType": "p3.2xlarge" } } ] } }