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AWS Deadline Cloud client is a multi-purpose Python library and command line tool for interacting with and submitting Open Job Description (OpenJD) jobs to AWS Deadline Cloud.

To support building workflows on top of AWS Deadline Cloud, it implements its own user interaction, job creation, file upload/download, and other useful helpers around the service's API. It can function as a pipeline tool, a standalone GUI application, or even be embedded within other applications' runtimes.

Notable features include:

A command-line interface with subcommands for querying your AWS Deadline Cloud resources, and submitting jobs to your AWS Deadline Cloud Farm.
A library of functions that implement AWS Deadline Cloud's Job Attachments functionality.
A library of functions for creating a job submission UI within any content creation tool that supports Python 3.8+ based plugins and the Qt GUI framework.
A Model Context Protocol (MCP) server for AI assistant integration, enabling natural language interaction with AWS Deadline Cloud resources.

Getting Started¶

The AWS Deadline Cloud client can be installed by the standard python packaging mechanisms:

$ pip install deadline

or if you want the optional gui dependencies:

$ pip install "deadline[gui]"

if you want the optional mcp dependencies:

$ pip install "deadline[mcp]"

Usage¶

After installation it can then be used as a command line tool:

$ deadline farm list
- farmId: farm-1234567890abcdefg
  displayName: my-first-farm

or as a python library:

from deadline.client import api
api.list_farms()
# {'farms': [{'farmId': 'farm-1234567890abcdefg', 'displayName': 'my-first-farm', ...},]}

The deadlinew command can be used from GUIs to avoid displaying a terminal window in the background when on Windows. You can use the --redirect-output option to write the terminal output to a file.

$ deadlinew --redirect-output out.txt farm list
$ cat out.txt
- farmId: farm-1234567890abcdefg
  displayName: my-first-farm

An example usage is to create a shortcut called "Deadline Settings" on your desktop that runs C:\path\to\deadlinew.exe config gui. Opening the shortcut will show the Deadline Settings dialog without a terminal window behind it.

For job-related files and data, AWS Deadline Cloud supports either transferring files to AWS using job attachments or reading files from network storage that is shared between both your local workstation and your farm.

Job attachments¶

Job attachments enable you to transfer files between your workstations and AWS Deadline Cloud using Amazon S3 buckets as content-addressed storage in your AWS account. The use of a content-addressed storage means that a file will never need to be uploaded again once it has been uploaded once.

See job attachments for a more in-depth look at how files are uploaded, stored, and retrieved.

Shared storage and storage profiles¶

Jobs can reference files that are stored on shared network storage. The Deadline Client uses a storage profile to determine which paths on the workstation are part of the network storage and do not need to be transferred using job attachments.

To use an existing storage profile with the Deadline Client, you can configure your default storage profile via CLI:

deadline config set settings.storage_profile_id sp-10b2e48ad6ac4fc88595dfcbef6271f2

Or with the configuration GUI:

deadline config gui

Job Bundles¶

A job bundle is one of the tools that you can use to define jobs for AWS Deadline Cloud. They group an Open Job Description (OpenJD) template with additional information such as files and directories that your jobs use with job attachments. You can use this package's command-line interface and/or its Python interface to use a job bundle to submit jobs for a queue to run. Please see the Job Bundles section of the AWS Deadline Cloud Developer Guide for detailed information on job bundles.

At a minimum, a job bundle is a folder that contains an OpenJD template as a file named template.json or template.yaml. However, it can optionally include: 1. An asset_references.yaml file - lists file inputs and outputs. 2. A parameter_values.yaml file - contains the selected values for the job template's parameters. 3. Any number of additional files required for the job.

For example job bundles, visit the samples repository.

To submit a job bundle, you can run

$ deadline bundle submit <path/to/bundle>

or if you have the optional GUI components installed, you can load up a job bundle for submission by running:

$ deadline bundle gui-submit --browse

On submission, a job bundle will be created in the job history directory (default: ~/.deadline/job_history).

Configuration¶

You can see the current configuration by running:

$ deadline config show

and change the settings by running the associated get, set and clear commands.

If you need to parse the settings as json, you can specify the output by running:

$ deadline config show --output json

Which will output:

{"settings.config_file_path": "~/.deadline/config", "deadline-cloud-monitor.path": "", "defaults.aws_profile_name": "(default)", "settings.job_history_dir": "~/.deadline/job_history/(default)", "defaults.farm_id": "", "settings.storage_profile_id": "", "defaults.queue_id": "", "defaults.job_id": "", "settings.auto_accept": "false", "settings.conflict_resolution": "NOT_SELECTED", "settings.log_level": "WARNING", "telemetry.opt_out": "false", "telemetry.identifier": "00000000-0000-0000-0000-000000000000", "defaults.job_attachments_file_system": "COPIED", "settings.s3_max_pool_connections": "50", "settings.small_file_threshold_multiplier": "20"}

To see a list of settings that can be configured, run:

$ deadline config --help

Or you can manage settings by a graphical user-interface if you have the optional GUI dependencies:

$ deadline config gui

By default, configuration of AWS Deadline Cloud is provided at ~/.deadline/config, however this can be overridden by the DEADLINE_CONFIG_FILE_PATH environment variable.

Authentication¶

In addition to the standard AWS credential mechanisms (AWS Profiles, instance profiles, and environment variables), AWS Deadline Cloud monitor credentials are also supported.

To view the currently configured credentials authentication status, run:

$ deadline auth status
    Profile Name: (default)
          Source: HOST_PROVIDED
          Status: AUTHENTICATED
API Availability: True

If the currently selected AWS Profile is set-up to use AWS Deadline Cloud monitor credentials, you can authenticate by logging in:

$ deadline auth login

and removing them by logging out:

$ deadline auth logout

Job Monitoring and Logs¶

Waiting for Job Completion¶

After submitting a job, you can wait for it to complete using the wait command:

# Wait for a job to complete with default settings
$ deadline job wait --job-id job-12345

# Customize the maximum polling interval (default is 120 seconds)
# The polling interval starts at 0.5 seconds and doubles until reaching this maximum
$ deadline job wait --job-id job-12345 --max-poll-interval 30

# Set a timeout (default is 0, meaning no timeout)
$ deadline job wait --job-id job-12345 --timeout 3600

# Get the result in JSON format
$ deadline job wait --job-id job-12345 --output json

The command blocks until the job reaches a terminal state (SUCCEEDED, FAILED, CANCELED, SUSPENDED, NOT_COMPATIBLE), then returns information about the job's status and any failed tasks. It uses exponential backoff for polling, starting at 0.5 seconds and doubling the interval after each check until it reaches the maximum polling interval.

Exit Codes: - 0 - Job succeeded - 1 - Timeout waiting for job completion - 2 - Job failed or has failed tasks - 3 - Job was canceled - 4 - Job was archived - 5 - Job is not compatible

Retrieving Job Logs¶

You can monitor job status and retrieve logs using the CLI. The logs lines are returned starting from the most recent log event with timestamps in ISO 8601 format:

# Get logs for a specific session
$ deadline job logs --session-id session-12345

# Get logs for a job (automatically selects session: ongoing sessions preferred, then most recently started/ended)
$ deadline job logs --job-id job-12345

# Limit the number of log lines returned to the 50 most recent.
$ deadline job logs --session-id session-12345 --limit 50

# Filter logs by time range
$ deadline job logs --session-id session-12345 --start-time 2023-01-01T12:00:00Z --end-time 2023-01-01T13:00:00Z

# Get logs in JSON format
$ deadline job logs --session-id session-12345 --output json

# Get logs with timestamps in local timezone (default is UTC)
$ deadline job logs --session-id session-12345 --timezone local

# Get logs with explicit UTC timestamps (default behavior)
$ deadline job logs --session-id session-12345 --timezone utc

# Combine timezone option with JSON output
$ deadline job logs --session-id session-12345 --timezone local --output json

# Paginate through logs
$ deadline job logs --session-id session-12345 --next-token next-token-value

Timestamp Format: All timestamps are displayed in ISO 8601 format with full microsecond precision and timezone information: - UTC format: 2025-07-03T10:49:33.821306+00:00 - Local format: 2025-07-03T03:49:33.821306-07:00 (example for PST)

Timezone Options: - --timezone utc (default): Display timestamps in UTC with +00:00 offset - --timezone local: Display timestamps converted to your local system timezone

When using a Deadline Cloud monitor profile, the job logs command will use the Queue role credentials to read logs. Otherwise, the chosen profile credentials are used for all API invocations. This allows you to access logs with the appropriate permissions based on your authentication method.

AWS Credentials Integration¶

You can use the Deadline Cloud client to obtain temporary AWS credentials for a queue and use them with the AWS CLI or SDK. This enables you to create AWS profiles that have queue-specific permissions for use in programmatic workflows.

To export credentials for a queue in a format compatible with the AWS SDK credentials_process interface:

$ deadline queue export-credentials --farm-id farm-1234567890abcdefg --queue-id q-12345abcdef --mode USER
{
  "Version": 1,
  "AccessKeyId": "ASIA...",
  "SecretAccessKey": "wJalr...",
  "SessionToken": "AQoD...",
  "Expiration": "2025-04-08T20:00:46Z"
}

You can then reference this command in your AWS config file to create a profile that uses these credentials. Often this will be a profile from a Deadline Cloud monitor session:

[profile deadline-queue]
credential_process = deadline queue export-credentials --farm-id farm-1234567890abcdefg --queue-id q-12345abcdef --mode USER --profile myfarm-us-west-2
region = us-west-2

Then use this profile with AWS CLI commands:

$ aws s3 ls --profile deadline-queue

Available modes: - USER: Credentials with full queue-role permissions. - READ: Credentials with read-only permissions for queue logs

Model Context Protocol (MCP) Server¶

The AWS Deadline Cloud client includes an MCP server that enables AI assistants to interact with AWS Deadline Cloud resources through natural language. The MCP server uses the MCP Python SDK for simplified tool registration while maintaining full protocol compliance.

See MCP Guide for more information.