This library provides bindings for the Domino APIs. It ships with the Domino Standard Environment (DSE).
See this documentation for details about the APIs:
The latest released version of python-domino is 1.4.3.
The python-domino library is compatible with different versions of
Domino:
| Domino Versions | Python-Domino |
|---|---|
| 3.6.x or lower | 0.3.5 |
| 4.1.0 or higher | 1.0.0 or Higher |
| 5.3.0 or higher | 1.2.0 or Higher |
| 5.5.0 or higher | 1.2.2 or Higher |
| 5.10.0 or higher | 1.3.1 or Higher |
| 5.11.0 or higher | 1.4.1 or Higher |
| 6.0.0 or higher | 1.4.3 or Higher |
The current python-domino is based on Python 3.9, which is therefore recommended for development. Pipenv is also recommended to manage the dependencies.
To use the Python binding in a Domino workbook session, include dominodatalab in your project's requirements.txt file.
This makes the Python binding available for each new workbook session (or batch run) started within the project.
To install dependencies from setup.py for development:
pipenv install -e ".[dev]"
Use the same process for Airflow and data:
pipenv install -e ".[data]" ".[airflow]"
You can set up the connection by creating a new instance of Domino:
_class_ Domino(project, api_key=None, host=None, domino_token_file=None, auth_token=None)
project: A project identifier (in the form of ownerusername/projectname).
api_proxy: (Optional) Location of the Domino API reverse proxy as host:port.
If set, this proxy is used to intercept any Domino API requests and insert an authentication token.
This is the preferred method of authentication.
Alternatively, set the DOMINO_API_PROXY environment variable.
In Domino 5.4.0 or later, this variable is set inside a Domino run container.
NOTE: This mechanism does not work when connecting to an HTTPS endpoint; it is meant to be used inside Domino runs.
api_key: (Optional) An API key to authenticate with.
If not provided, the library expects to find one in the DOMINO_USER_API_KEY environment variable.
If you are using the Python package in code that is already running in Domino, the DOMINO_API_USER_KEY variable is set automatically to be the key for the user who started the run.
host: (Optional) A host URL.
If not provided, the library expects to find one in the DOMINO_API_HOST environment variable.
domino_token_file: (Optional) Path to the Domino token file containing the auth token.
If not provided, the library expects to find one in the DOMINO_TOKEN_FILE environment variable.
If you are using Python package in code that is already running in Domino, the DOMINO_TOKEN_FILE is set automatically to be the token file for the user who started the run.
auth_token: (Optional) Authentication token.
Domino looks for the authentication method in the following order and uses the first one it finds:
api_proxyauth_tokendomino_token_fileapi_keyDOMINO_API_PROXYDOMINO_TOKEN_FILEDOMINO_USER_API_KEYThe API proxy is the preferred method of authentication. See Use the API Proxy to Authenticate Calls to the Domino API.
DOMINO_LOG_LEVEL
The default log level is INFO.
You can change the log level by setting DOMINO_LOG_LEVEL, for example to DEBUG.
DOMINO_VERIFY_CERTIFICATE
For testing purposes and issues with SSL certificates, set DOMINO_VERIFY_CERTIFICATE to false.
Be sure to unset this variable when not in use.
DOMINO_MAX_RETRIES
Default Retry is set to 4 Determines the number of attempts for the request session in case of a ConnectionError Get more info on request max timeout/error durations based on Retry and backoff factors here
See
example_budget_manager.py
for example code.
Get a list of the available default budgets with the assigned (if any) limits Requires Admin permission
Update default budgets by BudgetLabel Requires Admin roles
budget_label: (required) label of budget to be updated ex: BillingTag, Organization
budget_limit: (required) new budget quota to assign to default label
Get a list of the available budgets overrides with the assigned limits. Requires Admin permission
Create Budget overrides based on BudgetLabels, ie BillingTags, Organization, or Projects the object id is used as budget ids Requires Admin roles
budget_label: label of budget to be updated
budget_id: id of project or organization to be used as new budget override id.
budget_limit: budget quota to assign to override
Update Budget overrides based on BudgetLabel and budget id Requires Admin roles
budget_label: label of budget to be updated
budget_id: id of budget override to be updated.
budget_limit: new budget quota to assign to override
Delete an existing budget override Requires Admin roles
Get the current budget alerts settings Requires Admin permission
Update the current budget alerts settings to enable/disable budget notifications and whether to notify org owners on projects notifications Requires Admin permission
alerts_enabled: whether to enable or disable notifications.
notify_org_owner: whether to notify organizations owners on projects reaching threshold.
Update the current budget alerts settings with additional email targets per budget label Requires Admin permission
Get a list of active billing tags Requires Admin permission
Create a list of active billing tags Requires Admin permission
Get detailed info on active or archived billing tag Requires Admin permission
Archive an active billing tag Requires Admin permission
Get the current billing tag settings Requires Admin permission
Get the current billing tag settings mode Requires Admin permission
Update the current billing tag settings mode Requires Admin permission
Get a billing tag assigned to a particular project by project id Requires Admin permission
Update project's billing tag with new billing tag. Requires Admin permission
billing_tag: billing tag to assign to a project
project_id: id of the project to assign a billing tag
Remove a billing tag from a specified project Requires Admin permission
Remove a billing tag from a specified project Requires Admin permission
billing_tag: billing tag string to filter projects by
offset: The index of the start of the page, where checkpointProjectId is index 0. If the offset is negative the project it points to will be the end of the page.
page_size: The number of records to return per page.
name_filter: matches projects by name substring
sort_by: (Optional) field to sort the projects on
sort_order: (Optional) Whether to sort in asc or desc order
missing_tag_only: (Optional) determine whether to only return projects with missing tag
Update project's billing tags in bulk Requires Admin permission
See
example_projects_usage.py
for example code.
Newer version of projects creation using the v4 endpoints which allows more optional fields.
project_name: (required) The name of the project.
owner_id: (Optional) user id of the owner of the new project to be created (must be admin to create projects for other users) owner_id or owner_username can be used, both are not needed (Defaults to current owner_username)
owner_username: (Optional) username of the owner of the new project to be created (must be admin to create projects for other users) owner_id or owner_username can be used, both are not needed (Defaults to current owner_username)
description: (Optional) description of the project
collaborators: (Optional) list of collaborators to be added to the project
tags: (Optional) list of tags to add to project
billing_tag: (Optional unless billingTag settings mode is Required) active billing tag to be added to projects for governance
visibility: (Optional) (Defaults to Public) project visibility
Create a new project with given project name.
project_name: The name of the project.
owner_username: (Optional) The owner username for the project. This parameter is useful when you need to create a project under an organization.
Get the list of collaborators on a project.
Add collaborators to a project.
username_or_email: Name or email of the Domino user to add as collaborator to the current project.
message: Optional - Message related to the user’s role or purpose to the project.
Project tags are an easy way to add freeform metadata to a project. Tags help colleagues and consumers organize and find the Domino projects that interest them. Tags can be used to describe the subject explored by a project, the packages and libraries it uses, or the source of the data within.
See
example_projects_usage.py
for example code.
List a project’s tags.
Get details about a tag.
Create a tag, if it does not exist, and add it to a project.
tags (list): One or more tag names.
project_id: (Defaults to current project ID) The project identifier.
Get the tag ID using the tag string name.
tag_name (string): The tag name.
project_id: (Defaults to current project id) The project ID.
Remove a tag from a project.
tag_name (string): The tag name.
project_id: (Defaults to current project id) The project ID.
See these code example files:
List the executions on the selected project.
Start a new execution on the selected project.
command: The command to execution as an array of strings where
members of the array represent arguments of the command. For
example: ["main.py", "hi mom"]
isDirect: (Optional) Whether this command should be passed directly to a shell.
commitId: (Optional) The commitId to launch from. If not
provided, the project launches from the latest commit.
title: (Optional) A title for the execution.
tier: (Optional) The hardware tier to use for the execution. This is the human-readable name of the hardware tier, such as "Free", "Small", or "Medium". If not provided, the project’s default tier is used.
publishApiEndpoint: (Optional) Whether to publish an API endpoint from the resulting output.
Start a new execution on the selected project and make a blocking request that waits until job is finished.
command: The command to execution as an array of strings where
members of the array represent arguments of the command. For
example: ["main.py", "hi mom"]
isDirect: (Optional) Whether this command should be passed directly to a shell.
commitId: (Optional) The commitId to launch from. If not
provided, the project launches from the latest commit.
title: (Optional) A title for the execution.
tier: (Optional) The hardware tier to use for the execution. Will use project’s default tier if not provided. If not provided, the project’s default tier is used.
publishApiEndpoint: (Optional) Whether to publish an API endpoint from the resulting output.
poll_freq: (Optional) Number of seconds between polling of the Domino server for status of the task that is running.
max_poll_time: (Optional) Maximum number of seconds to wait for a task to complete. If this threshold is exceeded, an exception is raised.
retry_count: (Optional) Maximum number of polling retries (in case of transient HTTP errors). If this threshold is exceeded, an exception is raised.
Stop an existing execution in the selected project.
runId: String that identifies the execution.
saveChanges: (Defaults to True) If false, execution results are discarded.
Get stdout emitted by a particular execution.
See these code example files:
List the files in a folder in the Domino project.
commitId: The commitId to list files from.
path: (Defaults to "/") The path to list from.
Upload a Python file object into the specified path inside the project.
See examples/upload_file.py for an example. All parameters are
required.
path: The path to save the file to. For example, /README.md
writes to the root directory of the project while
/data/numbers.csv saves the file to a sub folder named data. If
the specified folder does not yet exist, it is created.
file: A Python file object. For example:
f = open("authors.txt","rb")
Deprecated Retrieve a file from the Domino server by blob key. Use blobs_get_v2(path, commit_id, project_id) instead.
Retrieve a file from the Domino server in a project from its path and commit id.
Publish an app within a project, or republish an existing app.
unpublishRunningApps: (Defaults to True) Check for an active app instance in the current project and unpublish it before re/publishing.
hardwareTierId: (Optional) Launch the app on the specified hardware tier.
Stop the running app in the project.
Start a new job (execution) in the project.
command (string): Command to execute in Job. For example:
domino.job_start(command="main.py arg1 arg2")
commit_id (string): (Optional) The commitId to launch from. If
not provided, the job launches from the latest commit.
hardware_tier_name (string): (Optional) The hardware tier NAME to launch job in. If not provided, the project’s default tier is used.
environment_id (string): (Optional) The environment ID with which to launch the job. If not provided, the project’s default environment is used.
on_demand_spark_cluster_properties (dict): (Optional) On demand spark cluster properties. The following properties can be provided in the Spark cluster:
{
"computeEnvironmentId": "<Environment ID configured with spark>"
"executorCount": "<Number of Executors in cluster>"
(optional defaults to 1)
"executorHardwareTierId": "<Hardware tier ID for Spark Executors>"
(optional defaults to last used historically if available)
"masterHardwareTierId": "<Hardware tier ID for Spark master"
(optional defaults to last used historically if available)
"executorStorageMB": "<Executor's storage in MB>"
(optional defaults to 0; 1GB is 1000MB Here)
}
param compute_cluster_properties (dict): (Optional) The
compute-cluster properties definition contains parameters for
launching any Domino supported compute cluster for a job. Use this
to launch a job that uses a compute-cluster instead of the
deprecated on_demand_spark_cluster_properties field. If
on_demand_spark_cluster_properties and
compute_cluster_properties are both present,
on_demand_spark_cluster_properties is ignored.
compute_cluster_properties contains the following fields:
{
"clusterType": <string, one of "Ray", "Spark", "Dask", "MPI">,
"computeEnvironmentId": <string, The environment ID for the cluster's nodes>,
"computeEnvironmentRevisionSpec": <one of "ActiveRevision", "LatestRevision",
{"revisionId":"<environment_revision_id>"} (optional)>,
"masterHardwareTierId": <string, the Hardware tier ID for the cluster's master node (required unless clusterType is MPI)>,
"workerCount": <number, the total workers to spawn for the cluster>,
"workerHardwareTierId": <string, The Hardware tier ID for the cluster workers>,
"workerStorage": <{ "value": <number>, "unit": <one of "GiB", "MB"> },
The disk storage size for the cluster's worker nodes (optional)>
"maxWorkerCount": <number, The max number of workers allowed. When
this configuration exists, autoscaling is enabled for the cluster and
"workerCount" is interpreted as the min number of workers allowed in the cluster
(optional)>
}
external_volume_mounts (List[string]): (Optional) External volume mount IDs to mount to execution. If not provided, the job launches with no external volumes mounted.
*title (string): (Optional) Title for Job.
Stop the Job (execution) in the project.
job_id (string): Job identifier.
commit_results (boolean): (Defaults to true) If false, the
job results are not committed.
Get the status of a job.
Start a job and poll until the job is finished. Additionally, this
method supports all the parameters in the job_start method.
poll_freq: Poll frequency interval in seconds.
max_poll_time: Max poll time in seconds.
A Domino dataset is a collection of files that are available in user executions as a filesystem directory. A dataset always reflects the most recent version of the data. You can modify the contents of a dataset through the Domino UI or through workload executions.
See Domino
Datasets
for more details, and
example_dataset.py
for example code.
Provide a JSON list of all the available datasets.
List the IDs the datasets for a particular project.
List the names the datasets for a particular project.
Provide details about a dataset.
Create a new dataset.
dataset_name: Name of the new dataset. NOTE: The name must be unique.
dataset_description: Description of the dataset.
Update a dataset’s name or description.
dataset_id: The dataset identifier.
dataset_name: (Optional) New name of the dataset.
dataset_description: (Optional) New description of the dataset.
Delete a set of datasets.
Uploads a file or entire directory to a dataset.
Overwrite, Rename, Ignore (default).The python-domino client comes bundled with an
Operator
for use with Apache Airflow as an extra.
When installing the client from PyPI, add the airflow flag to extras:
pip install "dominodatalab[airflow]"
Similarly, when installing the client from GitHub, use the following command:
pip install -e git+https://github.com/dominodatalab/python-domino.git@1.0.6#egg="dominodatalab[airflow]"
See also example_airflow_dag.py for example code.
from domino.airflow import DominoOperator
Allows a user to schedule Domino executions via Airflow. Follows the
same function signature as domino.runs_start with two extra arguments:
startup_delay: Optional[int] = 10 | Add a startup delay to your job, useful if you want to delay execution until after other work finishes.include_setup_log: Optional[bool] = True | Determine whether or not to publish the setup log of the job as the log prefix before stdout.from domino.airflow import DominoSparkOperator
Allows a user to schedule Domino executions via the v4 API, which
supports onDemandSparkClusters. Follows the same function signature as
domino.job_start, with the addition of startup_delay from above.
from domino import Domino
# By and large your commands will run against a single project,
# so you must specify the full project name
domino = Domino("chris/canon")
# List all runs in the project, most-recently queued first
all_runs = domino.runs_list()['data']
latest_100_runs = all_runs[0:100]
print(latest_100_runs)
# all runs have a commitId (the snapshot of the project when the
# run starts) and, if the run completed, an "outputCommitId"
# (the snapshot of the project after the run completed)
most_recent_run = all_runs[0]
commitId = most_recent_run['outputCommitId']
# list all the files in the output commit ID -- only showing the
# entries under the results directory. If not provided, will
# list all files in the project. Or you can say path=“/“ to
# list all files
files = domino.files_list(commitId, path='results/')['data']
for file in files:
print file['path'], '->', file['url']
print(files)
# Get the content (i.e. blob) for the file you're interested in.
# blobs_get returns a connection rather than the content, because
# the content can get quite large and it's up to you how you want
# to handle it
print(domino.blobs_get(files[0]['key']).read())
# Start a run of file main.py using the latest copy of that file
domino.runs_start(["main.py", "arg1", "arg2"])
# Start a "direct" command
domino.runs_start(["echo 'Hello, World!'"], isDirect=True)
# Start a run of a specific commit
domino.runs_start(["main.py"], commitId="aabbccddee")
Because python-domino ships with the DSE, normally you do not need to install it.
This section provides instructions for installing it in another environment or updating it to a newer version.
Starting from version 1.0.6, python-domino is available on PyPI as dominodatalab:
pip install dominodatalab
If you are adding install instructions for python-domino to your Domino Environment Dockerfile Instructions field, you must add RUN to the beginning:
RUN pip install dominodatalab
To install a specific version of the library from PyPI, such as 1.0.6:
pip install dominodatalab==1.0.6
To install a specific version of the library from GitHub, such as
1.0.6:
pip install https://github.com/dominodatalab/python-domino/archive/1.0.6.zip
This library is made available under the Apache 2.0 License. This is an open-source project of Domino Data Lab.
FAQs
Python bindings for the Domino API
We found that dominodatalab demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.
Security News
The Ultralytics' PyPI Package was compromised four times in one weekend through GitHub Actions cache poisoning and failure to rotate previously compromised API tokens.
Security News
Research
Socket researchers found a malicious Maven package impersonating the legitimate ‘XZ for Java’ library, introducing a backdoor for remote code execution.