Additional supported SDK

Import the sys module to install the SDKs.

import sys

(Optional) The Amazon SageMaker Python SDK, comes preinstalled in Amazon SageMaker Studio Classic. If you plan to run your code outside Studio Classic, install the SageMaker Python SDK.

!{sys.executable} -m pip install sagemaker

Install the SageMaker Experiments Python SDK.

!{sys.executable} -m pip install sagemaker-experiments

Import modules.

import time
from time import strftime

import sagemaker

from smexperiments.experiment import Experiment
from smexperiments.trial import Trial
from smexperiments.trial_component import TrialComponent
from smexperiments.tracker import Tracker

Get the execution role and create the SageMaker session.

role = sagemaker.get_execution_role()
sm_sess = sagemaker.session.Session()

Create a SageMaker experiment. The experiment name must be unique in your account.

create_date = strftime("%Y-%m-%d-%H-%M-%S")
demo_experiment = Experiment.create(experiment_name = "DEMO-{}".format(create_date),
                                    description = "Demo experiment",
                                    tags = [{'Key': 'demo-experiments', 'Value': 'demo1'}])

(Studio Classic) To view the experiment in Amazon SageMaker Studio Classic, in the left sidebar, choose the Experiments.

After the code runs, the experiment list contains the new experiment. It might take a moment for the list to refresh and display the experiment. The filter on the experiment tag is also displayed. Only experiments that have a matching tag are displayed. Your list should look similar to the following:

Create a trial for the experiment. The trial name must be unique in your account.

demo_trial = Trial.create(trial_name = "DEMO-{}".format(create_date),
                          experiment_name = demo_experiment.experiment_name,
                          tags = [{'Key': 'demo-trials', 'Value': 'demo1'}])

Create a trial component as part of the trial. The trial component is the SageMaker job.

Add the ExperimentConfig parameter to the appropriate method. The SageMaker jobs listed in the following table are supported.

The following examples are for a training job. The Tags parameter adds a tag to the trial component. ExperimentName isn't specified because the trial was associated with the experiment when the trial was created in an earlier step.

Using the SageMaker Python SDK

sagemaker.estimator.Estimator(
    ...,
    sagemaker_session = sm_sess,
    tags = [{'Key': 'demo-jobs', 'Value': 'demo2'}])

estimator.fit(
    ...,
    experiment_config = {
        # "ExperimentName"
        "TrialName" : demo_trial.trial_name,
        "TrialComponentDisplayName" : "TrainingJob",
    })

Using Boto3

create_training_job(
    ...,
    "ExperimentConfig": {
        # "ExperimentName"
        "TrialName" : demo_trial.trial_name,
        "TrialComponentDisplayName" : "TrainingJob",
    },
    "Tags": [{'Key': 'demo-jobs', 'Value': 'demo2'}])

(Studio Classic) In the experiment list, double-click the experiment to display a list of the trials in the experiment. In the Studio Classic UI, trials are referred to as run groups and trial components are referred to as runs. Your list should look similar to the following:

(Studio Classic) To view information about the experiment, trial, and job (trial component), see View, search, and compare experiment runs.