Log And Get Parameters

Parameters or HyperParameters that define your experiment and Machine Learning model. For example, learning_rate, cache_size

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Logging the hyperparameters

Hyperparameters are independent variables for a run used to control the learning process. We can capture the hyperparameters using the log_params method.
Once set, the hyperparameters are immutable. If you need to change the hyperparameter, it basically means that you are changing your model and it’s best to create a new run for that. This way, the system exactly tracks the model along with the exact configuration to train it.

Note that parameter values are stringified before storing.

from truefoundry.ml import get_client

client = get_client()
run = client.create_run(ml_repo="iris-demo", run_name="svm-model")

run.log_params({"cache_size": 200.0, "kernel": "linear"})

run.end()

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Viewing logged parameter in dashboard

These logged parameters can be seen in the MLFoundry dashboard.

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Accessing parameters for a run

You can use the get_params method. It returns a dictionary

from truefoundry.ml import get_client

client = get_client()
run = client.get_run("run-id-of-the-run")

print(run.get_params())

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Filtering runs bases on parameter value

To filters runs, click on top right corner of the screen to apply the required filter.

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Capturing command-line arguments

We can capture command-line arguments directly from the argparse.Namespace object.

import argparse
from truefoundry.ml import get_client

parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", type=int, required=True)
args = parser.parse_args()

client = get_client()
run = client.create_run(ml_repo="iris-demo")

run.log_params(args)

run.end()

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Can I change the param value once logged?

No you cannot change the value of param once logged.