Creating A Run

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Runs

A run represents a single experiment which in the context of Machine Learning is one specific model (say Logistic Regression), with a fixed set of hyper-parameters. Metrics, and parameters (details below) are all logged under a specific run.

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Creating Runs in Truefoundry

A run is an entity that represents a single experiment. Create a run at the beginning of your script or notebook to start capturing system metrics.

from truefoundry.ml import get_client

client = get_client()
run = client.create_run(ml_repo="iris-demo", run_name="svm-model")
# Your code here.
run.end()

You can organize multiple runs under a single ml_repo. For example, the run svm-model will be created under the ml_repo iris-sklearn-example.

You can view these runs in the TrueFoundry dashboard.

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Accessing Runs in TrueFoundry

To interact with runs in Truefoundry, you can use the provided methods in the TrueFoundryClient class. Here are the different possibilities to access runs:

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Get a Run by ID

To retrieve an existing run by its ID, use theget_run_by_id method:

client = TrueFoundryClient();
run = client.get_run_by_id("run_id_here");

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Get a Run by Fully Qualified Name (FQN)

If you have the fully qualified name (FQN) of a run, which follows the pattern tenant_name/ml_repo/run_name, you can use the get_run_by_fqn method:

client = TrueFoundryClient();
run = client.get_run_by_fqn("tenant_name/ml_repo/run_name");

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Get All Runs for a Project

To retrieve all the runs’ names and IDs for a project, use the get_all_runs method:

client = TrueFoundryClient();
runs_df = client.get_all_runs((ml_repo = "project_name_here"));

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Search Runs

You can search for runs that match specific criteria using the search_runs method:

client = TrueFoundryClient()
runs = client.search_runs(
    ml_repo="project_name_here",
    filter_string="metrics.accuracy > 0.75",
    order_by=["metric.accuracy DESC"],
)
for run in runs:
    print(run)

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FAQs

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What is a ml_repo?

A ml_repo embodies the high-level goal of the experiments, like “predicting the sentiment of product reviews”. To reach the goal, you can experiment with different machine learning algorithms with different parameters. A single run represents a single experiment. TrueFoundry helps you organize these runs and find the best-performing ones under a ml_repo.

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How can I create a ml_repo?

A ml_repo is automatically created when you call the create_run method. A ml_repo is identified by it’s owner and name.

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Can anyone create a run under my ml_repo?

No. TrueFoundry provides ml_repo-level authorization. If someone in your team wants to view or create a run under your ml_repo, you need to add them as a collaborator to your ml_repo.

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How can I create a run under a ml_repo owned by someone else?

You can pass the ml_repo argument in the the create run. You should at least have WRITE permission for the ml_repo. If you don’t have write access to the ml_repo, the admin needs to provide you atleast WRITE permission to the ml_repo.

client.create_ml_repo("iris-demo")

run = client.create_run(
    ml_repo="iris-demo",
    run_name="svm-model",
)
# Your code here.
run.end()

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Can I use runs as a context manager?

Yes, we can use runs as a context manager. A run will be automatically ended after the execution exits the with block.

client.create_ml_repo("iris-demo")

run = client.create_run(ml_repo="iris-demo", run_name="svm-model")
with run:
    # Your code here.
    ...

# No need to call run.end()

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Are run names unique?

Yes. run names under a ml_repo are unique. If a run name already exists, we add a suffix to make it unique.
If you do not pass a run name while creating a run, we generate a random name.

from truefoundry.ml import get_client

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

print(run.run_name)
run.end()

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How runs are identified?

Runs are identified by by their id.

from truefoundry.ml import get_client

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

print(run.run_id)
run.end()