Experiment Tracking
Add Tags
Service
- Introduction To A Service
- Deploy your first Service
- Interacting With Your Service
- Configuring your service
- Update, Rollback, Promote
- Monitoring Your Service
Job
- Introduction To A Job
- Running your first Job
- Interacting With Your Job
- Monitor Your Job
- Configuring your Job
Workflow
Large Language Models (LLMs)
Workbenches: Notebooks and SSH
Volumes
Async Service
- Introduction to Async Service
- Deploy your first Async Service
- Configure async service
- Queue Integrations
- Monitor Your Async Service
Secret Management
ML Repository
- Introduction To ML Repo
- Experiment Tracking
- Create Tracing Project
Deploying On Your Own Cloud
- Modes Of Deployment
- Deploy Compute Plane
- Deploy Control Plane Only
- Advanced Configuration
- Integrations
- Deploy Truefoundry In An Air Gapped Environment
Experiment Tracking
Add Tags
Tags are labels for a run. A tag is represented by a string tag name and value.
Adding tags programmatically
from truefoundry.ml import get_client
client = get_client()
run = client.create_run(ml_repo="iris-demo", run_name="svm-model")
run.set_tags({"env": "development", "task": "classification"})
run.end()
How can I programmatically fetch the tags for a run?
You can use the get_tags
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_tags())
Adding tags with UI
You can view the tags from the dashboard and also create new tags.
Adding tags
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