Getting Started

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"})
# Your code here.
run.end()

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()

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.log_metrics(metric_dict={"accuracy": 0.7, "loss": 0.6})

run.end()

import os
from truefoundry.ml import get_client
from truefoundry.ml import ArtifactPath

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.log_metrics(metric_dict={"accuracy": 0.7, "loss": 0.6})

# Just creating sample files to log as artifacts
# os.makedirs("my-folder", exist_ok=True)
# with open("my-folder/file-inside-folder.txt", "w") as f:
#     f.write("Hello!")

# with open("just-a-file.txt", "w") as f:
#     f.write("Hello from file!")

artifact_version = run.log_artifact(
    name="my-artifact",
    artifact_paths=[
        # Add files and folders here, `ArtifactPath` takes source and destination
        # source can be single file path or folder path
        # destination can be file path or folder path
        # Note: When source is a folder path, destination is always interpreted as folder path
        ArtifactPath(src="just-a-file.txt"),
        ArtifactPath(src="my-folder/", dest="cool-dir"),
        ArtifactPath(src="just-a-file.txt", dest="cool-dir/copied-file.txt")
    ],
    description="This is a sample artifact",
    metadata={"created_by": "my-username"}
)
print(artifact_version.fqn)
run.end()

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.log_metrics(metric_dict={"accuracy": 0.7, "loss": 0.6})

model_version = run.log_model(
    name="name-for-the-model",
    model_file_or_folder="path/to/model/file/or/folder/on/disk",
    framework=<None or Framework> # Check 
)
run.end()