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Capture code versions and model dependencies

Capture code versions

You can log and track the exact version of the model code of Registered Model Version.
  • If a model is registered via experiment run, the code version logged will automatocally be synced in model registry
  • If you are directly registering a model, you can log code version by capturing metadata about the git commit with the specified branch, tag, or commit_hash.
Here is the example to automatically capture git commit information:
#log code versions during model registration
from verta import Client
from verta.code import Git
HOST = "XXXX.XXXX.verta.ai"
client = Client(HOST)
model_version = client.create_registered_model().create_version()
demo_model_code = Git()
model_version.log_code_version("demo model code", demo_model_code)
This information can be visualized in the web UI.
Here is the example to manually provide git commit information if Verta system does not have access to git:
#Model training
#log code versions during model registration (manually)
from verta import Client
from verta.code import Git
HOST = "XXXX.XXXX.verta.ai"
client = Client(HOST)
model_version = client.create_registered_model().create_version()
demo_model_code = Git(
repo_url="[email protected]:VertaAI/models.git",
commit_hash="52f3d22",
autocapture=False,
)
model_version.log_code_version("demo model code", demo_model_code)

Capture OS level dependencies

Capturing OS level dependencies are helpful in the following scenario:
  • If you have a python dependency that requires a specific package to be installed on the image to build or to run (e.g. GCC)
  • If you need a specific version of a package that for python dependency and python doesn't have a way to specify OS-level dependencies
Here is the example to capture OS level dependencies (e.g. for computer vision models):
#capture os dependencies during model registration
from verta import Client
from verta.environment import Python
HOST = "XXXX.XXXX.verta.ai"
client = Client(HOST)
model_version.log_environment(
Python(
requirements=["tensorflow"],
apt_packages=["python3-opencv"],
),
overwrite=True
)
This information can be visualized in the web UI.