docs: tweak intro to model registry tutorial
I think it's cleaner to talk about use-cases first before we get into the data
model.
@@ -4,11 +4,7 @@ Organizing Models in the Model Registry
=======================================
Determined includes built-in support for a *model registry*, which makes it easy
-to organize trained models and their respective versions.
-
-The model registry contains a set of *models*. Each model has a unique name and
-zero or more *model versions*. A model version consists of a version number and
-a checkpoint, which represents the state of a trained model. Common use-cases
+to organize trained models and their respective versions. Common use-cases
for the model registry include:
* Grouping related checkpoints together, including checkpoints across experiments.
@@ -18,13 +14,17 @@ for the model registry include:
* Retrieving the latest version of a model for downstream tasks like serving or
batch inference.
+The model registry contains a set of *models*. Each model has a unique name and
+zero or more *model versions*. A model version consists of a version number and
+a checkpoint, which represents the state of a trained model.
+
The model registry is designed to be flexible, and the best way to use it
depends on your organization's requirements and workflow. For example, one
approach is to define a model for each high-level task you want to use machine
learning for (e.g., "object-detection", "sentiment-analysis", etc.). Then each
version of this model would correspond to a new approach to solving that
task. Note that different versions of a model might come from different
-experiments, use different network architectures, or can even use different deep
+experiments, use different network architectures, or even use different deep
learning frameworks. Another approach would be to register a model named
"FasterRCNN", and ensure that each version of the model uses that network
architecture.
@@ -91,7 +91,7 @@ options, see :class:`~determined.experimental.ModelSortBy` and
.. code:: python
- from determined.experimental import Determined
+ from determined.experimental import Determined, ModelOrderBy
d = Determined()
