added snippets for Connection, Model Registry

[rktraz]

No description provided.

[jimdowling]

I think Javier should review this.

[jimdowling]

This sentence doesn't mean anything.
configures itself how?
Technically, it should be
"The library automatically configures itself based on the environment it is run in."
Do you mean?
The library automatically configures itself based on the environment it is run in, either Python or Spark.
If you mean - it configures itself based on whether it is run inside Hopsworks or outside Hopsworks - in this case, just remove the sentence. Users will expect it should work inside/outside Hopsworks. We shouldn't need to tell them.

[jimdowling]

If you connect to Hopsworks from an external Python environment to your own managed Hopsworks cluster, you need to provide additional connection information - the Hopsworks hostname (or IP address) and port (if it is not port 443).

[jimdowling]

put the hostname and project in the example here:

connection = hsml.connection(host="my.cluster.com", project="fraud")

[jimdowling]

Add a line of code before this where you export the tensorflow model to the local directory:
"/tmp/model_directory"

Otherwise this code snippet doesn't actually upload a tensorflow model

[javierdlrm]

Suggested change

	mr = conn.get_model_registry() # Get the project's default model registry
	mr = conn.get_model_registry()

[javierdlrm]

Suggested change

	model.save('model.pkl')
	model.save('model.pkl') # save a single model file, or
	model.save('path/to/model_directory') # save a model with multiple files

[javierdlrm]

Suggested change

	# get model object
	model = mr.get_model("citibike_mlp_model", version=1)
	# delete this model version
	model.delete()
	# get model object of a specific version
	model = mr.get_model("mnist", version=1)
	# delete the model version
	model.delete()

[javierdlrm]

Suggested change

	models = mr.get_models("churnmodel")
	models = mr.get_models("mnist")

[javierdlrm]

Suggested change

	best_model = mr.get_best_model("citibike_mlp_model",
	best_model = mr.get_best_model("mnist",

[javierdlrm]

Suggested change

	model = mr.tensorflow.create_model(
	name="bitcoin_price_model",
	metrics=metrics,
	description="bitcoin daily price detection model.",
	input_example=[1613512800000]
	)
	model = mr.tensorflow.create_model(
	name="mnist_classifier",
	metrics=metrics,
	model_schema=model_schema,
	input_example=input_example,
	description="MNIST Classifier"
	)

[javierdlrm]

Suggested change

	model = mr.torch.create_model(
	name="electricity_price_prediction_model",
	metrics=metrics,
	description="Daily electricity price prediction model.",
	input_example=n_step_window.example[0].numpy()
	)
	model = mr.torch.create_model(
	name="mnist_classifier",
	metrics=metrics,
	model_schema=model_schema,
	input_example=input_example,
	description="MNIST Classifier"
	)

[javierdlrm]

I think we can keep the first sentence as

Suggested change

	However, to connect from an external Python environment additional connection information, such as the Hopsworks hostname (or IP address) and port (if it is not port 443), is required. For more information about the setup from external environments, see the setup section.
	The library automatically configures itself based on the environment it is run. However, to connect from an external Python environment additional connection information, such as the Hopsworks hostname (or IP address) and port (if it is not port 443), is required. For more information about the setup from external environments, see the setup section.

[javierdlrm]

Suggested change

	connection = hsml.connection(host="my.cluster.com", project="fraud")
	connection = hsml.connection()
	# or connection = hsml.connection(host="my.hopsworks.cluster", project="my_project")

[javierdlrm]

Suggested change

	import tensorflow as tf
	model = mr.tensorflow.create_model(name="mnist",
	version=1,
	metrics={"accuracy": 0.94},
	description="mnist model description")
	export_path = "/tmp/model_directory" # or "/tmp/model_file"
	tf.saved_model.save(model, export_path)
	model.save(export_path)
	import tensorflow as tf
	export_path = "/tmp/model_directory"
	tf.saved_model.save(model, export_path)
	model = mr.tensorflow.create_model(name="mnist",
	version=1,
	metrics={"accuracy": 0.94},
	description="mnist model description")
	model.save(export_path) # "/tmp/model_directory" or "/tmp/model_file"