hsml.deployment #

Delete the deployment.

Print a JSON description of the deployment.

Download the artifact files served by the deployment.

Get the base endpoint URL for this deployment.

Returns the base URL that can be used with external HTTP clients. This is the path-based routing base endpoint without any protocol-specific suffixes like :predict or /v1.

If Istio client is not available, returns None.

Examples:

deployment = ms.get_deployment("my_deployment")
url = deployment.get_endpoint_url()
# url = "https://host:port/v1/project/name"

Get the KServe inference URL for standard model deployments.

Returns the full URL with :predict suffix for KServe inference protocol. This method only returns a URL for standard model deployments (non-vLLM, with a model attached).

If Istio client is not available, falls back to Hopsworks REST API path.

Examples:

deployment = ms.get_deployment("my_deployment")
url = deployment.get_inference_url()
# Use with any HTTP client
import requests
response = requests.post(url, json={"instances": [[1, 2, 3]]})

Prints the deployment logs of the predictor or transformer.

.. note:: Legacy: this method prints to stdout and returns None. New code (and any agent / scripted use) should call meth:read_logs for a string return value or meth:tail_logs for incremental streaming.

Retrieve the metadata object for the model being used by this deployment.

Get the OpenAI-compatible API URL for vLLM deployments.

Returns the URL for OpenAI-compatible API endpoints (e.g., /v1/chat/completions). This method only returns a URL for LLM (vLLM) deployments.

Examples:

deployment = ms.get_deployment("my_llm_deployment")
url = deployment.get_openai_url()
# url = "https://host:port/v1/project/name/v1"
# Then use: url + "/chat/completions"

Get the current state of the deployment.

Get url to the deployment in Hopsworks.

Check whether the deployment is created.

Check whether the deployment is ready to handle inference requests.

Check whether the deployment is stopped.

Send inference requests to the deployment.

One of data or inputs parameters must be set. If both are set, inputs will be ignored.

Examples:

# login into Hopsworks using hopsworks.login()

# get Hopsworks Model Serving handle
ms = project.get_model_serving()

# retrieve deployment by name
my_deployment = ms.get_deployment("my_deployment")

# (optional) retrieve model input example
my_model = project.get_model_registry()                                .get_model(my_deployment.model_name, my_deployment.model_version)

# make predictions using model inputs (single or batch)
predictions = my_deployment.predict(inputs=my_model.input_example)

# or using more sophisticated inference request payloads
data = { "instances": [ my_model.input_example ], "key2": "value2" }
predictions = my_deployment.predict(data)

Return deployment logs as a single plain-text string.

Programmatic counterpart to meth:get_logs. Suitable for agents and scripts: never prints, never short-circuits on deployment state. The default source="opensearch" reads the project's serving index and works for stopped or restarted deployments — meth:get_logs only reads live pod stdout and returns None when the deployment isn't running.

Restart the deployment so it picks up the latest code and environment state.

If the deployment is already stopped, it is started in place.

Persist this deployment including the predictor and metadata to Model Serving.

Start the deployment.

Stop the deployment.

Yield only newly observed log chunks as plain text.

Client-side polling, not server-streaming: each tick calls meth:read_logs with a moving cursor and yields the portion not already seen. Deduplication uses the OpenSearch timestamp + doc_id pair; a content-hash fallback covers the Kubernetes path.

Example::

for chunk in dep.tail_logs(timeout=120):
    print(chunk, end="")