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chassis_client

ChassisClient 

ChassisClient(base_url='http://localhost:5000', auth_header=None, ssl_verification=True)

DEPRECATED

Please use chassis.builder.RemoteBuilder moving forward.

The Chassis Client object.

This class is used to interact with the Chassis remote build service.

Parameters:

Name Type Description Default
base_url str

The base url for the API.

 'http://localhost:5000'
auth_header Optional[str]

Optional authorization header to be included with all requests.

 None
ssl_verification bool

Verify TLS connections.

 True

get_job_status 

get_job_status(job_id)

DEPRECATED

Please use chassis.builder.RemoteBuilder.get_build_status moving forward.

Checks the status of a chassis job

Parameters:

Name Type Description Default
job_id str

Chassis job identifier generated from ChassisModel.publish method.

 required

Returns:

Type Description
BuildResponse

Chassis job status.

Example: 

# Create Chassisml model
chassis_model = chassis_client.create_model(process_fn=process)

# Define Dockerhub credentials
dockerhub_user = "user"
dockerhub_pass = "password"

# Publish model to Docker registry
response = chassis_model.publish(
    model_name="Chassisml Regression Model",
    model_version="0.0.1",
    registry_user=dockerhub_user,
    registry_pass=dockerhub_pass,
)

job_id = response.get('job_id')
job_status = chassis_client.get_job_status(job_id)

get_job_logs 

get_job_logs(job_id)

DEPRECATED

Please use chassis.builder.RemoteBuilder.get_build_logs moving forward.

Checks the status of a chassis job

Parameters:

Name Type Description Default
job_id str

Chassis job identifier generated from ChassisModel.publish method.

 required

Returns:

Type Description
str

The job logs.

Example: 

# Create Chassisml model
chassis_model = chassis_client.create_model(process_fn=process)
# Define Dockerhub credentials
dockerhub_user = "user"
dockerhub_pass = "password"
# Publish model to Docker registry
response = chassis_model.publish(
    model_name="Chassisml Regression Model",
    model_version="0.0.1",
    registry_user=dockerhub_user,
    registry_pass=dockerhub_pass,
)
job_id = response.get('job_id')
job_status = chassis_client.get_job_logs(job_id)

block_until_complete 

block_until_complete(job_id, timeout=None, poll_interval=5)

DEPRECATED

Please use chassis.builder.RemoteBuilder.block_until_complete moving forward.

Blocks until Chassis job is complete or timeout is reached. Polls Chassis job API until a result is marked finished.

Parameters:

Name Type Description Default
job_id str

Chassis job identifier generated from ChassisModel.publish method.

 required
timeout Optional[int]

Timeout threshold in seconds.

 None
poll_interval int

Amount of time to wait in between API polls to check status of job.

 5

Returns:

Type Description
BuildResponse

Final job status.

Example: 

# Create Chassisml model
chassis_model = chassis_client.create_model(process_fn=process)

# Define Dockerhub credentials
dockerhub_user = "user"
dockerhub_pass = "password"

# Publish model to Docker registry
response = chassis_model.publish(
    model_name="Chassisml Regression Model",
    model_version="0.0.1",
    registry_user=dockerhub_user,
    registry_pass=dockerhub_pass,
)

job_id = response.get('job_id')
final_status = chassis_client.block_until_complete(job_id)

download_tar 

download_tar(job_id, output_filename)

This method is no longer available.

create_model 

create_model(process_fn=None, batch_process_fn=None, batch_size=None)

DEPRECATED

Please use chassisml.ChassisModel moving forward.

Builds chassis model locally

Parameters:

Name Type Description Default
process_fn Optional[LegacyNormalPredictFunction]

Python function that must accept a single piece of input data in raw bytes form. This method is responsible for handling all data preprocessing, executing inference, and returning the processed predictions. Defining additional functions is acceptable as long as they are called within the process method

 None
batch_process_fn Optional[LegacyBatchPredictFunction]

Python function that must accept a batch of input data in raw bytes form. This method is responsible for handling all data preprocessing, executing inference, and returning the processed predictions. Defining additional functions is acceptable as long as they are called within the process method

 None
batch_size Optional[int]

Maximum batch size if batch_process_fn is defined

 None

Returns:

Type Description
ChassisModel

Chassis Model object that can be tested locally and published to a
ChassisModel

Docker Registry.

Examples:

The following snippet was taken from this example. 

# Import and normalize data
X_digits, y_digits = datasets.load_digits(return_X_y=True)
X_digits = X_digits / X_digits.max()

n_samples = len(X_digits)

# Split data into training and test sets
X_train = X_digits[: int(0.9 * n_samples)]
y_train = y_digits[: int(0.9 * n_samples)]
X_test = X_digits[int(0.9 * n_samples) :]
y_test = y_digits[int(0.9 * n_samples) :]

# Train Model
logistic = LogisticRegression(max_iter=1000)
print(
    "LogisticRegression mean accuracy score: %f"
    % logistic.fit(X_train, y_train).score(X_test, y_test)
)

# Save small sample input to use for testing later
sample = X_test[:5].tolist()
with open("digits_sample.json", 'w') as out:
    json.dump(sample, out)

# Define Process function
def process(input_bytes):
    inputs = np.array(json.loads(input_bytes))
    inference_results = logistic.predict(inputs)
    structured_results = []
    for inference_result in inference_results:
        structured_output = {
            "data": {
                "result": {
                    "classPredictions": [
                        {
                            "class": str(inference_result),
                            "score": str(1)
                        }
                    ]
                }
            }
        }
        structured_results.append(structured_output)
    return structured_results

# create Chassis model
chassis_model = chassis_client.create_model(process_fn=process)

run_inference 

run_inference(input_data, container_url='localhost', host_port=45000)

No Longer Available

Please use chassis.client.OMIClient.run moving forward.

This is the method you use to submit data to a container chassis has built for inference. It assumes the container has been downloaded from dockerhub and is running somewhere you have access to.

Parameters:

Name Type Description Default
input_data Dict[str, bytes]

dictionary of the form {"input": b"binary representation of your data"}.

 required
container_url str

URL where container is running.

 'localhost'
host_port int

host port that forwards to container's grpc server port

 45000

Returns:

Type Description
Iterable[OutputItem]

Success -> results from model processing as specified in the process function.
Iterable[OutputItem]

Failure -> Error codes from processing errors. All errors should container the word "Error."

Example: 

# assume that the container is running locally, and that it was started with this docker command
#  docker run -it -p 5001:45000 <docker_uname>/<container_name>:<tag_id>

from chassisml_sdk.chassisml import chassisml

client = chassisml.ChassisClient("https://chassis.app.modzy.com/")

input_data = {"input": b"[[0.0, 0.0, 0.0, 1.0, 12.0, 6.0, 0.0, 0.0, 0.0, 0.0, 0.0, 11.0, 15.0, 2.0, 0.0, 0.0, 0.0, 0.0, 8.0, 16.0, 6.0, 1.0, 2.0, 0.0, 0.0, 4.0, 16.0, 9.0, 1.0, 15.0, 9.0, 0.0, 0.0, 13.0, 15.0, 6.0, 10.0, 16.0, 6.0, 0.0, 0.0, 12.0, 16.0, 16.0, 16.0, 16.0, 1.0, 0.0, 0.0, 1.0, 7.0, 4.0, 14.0, 13.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 9.0, 0.0, 0.0], [0.0, 0.0, 8.0, 16.0, 3.0, 0.0, 1.0, 0.0, 0.0, 0.0, 16.0, 14.0, 5.0, 14.0, 12.0, 0.0, 0.0, 0.0, 8.0, 16.0, 16.0, 9.0, 0.0, 0.0, 0.0, 0.0, 3.0, 16.0, 14.0, 1.0, 0.0, 0.0, 0.0, 0.0, 12.0, 16.0, 16.0, 2.0, 0.0, 0.0, 0.0, 0.0, 16.0, 11.0, 16.0, 4.0, 0.0, 0.0, 0.0, 3.0, 16.0, 16.0, 16.0, 6.0, 0.0, 0.0, 0.0, 0.0, 10.0, 16.0, 10.0, 1.0, 0.0, 0.0], [0.0, 0.0, 5.0, 12.0, 8.0, 0.0, 1.0, 0.0, 0.0, 0.0, 11.0, 16.0, 5.0, 13.0, 6.0, 0.0, 0.0, 0.0, 2.0, 15.0, 16.0, 12.0, 1.0, 0.0, 0.0, 0.0, 0.0, 10.0, 16.0, 6.0, 0.0, 0.0, 0.0, 0.0, 1.0, 15.0, 16.0, 7.0, 0.0, 0.0, 0.0, 0.0, 8.0, 16.0, 16.0, 11.0, 0.0, 0.0, 0.0, 0.0, 11.0, 16.0, 16.0, 9.0, 0.0, 0.0, 0.0, 0.0, 6.0, 12.0, 12.0, 3.0, 0.0, 0.0], [0.0, 0.0, 0.0, 3.0, 15.0, 4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 16.0, 12.0, 0.0, 0.0, 0.0, 0.0, 0.0, 12.0, 15.0, 3.0, 4.0, 3.0, 0.0, 0.0, 7.0, 16.0, 5.0, 3.0, 15.0, 8.0, 0.0, 0.0, 13.0, 16.0, 13.0, 15.0, 16.0, 2.0, 0.0, 0.0, 12.0, 16.0, 16.0, 16.0, 13.0, 0.0, 0.0, 0.0, 0.0, 4.0, 5.0, 16.0, 8.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 16.0, 4.0, 0.0, 0.0], [0.0, 0.0, 10.0, 14.0, 8.0, 1.0, 0.0, 0.0, 0.0, 2.0, 16.0, 14.0, 6.0, 1.0, 0.0, 0.0, 0.0, 0.0, 15.0, 15.0, 8.0, 15.0, 0.0, 0.0, 0.0, 0.0, 5.0, 16.0, 16.0, 10.0, 0.0, 0.0, 0.0, 0.0, 12.0, 15.0, 15.0, 12.0, 0.0, 0.0, 0.0, 4.0, 16.0, 6.0, 4.0, 16.0, 6.0, 0.0, 0.0, 8.0, 16.0, 10.0, 8.0, 16.0, 8.0, 0.0, 0.0, 1.0, 8.0, 12.0, 14.0, 12.0, 1.0, 0.0]]"}
input_list = [input_data for _ in range(30)]

print("single input")
print(client.run_inference(input_data, container_url="localhost", host_port=5001))
print("multi inputs")
results = client.run_inference(input_list, container_url="localhost", host_port=5001)
for x in results:
    print(x)

docker_infer 

docker_infer(image_id, input_data, container_url='localhost', host_port=5001, container_port=None, timeout=20, clean_up=True, pull_container=False)

No Longer Available

Please use chassis.client.OMIClient.test_container moving forward.

Runs inference on an OMI compliant container. This method checks to see if a container is running and if not starts it. The method then runs inference against the input_data with the model in the container, and optionally shuts down the container.

Parameters:

Name Type Description Default
image_id str

the name of an OMI container image usually of the form /:

 required
input_data Mapping[str, bytes]

dictionary of the form

 required
container_url str

No longer used.

 'localhost'
host_port int

host port that forwards to container's grpc server port

 5001
container_port Optional[str]

No longer used.

 None
timeout int

number of seconds to wait for gRPC server to spin up

 20
clean_up bool

No longer used.

 True
pull_container bool

if True pulls missing container from repo

 False

Returns:

Type Description
Iterable[OutputItem]

Success -> model output as defined in the process function
Iterable[OutputItem]

Failure -> Error message if any success criteria is missing.

Example: 

host_port = 5002
client = chassisml.ChassisClient()


input_data = {"input": b"[[0.0, 0.0, 0.0, 1.0, 12.0, 6.0, 0.0, 0.0, 0.0, 0.0, 0.0, 11.0, 15.0, 2.0, 0.0, 0.0, 0.0, 0.0, 8.0, 16.0, 6.0, 1.0, 2.0, 0.0, 0.0, 4.0, 16.0, 9.0, 1.0, 15.0, 9.0, 0.0, 0.0, 13.0, 15.0, 6.0, 10.0, 16.0, 6.0, 0.0, 0.0, 12.0, 16.0, 16.0, 16.0, 16.0, 1.0, 0.0, 0.0, 1.0, 7.0, 4.0, 14.0, 13.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 9.0, 0.0, 0.0], [0.0, 0.0, 8.0, 16.0, 3.0, 0.0, 1.0, 0.0, 0.0, 0.0, 16.0, 14.0, 5.0, 14.0, 12.0, 0.0, 0.0, 0.0, 8.0, 16.0, 16.0, 9.0, 0.0, 0.0, 0.0, 0.0, 3.0, 16.0, 14.0, 1.0, 0.0, 0.0, 0.0, 0.0, 12.0, 16.0, 16.0, 2.0, 0.0, 0.0, 0.0, 0.0, 16.0, 11.0, 16.0, 4.0, 0.0, 0.0, 0.0, 3.0, 16.0, 16.0, 16.0, 6.0, 0.0, 0.0, 0.0, 0.0, 10.0, 16.0, 10.0, 1.0, 0.0, 0.0], [0.0, 0.0, 5.0, 12.0, 8.0, 0.0, 1.0, 0.0, 0.0, 0.0, 11.0, 16.0, 5.0, 13.0, 6.0, 0.0, 0.0, 0.0, 2.0, 15.0, 16.0, 12.0, 1.0, 0.0, 0.0, 0.0, 0.0, 10.0, 16.0, 6.0, 0.0, 0.0, 0.0, 0.0, 1.0, 15.0, 16.0, 7.0, 0.0, 0.0, 0.0, 0.0, 8.0, 16.0, 16.0, 11.0, 0.0, 0.0, 0.0, 0.0, 11.0, 16.0, 16.0, 9.0, 0.0, 0.0, 0.0, 0.0, 6.0, 12.0, 12.0, 3.0, 0.0, 0.0], [0.0, 0.0, 0.0, 3.0, 15.0, 4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 16.0, 12.0, 0.0, 0.0, 0.0, 0.0, 0.0, 12.0, 15.0, 3.0, 4.0, 3.0, 0.0, 0.0, 7.0, 16.0, 5.0, 3.0, 15.0, 8.0, 0.0, 0.0, 13.0, 16.0, 13.0, 15.0, 16.0, 2.0, 0.0, 0.0, 12.0, 16.0, 16.0, 16.0, 13.0, 0.0, 0.0, 0.0, 0.0, 4.0, 5.0, 16.0, 8.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 16.0, 4.0, 0.0, 0.0], [0.0, 0.0, 10.0, 14.0, 8.0, 1.0, 0.0, 0.0, 0.0, 2.0, 16.0, 14.0, 6.0, 1.0, 0.0, 0.0, 0.0, 0.0, 15.0, 15.0, 8.0, 15.0, 0.0, 0.0, 0.0, 0.0, 5.0, 16.0, 16.0, 10.0, 0.0, 0.0, 0.0, 0.0, 12.0, 15.0, 15.0, 12.0, 0.0, 0.0, 0.0, 4.0, 16.0, 6.0, 4.0, 16.0, 6.0, 0.0, 0.0, 8.0, 16.0, 10.0, 8.0, 16.0, 8.0, 0.0, 0.0, 1.0, 8.0, 12.0, 14.0, 12.0, 1.0, 0.0]]"}
input_list = [input_data for _ in range(30)]
print("single input")
print(client.docker_infer(image_id="claytondavisms/sklearn-digits-docker-test:0.0.7", input_data=input_data, container_url="localhost", host_port=host_port, clean_up=False, pull_container=True))

print("multi inputs")
results = client.run_inference(input_list, container_url="localhost", host_port=host_port)
results = client.docker_infer(image_id="claytondavisms/sklearn-digits-docker-test:0.0.7", input_data=input_list, container_url="localhost", host_port=host_port)
for x in results:
    print(x)