Python SDK Documentation Page

Installation

  pip install modzy-sdk

Usage/Examples

Initializing the SDK

frommodzyimportApiClient# Sets BASE_URL and API_KEY values# Best to set these as environment variablesBASE_URL="Valid Modzy URL"# e.g., "https://trial.modzy.com"API_KEY="Valid Modzy API Key"# e.g., "JbFkWZMx4Ea3epIrxSgA.a2fR36fZi3sdFPoztAXT"client=ApiClient(base_url=BASE_URL,api_key=API_KEY)

Running Inferences

Raw Text Inputs

# Creates a dictionary for text input(s)sources= {}# Adds any number of inputssources["first-phone-call"]= {"input.txt":"Mr Watson, come here. I want to see you.",}# Submit the text to v1.0.1 of a Sentiment Analysis model, and to make the job explainable, change explain=Truejob=client.jobs.submit_text("ed542963de","1.0.1",sources,explain=False)

File Inputs

# Generate a mapping of your local file (nyc-skyline.jpg) to the input filename the model expectssources= {"nyc-skyline": {"image":"./images/nyc-skyline.jpg"}}# Submit the image to v1.0.1 of an Image-based Geolocation modeljob=client.jobs.submit_file("aevbu1h3yw","1.0.1",sources)

Embedded Inputs

frommodzy._utilimportfile_to_bytes# Embed input as a string in base64image_bytes=file_to_bytes('./images/tower-bridge.jpg')# Prepare the source dictionarysources= {"tower-bridge": {"image":image_bytes}}# Submit the image to v1.0.1 of an Imaged-based Geolocation modeljob=client.jobs.submit_embedded("aevbu1h3yw","1.0.1",sources)

Inputs from Databases

# Add database connection and query informationdb_url="jdbc:postgresql://db.bit.io:5432/bitdotio"db_username=DB_USER_NAMEdb_password=DB_PASSWORDdb_driver="org.postgresql.Driver"# Select as "input.txt" becase that is the required input name for this modeldb_query="SELECT\"mailaddr\" as\"input.txt\" FROM\"user/demo_repo\".\"atl_parcel_attr\" LIMIT 10;"# Submit the database query to v0.0.12 of a Named Entity Recognition modeljob=client.jobs.submit_jdbc("a92fc413b5","0.0.12",db_url,db_username,db_password,db_driver,db_query)

Inputs from Cloud Storage

AWS S3

# Define sources dictionary with bucket and key that points to the correct file in your s3 bucketsources= {"first-amazon-review": {"input.txt": {"bucket":"s3-bucket-name","key":"key-to-file.txt"    }  }}AWS_ACCESS_KEY="aws-acces-key"AWC_SECRET_ACCESS_KEY="aws-secret-access-key"AWS_REGION="us-east-1"# Submit s3 input to v1.0.1 of a Sentiment Analysis modeljob=client.jobs.submit_aws_s3("ed542963de","1.0.1",sources,AWS_ACCESS_KEY,AWS_SECRET_ACCESS_KEY,AWS_REGION)

Azure Blob Storage

# Define sources dictionary with container name and filepath that points to the correct file in your Azure Blob containersources= {"first-amazon-review": {"input.txt": {"container":"azure-blob-container-name","filePath":"key-to-file.txt"    }  }}AZURE_STORAGE_ACCOUNT="Azure-Storage-Account"AZURE_STORAGE_ACCOUNT_KEY="cvx....ytw=="# Submit Azure Blob input to v1.0.1 of a Sentiment Analysis modeljob=client.jobs.submit_azureblob("ed542963de","1.0.1",sources,AZURE_STORAGE_ACCOUNT,AZURE_STORAGE_ACCOUNT_KEY)

NetApp StorageGRID

# Define sources dictionary with bucket name and key that points to the correct file in your NetApp StorageGRID bucketsources= {"first-amazon-review": {"input.txt": {"bucket":"bucket-name","key":"key-to-file.txt"    }  }}ACCESS_KEY="access-key"SECRET_ACCESS_KEY="secret-access-key"STORAGE_GRID_ENDPOINT="https://endpoint.storage-grid.example"# Submit StorageGRID input to v1.0.1 of a Sentiment Analysis modeljob=client.jobs.submit_storagegrid("ed542963de","1.0.1",sources,ACCESS_KEY,SECRET_ACCESS_KEY,STORAGE_GRID_ENDPOINT)

Getting Results

Block Job until it completes

# Define sources dictionary with input datasources= {"my-input": {"input.txt":"Today is a beautiful day!"}}# Submit the text to v1.0.1 of a Sentiment Analysis model, and to make the job explainable, change explain=Truejob=client.jobs.submit_text("ed542963de","1.0.1",sources,explain=False)# Use block until complete method to periodically ping the results API until job completesresults=client.results.block_until_complete(job,timeout=None,poll_interval=5)

Query a Job's Result

#  Query results for a job at any point in timeresults=client.results.get(job)#  Print the inference resultsresults_json=result.get_first_outputs()['results.json']print(results_json)

Deploying Models

frommodzyimportApiClient# Sets BASE_URL and API_KEY values# Best to set these as environment variablesBASE_URL="Valid Modzy URL"# e.g., "https://trial.modzy.com"API_KEY="Valid Modzy API Key"# e.g., "JbFkWZMx4Ea3epIrxSgA.a2fR36fZi3sdFPoztAXT"client=ApiClient(base_url=BASE_URL,api_key=API_KEY)model_data=client.models.deploy(container_image="modzy/grpc-echo-model:1.0.0",model_name="Echo Model",model_version="0.0.1",sample_input_file="./test.txt",run_timeout="60",status_timeout="60",short_description="This model returns the same text passed through as input, similar to an 'echo.'",long_description="This model returns the same text passed through as input, similar to an 'echo.'",technical_details="This section can include any technical information abot your model. Include information about how your model was trained, any underlying architecture details, or other pertinant information an end-user would benefit from learning.",performance_summary="This is the performance summary.")print(model_data)

Running Inferences at the Edge

Initialize Edge Client

frommodzyimportEdgeClient# Initialize edge client# Use 'localhost' for local inferences, otherwise use the device's full IP addressclient=EdgeClient('localhost',55000)

Submit Inference withJob API

# Submit text job to Sentiment Analysis model deployed on edge device by providing a model ID, version, and raw text data, wait for completionjob=client.jobs.submit_text("ed542963de","1.0.27",{"input.txt":"this is awesome"})# Block until results are readyfinal_job_details=client.jobs.block_until_complete(job)results=client.jobs.get_results(job)

Query Details about Inference withJob API

# get job details for a particular jobjob_details=client.jobs.get_job_details(job)# get job details for all jobs run on your Modzy Edge instanceall_job_details=client.jobs.get_all_job_details()

Submit Inference withInference API

frommodzyimportEdgeClientfrommodzy.edgeimportInputSourceimage_bytes=open("image_path.jpg","rb").read()input_object=InputSource(key="image",# input filename defined by model authordata=image_bytes,)withEdgeClient('localhost',55000)asclient:inference=client.inferences.run("<model-id>","<model-version>",input_object,explain=False,tags=None)results=inference.result.outputs

importosfrommodzyimportEdgeClientfrommodzy.edgeimportInputSource# submit inferencesimg_folder="./images"inferences= []forimginos.listdir(img_folder):input_object=InputSource(key="image",# input filename defined by model authordata=open(os.path.join(img_folder,img),'rb').read()  )withEdgeClient('localhost',55000)asclient:inference=client.inferences.perform_inference("<model-id>","<model-version>",input_object,explain=False,tags=None)inferences.append(inference)# query resultswithEdgeClient('localhost',55000)asclient:results= [client.inferences.block_until_complete(inference.identifier)forinferencesininferences]

importosfrommodzyimportEdgeClientfrommodzy.edgeimportInputSource# generate requests iterator to pass to stream methodrequests= []forimginos.listdir(img_folder):input_object=InputSource(key="image",# input filename defined by model authordata=open(os.path.join(img_folder,img),'rb').read()  )withEdgeClient('localhost',55000)asclient:requests.append(client.inferences.build_inference_request("<model-id>","<model-version>",input_object,explain=False,tags=None))# submit list of inference requests to streaming APIwithEdgeClient('localhost',55000)asclient:streaming_results=client.inferences.stream(requests)

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