Using SageMaker PyTorch Estimator with tdapiclient | API Integration - Using SageMaker PyTorch Estimator with tdapiclient

This use case shows the steps to use SageMaker PyTorch Estimator with tdapiclient.

You can download the aws-usecases.zip file in the attachment as a reference. The pytorch folder in the zip file includes a Jupyter notebook file (ipynb) and a Python file (py) required to run this notebook file.

Import necessary packages.

import os
import getpass
from tdapiclient import create_tdapi_context, remove_tdapi_context, TDApiClient
from teradataml import create_context, DataFrame, load_example_data
import pandas as pd
from teradatasqlalchemy.types import  *

Create the connection.

host = input("Host: ")
username = input("Username: ")
password = getpass.getpass("Password: ")

td_context = create_context(host=host, username=username, password=password)

Create TDAPI context and TDApiClient object.

s3_bucket = input("S3 Bucket(Please provide just the bucket name, for example: test-bucket): ")
access_id = input("Access ID:")
access_key = getpass.getpass("Acess Key: ")
region = input("AWS Region: ")

os.environ["AWS_ACCESS_KEY_ID"] = access_id
os.environ["AWS_SECRET_ACCESS_KEY"] = access_key
os.environ["AWS_REGION"] = region

tdapi_context = create_tdapi_context("aws", bucket_name=s3_bucket)

td_apiclient = TDApiClient(tdapi_context)

Set up data to be used for this workflow.

feature = ['crim', 'zn', 'indus', 'chas', 'nox', 'rm',
           'age', 'dis', 'rad', 'tax', 'ptratio', 'black', 'lstat']
target = 'medv'

# Load the data to run the example
load_example_data("decisionforest", "boston")

# Create teradataml DataFrame.
boston = DataFrame.from_table("boston")

boston

The output:

id	crim	zn	indus	chas	nox	rm	age	dis	rad	tax	ptratio	black	lstat	medv
326	0.19186	0	7.38	0	0.493	6.431	14	5.4159	5	287	19.6	393.68	5.08	24.6
40	0.02763	75	2.95	0	0.428	6.595	21	5.4011	3	252	18.3	395.63	4.32	30.8
366	4.55587	0	18.1	0	0.718	3.561	87	1.6132	24	666	20.2	354.7	7.12	27.5
265	0.55007	20	3.97	0	0.647	7.206	91	1.9301	5	264	13.0	387.89	8.1	36.5
387	24.3938	0	18.1	0	0.7	4.652	100	1.4672	24	666	20.2	396.9	28.28	10.5
448	9.92485	0	18.1	0	0.74	6.251	96	2.198	24	666	20.2	388.52	16.44	12.6
244	0.12757	30	4.93	0	0.428	6.393	7	7.0355	6	300	16.6	374.71	5.19	23.7
305	0.05515	33	2.18	0	0.472	7.236	41	4.022	7	222	18.4	393.68	6.93	36.1
122	0.07165	0	25.65	0	0.581	6.004	84	2.1974	2	188	19.1	377.67	14.27	20.3
183	0.09103	0	2.46	0	0.488	7.155	92	2.7006	3	193	17.8	394.12	4.82	37.9

Create PyTorch SageMaker estimator instance through tdapiclient.

exec_role_arn = "arn:aws:iam::076782961461:role/service-role/AmazonSageMaker-ExecutionRole-20210112T215668"
FRAMEWORK_VERSION = "1.10.0"

# Create an estimator object based on PyTorch sagemaker class
pyTorch_estimator = td_apiclient.PyTorch(
    entry_point="pytorch-script.py",
    role=exec_role_arn,
    instance_count=1,
    instance_type="ml.m5.large",
    py_version='py38',
    framework_version=FRAMEWORK_VERSION,
    metric_definitions=[{"Name": "median-AE",
                         "Regex": "AE-at-50th-percentile: ([0-9.]+).*$"}],
    hyperparameters={
        "epochs": 10,
        "seed": 42,
        "batch_size": 10,
        "features": "crim zn indus chas nox rm age dis rad tax ptratio black lstat",
        "target": target,
    },
)

Create test and training DataFrames, and start training.

Create two samples of input data - sample 1 has 80% of total rows and sample 2 has 20% of total rows.

boston_sample = boston.sample(frac=[0.8, 0.2])

train_df = boston_sample[boston_sample.sampleid =="1"].drop("sampleid", axis=1)

test_df = boston_sample[boston_sample.sampleid == "2"].drop("sampleid", axis=1)

Show the test DataFrame.

test_df

The output:

id	crim	zn	indus	chas	nox	rm	age	dis	rad	tax	ptratio	black	lstat	medv
120	0.14476	0	10.01	0	0.547	5.731	65	2.7592	6	432	17.8	391.5	13.61	19.3
261	0.54011	20	3.97	0	0.647	7.203	81	2.1121	5	264	13.0	392.8	9.59	33.8
118	0.15098	0	10.01	0	0.547	6.021	82	2.7474	6	432	17.8	394.51	10.3	19.2
162	1.46336	0	19.58	0	0.605	7.489	90	1.9709	5	403	14.7	374.43	1.73	50.0
80	0.08387	0	12.83	0	0.437	5.874	36	4.5026	5	398	18.7	396.06	9.1	20.3
341	0.06151	0	5.19	0	0.515	5.968	58	4.8122	5	224	20.2	396.9	9.29	18.7
259	0.66351	20	3.97	0	0.647	7.333	100	1.8946	5	264	13.0	383.29	7.79	36.0
55	0.0136	75	4.0	0	0.41	5.888	47	7.3197	3	469	21.1	396.9	14.8	18.9
19	0.80271	0	8.14	0	0.538	5.456	36	3.7965	4	307	21.0	288.99	11.69	20.2
282	0.03705	20	3.33	0	0.4429	6.968	37	5.2447	5	216	14.9	392.23	4.59	35.4

Show the training DataFrame.

train_df

The output:

id	crim	zn	indus	chas	nox	rm	age	dis	rad	tax	ptratio	black	lstat	medv
244	0.12757	30	4.93	0	0.428	6.393	7	7.0355	6	300	16.6	374.71	5.19	23.7
101	0.14866	0	8.56	0	0.52	6.727	79	2.7778	5	384	20.9	394.76	9.42	27.5
427	12.2472	0	18.1	0	0.584	5.837	59	1.9976	24	666	20.2	24.65	15.69	10.2
469	15.5757	0	18.1	0	0.58	5.926	71	2.9084	24	666	20.2	368.74	18.13	19.1
40	0.02763	75	2.95	0	0.428	6.595	21	5.4011	3	252	18.3	395.63	4.32	30.8
366	4.55587	0	18.1	0	0.718	3.561	87	1.6132	24	666	20.2	354.7	7.12	27.5
162	1.46336	0	19.58	0	0.605	7.489	90	1.9709	5	403	14.7	374.43	1.73	50.0
223	0.62356	0	6.2	1	0.507	6.879	77	3.2721	8	307	17.4	390.39	9.93	27.5
326	0.19186	0	7.38	0	0.493	6.431	14	5.4159	5	287	19.6	393.68	5.08	24.6
305	0.05515	33	2.18	0	0.472	7.236	41	4.022	7	222	18.4	393.68	6.93	36.1

Train the model using teradataml DataFrames.

pyTorch_estimator.fit({"train": train_df, "test": test_df}, content_type="csv", wait=True)

The output:

Updated input is : {'train': 's3://pttest-bucket/tdsg-vtkwcsmurk-csv', 'test': 's3://pttest-bucket/tdsg-darwkimmvs-csv'}
2022-03-23 08:50:00 Starting - Starting the training job...
2022-03-23 08:50:23 Starting - Preparing the instances for trainingProfilerReport-1648025398: InProgress
......
......
......
[2022-03-23 08:53:45.626 algo-1:29 INFO hook.py:591] name:layers.0.weight count_params:832
[2022-03-23 08:53:45.627 algo-1:29 INFO hook.py:591] name:layers.0.bias count_params:64
[2022-03-23 08:53:45.627 algo-1:29 INFO hook.py:591] name:layers.2.weight count_params:2048
[2022-03-23 08:53:45.627 algo-1:29 INFO hook.py:591] name:layers.2.bias count_params:32
[2022-03-23 08:53:45.627 algo-1:29 INFO hook.py:591] name:layers.4.weight count_params:32
[2022-03-23 08:53:45.627 algo-1:29 INFO hook.py:591] name:layers.4.bias count_params:1
[2022-03-23 08:53:45.627 algo-1:29 INFO hook.py:593] Total Trainable Params: 3009
[2022-03-23 08:53:45.627 algo-1:29 INFO hook.py:424] Monitoring the collections: losses
[2022-03-23 08:53:45.631 algo-1:29 INFO hook.py:488] Hook is writing from the hook with pid: 29
Epoch : 1 ,  Loss : 22.646
Epoch : 2 ,  Loss : 22.552
Epoch : 3 ,  Loss : 22.577
Epoch : 4 ,  Loss : 22.304
Epoch : 5 ,  Loss : 22.167
Epoch : 6 ,  Loss : 21.928
Epoch : 7 ,  Loss : 21.615
Epoch : 8 ,  Loss : 21.226
Epoch : 9 ,  Loss : 20.747
Epoch : 10 ,  Loss : 20.271
Training process has finished.
validating model
/opt/conda/lib/python3.8/site-packages/torch/utils/smdebug.py:68: UserWarning: Using a target size (torch.Size([10])) that is different to the input size (torch.Size([10, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
  return_value = function(*args, **kwargs)
/opt/conda/lib/python3.8/site-packages/torch/utils/smdebug.py:68: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([1, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
  return_value = function(*args, **kwargs)
Mean Squared Error => 59.184
model persisted at /opt/ml/model/model.pth
2022-03-23 08:53:48,209 sagemaker-training-toolkit INFO     Reporting training SUCCESS

2022-03-23 08:54:04 Uploading - Uploading generated training model
2022-03-23 08:54:04 Completed - Training job completed
Training seconds: 152
Billable seconds: 152

Create Serializer and Deserializer, so predictor can handle CSV input and output.

from sagemaker.serializers import CSVSerializer
from sagemaker.deserializers import CSVDeserializer
csv_ser = CSVSerializer()
csv_dser = CSVDeserializer()

predictor = pyTorch_estimator.deploy("aws-endpoint",
                                     sagemaker_kw_args={"instance_type": "ml.m5.large", "initial_instance_count": 1, "serializer": csv_ser, "deserializer": csv_dser})

Score the model using teradataml DataFrame and the predictor object created in previous step.

Try the predictor with simple CSV data to see if it works as expected.

item = '-0.4114,  1.4437, -1.1230, -0.2726, -1.0167,  0.7086, -0.9770, -0.0031,-0.5230, -0.0608, -1.5052,  0.4078, -0.8373'

print(predictor.cloudObj.accept)

print(predictor.cloudObj.predict(item))

The output:

('text/csv',)
[['4.00261']]

Try prediction with UDF and Client options.

Input:

input = test_df.sample(n=5).select(feature)

input

crim	zn	indus	chas	nox	rm	age	dis	rad	tax	ptratio	black	lstat
0.12204	0	2.89	0	0.445	6.625	57	3.4952	2	276	18.0	357.98	6.65
0.06211	40	1.25	0	0.429	6.49	44	8.7921	1	335	19.7	396.9	5.98
11.1081	0	18.1	0	0.668	4.906	100	1.1742	24	666	20.2	396.9	34.77
0.11329	30	4.93	0	0.428	6.897	54	6.3361	6	300	16.6	391.25	11.38
0.0578	0	2.46	0	0.488	6.98	58	2.829	3	193	17.8	396.9	5.04

Prediction with UDF option:

output = predictor.predict(input, mode="UDF",content_type='csv')

output

The output:

crim	zn	indus	chas	nox	rm	age	dis	rad	tax	ptratio	black	lstat	Output
0.19186	0	7.38	0	0.493	6.431	14	5.4159	5	287	19.6	393.68	5.08	275.26831
0.14455	12	7.87	0	0.524	6.172	96	5.9505	5	311	15.2	396.9	19.15	274.58148
0.06888	0	2.46	0	0.488	6.144	62	2.5979	3	193	17.8	396.9	9.45	283.88132
0.12579	45	3.44	0	0.437	6.556	29	4.5667	5	398	15.2	382.84	4.56	275.25662
0.06664	0	4.05	0	0.51	6.546	33	3.1323	5	296	16.6	390.96	5.33	273.52322

Prediction with Client option:

output = predictor.predict(input, mode="Client", content_type='csv')

output

The output:

[['270.57440', '328.61087', '273.84720', '282.11841', '270.84802']]

Clean up.

predictor.cloudObj.delete_model()
predictor.cloudObj.delete_endpoint()
remove_tdapi_context(tdapi_context)

Using SageMaker PyTorch Estimator with tdapiclient | API Integration - Using SageMaker PyTorch Estimator with tdapiclient - Teradata Vantage

Teradata Vantage™ - API Integration Guide for Cloud Machine Learning