Tutorial 5-Build,Train, Deploy Machine Learning Model In AWS SageMaker-Training Xgboost ML ALgo

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Sage Maker Instance has been depreciated, please use below format

estimator = sagemaker.estimator.Estimator(image_uri=container,
hyperparameters=hyperparameters,
role=sagemaker.get_execution_role(),
instance_type = 'ml.m5.2xlarge',
instance_count= 1,
volume_size = 5,
output_path=output_path,
use_spot_instances=True,
max_run=300,
max_wait= 600)

boringhuman

Awsm video sir, actually I am working on this technology in one of the IT firm, many doubts got cleared. Thanks.

ASinha

for the container part there is an error that one may get:
the following code worked for me, for you it may vary just change the parameters and keep trying . hope this helps

from sagemaker import image_uris
container = sagemaker.image_uris.retrieve("xgboost", boto3.Session().region_name, version ="latest")

adityagavali

Thanks Krish sir, for having this wonderful video on SageMaker. Really loving it.

mayanktripathiu

Thanks alot for explaining in details, you have helped me to understand faster .

sujatabehera

Thanks Krish, Please take a live session on End to End implementation and deployment in Docker.

sachinborgave

You no need to download data into SageMaker instance, read csv file directly from S3 in pandas

import pandas as pd
import boto3
import io

s3_file_key = '_DataSet_Name_.csv'
bucket = '_YourBucketName__'

s3 = boto3.client('s3')
obj = s3.get_object(Bucket=bucket, Key=s3_file_key)

df=

pubgkiller

In case if anyone is getting this error "The method get_image_uri has been renamed in sagemaker>=2."
Then change that line with this -> container = image_uris.retrieve(region=boto3.Session().region_name, framework='xgboost', version='1.0-1')

sapnilpatel

If we use Spot Instance then there will be guarantee that you will get spot instance an that moment, may be 100% run on your instance.

pubgkiller

how to know the repo_version you have hardcoded it

vashistnarayansingh

While trying to execute estimator to train the model: to parse hyperparameter objective value binary:logistic to Json.

dilse-desi-techie-musiclover

Could you tell prerequisite to watch aws sage maker playlists

krisnavodaya

guys just change image_name to image_uri if youre getting a image uri error in estimator training cell

yaswanthyalamuri

kindly also make a video with keras cnn model

hashamfaridy

@krish Naik, how are you going to save billing please share the video link?

ravindarmadishetty

Is there any way to avoid charges while training, eg choosing lower instance type or something else?

rachity

In real life, will you allow to do hyperparameter tuning in local machine? I think NO, even if we can execute tuning in local machine then we can train the model as well. What about data security ? there are a lot questions will come if your approach will be followed in real project for client.

pubgkiller

Hi Krish,
Bit confused with the built-in algorithm.
so with sagemaker built-in xgboost algorithm does not require to have data set in X_train, y_train and X_test, y_test format?
or in the video you just showed one of the way to feed the input data to the model?
Just wanted to compare with the usual model training steps. Also in SageMaker we did not define if it is Classification or Regression (such as xgbclassifier or xgbregressor). Please put some light on this as well. I am also doing my research, and share my findings, so that others will also be benefited.

code:
""""
xgbc = XGBClassifier()

xgbc.fit(X_train, y_train)

mayanktripathiu

When do estimator.fit({'train': s3_input_train, 'validation': s3_input_test}), got an error 'TypeError: argument of type 'function' is not iterable', how to fix it?

maggietang

How to deal with “The method get_image_uri has been renamed in sagemaker>=2” error

mpumisetshedii

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