Feature transformation with Amazon SageMaker processing job and Feature Store
Introduction
In this lab you will start with the raw Women's Clothing Reviews dataset and prepare it to train a BERT-based natural language processing (NLP) model. The model will be used to classify customer reviews into positive (1), neutral (0) and negative (-1) sentiment.
You will convert the original review text into machine-readable features used by BERT. To perform the required feature transformation you will configure an Amazon SageMaker processing job, which will be running a custom Python script.
# please ignore warning messages during the installation
!pip install --disable-pip-version-check -q sagemaker==2.35.0
!conda install -q -y pytorch==1.6.0 -c pytorch
!pip install --disable-pip-version-check -q transformers==3.5.1
[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv[0m[33m
[0mCollecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
## Package Plan ##
environment location: /opt/conda
added / updated specs:
- pytorch==1.6.0
The following packages will be downloaded:
package | build
---------------------------|-----------------
ca-certificates-2023.05.30 | h06a4308_0 120 KB
certifi-2022.12.7 | py37h06a4308_0 150 KB
cudatoolkit-10.2.89 | hfd86e86_1 365.1 MB
ninja-1.10.2 | h06a4308_5 8 KB
ninja-base-1.10.2 | hd09550d_5 109 KB
pytorch-1.6.0 |py3.7_cuda10.2.89_cudnn7.6.5_0 537.7 MB pytorch
------------------------------------------------------------
Total: 903.1 MB
The following NEW packages will be INSTALLED:
cudatoolkit pkgs/main/linux-64::cudatoolkit-10.2.89-hfd86e86_1 None
ninja pkgs/main/linux-64::ninja-1.10.2-h06a4308_5 None
ninja-base pkgs/main/linux-64::ninja-base-1.10.2-hd09550d_5 None
pytorch pytorch/linux-64::pytorch-1.6.0-py3.7_cuda10.2.89_cudnn7.6.5_0 None
The following packages will be UPDATED:
ca-certificates conda-forge::ca-certificates-2022.12.~ --> pkgs/main::ca-certificates-2023.05.30-h06a4308_0 None
The following packages will be SUPERSEDED by a higher-priority channel:
certifi conda-forge/noarch::certifi-2022.12.7~ --> pkgs/main/linux-64::certifi-2022.12.7-py37h06a4308_0 None
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
Retrieving notices: ...working... done
[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv[0m[33m
[0m
import boto3
import sagemaker
import botocore
config = botocore.config.Config(user_agent_extra='dlai-pds/c2/w1')
# low-level service client of the boto3 session
sm = boto3.client(service_name='sagemaker',
config=config)
featurestore_runtime = boto3.client(service_name='sagemaker-featurestore-runtime',
config=config)
sess = sagemaker.Session(sagemaker_client=sm,
sagemaker_featurestore_runtime_client=featurestore_runtime)
bucket = sess.default_bucket()
role = sagemaker.get_execution_role()
region = sess.boto_region_name
1. Configure the SageMaker Feature Store
1.1. Configure dataset
The raw dataset is in the public S3 bucket. Let's start by specifying the S3 location of it:
raw_input_data_s3_uri = 's3://dlai-practical-data-science/data/raw/'
print(raw_input_data_s3_uri)
s3://dlai-practical-data-science/data/raw/
List the files in the S3 bucket (in this case it will be just one file):
!aws s3 ls $raw_input_data_s3_uri
2021-04-30 02:21:06 8457214 womens_clothing_ecommerce_reviews.csv
1.2. Configure the SageMaker feature store
As the result of the transformation, in addition to generating files in S3 bucket, you will also save the transformed data in the Amazon SageMaker Feature Store to be used by others in your organization, for example.
To configure a Feature Store you need to setup a Feature Group. This is the main resource containing all of the metadata related to the data stored in the Feature Store. A Feature Group should contain a list of Feature Definitions. A Feature Definition consists of a name and the data type. The Feature Group also contains an online store configuration and an offline store configuration controlling where the data is stored. Enabling the online store allows quick access to the latest value for a record via the GetRecord API. The offline store allows storage of the data in your S3 bucket. You will be using the offline store in this lab.
Let's setup the Feature Group name and the Feature Store offline prefix in S3 bucket (you will use those later in the lab):
import time
timestamp = int(time.time())
feature_group_name = 'reviews-feature-group-' + str(timestamp)
feature_store_offline_prefix = 'reviews-feature-store-' + str(timestamp)
print('Feature group name: {}'.format(feature_group_name))
print('Feature store offline prefix in S3: {}'.format(feature_store_offline_prefix))
Feature group name: reviews-feature-group-1686463816
Feature store offline prefix in S3: reviews-feature-store-1686463816
Taking two features from the original raw dataset (Review Text and Rating), you will transform it preparing to be used for the model training and then to be saved in the Feature Store. Here you will define the related features to be stored as a list of FeatureDefinition.
from sagemaker.feature_store.feature_definition import (
FeatureDefinition,
FeatureTypeEnum,
)
feature_definitions= [
# unique ID of the review
FeatureDefinition(feature_name='review_id', feature_type=FeatureTypeEnum.STRING),
# ingestion timestamp
FeatureDefinition(feature_name='date', feature_type=FeatureTypeEnum.STRING),
# sentiment: -1 (negative), 0 (neutral) or 1 (positive). It will be found the Rating values (1, 2, 3, 4, 5)
FeatureDefinition(feature_name='sentiment', feature_type=FeatureTypeEnum.STRING),
# label ID of the target class (sentiment)
FeatureDefinition(feature_name='label_id', feature_type=FeatureTypeEnum.STRING),
# reviews encoded with the BERT tokenizer
FeatureDefinition(feature_name='input_ids', feature_type=FeatureTypeEnum.STRING),
# original Review Text
FeatureDefinition(feature_name='review_body', feature_type=FeatureTypeEnum.STRING),
# train/validation/test label
FeatureDefinition(feature_name='split_type', feature_type=FeatureTypeEnum.STRING)
]
Exercise 1
Create the feature group using the feature definitions defined above.
Instructions: Use the FeatureGroup function passing the defined above feature group name and the feature definitions.
feature_group = FeatureGroup(
name=..., # Feature Group name
feature_definitions=..., # a list of Feature Definitions
sagemaker_session=sess # SageMaker session
)
from sagemaker.feature_store.feature_group import FeatureGroup
feature_group = FeatureGroup(
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
name=feature_group_name, # Replace None
feature_definitions=feature_definitions, # Replace None
### END SOLUTION - DO NOT delete this comment for grading purposes
sagemaker_session=sess
)
print(feature_group)
FeatureGroup(name='reviews-feature-group-1686463816', sagemaker_session=<sagemaker.session.Session object at 0x7f0b3d12bd50>, feature_definitions=[FeatureDefinition(feature_name='review_id', feature_type=<FeatureTypeEnum.STRING: 'String'>), FeatureDefinition(feature_name='date', feature_type=<FeatureTypeEnum.STRING: 'String'>), FeatureDefinition(feature_name='sentiment', feature_type=<FeatureTypeEnum.STRING: 'String'>), FeatureDefinition(feature_name='label_id', feature_type=<FeatureTypeEnum.STRING: 'String'>), FeatureDefinition(feature_name='input_ids', feature_type=<FeatureTypeEnum.STRING: 'String'>), FeatureDefinition(feature_name='review_body', feature_type=<FeatureTypeEnum.STRING: 'String'>), FeatureDefinition(feature_name='split_type', feature_type=<FeatureTypeEnum.STRING: 'String'>)])
You will use the defined Feature Group later in this lab, the actual creation of the Feature Group will take place in the processing job. Now let's move into the setup of the processing job to transform the dataset.
2. Transform the dataset
You will configure a SageMaker processing job to run a custom Python script to balance and transform the raw data into a format used by BERT model.
Set the transformation parameters including the instance type, instance count, and train/validation/test split percentages. For the purposes of this lab, you will use a relatively small instance type. Please refer to this link for additional instance types that may work for your use case outside of this lab.
You can also choose whether you want to balance the dataset or not. In this case, you will balance the dataset to avoid class imbalance in the target variable, sentiment.
Another important parameter of the model is the max_seq_length, which specifies the maximum length of the classified reviews for the RoBERTa model. If the sentence is shorter than the maximum length parameter, it will be padded. In another case, when the sentence is longer, it will be truncated from the right side.
Since a smaller max_seq_length leads to faster training and lower resource utilization, you want to find the smallest power-of-2 that captures 100% of our reviews. For this dataset, the 100th percentile is 115. However, it's best to stick with powers-of-2 when using BERT. So let's choose 128 as this is the smallest power-of-2 greater than 115. You will see below how the shorter sentences will be padded to a maximum length.
mean 52.512374
std 31.387048
min 1.000000
10% 10.000000
20% 22.000000
30% 32.000000
40% 41.000000
50% 51.000000
60% 61.000000
70% 73.000000
80% 88.000000
90% 97.000000
100% 115.000000
max 115.000000
processing_instance_type='ml.c5.xlarge'
processing_instance_count=1
train_split_percentage=0.90
validation_split_percentage=0.05
test_split_percentage=0.05
balance_dataset=True
max_seq_length=128
To balance and transform our data, you will use a scikit-learn-based processing job. This is essentially a generic Python processing job with scikit-learn pre-installed. You can specify the version of scikit-learn you wish to use. Also pass the SageMaker execution role, processing instance type and instance count.
from sagemaker.sklearn.processing import SKLearnProcessor
processor = SKLearnProcessor(
framework_version='0.23-1',
role=role,
instance_type=processing_instance_type,
instance_count=processing_instance_count,
env={'AWS_DEFAULT_REGION': region},
max_runtime_in_seconds=7200
)
The processing job will be running the Python code from the file src/prepare_data.py. In the following exercise you will review the contents of the file and familiarize yourself with main parts of it.
Exercise 2
- Open the file src/prepare_data.py. Go through the comments to understand its content.
- Find and review the
convert_to_bert_input_ids()function, which contains the RoBERTatokenizerconfiguration. - Complete method
encode_plusof the RoBERTatokenizer. Pass themax_seq_lengthas a value for the argumentmax_length. It defines a pad to a maximum length specified. - Save the file src/prepare_data.py (with the menu command File -> Save Python File).
This cell will take approximately 1-2 minutes to run.
import sys, importlib
sys.path.append('src/')
# import the `prepare_data.py` module
import prepare_data
# reload the module if it has been previously loaded
if 'prepare_data' in sys.modules:
importlib.reload(prepare_data)
input_ids = prepare_data.convert_to_bert_input_ids("this product is great!", max_seq_length)
updated_correctly = False
if len(input_ids) != max_seq_length:
print('#######################################################################################################')
print('Please check that the function \'convert_to_bert_input_ids\' in the file src/prepare_data.py is complete.')
print('#######################################################################################################')
raise Exception('Please check that the function \'convert_to_bert_input_ids\' in the file src/prepare_data.py is complete.')
else:
print('##################')
print('Updated correctly!')
print('##################')
updated_correctly = True
HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898823.0, style=ProgressStyle(descripti…
HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…
##################
Updated correctly!
##################
Review the results of tokenization for the given example (\"this product is great!\"):
input_ids = prepare_data.convert_to_bert_input_ids("this product is great!", max_seq_length)
print(input_ids)
print('Length of the sequence: {}'.format(len(input_ids)))
[0, 9226, 1152, 16, 372, 328, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
Length of the sequence: 128
Launch the processing job with the custom script passing defined above parameters.
from sagemaker.processing import ProcessingInput, ProcessingOutput
if (updated_correctly):
processor.run(code='src/prepare_data.py',
inputs=[
ProcessingInput(source=raw_input_data_s3_uri,
destination='/opt/ml/processing/input/data/',
s3_data_distribution_type='ShardedByS3Key')
],
outputs=[
ProcessingOutput(output_name='sentiment-train',
source='/opt/ml/processing/output/sentiment/train',
s3_upload_mode='EndOfJob'),
ProcessingOutput(output_name='sentiment-validation',
source='/opt/ml/processing/output/sentiment/validation',
s3_upload_mode='EndOfJob'),
ProcessingOutput(output_name='sentiment-test',
source='/opt/ml/processing/output/sentiment/test',
s3_upload_mode='EndOfJob')
],
arguments=['--train-split-percentage', str(train_split_percentage),
'--validation-split-percentage', str(validation_split_percentage),
'--test-split-percentage', str(test_split_percentage),
'--balance-dataset', str(balance_dataset),
'--max-seq-length', str(max_seq_length),
'--feature-store-offline-prefix', str(feature_store_offline_prefix),
'--feature-group-name', str(feature_group_name)
],
logs=True,
wait=False)
else:
print('#######################################')
print('Please update the code correctly above.')
print('#######################################')
Job Name: sagemaker-scikit-learn-2023-06-11-06-19-29-466
Inputs: [{'InputName': 'input-1', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://dlai-practical-data-science/data/raw/', 'LocalPath': '/opt/ml/processing/input/data/', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'ShardedByS3Key', 'S3CompressionType': 'None'}}, {'InputName': 'code', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-047418094836/sagemaker-scikit-learn-2023-06-11-06-19-29-466/input/code/prepare_data.py', 'LocalPath': '/opt/ml/processing/input/code', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]
Outputs: [{'OutputName': 'sentiment-train', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-east-1-047418094836/sagemaker-scikit-learn-2023-06-11-06-19-29-466/output/sentiment-train', 'LocalPath': '/opt/ml/processing/output/sentiment/train', 'S3UploadMode': 'EndOfJob'}}, {'OutputName': 'sentiment-validation', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-east-1-047418094836/sagemaker-scikit-learn-2023-06-11-06-19-29-466/output/sentiment-validation', 'LocalPath': '/opt/ml/processing/output/sentiment/validation', 'S3UploadMode': 'EndOfJob'}}, {'OutputName': 'sentiment-test', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-east-1-047418094836/sagemaker-scikit-learn-2023-06-11-06-19-29-466/output/sentiment-test', 'LocalPath': '/opt/ml/processing/output/sentiment/test', 'S3UploadMode': 'EndOfJob'}}]
You can see the information about the processing jobs using the describe function. The result is in dictionary format. Let's pull the processing job name:
scikit_processing_job_name = processor.jobs[-1].describe()['ProcessingJobName']
print('Processing job name: {}'.format(scikit_processing_job_name))
Processing job name: sagemaker-scikit-learn-2023-06-11-06-19-29-466
Exercise 3
Pull the processing job status from the processing job description.
Instructions: Print the keys of the processing job description dictionary, choose the one related to the status of the processing job and print the value of it.
print(processor.jobs[-1].describe().keys())
dict_keys(['ProcessingInputs', 'ProcessingOutputConfig', 'ProcessingJobName', 'ProcessingResources', 'StoppingCondition', 'AppSpecification', 'Environment', 'RoleArn', 'ProcessingJobArn', 'ProcessingJobStatus', 'LastModifiedTime', 'CreationTime', 'ResponseMetadata'])
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
scikit_processing_job_status = processor.jobs[-1].describe()['ProcessingJobStatus'] # Replace None
### END SOLUTION - DO NOT delete this comment for grading purposes
print('Processing job status: {}'.format(scikit_processing_job_status))
Processing job status: InProgress
Review the created processing job in the AWS console.
Instructions:
- open the link
- notice that you are in the section Amazon SageMaker -> Processing jobs
- check the name of the processing job, its status and other available information
from IPython.core.display import display, HTML
display(HTML('<b>Review <a target="blank" href="https://console.aws.amazon.com/sagemaker/home?region={}#/processing-jobs/{}">processing job</a></b>'.format(region, scikit_processing_job_name)))
Review processing job
Wait for about 5 minutes to review the CloudWatch Logs. You may open the file src/prepare_data.py again and examine the outputs of the code in the CloudWatch logs.
from IPython.core.display import display, HTML
display(HTML('<b>Review <a target="blank" href="https://console.aws.amazon.com/cloudwatch/home?region={}#logStream:group=/aws/sagemaker/ProcessingJobs;prefix={};streamFilter=typeLogStreamPrefix">CloudWatch logs</a> after about 5 minutes</b>'.format(region, scikit_processing_job_name)))
Review CloudWatch logs after about 5 minutes
After the completion of the processing job you can also review the output in the S3 bucket.
from IPython.core.display import display, HTML
display(HTML('<b>Review <a target="blank" href="https://s3.console.aws.amazon.com/s3/buckets/{}/{}/?region={}&tab=overview">S3 output data</a> after the processing job has completed</b>'.format(bucket, scikit_processing_job_name, region)))
Review S3 output data after the processing job has completed
Wait for the processing job to complete.
This cell will take approximately 15 minutes to run.
%%time
running_processor = sagemaker.processing.ProcessingJob.from_processing_name(
processing_job_name=scikit_processing_job_name,
sagemaker_session=sess
)
running_processor.wait(logs=False)
................................................................................!CPU times: user 367 ms, sys: 20.1 ms, total: 387 ms
Wall time: 6min 45s
Please wait until ^^ Processing Job ^^ completes above
Inspect the transformed and balanced data in the S3 bucket.
processing_job_description = running_processor.describe()
output_config = processing_job_description['ProcessingOutputConfig']
for output in output_config['Outputs']:
if output['OutputName'] == 'sentiment-train':
processed_train_data_s3_uri = output['S3Output']['S3Uri']
if output['OutputName'] == 'sentiment-validation':
processed_validation_data_s3_uri = output['S3Output']['S3Uri']
if output['OutputName'] == 'sentiment-test':
processed_test_data_s3_uri = output['S3Output']['S3Uri']
print(processed_train_data_s3_uri)
print(processed_validation_data_s3_uri)
print(processed_test_data_s3_uri)
s3://sagemaker-us-east-1-047418094836/sagemaker-scikit-learn-2023-06-11-06-19-29-466/output/sentiment-train
s3://sagemaker-us-east-1-047418094836/sagemaker-scikit-learn-2023-06-11-06-19-29-466/output/sentiment-validation
s3://sagemaker-us-east-1-047418094836/sagemaker-scikit-learn-2023-06-11-06-19-29-466/output/sentiment-test
!aws s3 ls $processed_train_data_s3_uri/
2023-06-11 06:32:30 4898781 part-algo-1-womens_clothing_ecommerce_reviews.tsv
!aws s3 ls $processed_validation_data_s3_uri/
2023-06-11 06:32:30 270676 part-algo-1-womens_clothing_ecommerce_reviews.tsv
!aws s3 ls $processed_test_data_s3_uri/
2023-06-11 06:32:31 273383 part-algo-1-womens_clothing_ecommerce_reviews.tsv
Copy the data into the folder balanced.
!aws s3 cp $processed_train_data_s3_uri/part-algo-1-womens_clothing_ecommerce_reviews.tsv ./balanced/sentiment-train/
!aws s3 cp $processed_validation_data_s3_uri/part-algo-1-womens_clothing_ecommerce_reviews.tsv ./balanced/sentiment-validation/
!aws s3 cp $processed_test_data_s3_uri/part-algo-1-womens_clothing_ecommerce_reviews.tsv ./balanced/sentiment-test/
download: s3://sagemaker-us-east-1-047418094836/sagemaker-scikit-learn-2023-06-11-06-19-29-466/output/sentiment-train/part-algo-1-womens_clothing_ecommerce_reviews.tsv to balanced/sentiment-train/part-algo-1-womens_clothing_ecommerce_reviews.tsv
download: s3://sagemaker-us-east-1-047418094836/sagemaker-scikit-learn-2023-06-11-06-19-29-466/output/sentiment-validation/part-algo-1-womens_clothing_ecommerce_reviews.tsv to balanced/sentiment-validation/part-algo-1-womens_clothing_ecommerce_reviews.tsv
download: s3://sagemaker-us-east-1-047418094836/sagemaker-scikit-learn-2023-06-11-06-19-29-466/output/sentiment-test/part-algo-1-womens_clothing_ecommerce_reviews.tsv to balanced/sentiment-test/part-algo-1-womens_clothing_ecommerce_reviews.tsv
Review the training, validation and test data outputs:
!head -n 5 ./balanced/sentiment-train/part-algo-1-womens_clothing_ecommerce_reviews.tsv
review_id sentiment label_id input_ids review_body date
12096 1 2 [0, 45065, 16, 372, 1437, 10698, 182, 157, 23, 3844, 1437, 939, 524, 195, 108, 306, 16157, 17243, 2162, 1836, 290, 642, 8, 24, 18, 205, 734, 939, 74, 2703, 3568, 8872, 25, 24, 11541, 8, 6476, 15, 5, 1929, 4, 51, 32, 55, 1136, 73, 36741, 73, 31421, 9304, 25, 1468, 1341, 7992, 4, 1374, 1437, 939, 524, 1372, 19, 106, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] Quality is great fits very well at waste i am 5'4 135lb bought size 8p and it's good... i would require wear heels as it reaches and sits on the floor. they are more fall/spring/winter pants as material quite thick. overall i am happy with them 2023-06-11T06:26:11Z
10118 -1 0 [0, 713, 3588, 34, 1256, 8089, 4, 5, 299, 2564, 157, 53, 5, 2576, 1415, 101, 41, 7237, 159, 16676, 4, 24, 34, 615, 1468, 7, 146, 132, 31296, 8, 5, 380, 12189, 15, 5, 2380, 146, 5, 16576, 4757, 66, 29445, 4, 5, 1345, 9, 1421, 16, 31, 526, 1437, 45, 5, 760, 4, 182, 29747, 24203, 15, 162, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] This dress has pretty colors. the top fit well but the bottom looked like an upside down umbrella. it has enough material to make 2 skirts and the big pockets on the sides make the skirt stick out sideways. the photo of model is from side not the front. very unflattering on me. 2023-06-11T06:26:11Z
7852 -1 0 [0, 347, 4467, 11, 6680, 1437, 939, 657, 5, 4520, 8089, 1437, 5, 3016, 909, 19008, 579, 1671, 8, 5, 1510, 18, 16464, 1437, 959, 1437, 5, 10199, 16, 6162, 1437, 8, 5, 2373, 233, 11, 127, 1217, 16, 14, 5, 39336, 14, 422, 552, 5, 760, 1312, 9, 5, 6399, 109, 45, 12432, 5, 6184, 4, 2085, 42, 21, 18797, 1437, 53, 19, 5, 6399, 939, 829, 1437, 24, 1415, 11385, 4, 939, 1051, 24, 124, 4, 939, 3120, 11, 5, 2170, 14, 5, 909, 3405, 32, 19055, 2325, 81, 5, 1312, 760, 27595, 1437, 98, 24, 16, 10, 828, 34575, 9404, 4, 939, 1979, 75, 907, 42, 299, 23, 143, 425, 716, 15, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] Cute in theory i love the bright colors the tied black collar sash and the 70's vibe however the fabric is cheap and the worst part in my view is that the seams that run along the front center of the shirt do not align the pattern. maybe this was intentional but with the shirt i received it looked horrible. i sent it back. i notice in the picture that the black ties are strategically placed over the center front seam so it is a bit deceiving. i wouldn't buy this top at any price based on 2023-06-11T06:26:11Z
8068 1 2 [0, 19065, 8089, 1437, 2496, 8, 2564, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] Great colors style and fit 2023-06-11T06:26:11Z
!head -n 5 ./balanced/sentiment-validation/part-algo-1-womens_clothing_ecommerce_reviews.tsv
review_id sentiment label_id input_ids review_body date
12126 1 2 [0, 713, 16, 804, 5451, 98, 939, 2740, 258, 10070, 3023, 29, 8, 579, 4, 24, 1237, 1256, 739, 98, 939, 1682, 1836, 3023, 29, 4, 5, 1468, 64, 28, 13596, 4, 53, 939, 101, 5, 2157, 9, 24, 8, 182, 3473, 7, 110, 3024, 4, 939, 524, 98, 1372, 2806, 98, 171, 33391, 11263, 939, 213, 16506, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] This is online exclusive so i ordered both sizes xs and s. it runs pretty large so i kept size xs. the material can be stretched. but i like the feeling of it and very comfortable to your skin. i am so happy receiving so many compliments wherever i go!!! 2023-06-11T06:26:11Z
2430 0 1 [0, 100, 2162, 155, 9, 209, 13657, 11, 430, 8089, 1437, 5, 2440, 65, 56, 65, 24150, 28840, 10941, 4, 1051, 24, 124, 4, 122, 540, 87, 158, 360, 423, 51, 32, 15, 1392, 4, 5, 2496, 16, 30213, 8, 120, 171, 14156, 2963, 15, 5, 80, 939, 33, 4, 856, 28831, 939, 437, 10, 2491, 417, 8325, 29882, 8, 5679, 2564, 372, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] I bought 3 of these tops in different colors the blue one had one sleeve noticeably shorter. sent it back. now less than 10 days later they are on sale. the style is darling and get many complements on the two i have. fyi i'm a 36d 130lbs and med fit great 2023-06-11T06:26:11Z
4395 -1 0 [0, 100, 794, 42, 15, 10, 313, 858, 18017, 11, 5, 1400, 8, 98, 770, 24, 7, 173, 53, 21, 95, 350, 24901, 8, 35156, 4, 182, 205, 1318, 8, 1468, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] I saw this on a mannequin in the store and so wanted it to work but was just too oversized and bulky. very good quality and material. 2023-06-11T06:26:11Z
2386 1 2 [0, 597, 20554, 833, 53, 45, 350, 856, 28102, 13454, 16, 10, 2579, 37919, 12, 3341, 1468, 4, 939, 300, 5, 1104, 65, 61, 40, 213, 19, 10, 319, 9, 127, 31296, 1437, 9304, 8, 13344, 4, 7328, 97, 34910, 1437, 939, 399, 75, 465, 24, 350, 765, 13, 162, 23, 70, 1437, 53, 939, 524, 239, 12, 2739, 15346, 4, 9881, 53, 19, 30109, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] Feminine but not too fussy tee is a nice linen-like material. i got the white one which will go with a lot of my skirts pants and shorts. unlike other reviewers i didn't find it too short for me at all but i am high-waisted. casual but with extras. 2023-06-11T06:26:11Z
!head -n 5 ./balanced/sentiment-test/part-algo-1-womens_clothing_ecommerce_reviews.tsv
review_id sentiment label_id input_ids review_body date
14693 -1 0 [0, 100, 269, 770, 7, 657, 42, 6399, 328, 5, 8089, 32, 12058, 1437, 182, 11577, 8, 95, 25, 2343, 4, 5, 2564, 21, 70, 1593, 15, 162, 4, 939, 2740, 5, 4716, 1459, 37863, 29, 8, 24, 21, 202, 350, 2233, 219, 4, 8, 1437, 24, 1682, 9255, 98, 5, 760, 19008, 9, 5, 6399, 74, 3068, 62, 88, 127, 5397, 4, 24, 1415, 101, 10, 14072, 15, 162, 4, 13, 5135, 12, 939, 524, 195, 108, 176, 113, 1437, 12730, 29882, 734, 5632, 10, 650, 5120, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] "I really wanted to love this shirt! the colors are gorgeous very vibrant and just as shown. the fit was all wrong on me. i ordered the petite xxs and it was still too boxy. and it kept shifting so the front collar of the shirt would ride up into my neck. it looked like a sack on me. for reference- i am 5'2"" 112lbs...with a small frame." 2023-06-11T06:26:11Z
1209 1 2 [0, 100, 2638, 5, 3195, 8, 5, 2496, 9, 5, 21764, 4, 1437, 24, 21, 10, 828, 350, 765, 15, 162, 8, 939, 802, 24, 156, 162, 356, 2233, 219, 98, 939, 399, 75, 253, 62, 2396, 24, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] I loved the color and the style of the sleeves. it was a bit too short on me and i thought it made me look boxy so i didn't end up keeping it 2023-06-11T06:26:11Z
17209 -1 0 [0, 133, 2564, 9, 5, 299, 16, 1528, 7, 1836, 1437, 53, 939, 269, 218, 75, 101, 5, 326, 1180, 2379, 8470, 1506, 41262, 12, 3341, 22861, 28517, 223, 5, 1104, 22990, 4, 5, 1104, 22990, 16, 4520, 1104, 8, 1302, 182, 28685, 1437, 53, 24, 817, 5, 22861, 28517, 12213, 356, 182, 11216, 8, 182, 28840, 326, 1180, 2379, 4, 24, 16, 6587, 11153, 561, 4, 939, 67, 399, 75, 101, 5, 619, 9, 5, 8470, 1506, 41262, 12, 3341, 10199, 136, 127, 3024, 4, 190, 600, 42, 16, 10, 205, 1318, 2125, 19, 10, 319, 9, 2679, 4617, 1437, 939, 437, 3668, 3981, 24, 124, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] The fit of the top is true to size but i really don't like the taupe terrycloth-like sweatshirt under the white lace. the white lace is bright white and seems very sturdy but it makes the sweatshirt underneath look very dirty and very noticeably taupe. it is terrible paired together. i also didn't like the feel of the terrycloth-like fabric against my skin. even though this is a good quality piece with a lot of interesting detail i'm absolutely sending it back. 2023-06-11T06:26:11Z
14582 -1 0 [0, 100, 269, 770, 7, 657, 42, 1437, 53, 24, 21, 95, 350, 13103, 18140, 1827, 7, 28, 34203, 4, 939, 524, 3700, 10, 1836, 158, 50, 316, 11, 14442, 8, 13855, 27734, 1437, 8, 5, 4761, 21, 1087, 6932, 198, 162, 4, 67, 5, 33827, 13977, 478, 162, 614, 8, 15713, 3804, 11, 41, 29747, 24203, 169, 4, 24, 56, 7, 213, 124, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] I really wanted to love this but it was just too voluminous to be flattering. i am typically a size 10 or 12 in dresses and jumpsuits and the medium was billowing around me. also the elastic waist hit me low and bunched in an unflattering way. it had to go back. 2023-06-11T06:26:11Z
3. Query the Feature Store
In addition to transforming the data and saving in S3 bucket, the processing job populates the feature store with the transformed and balanced data. Let's query this data using Amazon Athena.
3.1. Export training, validation, and test datasets from the Feature Store
Here you will do the export only for the training dataset, as an example.
Use athena_query() function to create an Athena query for the defined above Feature Group. Then you can pull the table name of the Amazon Glue Data Catalog table which is auto-generated by Feature Store.
feature_store_query = feature_group.athena_query()
feature_store_table = feature_store_query.table_name
query_string = """
SELECT date,
review_id,
sentiment,
label_id,
input_ids,
review_body
FROM "{}"
WHERE split_type='train'
LIMIT 5
""".format(feature_store_table)
print('Glue Catalog table name: {}'.format(feature_store_table))
print('Running query: {}'.format(query_string))
Glue Catalog table name: reviews_feature_group_1686463816_1686464741
Running query:
SELECT date,
review_id,
sentiment,
label_id,
input_ids,
review_body
FROM "reviews_feature_group_1686463816_1686464741"
WHERE split_type='train'
LIMIT 5
Configure the S3 location for the query results. This allows us to re-use the query results for future queries if the data has not changed. We can even share this S3 location between team members to improve query performance for common queries on data that does not change often.
output_s3_uri = 's3://{}/query_results/{}/'.format(bucket, feature_store_offline_prefix)
print(output_s3_uri)
s3://sagemaker-us-east-1-047418094836/query_results/reviews-feature-store-1686463816/
Exercise 4
Query the feature store.
Instructions: Use feature_store_query.run function passing the constructed above query string and the location of the output S3 bucket.
feature_store_query.run(
query_string=..., # query string
output_location=... # location of the output S3 bucket
)
feature_store_query.run(
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
query_string=query_string, # Replace None
output_location=output_s3_uri # Replace None
### END SOLUTION - DO NOT delete this comment for grading purposes
)
feature_store_query.wait()
import pandas as pd
pd.set_option("max_colwidth", 100)
df_feature_store = feature_store_query.as_dataframe()
df_feature_store
| date | review_id | sentiment | label_id | input_ids | review_body | |
|---|---|---|---|---|---|---|
| 0 | 2023-06-11T06:26:11Z | 18569 | 1 | 2 | [0, 16587, 1437, 657, 42, 299, 4, 10199, 16, 2579, 8, 3793, 1437, 1326, 372, 19, 2084, 6149, 103... | Love love this top. fabric is nice and soft looks great with leggings. love the way it falls i... |
| 1 | 2023-06-11T06:26:11Z | 11103 | 0 | 1 | [0, 713, 16, 10, 7992, 23204, 4, 24, 16, 1256, 1437, 53, 34, 10, 765, 5933, 4, 2, 1, 1, 1, 1, 1,... | This is a thick sweater. it is pretty but has a short length. |
| 2 | 2023-06-11T06:26:11Z | 1038 | -1 | 0 | [0, 20328, 282, 75, 173, 15, 42, 5350, 11454, 22101, 4, 1437, 39328, 62, 74, 33, 1147, 19, 5, 61... | Didn't work on this curvy gal. sizing up would have helped with the button gaps but the flowy ... |
| 3 | 2023-06-11T06:26:11Z | 7946 | 0 | 1 | [0, 28965, 1437, 10, 269, 2579, 299, 14, 16, 3793, 8, 3473, 4, 5, 3124, 5933, 2323, 15481, 15, 5... | Overall a really nice top that is soft and comfortable. the arm length hits nicely on the forea... |
| 4 | 2023-06-11T06:26:11Z | 3267 | -1 | 0 | [0, 100, 269, 6640, 42, 6399, 5, 65, 86, 939, 300, 7, 3568, 24, 734, 4297, 77, 939, 15158, 24, 1... | I really liked this shirt the one time i got to wear it...but when i washed it according to the... |
Review the Feature Store in SageMaker Studio
3.2. Export TSV from Feature Store
Save the output as a TSV file:
df_feature_store.to_csv('./feature_store_export.tsv',
sep='\t',
index=False,
header=True)
!head -n 5 ./feature_store_export.tsv
date review_id sentiment label_id input_ids review_body
2023-06-11T06:26:11Z 18569 1 2 [0, 16587, 1437, 657, 42, 299, 4, 10199, 16, 2579, 8, 3793, 1437, 1326, 372, 19, 2084, 6149, 1033, 4, 657, 5, 169, 24, 5712, 11, 5, 760, 41, 251, 615, 11, 5, 124, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] Love love this top. fabric is nice and soft looks great with leggings. love the way it falls in the front an long enough in the back.
2023-06-11T06:26:11Z 11103 0 1 [0, 713, 16, 10, 7992, 23204, 4, 24, 16, 1256, 1437, 53, 34, 10, 765, 5933, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] This is a thick sweater. it is pretty but has a short length.
2023-06-11T06:26:11Z 1038 -1 0 [0, 20328, 282, 75, 173, 15, 42, 5350, 11454, 22101, 4, 1437, 39328, 62, 74, 33, 1147, 19, 5, 6148, 10778, 1437, 53, 5, 3041, 219, 3989, 9, 5, 3089, 11556, 74, 33, 1415, 350, 21592, 15, 10, 2514, 1836, 4, 1437, 538, 1323, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] Didn't work on this curvy gal. sizing up would have helped with the button gaps but the flowy shape of the blouse would have looked too maternity on a larger size. major pass.
2023-06-11T06:26:11Z 7946 0 1 [0, 28965, 1437, 10, 269, 2579, 299, 14, 16, 3793, 8, 3473, 4, 5, 3124, 5933, 2323, 15481, 15, 5, 30357, 8, 5, 29815, 4617, 23, 299, 16, 157, 12, 3865, 28354, 4, 399, 75, 489, 42, 187, 5, 299, 156, 162, 356, 1341, 380, 11, 127, 11044, 1437, 7, 5, 477, 9, 45, 145, 34203, 4, 299, 16, 67, 10, 828, 2233, 219, 1437, 98, 939, 1017, 1095, 409, 114, 47, 32, 4716, 1459, 1437, 19, 10, 2514, 11044, 3867, 47, 32, 8578, 19, 14, 9723, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] Overall a really nice top that is soft and comfortable. the arm length hits nicely on the forearm and the decorative detail at top is well-constructed. didn't keep this since the top made me look quite big in my bust to the point of not being flattering. top is also a bit boxy so i'd stay away if you are petite with a larger bust unless you are okay with that emphasis.
Upload TSV to the S3 bucket:
!aws s3 cp ./feature_store_export.tsv s3://$bucket/feature_store/feature_store_export.tsv
upload: ./feature_store_export.tsv to s3://sagemaker-us-east-1-047418094836/feature_store/feature_store_export.tsv
Check the file in the S3 bucket:
!aws s3 ls --recursive s3://$bucket/feature_store/feature_store_export.tsv
2023-06-11 06:37:31 3622 feature_store/feature_store_export.tsv
3.3. Check that the dataset in the Feature Store is balanced by sentiment
Now you can setup an Athena query to check that the stored dataset is balanced by the target class sentiment.
Exercise 5
Write an SQL query to count the total number of the reviews per sentiment stored in the Feature Group.
Instructions: Pass the SQL statement of the form
SELECT category_column, COUNT(*) AS new_column_name
FROM table_name
GROUP BY category_column
into the variable query_string_count_by_sentiment. Here you would need to use the column sentiment and give a name count_reviews to the new column with the counts.
feature_store_query_2 = feature_group.athena_query()
# Replace all None
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
query_string_count_by_sentiment = """
SELECT sentiment, COUNT(*) AS count_reviews
FROM "{}"
GROUP BY sentiment
""".format(feature_store_table)
### END SOLUTION - DO NOT delete this comment for grading purposes
Exercise 6
Query the feature store.
Instructions: Use run function of the Feature Store query, passing the new query string query_string_count_by_sentiment. The output S3 bucket will remain unchanged. You can follow the example above.
feature_store_query_2.run(
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
query_string=query_string_count_by_sentiment, # Replace None
output_location=output_s3_uri # Replace None
### END SOLUTION - DO NOT delete this comment for grading purposes
)
feature_store_query_2.wait()
df_count_by_sentiment = feature_store_query_2.as_dataframe()
df_count_by_sentiment
| sentiment | count_reviews | |
|---|---|---|
| 0 | -1 | 2051 |
| 1 | 0 | 2051 |
| 2 | 1 | 2051 |
Exercise 7
Visualize the result of the query in the bar plot, showing the count of the reviews by sentiment value.
Instructions: Pass the resulting data frame df_count_by_sentiment into the barplot function of the seaborn library.
sns.barplot(
data=...,
x='...',
y='...',
color="blue"
)
%matplotlib inline
import seaborn as sns
sns.barplot(
### BEGIN SOLUTION - DO NOT delete this comment for grading purposes
data=df_count_by_sentiment, # Replace None
x='sentiment', # Replace None
y='count_reviews', # Replace None
### END SOLUTION - DO NOT delete this comment for grading purposes
color="blue"
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f0ad1ecddd0>
Upload the notebook and prepare_data.py file into S3 bucket for grading purposes.
Note: you may need to save the file before the upload.
!aws s3 cp ./C2_W1_Assignment.ipynb s3://$bucket/C2_W1_Assignment_Learner.ipynb
!aws s3 cp ./src/prepare_data.py s3://$bucket/src/C2_W1_prepare_data_Learner.py
Please go to the main lab window and click on Submit button (see the Finish the lab section of the instructions).
Last update:
November 24, 2024
Created: November 24, 2024
Created: November 24, 2024