how to link s3 bucket to sagemaker notebook

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I am trying to link my s3 bucket to a notebook instance, however i am not able to:

Here is how much I know:

from sagemaker import get_execution_role

role = get_execution_role
bucket = 'atwinebankloadrisk'
datalocation = 'atwinebankloadrisk'

data_location = 's3://{}/'.format(bucket)
output_location = 's3://{}/'.format(bucket)

to call the data from the bucket:

df_test = pd.read_csv(data_location/'application_test.csv')
df_train = pd.read_csv('./application_train.csv')
df_bureau = pd.read_csv('./bureau_balance.csv')

However I keep getting errors and unable to proceed. I haven't found answers that can assist much.

PS: I am new to this AWS

You can load S3 Data into AWS SageMaker Notebook by using the sample code below. Do make sure the Amazon SageMaker role has policy attached to it to have access to S3.

[1] https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html

import boto3 
import botocore 
import pandas as pd 
from sagemaker import get_execution_role 

role = get_execution_role() 

bucket = 'Your_bucket_name' 
data_key = your_data_file.csv' 
data_location = 's3://{}/{}'.format(bucket, data_key) 

pd.read_csv(data_location) 

Specify a Bucket and Data Output Location, The name of the bucket must contain sagemaker , and be globally unique. The bucket must be in the same AWS Region as the notebook instance that you use  If you already have S3 buckets, you can use them, or you can create new ones. To create a bucket, follow the instructions in Create a Bucket in the Amazon Simple Storage Service Console User Guide. Include sagemaker in the bucket name. For example, sagemaker-datetime .

You're trying to use Pandas to read files from S3 - Pandas can read files from your local disk, but not directly from S3. Instead, download the files from S3 to your local disk, then use Pandas to read them.

import boto3
import botocore

BUCKET_NAME = 'my-bucket' # replace with your bucket name
KEY = 'my_image_in_s3.jpg' # replace with your object key

s3 = boto3.resource('s3')

try:
    # download as local file
    s3.Bucket(BUCKET_NAME).download_file(KEY, 'my_local_image.jpg')

    # OR read directly to memory as bytes:
    # bytes = s3.Object(BUCKET_NAME, KEY).get()['Body'].read() 
except botocore.exceptions.ClientError as e:
    if e.response['Error']['Code'] == "404":
        print("The object does not exist.")
    else:
        raise

Load S3 Data into AWS SageMaker Notebook, I've just started to experiment with AWS SageMaker and would like to load data from an S3 bucket into a pandas dataframe in my SageMaker  The name of the bucket must contain sagemaker , and be globally unique. The bucket must be in the same AWS Region as the notebook instance that you use for this example. You can use the bucket that you created when you set up Amazon SageMaker, or you can create a new bucket.

You can use the https://s3fs.readthedocs.io/en/latest/ to read s3 files directly with pandas. The code below is taken from here

import os
import pandas as pd
from s3fs.core import S3FileSystem

os.environ['AWS_CONFIG_FILE'] = 'aws_config.ini'

s3 = S3FileSystem(anon=False)
key = 'path\to\your-csv.csv'
bucket = 'your-bucket-name'

df = pd.read_csv(s3.open('{}/{}'.format(bucket, key), mode='rb'))

Setup of an AWS S3 Bucket and SageMaker Notebook Instance, AZs in the same region connect between them through low-latency links so there is fast and easy communication between them. Almost all AWS  After successfully uploading CSV files from S3 to SageMaker notebook instance, I am stuck on doing the reverse. I have a dataframe and want to upload that to S3 Bucket as CSV or JSON. The code that I have is below: bucket='bucketname' data_key = 'test.csv' data_location = 's3://{}/{}'.format(bucket, data_key) df.to_csv(data_location)

import boto3

# files are referred as objects in S3.  
# file name is referred as key name in S3

def write_to_s3(filename, bucket_name, key):
    with open(filename,'rb') as f: # Read in binary mode
        return boto3.Session().resource('s3').Bucket(bucket).Object(key).upload_fileobj(f)

# Simple call the write_to_s3 function with required arguement  

write_to_s3('file_name.csv', 
            bucket_name,
            'file_name.csv')

Is it possible to mount an S3 bucket onto a Sagemaker notebook , How do you mount an S3 bucket onto a Sagemaker notebook? Mounting would make it easier to download any large datasets (~1TB) directly  You’ll start by creating an Amazon S3 bucket that will be used throughout the workshop. You’ll then create a SageMaker notebook instance, which you will use for the other workshop modules. Create a S3 Bucket. SageMaker typically uses S3 as storage for data and model artifacts. In this step you’ll create a S3 bucket for this purpose.

AWS SageMaker Machine Learning Data handling, As soon as you are working with or migrating to AWS SageMaker, you will be The notebook instance might need much less of storage and compute power than the training instance. A very simple and easy way to copy data from your S3 bucket to your Valid URL schemes include http, ftp, s3, and file. Copy the following Python code and paste it into the first cell in your notebook. Add the name of the S3 bucket that you created in Set Up Amazon SageMaker, and run the code. The get_execution_role function retrieves the IAM role you created when you created your notebook instance.

Training data in S3 in AWS Sagemaker, I've uploaded my own Jupyter notebook to Sagemaker, and am trying to ImageRecordIter( path_imgrec = 's3://bucket-name/train.rec' …… ). But as Prateek stated make sure to configure your SageMaker notebook instance. to have access to s3. Do make sure the Amazon SageMaker role has policy attached to it to have access to S3. It can be done in IAM. @Hack-R The pro is that you are able to use the python file pointer interface/object throughout the code.

How To Pull Data into S3 using AWS Sagemaker, Restricting access to AWS SageMaker S3 Buckets. notebook and not the AWS console, you can create a presigned notebook instance URL  Download and Explore the Training Dataset. To download and explore the dataset, run the following code in your notebook: Prepare and Upload Data. Before creating the hyperparameter tuning job, prepare the data and upload it to an S3 bucket where the hyperparameter tuning job can access it.

Comments
  • You can pass s3 locations to your training jobs. I never saw that you can do this with a notebook instance. If you want the s3 data inside your notebook, than just download it via boto3 s3 client.
  • I want to read the s3 bucket in a sagemaker notebook instance without having to download the hard disk. Can I get help with that?
  • @AtwineMugume bytes = s3.Object(bucket, key).get()['Body'].read()