How to Upload a Zip File With Read Access Permission on S3 Python
How to Read Data Files on S3 from Amazon SageMaker
Keeping your information science workflow in the deject
Amazon SageMaker is a powerful, cloud-hosted Jupyter Notebook service offered by Amazon Spider web Services (AWS). It's used to create, train, and deploy car learning models, only information technology's too great for doing exploratory data assay and prototyping.
While information technology may not exist quite as beginner-friendly as some alternatives, such equally Google CoLab or Kaggle Kernels, there are some proficient reasons why y'all may want to be doing data scientific discipline work inside Amazon SageMaker.
Let'southward hash out a few.
Private data hosted in S3
Machine learning models chiliad ust be trained on data. If yous're working with private data, and so special care must be taken when accessing this information for model training. Downloading the entire data fix to your laptop may exist confronting your company'south policy or may exist simply imprudent. Imagine having your laptop lost or stolen, knowing that it contains sensitive data. Equally a side notation, this another reason why yous should employ e'er disk encryption.
The information being hosted in the cloud may also be too large to fit on your personal computer'southward disk, and then it's easier but to keep it hosted in the cloud and accessed straight.
Compute resource
Working in the deject ways yous tin access powerful compute instances. AWS or your preferred deject services provider will unremarkably let you select and configure your compute instances. Peradventure you need high CPU or high memory — more than what you take available on your personal car. Or maybe you lot need to railroad train your models on GPUs. Cloud providers take a host of different instance types on offer.
Model deployment
How to deploy ML models directly from SageMaker is a topic for another article, but AWS gives yous this pick. You lot won't need to build a circuitous deployment compages. SageMaker will spin off a managed compute instance hosting a Dockerized version of your trained ML model backside an API for performing inference tasks.
Loading data into a SageMaker notebook
Now let's move on to the master topic of this article. I will bear witness yous how to load data saved as files in an S3 saucepan using Python. The example data are pickled Python dictionaries that I'd similar to load into my SageMaker notebook.
The procedure for loading other data types (such as CSV or JSON) would be like, but may crave additional libraries.
Step 1: Know where y'all continue your files
Y'all will demand to know the name of the S3 bucket. Files are indicated in S3 buckets every bit "keys", but semantically I discover it easier just to think in terms of files and folders.
Let'due south define the location of our files:
bucket = 'my-bucket'
subfolder = '' Step two: Go permission to read from S3 buckets
SageMaker and S3 are divide services offered by AWS, and for 1 service to perform actions on another service requires that the advisable permissions are set. Thankfully, it'southward expected that SageMaker users will be reading files from S3, then the standard permissions are fine.
Withal, you'll need to import the necessary execution role, which isn't hard.
from sagemaker import get_execution_role
office = get_execution_role() Stride iii: Apply boto3 to create a connection
The boto3 Python library is designed to aid users perform actions on AWS programmatically. It will facilitate the connection between the SageMaker notebook at the S3 bucket.
The code below lists all of the files contained within a specific subfolder on an S3 bucket. This is useful for checking what files exist.
You may conform this code to create a listing object in Python if you will be iterating over many files.
Step iv: Load pickled data directly from the S3 bucket
The pickle library in Python is useful for saving Python data structures to a file then that you can load them later.
In the example below, I desire to load a Python dictionary and assign it to the information variable.
This requires using boto3 to become the specific file object (the pickle) on S3 that I want to load. Notice how in the example the boto3 client returns a response that contains a information stream. We must read the data stream with the pickle library into the data object.
This behavior is a fleck different compared to how you lot would use pickle to load a local file.
Since this is something I e'er forget how to practise right, I've compiled the steps into this tutorial so that others might benefit.
Culling: Download a file
There are times you may desire to download a file from S3 programmatically. Perhaps you desire to download files to your local machine or to storage attached to your SageMaker instance.
To do this, the code is a bit dissimilar:
Conclusion
I accept focussed on Amazon SageMaker in this article, but if you have the boto3 SDK set up correctly on your local machine, you can too read or download files from S3 there. Since much of my own information science work is washed via SageMaker, where you need to remember to set the correct access permissions, I wanted to provide a resource for others (and my future self).
Obviously SageMaker is not the only game in town. There are a variety of unlike cloud-hosted data science notebook environments on offer today, a huge leap forward from five years agone (2015) when I was completing my Ph.D.
One consideration that I did not mention is cost: SageMaker is not costless, just is billed by usage. Recollect to shut downward your notebook instances when you lot're finished.
Source: https://towardsdatascience.com/how-to-read-data-files-on-s3-from-amazon-sagemaker-f288850bfe8f
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