How to Download an Upload Folders in Sagemaker
How to Read Data Files on S3 from Amazon SageMaker
Keeping your information science workflow in the cloud
Amazon SageMaker is a powerful, cloud-hosted Jupyter Notebook service offered by Amazon Web Services (AWS). It'southward used to create, train, and deploy machine learning models, but information technology's also great for doing exploratory data analysis and prototyping.
While it may not be quite equally beginner-friendly equally some alternatives, such every bit Google CoLab or Kaggle Kernels, there are some good reasons why you may want to be doing data scientific discipline work inside Amazon SageMaker.
Let'southward discuss a few.
Private data hosted in S3
Machine learning models must exist trained on information. If you're working with private data, so special intendance must exist taken when accessing this information for model training. Downloading the unabridged data set to your laptop may exist confronting your visitor's policy or may be simply imprudent. Imagine having your laptop lost or stolen, knowing that information technology contains sensitive data. As a side annotation, this another reason why you should utilize e'er deejay encryption.
The data being hosted in the cloud may also be likewise large to fit on your personal estimator's disk, so information technology'due south easier just to keep it hosted in the cloud and accessed directly.
Compute resources
Working in the cloud means yous can access powerful compute instances. AWS or your preferred cloud services provider will usually allow you lot select and configure your compute instances. Perhaps you need loftier CPU or high retentiveness — more than what you accept available on your personal car. Or perhaps you lot need to train your models on GPUs. Deject providers accept a host of dissimilar instance types on offer.
Model deployment
How to deploy ML models direct from SageMaker is a topic for another article, but AWS gives you this selection. Y'all won't need to build a complex deployment compages. SageMaker will spin off a managed compute instance hosting a Dockerized version of your trained ML model behind an API for performing inference tasks.
Loading data into a SageMaker notebook
Now permit'due south motion on to the main topic of this article. I will show you how to load data saved equally files in an S3 bucket using Python. The instance data are pickled Python dictionaries that I'd like to load into my SageMaker notebook.
The procedure for loading other information types (such as CSV or JSON) would be like, but may crave additional libraries.
Step 1: Know where you lot keep your files
You will need to know the name of the S3 bucket. Files are indicated in S3 buckets as "keys", but semantically I find it easier only to recollect in terms of files and folders.
Let's ascertain the location of our files:
bucket = 'my-bucket'
subfolder = '' Stride two: Go permission to read from S3 buckets
SageMaker and S3 are separate services offered by AWS, and for i service to perform actions on another service requires that the appropriate permissions are set. Thankfully, it'south expected that SageMaker users will be reading files from S3, so the standard permissions are fine.
Still, you'll need to import the necessary execution role, which isn't difficult.
from sagemaker import get_execution_role
role = get_execution_role() Step 3: Use boto3 to create a connection
The boto3 Python library is designed to help users perform actions on AWS programmatically. Information technology will facilitate the connexion 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 be.
You may adapt this lawmaking to create a listing object in Python if you will exist iterating over many files.
Footstep 4: Load pickled data directly from the S3 saucepan
The pickle library in Python is useful for saving Python data structures to a file so that you tin can load them later on.
In the example below, I want to load a Python dictionary and assign it to the data variable.
This requires using boto3 to get the specific file object (the pickle) on S3 that I desire to load. Find how in the example the boto3 client returns a response that contains a information stream. Nosotros must read the information stream with the pickle library into the data object.
This beliefs is a chip different compared to how you would use pickle to load a local file.
Since this is something I e'er forget how to do right, I've compiled the steps into this tutorial and so that others might benefit.
Alternative: Download a file
In that location are times you may want to download a file from S3 programmatically. Perhaps you want to download files to your local machine or to storage fastened to your SageMaker instance.
To do this, the code is a bit dissimilar:
Conclusion
I have focussed on Amazon SageMaker in this article, merely if you have the boto3 SDK fix correctly on your local machine, you can also read or download files from S3 there. Since much of my own data scientific discipline work is washed via SageMaker, where you lot need to remember to set the correct access permissions, I wanted to provide a resources for others (and my futurity cocky).
Obviously SageMaker is not the merely game in town. In that location are a diversity of different cloud-hosted data scientific discipline notebook environments on offering today, a huge leap forward from v years ago (2015) when I was completing my Ph.D.
One consideration that I did non mention is cost: SageMaker is non gratis, but is billed by usage. Remember to shut down your notebook instances when y'all're finished.
Source: https://towardsdatascience.com/how-to-read-data-files-on-s3-from-amazon-sagemaker-f288850bfe8f
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