Bacalhau, a powerful and versatile data processing platform, has recently integrated Amazon Web Services (AWS) S3, allowing users to seamlessly access and process data stored in S3 buckets within their Bacalhau jobs. This integration not only simplifies data input, output, and processing operations but also streamlines the overall workflow by enabling users to store and manage their data effectively in S3 buckets. With Bacalhau, you can process several Large s3 buckets in parallel. In this example, we will walk you through the process of reading data from multiple S3 buckets, converting TIFF images to JPEG format.
There are several advantages to converting images from TIFF to JPEG format:
Reduced File Size: JPEG images use lossy compression, which significantly reduces file size compared to lossless formats like TIFF. Smaller file sizes lead to faster upload and download times, as well as reduced storage requirements.
Efficient Processing: With smaller file sizes, image processing tasks tend to be more efficient and require less computational resources when working with JPEG images compared to TIFF images.
Training Machine Learning Models: Smaller file sizes and reduced computational requirements make JPEG images more suitable for training machine learning models, particularly when dealing with large datasets, as they can help speed up the training process and reduce the need for extensive computational resources.
We will use the S3 mount feature to mount bucket objects from s3 buckets. Let’s have a look at the example below:
-i src=s3://sentinel-s1-rtc-indigo/tiles/RTC/1/IW/10/S/DH/2017/S1A_20170125_10SDH_ASC/Gamma0_VH.tif,dst=/sentinel-s1-rtc-indigo/,opt=region=us-west-2 It defines S3 object as input to the job:
sentinel-s1-rtc-indigo: bucket’s name
tiles/RTC/1/IW/10/S/DH/2017/S1A_20170125_10SDH_ASC/Gamma0_VH.tif: represents the key of the object in that bucket. The object to be processed is called Gamma0_VH.tif and is located in the subdirectory with the specified path.
But if you want to specify the entire objects located in the path, you can simply add * to the end of the path (tiles/RTC/1/IW/10/S/DH/2017/S1A_20170125_10SDH_ASC/*)
dst=/sentinel-s1-rtc-indigo: the destination to which to mount the s3 bucket object
opt=region=us-west-2 : specifying the region in which the bucket is located
To get started, you need to install the Bacalhau client, see more information here
In the example below, we will mount several bucket objects from public s3 buckets located in a specific region:
The job has been submitted and Bacalhau has printed out the related job_id. We store that in an environment variable so that we can reuse it later on:
Job status: You can check the status of the job using bacalhau list.
When it says Published or Completed, that means the job is done, and we can get the results.
Job information: You can find out more information about your job by using bacalhau describe.
Job download: You can download your job results directly by using bacalhau get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory (results) and downloaded our job output to be stored in that directory.
To view the images, we will use glob to return all file paths that match a specific pattern.
The code processes and displays all images in the specified directory by applying cropping and resizing with a specified reduction factor.
If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).
