Empowering Transformational Science
- 1. Empowering Transformational Science Chelle Gentemann (Farallon Institute) twitter: @ChelleGentemann Ryan Abernathey (Columbia / LDEO) twitter: @rabernat Aimee Barciauskas (Development Seed) twitter: @_aimeeb (there are lots of links in this presentation! click away!) SWOT NISAR NASA Physical Oceanography Program
- 3. Communities build open science. Open science is more efficient. Efficient science leads to transformational results.
- 4. Data: time to find, access, clean, & format data for analysis Software: what tools are easily available? Compute: access to compute == speed of results What impacts the velocity of science? Data, Software, & Compute 80% Data Preparation (download, clean, & organize files) 10% Batch Processing 10% Think about science
- 5. Traditional methods of data access cannot leverage large volumes of data
- 7. Analytics Optimized Data Store (AODS) a few examples of AODS formats Current method - NetCDF files - organized into ‘reasonable’ data sizes per file, usually by orbit, granule, or day. Filename has information about date, sensor, version. Reading usually involved calculating the filename, opening, reading, processing, closing. Analytics Optimized Data Store (one example of many different formats) Zarr - makes large datasets easily accessible to distributed computing. Original data is stored in directories each having chunked data corresponding to dataset dimensions. Metadata is read by zarr libraries to read only the chunks necessary to complete a subsetting request. Technology advances - Lazy loading - also known as asynchronous loading - defer initialization of an object until the point at which it is needed. Developed for webpages. Delays reading data until needed for compute. Advanced OSS libraries: Xarray - library for analyzing multi-dimensional arrays, lazy loading. Dask - able to break a large computational problems into a network of smaller problems for distribution across multiple processors Intake - lightweight set of tools for loading and sharing data in data science projects
- 8. NetCDF Zarr What does a data store look like? Organized so that each file can fit into RAM, usually by day, orbit, or granules organization and format invisible to user, data accessed by metadata
- 9. Time to access data? https://nbviewer.jupyter.org/github/cgentemann/Biophysical/blob/master/Test_AVISO_zarr_simple_version.ipynb Modern software tools use lazy loading to access large datasets Accessing netCDF data: 11 minutes (depends on computer) 1 - user creates list of filenames 2 - access dataset by reading the metadata distributed through files Accessing Zarr data: 0.1 seconds (metadata consolidated) 1 - access dataset by reading the consolidated metadata Calculate mean over region NetCDF - 12 minutes Zarr - 4 seconds My version of lazy loading before I knew python - on bedrest, pregnant with twins STOP ------------- THIS IS DIFFERENT ------------------ 1 line of code to access a 28-year, global, 25km dataset 1 line of code to select a region, calculate mean, & plot time series in LESS than 1 minute
- 10. Data, Software, Compute Inspiration: Stephan Hoyer, Jake Vanderplas (SciPy 2015) SciPy
- 11. Data, Software, Compute Analytics Optimized Data Store (AODS) Data Provider’s $ Data Consumer’s $ Scalable Parallel Computing Frameworks
- 14. 14 Pangeo Architecture Jupyter for interactive data analysis on remote systemsCloud / HPC Xarray provides data structures and intuitive interface for interacting with datasetsParallel computing system allows users deploy clusters of compute nodes for data processing. Dask tells the nodes what to do. Distributedstorage “Analytics Optimized Data Stores” stored on globally- available distributed storage. @pangeo_data
- 15. How can data providers reduce barriers? Reimagine how cloud data access and tools can enable transformational science Publish cloud- optimized data Interactive tutorials Contribute to OSS tools Increase user interactions/feedback
- 16. How does minimizing barriers to data change science? Levels the playing field for all who want to contribute
- 17. Traditional Project Timeline Impacts: Reduce Time to Science 80% Data Preparation (download, clean, & organize files) 10% Batch Processing 10% Think about science Cloud-based Project Timeline 5% Load AODS 5% Parallel Processing 90% Think about science
- 18. Traditional Project Code Impacts: Reproducibility Cloud-based Project Code # step 1: open data (stored on local hard drive) >>> data = open_data(“/path/to/private/files”) Error: files not found # step 1: open data (globally accessible) >>> data = open_data(“http://catalog.pangeo.io/path/to/dataset”) # step 2: process data >>> process(data) Reproducibility in data-driven science requires more than just code!
- 19. Thank you! Open source science What impacts the velocity of progress? Data, Software, & Compute STOP ------------- THIS IS DIFFERENT ------------------ 1 line of code to access a 28-year, global, 25km dataset 1 line of code to select a region, calculate mean, & plot time series in LESS than 1 minute