zarr-parallel 0.1.0

Package for efficiently parallelising zarr write operations based on awareness of source chunks

Zarr Parallel Cacher

This package has been developed as part of the FRAME-FM AI project. It has been separated into its own module for ease of reusability across multiple projects. AI-specific steps may form part of the package, but may also be disabled by default.

Basic Usage

from zarr_parallel.assembler import ZarrParallelAssembler

zp = ZarrParallelAssembler(data_uri=uri, preprocessors=preprocessors,
            chunks=chunks,
            engine='kerchunk',
            variables={'d2m':{}}, 
            cache_label='_v1')

zp.cache(
    cache_dir='/gws/ssde/j25b/eds_ai/frame-fm/data/zarr_cache',
    deploy_mode='dask_distributed',
    simultaneous_worker_limit=4)

The above code snippet demonstrates the use of this package. The data_uri and engine parameters refer to the xarray open_dataset method for accessing the source object. chunks are required to specify the output chunking in the zarr cache, which is also required for organising the parallel jobs. variables is optional to add, and includes the ability to run transforms on specific data arrays (such as renaming) which are applied individually.

The preprocessors list defines the set of preprocessing transforms to apply to the dataset (including selection) at the point of caching. This should include all transforms that should be applied to the dataset before writing to the zarr cache.

The num_jobs and simultaneous_worker_limit parameters are used to configure for parallel deployment. If no num_jobs is provided, the assembler will calculate the optimal number of jobs for your memory limit (recommended).

Transforms/Preprocessors

Transformations to the data may be specified via the selector option passed in the above example. Xarray-native transformations are supported, as well as transforms from the FRAME-FM package if installed.

Selection Recommendations

The assembler will halt to recommend alternative data selections based on the underlying chunk structure. Proceeding without recommendations is not advised, as mismatched chunk-region borders may involve duplicating chunk requests and significantly increasing memory requirements per worker.