data

Data

• Sharing data with the outside world

Filesystem nuances

WORK has only 5M inodes, so we can't pre-process large datasets there. e.g. openwebtext is 8M files.

So need to pre-process it on SCRATCH and then copy over just the arrow files to WORK preserving the full pass as datasets expects it.

SCRATCH is also much faster as it's SSD, WORK is not!

After finishing pre-processing could tarball the raw files and put them to STORE for long term storage.

Remember, anything on SCRATCH gets wiped out after 30 days of not being accessed - or immediately at midnight of the same day if the file happens to have its access timestamp set in the past!

To clear out the empty dirs (ghosts of the once full dirs) run:

find /gpfswork/rech/six/commun/datasets/downloads/extracted -empty -type d -delete
find /gpfswork/rech/six/commun/datasets/ -empty -type d -delete

Anatomy of datasets dataset:

Each stage is written in subdirectories of HF_DATASETS_CACHE so you can definitely rm any of these stages:

1. compressed source files are in HF_DATASETS_CACHE / “downloads”
2. uncompressed source files are in HF_DATASETS_CACHE / “downloads” / “extracted”
3. temporary build files are removed as soon as the dataset has been built (or if it failed) so in theory you shouldn’t have to do anything. But anyway they are at HF_DATASETS_CACHE / <dataset_name> / <config_name> / <version> / <script_hash> + “.incomplete”
4. cached arrow files are in HF_DATASETS_CACHE / <dataset_name> / <config_name> / <version> / <script_hash>

• So (1) can go to STORE
• 2 and 3 deleted
• 4 moved to WORK, preserving HF_DATASETS_CACHE / <dataset_name> / <config_name> / <version> / <script_hash>

Of course, this will also require fiddling with HF_DATASETS_CACHE for the duration of this process to point to WORK.

Code snippets

To get the full path expected by the cache checker:

from datasets.load import prepare_module, import_main_class
dataset_name = "openwebtext"
module_path, module_hash = prepare_module(dataset_name)
builder_cls = import_main_class(module_path)
builder = builder_cls(hash=module_hash)
print(builder.cache_dir)
#/Users/quentinlhoest/.cache/huggingface/datasets/openwebtext/plain_text/1.0.0/85b3ae7051d2d72e7c5fdf6dfb462603aaa26e9ed506202bf3a24d261c6c40a1

And ideally we want this:

from datasets import load_dataset_builder
dataset_name = "openwebtext"
dataset_builder = load_dataset_builder(dataset_name)
print(dataset_builder.cache_dir)

this feature was added in huggingface/datasets#2500