- Download metadata files from Huggingface Dataset
# install git lfs curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash sudo apt-get install git-lfs git lfs install # download coyo-700m git clone https://huggingface.co/datasets/kakaobrain/coyo-700m
- img2dataset can easily download and store to webdataset or tfrecord format for large-scale distributed training.
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Dataproc is a fully managed and highly scalable service for running Apache Spark and 30+ open source tools. With dataproc, you can easily configure multi-node clusters and process large amounts of data quickly.
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Copy
dataproc-initialiation.shto your gs bucket. It contains commands to initialize the environment when each node is launched. (e.g,pip install img2dataset)gsutil cp dataproc-initialization.sh gs://${YOUR_GS_BUCKET}/dataproc/dataproc-initialization.sh -
Download metadata and upload it to Google Cloud Storage
for i in {00000..00127}; do wget https://huggingface.co/datasets/kakaobrain/coyo-700m/resolve/main/data/part-$i-17da4908-939c-46e5-91d0-15f256041956-c000.snappy.parquet -O - | gsutil cp - gs://${YOUR_GS_BUCKET}/dataset/coyo-700m/parquet/part-$i-17da4908-939c-46e5-91d0-15f256041956-c000.snappy.parquet; done -
Create Dataproc Cluster
gcloud dataproc clusters create coyo-700m \ --project=${YOUR_PROJECT_NAME} \ --region=${YOUR_REGION} \ --zone=${YOUR_ZONE} \ --master-machine-type=n2-standard-16 \ --num-workers=2 \ --worker-machine-type=n2-standard-16 \ --num-secondary-workers=8 \ --secondary-worker-boot-disk-size=100 \ --image-version=2.0-ubuntu18 \ --scopes='https://www.googleapis.com/auth/cloud-platform' \ --properties='yarn:yarn.nodemanager.user-home-dir=/var/lib/hadoop-yarn' \ --initialization-actions=gs://${YOUR_GS_BUCKET}/dataproc/dataproc-initialization.sh- This command creates 10 nodes for the dataproc cluster
- 1 master machine
- 2 primary workers
- 8 secondary workers (preemptible/spot instances)
- This command creates 10 nodes for the dataproc cluster
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Run/Submit PySpark Job
gcloud dataproc jobs submit pyspark --cluster=coyo-700m dataproc-img2dataset.py -- \ --url_list=gcs://${YOUR_GS_BUCKET}/dataset/coyo-700m/parquet \ --input_format="parquet" \ --url_col="url" \ --caption_col="text" \ --output_format=webdataset \ --output_folder=gcs://${YOUR_GS_BUCKET}/dataset/coyo-700m/webdataset \ --distributor="pyspark" \ --processes_count=1 \ --thread_count=64 \ --image_size=384 \ --retries=1 \ --min_image_size=200 \ --max_aspect_ratio=3 \ --resize_only_if_bigger=True \ --resize_mode="keep_ratio" \ --skip_reencode=True \ --save_additional_columns='["clip_similarity_vitb32","clip_similarity_vitl14","nsfw_score_opennsfw2","nsfw_score_gantman","watermark_score","aesthetic_score_laion_v2"]' \ --enable_wandb=False
- For a detailed description of the arguments, see img2dataset#API.
- Please see img2dataset/distributed_img2dataset_tutorial for more details.
- COYO-700M was made in 2021, unfortunately, so many images may be gone now.
- As of August 2022, only about 85% of all images are available for download.