1/ Overview @AkaveCloud has released a guide on using an S3 gateway and Filecoin storage on Akave Cloud to process the Hugging Face dataset. Workflow: Select a dataset from Hugging Face, upload it to an Akave bucket, and then read it back via the same S3 path for inference. 2/ Requirements This setup relies on Python 3.9+, the Hugging Face dataset, s3fs, boto3, and Akave O3 credentials. Akaave writes the data to @Filecoin, so the dataset moves to a persistent storage layer. Any operation supported by load_dataset or load_from_disk will work for this workflow. 3/ Helper Scripts Akave includes several small Python tools for verifying S3 access permissions, listing buckets, and running quick transfer checks. These tools simplify setup and confirm your connection is working correctly. All of these tools can be found in the Hugging Face integration section of the documentation. 4/ The Role of Filecoin Akave's S3 gateway is built on Filecoin, thus providing a persistent and tamper-proof storage foundation for datasets. The team can continue using existing Hugging Face and S3 tools, while Filecoin handles data integrity and long-term stability in the background. Read the full article: