Comments (3)
fyi, added to docs: https://docarray.jina.ai/get-started/what-is/#to-huggingface-datasets
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Thanks for your attention.
There are a couple of features that we will release soon to allow big data loading with constant memory consumption.
But in general, I want to clarify that DocArray is designed to be a data type in transit, which means its IO interfaces and designs are focused on thread-to-thread, process-to-process, microservice-to-microservice network communication. This is as opposed to alternatives, e.g. HF datasets, which focus on data at rest: inactive data subject to very occasional changes (e.g. you rarely change the content in the training data).
The data in transit part will become much clear when Jina 3.0 is out (expect in Feb.)
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@hanxiao Thank you for your replies, I will keep my eyes on it..
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Related Issues (20)
- Add Field Description and example to Predefined Documents HOT 9
- Release Notes v0.37.0
- feat: delete index HOT 2
- Crash when using non-class type field in Document with QdrantDocumentIndex HOT 1
- filter_docs fail to apply `and` when both parts of query refer to same field HOT 2
- chore: draft release note v0.37.1
- refactor: count number of documents using hnswlib HOT 11
- _pickle.PicklingError encountered when attempting to serialize `BaseDoc` type HOT 4
- Document torch.compile workaround
- Inconsistent `to_json()` return type HOT 1
- Request for support for MongoDB Atlas Vector Search as DocIndex HOT 5
- chore: bump to latest tensorflow version HOT 6
- Release Note HOT 5
- Bug with DocList[] HOT 7
- [pydantic v2] csv and pandas reader DocList with Nested Documents is not working HOT 3
- [pydantic v2] Override config is not working
- [pydantic v2] nested json parsing is not working HOT 2
- [pydantic 2] create pydantic model from schema HOT 1
- [pydantic v2] Validation with default value is not working
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