Inference Endpoints provides a secure solution to easily deploy models hosted on the Hub in a production-ready infrastructure managed by Huggingface. With huggingface_hub>=0.19.0 integration, you can now manage your Inference Endpoints programmatically. Combined with the InferenceClient, this becomes the go-to solution to deploy models and run jobs in production, either sequentially or in batch!
Here is an example how to get an inference endpoint, wake it up, wait for initialization, run jobs in batch and pause back the endpoint. All of this in a few lines of code! For more details, please check out our dedicated guide.
>>> import asyncio
>>> from huggingface_hub import get_inference_endpoint
huggingface_hub is a library primarily used to transfer (huge!) files with the Huggingface Hub. Our goal is to keep improving the experience for this core part of the library. In this release, we introduce a more robust download mechanism for slow/limited connection while improving the UX for users with a high bandwidth available!
More robust downloads
Getting a connection error in the middle of a download is frustrating. That's why we've implemented a retry mechanism that automatically reconnects if a connection get closed or a ReadTimeout error is raised. The download restart exactly where it stopped without having to redownload any bytes.
Retry on ConnectionError/ReadTimeout when streaming file from server by @​Wauplin in #1766
Reset nb_retries if data has been received from the server by @​Wauplin in #1784
In addition to this, it is possible to configure huggingface_hub with higher timeouts thanks to @​Shahafgo. This should help getting around some issues on slower connections.
Adding the ability to configure the timeout of get request by @​Shahafgo in #1720
Fix a bug to respect the HF_HUB_ETAG_TIMEOUT. by @​Shahafgo in #1728
Progress bars while using hf_transfer
hf_transfer is a Rust-based library focused on improving upload and download speed on machines with a high bandwidth available. Once installed (pip install -U hf_transfer), it can transparently be used with huggingface_hub simply by setting HF_HUB_ENABLE_HF_TRANSFER=1 as environment variable. The counterpart of higher performances is the lack of some user-friendly features such as better error handling or a retry mechanism -meaning it is recommended only to power-users-. In this release we still ship a new feature to improve UX: progress bars. No need to update any existing code, a simple library upgrade is enough.
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Updates the requirements on huggingface-hub to permit the latest version.
Release notes
Sourced from huggingface-hub's releases.
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Commits
64c6dc9
Release: v0.19.0a89d2b6
Release: v0.19.0.rc0aa4d3fa
FixHfFileSystemFile
when init fails + improve error message (#1805)17be1c5
Add support for progress bars in hf_transfer uploads (#1804)d306c90
hf-transfer
progress bar (#1792)4c93d89
change prop name (#1803)6004abd
Add CLI guide to documentation (#1797)50306da
Do not promoteHF_ENDPOINT
environment variable (#1799)7f1fa6a
Fix SpaceInfo initialization + add test (#1802)9b56c3f
Update SpaceHardware enum (#1798)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
.Dependabot commands and options
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