This is a simple example that shows how to do OCR with Ray Datasets. It includes:
- How to upload and download the dataset to and from S3
- How to run OCR on the dataset with tesseract
- How to use actors to keep around and re-use a spaCy context for doing NLP on the data
Co-authored-by: Clark Zinzow <clarkzinzow@gmail.com>
- new section of doc for autoscaling (introduction of serve autoscaling and config parameter)
- Remove the version requirement note inside the doc
Co-authored-by: Simon Mo <simon.mo@hey.com>
Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
Co-authored-by: shrekris-anyscale <92341594+shrekris-anyscale@users.noreply.github.com>
Co-authored-by: Archit Kulkarni <architkulkarni@users.noreply.github.com>
Follow up on our last discussion for supporting piecemeal fashion air users.
Only did for tensorflow for now, want to collect some feedback on API naming, package structure etc and I will add others.
This PR adds a FAQ to Datasets docs.
Docs preview: https://ray--24932.org.readthedocs.build/en/24932/
## Checks
- [x] I've run `scripts/format.sh` to lint the changes in this PR.
- [x] I've included any doc changes needed for https://docs.ray.io/en/master/.
- [x] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
- Testing Strategy
- [x] Unit tests
- [ ] Release tests
- [ ] This PR is not tested :(
Co-authored-by: Eric Liang <ekhliang@gmail.com>
This PR adds a dedicated docs page for examples, and adds a basic e2e tabular data processing example on the NYC taxi dataset.
The goal of this example is to demonstrate basic data reading, inspection, transformations, and shuffling, along with ingestion into dummy model trainers and doing dummy batch inference, for tabular (Parquet) data.
This example simply doesn't run as is. We can bring it back up again later, if it makes sense. But it's not clear what the variables used there, like actor are. Fixes#21328
Signed-off-by: Max Pumperla <max.pumperla@googlemail.com>
This is a notebook showing how to tune an xgboost model and analyze the results.
Also adds a `get_dataframe()` method to `ResultsGrid` to fetch the trial results.
Depends on #24483 for toctree.
Ray SGD v1 has been denoted as a deprecated API for a while. This PR fully deprecates Ray SGD v1. An error will be raised if ray.util.sgd package is attempted to be imported.
Closes#16435
Adding a FAQ page. Currently has some basic questions that have come up in the past.
Explaining how to use Matplotlib due to threading in the distributed training function.
