semvec/pypi-packages huggingface dataset:
flamingo-ray 0.1.2
Ray-centric job library for training and evaluation.
License: Apache-2.0
Requires Python: >=3.10,<3.11

# flamingo

<p align="center">
    <img src="https://github.com/mozilla-ai/flamingo/blob/main/assets/flamingo.png" width="300">
</p>

## Getting started

### Minimum Python version

This library is developed with the same Python version as the Ray cluster
to avoid dependency/syntax errors when executing code remotely.
Currently, installation requires Python between `[3.10, 3.11)` to match the global
cluster environment (Ray cluster is running 3.10.8).

### Installation

run 

```
pip install flamingo-ray
```

This will install an editable version of the package along with all of its dependency groups.

Poetry should recognize your active virtual environment during installation
If you have an active Conda environment, Poetry should recognize it during installation
and install the package dependencies there. 
This hasn't been explicitly tested with other virtual python environments, but will likely work.

Alternatively, you can use poetry's own environment by running
```
poetry lock
poetry env use python3.10
poetry install
```
where `python3.10` is your python interpreter.

See the [contributing](CONTRIBUTING.md) guide for more information on development workflows and/or building locally.

### Usage

`flamingo` exposes a simple CLI with a few commands, one for each Ray job type.
Jobs are expected to take as input a YAML configuration file 
that contains all necessary parameters/settings for the work.
See the `examples/configs` folder for examples of the configuration structure.

Once installed in your environment, usage is as follows:
```
# Simple test
flamingo run simple --config simple_config.yaml

# LLM finetuning
flamingo run finetuning --config finetuning_config.yaml

# LLM evaluation
flamingo run lm-harness --config lm_harness_config.yaml
```
When submitting a job to Ray, the above commands should be used as your job entrypoints.