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Feat(doc): Reorganize documentation, fix broken syntax, update notes (#2348)

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* feat(doc): organize docs, add to menu bar, fix broken formatting

* feat: add link to custom integrations

* feat: update readme for integrations to include citations and repo link

* chore: update lm_eval info

* chore: use fullname

* Update docs/cli.qmd per suggestion

Co-authored-by: Dan Saunders <danjsaund@gmail.com>

* feat: add sweep doc

* feat: add kd doc

* fix: remove toc

* fix: update deprecation

* feat: add more info about chat_template issues

* fix: heading level

* fix: shell->bash code block

* fix: ray link

* fix(doc): heading level, header links, formatting

* feat: add grpo docs

* feat: add style changes

* fix: wrong cli arg for lm-eval

* fix: remove old run method

* feat: load custom integration doc dynamically

* fix: remove old cli way

* fix: toc

* fix: minor formatting

---------

Co-authored-by: Dan Saunders <danjsaund@gmail.com>
This commit is contained in:
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2025-02-25 16:09:37 +07:00
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6
docs/ray-integration.qmd
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@@ -1,5 +1,5 @@
---
title: Ray Train integration
title: Ray Train
description: How to use Axolotl with Ray Train
---
@@ -9,7 +9,7 @@ With the `--use-ray` CLI flag, Axolotl will use Ray Train's [`TorchTrainer`](htt
## Ray cluster setup
A prerequisite using the Ray Train integration is to setup a Ray cluster on your desired node(s). For a detailed guide on how you can get started with ray clusters, check the official Ray docs here: https://docs.ray.io/en/latest/cluster/getting-started.html
A prerequisite using the Ray Train integration is to setup a Ray cluster on your desired node(s). For a detailed guide on how you can get started with ray clusters, check the official Ray docs [here](https://docs.ray.io/en/latest/cluster/getting-started.html).
Every Ray cluster has one _head_ node and a set of worker nodes. The head node is just like any other worker node, but it also runs certain special processes related to scheduling and orchestration. Ray-enabled scripts are run on the head node and depending on the resources (number of CPUs, GPUs, etc) they request, will be scheduled to run certain tasks on the worker nodes. For more on key concepts behind a Ray cluster, you can refer this [doc](https://docs.ray.io/en/latest/cluster/key-concepts.html#cluster-key-concepts).
@@ -58,13 +58,11 @@ You can find an example configuration at `configs/llama-3/lora-1b-ray.yaml`.
The key parameters to note here are:
```yaml
...
use_ray: true
ray_num_workers: 4
# optional
resources_per_worker:
GPU: 1
...
```
- `use_ray`: This is the flag that enables the Ray Train integration. You can either use the corresponding `--use-ray` flag in the CLI or set `use_ray` in the config file.