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    _ Source component: Fine_Tuning.ray_fine_tuning_job


fine_tuning ray_fine_tuning_job
===============================

Run a simple Ray fine-tuning Job.




Parameters
----------


``name``  

* The name of the fine-tuning job to create


``namespace``  

* The name of the namespace where the scheduler load will be generated


``pvc_name``  

* The name of the PVC where the model and dataset are stored


``model_name``  

* The name of the model to use inside the /dataset directory of the PVC


``workload``  

* The name of the workload job to run (see the role's workload directory)

* default value: ``ray-finetune-llm-deepspeed``


``dataset_name``  

* The name of the dataset to use inside the /model directory of the PVC


``dataset_replication``  

* Number of replications of the dataset to use, to artificially extend or reduce the fine-tuning effort

* default value: ``1``


``dataset_transform``  

* Name of the transformation to apply to the dataset


``dataset_prefer_cache``  

* If True, and the dataset has to be transformed/duplicated, save and/or load it from the PVC

* default value: ``True``


``dataset_prepare_cache_only``  

* If True, only prepare the dataset cache file and do not run the fine-tuning.


``container_image``  

* The image to use for the fine-tuning container

* default value: ``quay.io/rhoai/ray:2.35.0-py39-cu121-torch24-fa26``


``ray_version``  

* The version identifier passed to the RayCluster object

* default value: ``2.35.0``


``gpu``  

* The number of GPUs to request for the fine-tuning job


``memory``  

* The number of RAM gigs to request for to the fine-tuning job (in Gigs)

* default value: ``10``


``cpu``  

* The number of CPU cores to request for the fine-tuning job (in cores)

* default value: ``1``


``request_equals_limits``  

* If True, sets the 'limits' of the job with the same value as the request.


``prepare_only``  

* If True, only prepare the environment but do not run the fine-tuning job.


``delete_other``  

* If True, delete the other PyTorchJobs before running


``pod_count``  

* Number of Pods to include in the job

* default value: ``1``


``hyper_parameters``  

* Dictionnary of hyper-parameters to pass to sft-trainer


``sleep_forever``  

* If true, sleeps forever instead of running the fine-tuning command.


``capture_artifacts``  

* If enabled, captures the artifacts that will help post-mortem analyses

* default value: ``True``


``shutdown_cluster``  

* If True, let the RayJob shutdown the RayCluster when the job terminates