pipeline

To automate checkpoint syncing across ML clusters, this pipeline detects training completion, syncs checkpoints to the evaluation cluster, runs evaluations on L40S hardware, and parses the resulting metrics automatically.

An end-to-end post-training pipeline automates ML workflows by detecting when training finishes, migrating checkpoints to a destination cluster, executing evaluations, and aggregating the results into an experiment store.

Yes, evaluations can be run on L40S hardware after syncing checkpoints from a source cluster. The pipeline submits evaluation jobs to the destination cluster and parses the output metrics for aggregation.

You can start the automated post-training workflow by running the pipeline with the source cluster, job ID, evaluation task name, and destination cluster, relying on sbatch outputs and cluster availability assumptions.

Yes, the pipeline requires access to an experiment store to update and optionally share evaluation results to external hubs, ensuring collaboration and traceability for your ML experiments.

Multicluster ML evaluations fail without checkpoint syncing because the pipeline relies on the find_checkpoint_path and sync_checkpoint tooling to migrate model states to the destination cluster before running evaluations.