ARVICA / DOCUMENTATION
Training and fine-tuning
Plan a reproducible training run and recovery path.
Estimate the working set
Describe model size, precision, optimizer, sequence length and batch size. Include dataset staging space and checkpoint storage. Benchmark a small run before committing to a long schedule.
Distributed jobs
For multi-GPU or multi-node work, confirm GPU interconnect, host networking and the framework strategy. The sum of GPU memory is not automatically available to a single process.
Checkpoint and recover
Save checkpoints to storage with confirmed retention. Test resuming from a checkpoint, record the dataset and code versions, and agree the behaviour for maintenance or interruptions.