Inference: from a loaded model to a useful service
You want to generate responses, extract representations or process a corpus. Start by defining the input format, its size and the request frequency. A trial with a short input does not describe a service that must handle several long requests in parallel.
The inference path helps you prepare a request set, an availability check and a reading of the results. You can then compare the GPU and settings without simultaneously changing the workload being measured. The expected outputs are a request profile, a launch procedure and a set of measurements kept with their configuration.
Adaptation: organizing an experiment you can resume
Adapting a model means linking training data, the method and the evaluation criteria. Before a long run, verify one computation step, the writing of a checkpoint and its reload. You will have a concrete basis for choosing the plan duration and the save frequency.
The adaptation path proposes an organization by trial: hypothesis, settings, outputs and next decision. Set aside an evaluation set for comparison, then keep the results even when the trial does not deliver the expected improvement. Understanding a negative result can prevent repeating the same work.
Multiple batches for clearly separated jobs
When the experiments are independent, give each one its own configuration, inputs and results destination. Then define which task uses which card. This arrangement suits, for example, a parameter comparison or the processing of corpus partitions.
For a single distributed model, prepare instead the distribution mechanism and the exchanges required by the program. The number of GPUs is then a component of the software architecture. The hardware sheets and the environments guide help you bring this decision closer to your order.