Use a Python training script
Upload your own Python script when you need complete control over the training code and runtime arguments.
GPU training
Run GPU training jobs from the OneInfer console.
Upload a Python training script or configure a job through guided fields. Choose the GPU resources, submit the workload, and track its lifecycle without leaving the console.
Configure
Script or guided parameters
Use your own training code or fill in model, dataset, and training settings.
Allocate
Job-specific GPU resources
Set the GPU type and count, disk capacity, spot usage, and provider preference.
Operate
Visible job lifecycle
Review job status and resources, refresh results, or cancel active work.
What the platform supports
The controls needed to submit and manage training work.
The training page is connected to the same GPU, storage, and job-management workflows already available in OneInfer.
Configure model and dataset inputs
Use the guided form to provide model and dataset paths, training parameters, and optional LoRA settings.
Choose the compute for each job
Select the GPU model and quantity, disk size, spot preference, and preferred cloud capacity for the workload.
Track the job lifecycle
See submitted jobs, refresh their latest status, review assigned resources, and cancel queued or running work.
Keep data and artifacts available
Use OneInfer storage for persistent datasets, checkpoints, and model artifacts that need to survive beyond a job.
Request clustered capacity
For workloads that need coordinated GPU capacity, submit a cluster request from the GPU marketplace workflow.
Training workflow
From configuration to a running batch job.
Each job keeps its training configuration and compute selection together, so the console can show what was requested and what is running.
Choose how to configure the job
Start with an uploaded Python script or use the guided parameter form.
Add training inputs
Provide the model, dataset, hyperparameters, and optional LoRA configuration.
Select GPU resources
Choose the GPU type, count, storage, spot preference, and cloud preference.
Submit and monitor
Follow the job status from the console and cancel queued or running work when needed.
Connected workflows
Keep training data and deployment work in one platform.
Use persistent storage for datasets and model artifacts. When a supported custom model is ready, continue through the dedicated endpoint workflow to prepare it for inference.
Submit your first GPU training job.
Open the Training console to configure resources, submit a batch job, and monitor its status.