AI DATA DELIVERY
The Data AI Needs,
Where AI Runs.
Deliver the datasets and models that training and inference need to your GPU servers and clusters, and collect the checkpoints produced along the way and the final results back into storage.
Wherever the data is kept — cloud, data centre or on-premises — and wherever AI runs, the files it needs move reliably between them. Your AI infrastructure and training environment stay as they are, while everything from data preparation to result collection is connected on one platform.
- DatasetTraining data2.4 TB
- ModelPretrained model48 GB
- Auxiliary DataConfig · Tokenizer12 GB
Training Job68%
- Epoch
- 68 / 100
- GPU
- 8 GPUs
- CheckpointTraining state320 GB
- ResultGenerated output120 GB
- LogsTraining logs8 GB
AI WORKFLOW
From the First Epoch to the Result,
At Every Stage of AI Work.
AI training starts with getting the dataset and the model into the GPU environment, and carries on through storing the checkpoints it produces and collecting the final result.
INNORIX moves the files each stage needs between storage and the GPU environment, and automates the data movement that keeps recurring. Preparing the files for training, keeping the intermediate results and delivering the final ones are all connected, so file transfer works the same way across the whole AI workflow.
Training run #4821resnet-vision · gpu-cluster-01 · started 09:52
DatasetTraining data ready
training-dataset.tar2.4 TB · 12,480 files
ModelModel delivered
foundation-model48 GB · 12 files
AI TrainingRunning
Epoch 64 / 10064%
GPU Cluster8 GPUs
CheckpointTraining state stored
checkpoint-064320 GB · 64 files
ResultResult collected
inference-result120 GB · 3,420 files
Checkpoints repeat, so a long run is never lost
DATA DELIVERY
Wherever the Storage Is,
Wherever the GPU Runs.
Whether the dataset and the model sit in cloud object storage, in a data centre or in private storage, they can be delivered to the GPU environment the work runs in.
Data moves between different clouds, regions and on-premises environments while the storage and GPU infrastructure stay as they are. The route and the target are set to match the environment — a cloud GPU, a data centre GPU cluster, or your own.
Cloud to GPUObject storage in one region, delivered to a GPU cluster in another.
SOURCEObject StorageUS West · S3
68%
1.63 TB of 2.4 TB transferred
TARGETCloud GPUAsia · NVIDIA H100
DATA READINESS
Terabyte Datasets, Millions of Files,
All Ready for AI.
The data AI training and inference need ranges from single files of several terabytes to millions of small images and documents. INNORIX takes the load that size and count create off the transfer, and delivers large data to the GPU environment reliably.
If the network fails or the transfer stops, what has already completed is kept and only the missing part is sent again. Automatic resume, retries and verification cut out needless repeat transfers and confirm that the data AI needs is ready.
Large transfers
A terabyte dataset in a single transfer
Many small files
Millions of files, handled reliably
No lost progress
What finished is kept; only the rest is sent again
Transfer #4821-01Object Storage → gpu-cluster-01
2.4 TB of 2.4 TB transferred
Parts of this transfer
AI INTEGRATION
Keep Your AI Stack.
Just Connect the Transfer.
File transfer connects to your existing AI orchestrator, scheduler and pipeline without changing them. Name the files and the target from Kubeflow, Airflow, SLURM or your own system through the API, and INNORIX runs the transfer and returns its progress and its result.
Your AI system schedules and runs the work exactly as it does today, while INNORIX handles the file movement between GPU clusters, cloud storage and the data centre. Completion callbacks and run results feed back into the workflow, so data preparation, training and result collection carry on automatically.
Simple to connect
A standard API, connected straight away
Nothing to change
Used without touching the AI system
Results you can see
Status, logs and a completion callback
Transfer APItr_8f3a2e6b · called by airflow-dag-vision
- AI OrchestratorKubeflow · Airflow · Custom
- SchedulerSLURM · PBS · LSF
- AI ApplicationTraining · Inference · Fine-tuning
- High-Speed Transfer
- Resume & Retry
- Verification
- GPU ClusterNVIDIA H100 · A100
- Cloud StorageS3 · GCS · Azure Blob
- Data CenterNAS · Object Storage
POST/api/v1/transferscURL
1{2 "source": "s3://training-dataset",3 "target": "gpu-cluster-01",4 "files": "dataset/*.tar",5 "callback": "https://ai.company.com/webhook"6}
RESPONSEJSON
1{2 "transferId": "tr_8f3a2e6b",3 "status": "completed",4 "files": "12,480",5 "size": "2.4 TB",6 "progress": "100%",7 "callback": "delivered"8}
Run status
- Requested10:14
- Transferring10:15
- Completed10:33
- Callback sent10:33
Every one of these capabilities,on a single platform.
The capability on this page is one of the many file transfer features INNORIX Platform provides. From file transfer to automation and unified operations, try every feature free.
Start FreeLooking at data delivery for AI?
We'll go through the transfer setup you need for the storage and GPU environment you already run.
- Review of storage and GPU environment connections
- Setting up large dataset transfers
- Connecting to an existing AI pipeline