The End-to-End
ML Ecosystem
Distributed training with multiple parallelism strategies. Inference as input → tensor out. Full lifecycle from raw data to deployed model — in one platform.
The actual platform.
Click through it yourself.
Deploy an endpoint, scale replicas, walk through the submit wizard, filter the cluster — all live.
Why we're faster:
True distribution.
We don't just run your script on multiple GPUs. We orchestrate genuine distributed training across a cluster — gradient synchronization, topology-aware node placement, and automatic parallelism configuration. Set your GPU count in the platform and we handle the rest.
- ✓Topology-aware node placement — NVLink / InfiniBand preferred
- ✓Automatic NCCL configuration — no manual rank setup
- ✓Gradient checkpointing and mixed precision by default
- ✓Checkpoint recovery resumes from last epoch — not zero
Prepare Data
Split dataset into .zip shards — one per GPU worker. Manual or via the Python SDK. Upload to your S3 bucket once.
Bucket Pull
We host the bucket — your private Garage storage. At dispatch each worker gets a short-lived presigned URL for its shard and pulls it straight from storage, bypassing the control plane.
Distributed Run
Workers run in parallel. Gradients sync. Checkpoints stream back to your bucket under the job folder.
Auto-Recovery
Node fails? Job reschedules. Resumes from last checkpoint in your bucket — not zero.
Get Your Model
Final weights land in jobs/<name>/model_out/ in your bucket. Download via browser, rclone, or SDK.
One import,
a whole cluster.
The resontech Python SDK handles authentication, job submission, live telemetry, and artifact retrieval — straight from your notebook or script.
- ✓One-call job submission — rt.submit(model=..., training=...)
- ✓Native PyTorch, HuggingFace, and federated workflows
- ✓Stream logs, metrics, and checkpoints from any running job
- ✓Artifacts synced back to your workspace when training finishes
Model + Input
→ Tensor out.
Push your trained model. Send your input. Get tensors back. No servers to manage, no scaling to configure. We handle all of it.
Push Model
Upload checkpoint, point to HuggingFace repo, or reference a completed training job. PyTorch, TensorFlow, HuggingFace, Triton.
Send Input
Send any input via our SDK or platform. Text, images, audio, embeddings. Format-agnostic. Batching handled automatically.
Get Tensors
Receive raw tensors or decoded outputs. Sub-10s cold start. Autoscales to zero when idle — pay per request.
From 4 days to 12 hours.
DeepLab fine-tuning · 61M parameters · 30GB dataset · identical final quality
Works with what
you already use.
Ready to run?
Start in 60 seconds.
No credit card required for the public pool. Book a demo for managed or private cluster.