framework · Meta
Synthetic Data Generation with PyTorch
Synthetic Data Generation built on PyTorch, chosen where it genuinely fits, and swapped where it does not.
- Category
- framework
- Vendor
- Meta
- Alternatives we also use
- 5
Why PyTorch for this
Synthetic is not automatically anonymous. A poorly generated set can leak information about the individuals it was derived from, which is why we test re-identification risk rather than assuming safety.
PyTorch is strongest at flexibility and the widest availability of pretrained models. For synthetic data generation that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: production serving needs deliberate optimisation work beyond the training code. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.
You own the code, the models where they are open-weight, and the documentation to run it without us.
The honest assessment
- What it is
- The deep learning framework behind most current research and production model work.
- Strongest at
- flexibility and the widest availability of pretrained models
- Trade-off
- production serving needs deliberate optimisation work beyond the training code
- Category
- framework
We are not a reseller for Meta and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Statistical profiling of the source so the synthetic set preserves real relationships
- Privacy evaluation, including re-identification risk testing
- Class balancing and rare-event augmentation where models need it
- Realistic test datasets for non-production environments
- Validation that models trained on synthetic data actually transfer
- Documentation for your DPO and auditors
Questions
Is synthetic data private by default?
No. Privacy depends on how it was generated and must be tested. We run re-identification risk assessment rather than asserting anonymity, because regulators ask for evidence.
Can we train production models on it?
Sometimes, particularly for augmentation and class balancing. We validate performance on held-out real data before recommending it for production training.
Does it satisfy DPDP requirements?
Properly generated and tested synthetic data can reduce personal-data exposure meaningfully. We document the method and the risk assessment so your DPO can make that determination.
Alternatives for synthetic data generation
Same capability, different stack. Each page states its own trade-off.
Building with PyTorch?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878