ML Ops & Training on PYRAX
ML Ops covers the pipeline that takes models from training to production — data prep, distributed training, versioning, evaluation, and deployment — and its central weakness is reproducibility: teams and regulators struggle to prove which data, code, and compute produced a given model. PYRAX makes the whole pipeline attestable: PYRAX Compute runs training and eval jobs against a ComputeReceipt, content-addressed storage pins the exact inputs, and eth_getProof makes every artifact and metric independently verifiable.
The market
Source: MarketsandMarkets, 2024. Figures are indicative and provided for context.
What's broken today
How PYRAX transforms it
Concrete network elements mapped to this business.
PYRAX Compute distributed training jobs
Training runs are metered in compute units at 8 PYRX/CU and can shard across a ShardedExecutor cohort, giving teams cluster-scale compute without a hyperscaler contract.
ComputeReceipt for reproducibility
Every training and evaluation job emits one ComputeReceipt binding the data digest, config, code hash, and resulting artifact — a cryptographic record that the run happened as claimed.
Content-addressed model & dataset registry
Datasets, checkpoints, and containers are addressed by hash, so a model version can be traced to the exact bytes that produced it and never silently drifts.
eth_getProof verifiable metrics
Evaluation scores and lineage are committed to state and provable via proofs, so an auditor can verify a model card's claims without re-running the pipeline.
4-rung verification for evals
Benchmark and safety-eval results can be independently re-executed or ZK-proven, replacing self-reported metrics with verifiable ones.
Buildathon: dApp ideas
Ship-ready concepts for ml ops & training on PYRAX.
RepoTrain
01Distributed training service that returns a ComputeReceipt pinning data, config, and code for a fully reproducible run.
ModelRegistry
02Content-addressed registry of checkpoints and datasets with immutable, verifiable lineage.
EvalProof
03Benchmark harness that re-executes evaluations on independent workers and publishes verifiable scores.
DriftWatch
04Monitoring dApp that anchors production metrics on-chain and proves when a model has drifted from its baseline.
ShardTrainer
05Cohort trainer that fans a large training job across pooled consumer GPUs via the ShardedExecutor.
AuditCard
06Model-card contract whose every claim is backed by an eth_getProof-verifiable artifact for regulators.
PipelineEscrow
07CI/CD for models where each stage escrows PYRX and pays workers on a verified receipt.
PrivateFineTune
08Compute-to-data fine-tuning that adapts a base model on private corpora without moving the data.