Today we're looking at some hyperparameter optimization workflows that get the most out of your GPU spend and power durable BERT training. We start with CheckpointedBertTrainingWorkflow, which materializes a dataset snapshot, runs fine-tuning, and records checkpoints through signals so runs can resume safely. 

Then BertEvalWorkflow scores each run, while CoordinatorWorkflow wires training and evaluation into a clean "config in, metrics out" API. Next, SweepWorkflow fans out randomized trials and returns a leaderboard. Training runs on a dedicated task queue, evaluation on another, so you can scale GPUs and CPUs independently. 

Finally, LadderSweepWorkflow scales the search: short, cheap rungs first, then promotes the best configs, using a TPE-style sampler to propose smarter candidates. Thanks to Temporal, every trial is durable, replayable, and recoverable after worker restarts. 

If you want reliable HPO that scales from laptop to GPU cluster without losing state, this is the pattern. Built for production, simple to adopt.

Code Sample: https://temporal.io/code-exchange/temporal-ml-ops-examples
Temporal 101: https://learn.temporal.io/courses/temporal_101/
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Temporal is the simple, scalable, open source way to write and run reliable cloud applications. 

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Temporal's platform is a central hub for developers interested in distributed systems, workflow orchestration, and fault-tolerant computing. Through articles, tutorials, and documentation, Temporal offers insights into building resilient and scalable distributed applications. Developers can learn about workflow automation, state management, and handling failure scenarios using Temporal's workflow engine and coordination APIs.

Temporal

Using Temporal for durable ML pipeline orchestration eliminates the need for custom crash recovery and state management code. A demo shows a PyTorch training job automatically resuming from the latest checkpoint after being killed, with zero orchestration code written by the developer. The talk walks through a hyperparameter optimization experiment using a scaling ladder approach (based on the Hyperband algorithm) applied to fine-tuning a DeBERTa model on the GLUE SST2 sentiment dataset. Four nested Temporal workflows handle training, evaluation, coordination, and ladder logic. Results show the scaling ladder approach achieved 10.1% better accuracy using only half the compute budget compared to random hyperparameter selection. Trade-offs include required code refactoring to separate deterministic workflow code from idempotent activities, and minor network latency. Temporal is not recommended for low-level multi-node training coordination, where specialist libraries like Ray are better suited.

Build durable ML pipelines with Temporal