Menu
Out-of-memory and resource pressure recovery without changing the user's training objective.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
MaxEvolve Triton/PyTorch to JAX/Pallas porting with GpuToJax 12-step pipeline, interpret=True validation, MAP-Elites evolution, and Colab/SSH hardware profiling.
Manage Google Colab sessions and execute code on remote Colab VMs via the colab-cli.
Use when you need to generate a synthetic dataset. You can use Afterimage Python package to create, conversational, tool-calling, multi-choice questions, or other types of structured output datasets for SFT, or preference data for DPO.
Schedule deferred or recurring work via the isanagent cron tool—add/remove jobs with correct runtime context fields.
Configure long wall clocks and use execution_run_background + job polling for ML or hours-long runs without blocking the agent loop.
Run numeric or ML code through isanagent execution tools without flooding context—artifacts, incremental runs, sandbox paths.
| name | oom-recovery-playbook |
| description | Out-of-memory and resource pressure recovery without changing the user's training objective. |
| always | false |
When a run fails with CUDA OOM, host OOM, or timeout:
per_device_train_batch_size (or equivalent) and raise gradient_accumulation_steps so effective batch size stays the same when possible.max_length / context window (changes what data the model sees).Re-run a small pilot, confirm loss/metrics move as expected, then restore full scale.