Rename dgx cloud executor to runai executor#253
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Thanks for doing this, @hemildesai! Looks good, just a branding update to NVIDIA Run:ai in the applicable places.
| def your_dgx_executor(nodes: int, gpus_per_node: int, container_image: str): | ||
| # Ensure these are set correctly for your DGX Cloud environment | ||
| def your_runai_executor(nodes: int, gpus_per_node: int, container_image: str): | ||
| # Ensure these are set correctly for your RunAI environment |
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This should technically be NVIDIA Run:ai. I'll add a comment to each of these I find.
| ``` | ||
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| For a complete end-to-end example using DGX Cloud with NeMo, refer to the [NVIDIA DGX Cloud NeMo End-to-End Workflow Example](https://docs.nvidia.com/dgx-cloud/run-ai/latest/nemo-e2e-example.html). | ||
| For a complete end-to-end example using RunAI with NeMo, refer to the [NVIDIA RunAI NeMo End-to-End Workflow Example](https://docs.nvidia.com/dgx-cloud/run-ai/latest/nemo-e2e-example.html). |
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Should be NVIDIA Run:ai for both
| The `DGXCloudExecutor` integrates with a DGX Cloud cluster's Run:ai API to launch distributed jobs. It uses REST API calls to authenticate, identify the target project and cluster, and submit the job specification. | ||
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| > **_WARNING:_** Currently, the `DGXCloudExecutor` is only supported when launching experiments *from* a pod running on the DGX Cloud cluster itself. Furthermore, this launching pod must have access to a Persistent Volume Claim (PVC) where the experiment/job directories will be created, and this same PVC must also be configured to be mounted by the job being launched. | ||
| The `RunAIExecutor` integrates with the Run:ai API to launch distributed jobs. It uses REST API calls to authenticate, identify the target project and cluster, and submit the job specification. |
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This should be NVIDIA Run:ai
| class RunAIExecutor(Executor): | ||
| """ | ||
| Dataclass to configure a DGX Executor. | ||
| Dataclass to configure a RunAI Executor. |
| Dataclass to configure a RunAI Executor. | ||
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| This executor integrates with a DGX cloud endpoint for launching jobs | ||
| This executor integrates with a RunAI cloud endpoint for launching jobs |
| DGXCloudState.COMPLETED: AppState.SUCCEEDED, | ||
| DGXCloudState.TERMINATING: AppState.RUNNING, | ||
| DGXCloudState.UNKNOWN: AppState.FAILED, | ||
| # Local placeholder for storing RunAI job states |
| class RunAIRequest: | ||
| """ | ||
| Wrapper around the torchx AppDef and the DGX executor. | ||
| Wrapper around the torchx AppDef and the RunAI executor. |
| RunAIRequest(app=app, executor=executor, cmd=cmd, name=role.name), | ||
| # Minimal function to show the config, if any | ||
| lambda req: f"DGX job for app: {req.app.name}, cmd: {' '.join(cmd)}, executor: {executor}", | ||
| lambda req: f"RunAI job for app: {req.app.name}, cmd: {' '.join(cmd)}, executor: {executor}", |
| def schedule(self, dryrun_info: AppDryRunInfo[RunAIRequest]) -> str: | ||
| """ | ||
| Launches a job on DGX using the DGXExecutor. Returns an app_id | ||
| Launches a job on RunAI using the RunAIExecutor. Returns an app_id |
| job_id, status = executor.launch(name=req.name, cmd=req.cmd) | ||
| if not job_id: | ||
| raise RuntimeError("Failed scheduling run on DGX: no job_id returned") | ||
| raise RuntimeError("Failed scheduling run on RunAI: no job_id returned") |
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Signed-off-by: Hemil Desai <hemild@nvidia.com>
Signed-off-by: Hemil Desai <hemild@nvidia.com>
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This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days. |
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This PR was closed because it has been inactive for 7 days since being marked as stale. |
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Closes #241