Add support for torchrun to DGXC Executor

src/nemo_run/core/execution/__init__.py

@@ -17,4 +17,4 @@
 from nemo_run.core.execution.skypilot import SkypilotExecutor
 from nemo_run.core.execution.slurm import SlurmExecutor
 
-__all__ = ["LocalExecutor", "SlurmExecutor", "SkypilotExecutor"]
+__all__ = ["LocalExecutor", "SlurmExecutor", "SkypilotExecutor", "DGXCloudExecutor"]

src/nemo_run/core/execution/dgxcloud.py

@@ -138,7 +138,7 @@ def create_distributed_job(
         return response
 
     def launch(self, name: str, cmd: list[str]) -> tuple[str, str]:
-        name = name.replace("_", "-")  # to meet K8s requirements
+        name = name.replace("_", "-").replace(".", "-")  # to meet K8s requirements
         token = self.get_auth_token()
         if not token:
             raise RuntimeError("Failed to get auth token")
@@ -156,6 +156,12 @@ def launch(self, name: str, cmd: list[str]) -> tuple[str, str]:
         status = r_json["actualPhase"]
         return job_id, status
 
+    def nnodes(self) -> int:
+        return self.nodes
+
+    def nproc_per_node(self) -> int:
+        return self.gpus_per_node
+
     def status(self, job_id: str) -> Optional[DGXCloudState]:
         url = f"{self.base_url}/workloads/distributed/{job_id}"
         token = self.get_auth_token()

src/nemo_run/run/torchx_backend/components/torchrun.py

@@ -59,6 +59,7 @@ def torchrun(
     rdzv_backend: str = "c10d",
     mounts: Optional[list[str]] = None,
     debug: bool = False,
+    dgxc: bool = False,
 ) -> specs.AppDef:
     """
     Distributed data parallel style application (one role, multi-replica).
@@ -92,6 +93,8 @@ def torchrun(
         mounts: mounts to mount into the worker environment/container (ex. type=<bind/volume>,src=/host,dst=/job[,readonly]).
                 See scheduler documentation for more info.
         debug: whether to run with preset debug flags enabled
+        use_env: whether to read config from environment variables
+        dgxc: whether to use a subset of settings for DGX Cloud
     """
     if (script is None) == (m is None):
         raise ValueError("exactly one of --script and -m must be specified")
@@ -130,24 +133,22 @@ def torchrun(
     if debug:
         env.update(_TORCH_DEBUG_FLAGS)
 
-    cmd = [
-        "--rdzv-backend",
-        rdzv_backend,
-        "--rdzv-endpoint",
-        rdzv_endpoint,
-        "--rdzv-id",
-        f"{random.randint(1, 10000)}",
-        "--nnodes",
-        num_nodes,
-        "--nproc-per-node",
-        nproc_per_node,
-        "--node-rank",
-        node_rank,
-        "--tee",
-        "3",
-        # "--role",
-        # "",
-    ]
+    cmd = ["--nnodes", num_nodes, "--nproc-per-node", nproc_per_node]
+    if not dgxc:
+        cmd += [
+            "--rdzv-backend",
+            rdzv_backend,
+            "--rdzv-endpoint",
+            rdzv_endpoint,
+            "--rdzv-id",
+            f"{random.randint(1, 10000)}",
+            "--node-rank",
+            node_rank,
+            "--tee",
+            "3",
+            # "--role",
+            # "",
+        ]
     if script is not None:
         if no_python:
             cmd += ["--no-python"]

src/nemo_run/run/torchx_backend/packaging.py

@@ -23,6 +23,7 @@
 
 from nemo_run.config import SCRIPTS_DIR, Partial, Script
 from nemo_run.core.execution.base import Executor, FaultTolerance, Torchrun
+from nemo_run.core.execution.dgxcloud import DGXCloudExecutor
 from nemo_run.core.execution.local import LocalExecutor
 from nemo_run.core.serialization.yaml import YamlSerializer
 from nemo_run.core.serialization.zlib_json import ZlibJSONSerializer
@@ -139,6 +140,7 @@ def package(
             mounts=mounts,
             debug=executor.packager.debug,
             max_retries=executor.retries,
+            dgxc=isinstance(executor, DGXCloudExecutor),
         )
     elif launcher and isinstance(launcher, FaultTolerance):
         app_def = ft_launcher.ft_launcher(