diff --git a/kubeflow/trainer/__init__.py b/kubeflow/trainer/__init__.py index 61a46b26..6c791e90d 100644 --- a/kubeflow/trainer/__init__.py +++ b/kubeflow/trainer/__init__.py @@ -15,6 +15,7 @@ # Import the Kubeflow Trainer client. from kubeflow.trainer.api.trainer_client import TrainerClient # noqa: F401 +from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig # import backends and its associated configs from kubeflow.trainer.backends.kubernetes.types import KubernetesBackendConfig @@ -58,5 +59,6 @@ "TrainerClient", "TrainerType", "LocalProcessBackendConfig", + "LocalDockerBackendConfig", "KubernetesBackendConfig", ] diff --git a/kubeflow/trainer/api/trainer_client.py b/kubeflow/trainer/api/trainer_client.py index 6b564c90..df287bed 100644 --- a/kubeflow/trainer/api/trainer_client.py +++ b/kubeflow/trainer/api/trainer_client.py @@ -16,6 +16,8 @@ import logging from typing import Optional, Union +from kubeflow.trainer.backends.docker.backend import LocalDockerBackend +from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig from kubeflow.trainer.backends.kubernetes.backend import KubernetesBackend from kubeflow.trainer.backends.kubernetes.types import KubernetesBackendConfig from kubeflow.trainer.backends.localprocess.backend import ( @@ -31,7 +33,11 @@ class TrainerClient: def __init__( self, - backend_config: Union[KubernetesBackendConfig, LocalProcessBackendConfig] = None, + backend_config: Union[ + KubernetesBackendConfig, + LocalProcessBackendConfig, + LocalDockerBackendConfig, + ] = None, ): """Initialize a Kubeflow Trainer client. @@ -52,6 +58,8 @@ def __init__( self.backend = KubernetesBackend(backend_config) elif isinstance(backend_config, LocalProcessBackendConfig): self.backend = LocalProcessBackend(backend_config) + elif isinstance(backend_config, LocalDockerBackendConfig): + self.backend = LocalDockerBackend(backend_config) else: raise ValueError(f"Invalid backend config '{backend_config}'") diff --git a/kubeflow/trainer/backends/docker/__init__.py b/kubeflow/trainer/backends/docker/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/kubeflow/trainer/backends/docker/backend.py b/kubeflow/trainer/backends/docker/backend.py new file mode 100644 index 00000000..c1ca4172 --- /dev/null +++ b/kubeflow/trainer/backends/docker/backend.py @@ -0,0 +1,536 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +DockerBackend +------------- + +Local execution backend for `CustomTrainer` jobs using Docker containers. + +Key behaviors: +- Multi-node jobs: one container per node connected via a per-job Docker network. +- Entry script generation: we serialize the user's training function to a small + Python file and invoke it inside the container using `torchrun` (preferred) or + `python` as a fallback. +- Runtimes: we use `config/local_runtimes` to define runtime images and + characteristics (e.g., torch). Defaults to `torch-distributed` if no runtime + is provided. +- Image pulling: controlled via `pull_policy` and performed automatically if + needed. +- Logs and lifecycle: streaming logs and deletion semantics similar to the + Kubernetes backend, but tailored to Docker. +""" + +from __future__ import annotations + +from collections.abc import Iterator +from dataclasses import dataclass +from datetime import datetime +import logging +import os +from pathlib import Path +import random +import shutil +import string +import uuid + +try: + import docker # type: ignore + from docker.models.containers import Container # type: ignore +except Exception: # pragma: no cover - optional dependency, validated at runtime + docker = None # type: ignore + Container = object # type: ignore + +from kubeflow.trainer.backends.base import ExecutionBackend +from kubeflow.trainer.backends.docker.runtime_loader import ( + get_local_runtime, + list_local_runtimes, +) +from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig +from kubeflow.trainer.constants import constants +from kubeflow.trainer.types import types + +logger = logging.getLogger(__name__) + + +DOCKER_LABEL_PREFIX = "trainer.kubeflow.ai" + + +@dataclass +class _Node: + name: str + container: Container + status: str = constants.TRAINJOB_CREATED + + +@dataclass +class _Job: + name: str + created: datetime + runtime: types.Runtime + network_name: str + nodes: list[_Node] + workdir_host: str + + +class LocalDockerBackend(ExecutionBackend): + def __init__(self, cfg: LocalDockerBackendConfig): + if docker is None: + raise ImportError( + "The 'docker' Python package is not installed. Install with extras: " + "pip install kubeflow[docker]" + ) + + # initialize docker client (env-based resolution by default) + if cfg.docker_host: + self.client = docker.DockerClient(base_url=cfg.docker_host) + else: + self.client = docker.from_env() + + self.cfg = cfg + self._jobs: dict[str, _Job] = {} + + # ---- Runtime APIs ---- + def list_runtimes(self) -> list[types.Runtime]: + return list_local_runtimes() + + def get_runtime(self, name: str) -> types.Runtime: + return get_local_runtime(name) + + def get_runtime_packages(self, runtime: types.Runtime): + """ + Spawn a short-lived container to report Python version, pip list, and nvidia-smi. + We follow the Kubernetes backend semantics: create a one-off job and print outputs. + """ + image = self._resolve_image(runtime) + self._maybe_pull_image(image) + + command = ( + "bash", + "-lc", + "python -c \"import sys; print(f'Python: {sys.version}')\" && " + "(pip list || echo 'pip not found') && " + "(nvidia-smi || echo 'nvidia-smi not found')", + ) + + logs = self._run_oneoff_container(image=image, command=command) + print(logs) + + # ---- Train/Jobs APIs ---- + def train( + self, + runtime: types.Runtime | None = None, + initializer: types.Initializer | None = None, + trainer: types.CustomTrainer | types.BuiltinTrainer | None = None, + ) -> str: + if runtime is None: + runtime = self.get_runtime("torch-distributed") + + if not isinstance(trainer, types.CustomTrainer): + raise ValueError("DockerBackend supports only CustomTrainer in v1") + + # Generate job name + job_name = random.choice(string.ascii_lowercase) + uuid.uuid4().hex[:11] + + # Create per-job working directory on host where we'll place the generated script + # Prefer a location under the user's home directory to ensure Docker Desktop file + # sharing includes it by default on macOS/Windows. + if self.cfg.workdir_base: + base = Path(self.cfg.workdir_base) + base.mkdir(parents=True, exist_ok=True) + workdir = str((base / f"{job_name}").resolve()) + os.makedirs(workdir, exist_ok=True) + else: + home_base = Path.home() / ".kubeflow_trainer" / "localdocker" + home_base.mkdir(parents=True, exist_ok=True) + workdir = str((home_base / f"{job_name}").resolve()) + os.makedirs(workdir, exist_ok=True) + _ = self._write_training_script(workdir, trainer) + + # Provision per-job network + network_name = self._ensure_network(job_name) + + # Resolve image and pull if needed + image = self._resolve_image(runtime) + self._maybe_pull_image(image) + + # Build base environment + env = dict(self.cfg.env or {}) + if trainer.env: + env.update(trainer.env) + + # Construct pre-run command to install packages, if any, inside the container. + pre_install_cmd = "" + pkgs = trainer.packages_to_install or [] + if pkgs: + # Respect pip_index_urls ordering (index-url then extra-index-urls) + index_urls = trainer.pip_index_urls or list(constants.DEFAULT_PIP_INDEX_URLS) + main_idx = index_urls[0] + extras = " ".join(f"--extra-index-url {u}" for u in index_urls[1:]) + quoted = " ".join(f'"{p}"' for p in pkgs) + pre_install_cmd = ( + "PIP_DISABLE_PIP_VERSION_CHECK=1 pip install --no-warn-script-location " + f"--index-url {main_idx} {extras} {quoted} && " + ) + + # Create N containers (one per node) + num_nodes = trainer.num_nodes or runtime.trainer.num_nodes or 1 + containers: list[_Node] = [] + + for rank in range(num_nodes): + container_name = f"{job_name}-node-{rank}" + + # torchrun rendezvous across nodes using network alias + # We use a simple host:port rendezvous; rank 0 acts as master. + master_addr = f"{job_name}-node-0" + master_port = 29500 + + # Prefer torchrun; fall back to python if torchrun is unavailable. + # IMPORTANT: Only add torchrun flags when torchrun is present. If we fall back to + # python, run the script directly without torchrun-specific arguments to avoid + # immediate failure and short-lived containers. + entry_cmd = ( + f"{pre_install_cmd}" + "if command -v torchrun >/dev/null 2>&1; then " + f" torchrun --nproc_per_node=1 --nnodes={num_nodes} " + f" --node_rank={rank} --rdzv_backend=c10d " + f" --rdzv_endpoint={master_addr}:{master_port} " + f" {self.cfgd_workspace_path()}train.py; " + "else " + f" python {self.cfgd_workspace_path()}train.py; " + "fi" + ) + + full_cmd = ("bash", "-lc", entry_cmd) + + labels = { + f"{DOCKER_LABEL_PREFIX}/trainjob-name": job_name, + f"{DOCKER_LABEL_PREFIX}/step": f"node-{rank}", + } + + # Bind mount the host workdir into the container working dir + binds = { + workdir: { + "bind": "/workspace", + "mode": "rw", + } + } + + container = self.client.containers.run( + image=image, + command=full_cmd, + name=container_name, + detach=True, + working_dir="/workspace", + network=network_name, + environment=env, + labels=labels, + volumes=binds, + auto_remove=False, # we control removal in delete_job + ) + + containers.append(_Node(name=container_name, container=container)) + + self._jobs[job_name] = _Job( + name=job_name, + created=datetime.now(), + runtime=runtime, + network_name=network_name, + nodes=containers, + workdir_host=workdir, + ) + + return job_name + + def list_jobs(self, runtime: types.Runtime | None = None) -> list[types.TrainJob]: + result: list[types.TrainJob] = [] + for job in self._jobs.values(): + if runtime and job.runtime.name != runtime.name: + continue + steps = [] + for node in job.nodes: + steps.append( + types.Step( + name=node.name.split(f"{job.name}-")[-1], + pod_name=node.name, + status=self._container_status(node.container), + ) + ) + result.append( + types.TrainJob( + name=job.name, + creation_timestamp=job.created, + runtime=job.runtime, + steps=steps, + num_nodes=len(job.nodes), + status=self._aggregate_status(job), + ) + ) + return result + + def get_job(self, name: str) -> types.TrainJob: + job = self._jobs.get(name) + if not job: + raise ValueError(f"No TrainJob with name {name}") + # Refresh container statuses on demand + steps: list[types.Step] = [] + for node in job.nodes: + status = self._container_status(node.container) + steps.append( + types.Step( + name=node.name.split(f"{job.name}-")[-1], + pod_name=node.name, + status=status, + ) + ) + return types.TrainJob( + name=job.name, + creation_timestamp=job.created, + runtime=job.runtime, + steps=steps, + num_nodes=len(job.nodes), + status=self._aggregate_status(job), + ) + + def get_job_logs( + self, + name: str, + follow: bool = False, + step: str = constants.NODE + "-0", + ) -> Iterator[str]: + job = self._jobs.get(name) + if not job: + raise ValueError(f"No TrainJob with name {name}") + + want_all = step == constants.NODE + "-0" + for node in job.nodes: + node_step = node.name.split(f"{job.name}-")[-1] + if not want_all and node_step != step: + continue + logs = node.container.logs(stream=bool(follow), follow=bool(follow)) + if follow: + for chunk in logs: + yield chunk.decode("utf-8", errors="ignore") + else: + yield logs.decode("utf-8", errors="ignore") + + def wait_for_job_status( + self, + name: str, + status: set[str] = {constants.TRAINJOB_COMPLETE}, + timeout: int = 600, + polling_interval: int = 2, + ) -> types.TrainJob: + # Simple polling loop similar to Kubernetes backend + import time + + end = time.time() + timeout + while time.time() < end: + tj = self.get_job(name) + logger.debug(f"TrainJob {name}, status {tj.status}") + if tj.status in status: + return tj + if constants.TRAINJOB_FAILED not in status and tj.status == constants.TRAINJOB_FAILED: + raise RuntimeError(f"TrainJob {name} is Failed") + time.sleep(polling_interval) + raise TimeoutError(f"Timeout waiting for TrainJob {name} to reach status: {status}") + + def delete_job(self, name: str): + job = self._jobs.get(name) + if not job: + raise ValueError(f"No TrainJob with name {name}") + + # Stop containers, collect final logs if needed, and remove + from contextlib import suppress + + for node in job.nodes: + with suppress(Exception): + node.container.stop(timeout=10) + with suppress(Exception): + node.container.remove(force=True) + + # Remove network (best-effort) + try: + net = self.client.networks.get(job.network_name) + net.remove() + except Exception: + pass + + # Remove working directory if configured + if self.cfg.auto_remove and os.path.isdir(job.workdir_host): + shutil.rmtree(job.workdir_host, ignore_errors=True) + + del self._jobs[name] + + # ---- Helpers ---- + def _write_training_script(self, workdir: str, trainer: types.CustomTrainer) -> Path: + script_path = Path(workdir) / "train.py" + # Serialize function source and immediate call (mirrors localprocess utils) + import inspect + import textwrap + + code = inspect.getsource(trainer.func) + code = textwrap.dedent(code) + if trainer.func_args is None: + code += f"\n{trainer.func.__name__}()\n" + else: + code += f"\n{trainer.func.__name__}({trainer.func_args})\n" + script_path.write_text(code) + return script_path + + def _resolve_image(self, runtime: types.Runtime) -> str: + # Prefer explicit image from config; else from runtime YAML. Runtime YAML + # must include the image field; otherwise we fail with a clear message. + if self.cfg.image: + return self.cfg.image + + # Load from local runtime YAMLs directory + # The loader already validated presence, but not the image; enforce here. + # We reuse the torch-distributed default when name matches. + # For extensibility, a mapping could be added in the YAML schema later. + name = runtime.name + # Best-effort: read the same YAML used for this runtime to fetch image. + # The simple loader does not expose image directly, so we re-read YAML by matching name. + from kubeflow.trainer.backends.docker.runtime_loader import LOCAL_RUNTIMES_DIR + + for f in sorted(LOCAL_RUNTIMES_DIR.glob("*.yaml")): + try: + import yaml + + data = yaml.safe_load(Path(f).read_text()) + # CRD-like structure only (ClusterTrainingRuntime or TrainingRuntime) + if ( + data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} + and data.get("metadata", {}).get("name") == name + ): + # locate the 'node' replicated job + replicated = ( + data.get("spec", {}) + .get("template", {}) + .get("spec", {}) + .get("replicatedJobs", []) + ) + node_jobs = [j for j in replicated if j.get("name") == "node"] + if node_jobs: + node_spec = ( + node_jobs[0] + .get("template", {}) + .get("spec", {}) + .get("template", {}) + .get("spec", {}) + ) + containers = node_spec.get("containers", []) + if containers and containers[0].get("image"): + return str(containers[0]["image"]) + except Exception: + continue + raise ValueError( + f"No image specified for runtime '{name}'. Provide DockerBackendConfig.image or " + f"add an 'image' field to its YAML in {LOCAL_RUNTIMES_DIR}." + ) + + def _maybe_pull_image(self, image: str): + policy = (self.cfg.pull_policy or "IfNotPresent").lower() + try: + if policy == "never": + # Ensure image exists locally + self.client.images.get(image) + return + if policy == "always": + logger.debug(f"Pulling image (Always): {image}") + self.client.images.pull(image) + return + # IfNotPresent + try: + self.client.images.get(image) + except Exception: + logger.debug(f"Pulling image (IfNotPresent): {image}") + self.client.images.pull(image) + except Exception as e: + raise RuntimeError(f"Failed to ensure image '{image}': {e}") from e + + def _ensure_network(self, job_name: str) -> str: + # Create a job-scoped network to allow name-based discovery between nodes + network_name = f"{job_name}-net" + try: + self.client.networks.get(network_name) + return network_name + except Exception: + pass + # Create network with labels for filtering and management + self.client.networks.create( + name=network_name, + check_duplicate=True, + labels={ + "trainer.kubeflow.org/trainjob-name": job_name, + }, + ) + return network_name + + def _container_status(self, container: Container) -> str: + try: + container.reload() + status = container.status # e.g., created, running, exited + if status == "running": + return constants.TRAINJOB_RUNNING + if status == "created": + return constants.TRAINJOB_CREATED + if status == "exited": + # Exit code 0 -> complete, else failed + inspect = self.client.api.inspect_container(container.id) + code = inspect.get("State", {}).get("ExitCode") + return constants.TRAINJOB_COMPLETE if code == 0 else constants.TRAINJOB_FAILED + except Exception: + return constants.UNKNOWN + return constants.UNKNOWN + + def _aggregate_status(self, job: _Job) -> str: + statuses = [self._container_status(n.container) for n in job.nodes] + if constants.TRAINJOB_FAILED in statuses: + return constants.TRAINJOB_FAILED + if constants.TRAINJOB_RUNNING in statuses: + return constants.TRAINJOB_RUNNING + if all(s == constants.TRAINJOB_COMPLETE for s in statuses if s != constants.UNKNOWN): + return constants.TRAINJOB_COMPLETE + if any(s == constants.TRAINJOB_CREATED for s in statuses): + return constants.TRAINJOB_CREATED + return constants.UNKNOWN + + def _run_oneoff_container(self, image: str, command: tuple[str, ...]) -> str: + """ + Run a short-lived container and return its stdout as a string. + + Implementation detail: + - Use detach=False and remove=True so Docker SDK runs the container to + completion, returns combined logs as bytes, and cleans it up. This + avoids races where auto-removed containers disappear before .wait/.logs. + """ + try: + output = self.client.containers.run( + image=image, + command=command, + detach=False, + remove=True, + ) + except Exception as e: + raise RuntimeError(f"One-off container failed to run: {e}") from e + + if isinstance(output, (bytes, bytearray)): + return output.decode("utf-8", errors="ignore") + # Fallback: ensure string return + return str(output) + + def cfgd_workspace_path(self) -> str: + # Location inside the container where the host workdir is mounted + return "/workspace/" diff --git a/kubeflow/trainer/backends/docker/runtime_loader.py b/kubeflow/trainer/backends/docker/runtime_loader.py new file mode 100644 index 00000000..d222624b --- /dev/null +++ b/kubeflow/trainer/backends/docker/runtime_loader.py @@ -0,0 +1,110 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Runtime loader for the Docker backend. + +We support loading local runtime definitions from +`kubeflow/trainer/config/local_runtimes/` (YAML files). The schema mirrors the +essential fields from the upstream CRD manifest but is tailored for Docker to +capture the container image and trainer characteristics needed to construct a +`types.Runtime` object. + +We ship a built-in `torch_distributed.yaml` as the default runtime. Users can +add additional YAML files to the same directory to define custom local runtimes. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import yaml + +from kubeflow.trainer.types import types as base_types + +LOCAL_RUNTIMES_DIR = Path(__file__).parents[2] / "config" / "local_runtimes" + + +def _load_runtime_from_yaml(path: Path) -> dict[str, Any]: + with open(path) as f: + data: dict[str, Any] = yaml.safe_load(f) + return data + + +def list_local_runtimes() -> list[base_types.Runtime]: + runtimes: list[base_types.Runtime] = [] + if not LOCAL_RUNTIMES_DIR.exists(): + return runtimes + + for f in sorted(LOCAL_RUNTIMES_DIR.glob("*.yaml")): + data = _load_runtime_from_yaml(f) + + # Require CRD-like schema strictly. Accept both ClusterTrainingRuntime + # and TrainingRuntime kinds. + if not ( + data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} + and data.get("metadata") + ): + raise ValueError( + f"Runtime YAML {f} must be a ClusterTrainingRuntime CRD-shaped document" + ) + + name = data["metadata"].get("name") + if not name: + raise ValueError(f"Runtime YAML {f} missing metadata.name") + + labels = data["metadata"].get("labels", {}) + framework = labels.get("trainer.kubeflow.org/framework") + if not framework: + raise ValueError( + f"Runtime {name} must set metadata.labels['trainer.kubeflow.org/framework']" + ) + + spec = data.get("spec", {}) + ml_policy = spec.get("mlPolicy", {}) + num_nodes = int(ml_policy.get("numNodes", 1)) + + # Validate presence of a 'node' replicated job with a container image + templ = spec.get("template", {}).get("spec", {}) + replicated = templ.get("replicatedJobs", []) + node_jobs = [j for j in replicated if j.get("name") == "node"] + if not node_jobs: + raise ValueError(f"Runtime {name} must define replicatedJobs with a 'node' entry") + node_spec = ( + node_jobs[0].get("template", {}).get("spec", {}).get("template", {}).get("spec", {}) + ) + containers = node_spec.get("containers", []) + if not containers or not containers[0].get("image"): + raise ValueError(f"Runtime {name} 'node' must specify containers[0].image") + + runtimes.append( + base_types.Runtime( + name=name, + trainer=base_types.RuntimeTrainer( + trainer_type=base_types.TrainerType.CUSTOM_TRAINER, + framework=framework, + num_nodes=num_nodes, + ), + pretrained_model=None, + ) + ) + return runtimes + + +def get_local_runtime(name: str) -> base_types.Runtime: + for rt in list_local_runtimes(): + if rt.name == name: + return rt + raise ValueError(f"Runtime '{name}' not found in {LOCAL_RUNTIMES_DIR}") diff --git a/kubeflow/trainer/backends/docker/types.py b/kubeflow/trainer/backends/docker/types.py new file mode 100644 index 00000000..1a87090b --- /dev/null +++ b/kubeflow/trainer/backends/docker/types.py @@ -0,0 +1,52 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Types and configuration for the Docker backend. + +We keep the configuration surface area intentionally small for v1: + - image: Optional explicit image. If omitted, use the image referenced by the + selected runtime (e.g., torch_distributed) from `config/local_runtimes`. + - network: Optional explicit Docker network name. If omitted, the backend will + create a per-job ephemeral network to interconnect all node containers. + - pull_policy: Controls image pulling. Supported values: "IfNotPresent", + "Always", "Never". The default is "IfNotPresent". + - auto_remove: Whether to remove containers and ephemeral network when jobs + are deleted. Defaults to True. + - gpus: GPU support is not implemented for v1 (kept for future extensibility). + - env: Optional global environment variables applied to all containers. + - docker_host: Optional override for connecting to a remote/local Docker + daemon; by default the Docker SDK resolves from environment and local + configuration. +""" + +from typing import Optional, Union + +from pydantic import BaseModel, Field + + +class LocalDockerBackendConfig(BaseModel): + image: Optional[str] = Field(default=None) + network: Optional[str] = Field(default=None) + pull_policy: str = Field(default="IfNotPresent") + auto_remove: bool = Field(default=True) + # In Python 3.9, avoid PEP 604 unions; use typing.Union/Optional instead. + # Define the type at module scope so Pydantic doesn't treat it as a field. + gpus: Optional[Union[int, bool]] = Field(default=None) + env: Optional[dict[str, str]] = Field(default=None) + docker_host: Optional[str] = Field(default=None) + # Base directory on the host to place per-job working dirs that are bind-mounted + # into containers as /workspace. Defaults to a path under the user's home to + # maximize compatibility with Docker Desktop file sharing on macOS/Windows. + workdir_base: Optional[str] = Field(default=None) diff --git a/kubeflow/trainer/config/local_runtimes/torch_distributed.yaml b/kubeflow/trainer/config/local_runtimes/torch_distributed.yaml new file mode 100644 index 00000000..60d36cfb --- /dev/null +++ b/kubeflow/trainer/config/local_runtimes/torch_distributed.yaml @@ -0,0 +1,25 @@ +apiVersion: trainer.kubeflow.org/v1alpha1 +kind: ClusterTrainingRuntime +metadata: + name: torch-distributed + labels: + trainer.kubeflow.org/framework: torch +spec: + mlPolicy: + numNodes: 1 + torch: + numProcPerNode: auto + template: + spec: + replicatedJobs: + - name: node + template: + metadata: + labels: + trainer.kubeflow.org/trainjob-ancestor-step: trainer + spec: + template: + spec: + containers: + - name: node + image: pytorch/pytorch:2.7.1-cuda12.8-cudnn9-runtime diff --git a/pyproject.toml b/pyproject.toml index 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