adapters/kfp: support distributed training

Author: d4l3k

.gitignore

@@ -138,3 +138,4 @@ dmypy.json
 cython_debug/
 
 wordlist.dic
+pipeline.yaml

docs/papermill.sh

@@ -7,8 +7,15 @@
 
 WORK_DIR=/tmp/papermill
 
-set -e
+set -ex
 mkdir -p "$WORK_DIR"
+
+# create empty master.tar.gz file and setup symlinks instead of pulling from
+# master so we can handle local changes
+tar -cJf "$WORK_DIR/master.tar.gz" -T /dev/null
+ROOT="$(pwd)/.."
+(cd "$WORK_DIR" && ln -s "$ROOT/torchx" . && ln -s "$ROOT/examples" .)
+
 files="$(find "$(pwd)"/build -name '*.ipynb')"
 for file in $files
 do

docs/source/pipelines.rst

@@ -13,6 +13,7 @@ torchx.pipelines.kfp
 .. currentmodule:: torchx.pipelines.kfp.adapter
 
 .. autofunction:: container_from_app
+.. autofunction:: resource_from_app
 .. autofunction:: component_from_app
 .. autofunction:: component_spec_from_app
 

examples/pipelines/kfp/dist_pipeline.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Distributed KubeFlow Pipelines Example
+======================================
+
+This is an example KFP pipeline that uses resource_from_app to launch a
+distributed operator using the kubernetes/volcano job scheduler. This only works
+in Kubernetes KFP clusters with https://volcano.sh/en/docs/ installed on them.
+"""
+
+import kfp
+from torchx import specs
+from torchx.pipelines.kfp.adapter import resource_from_app
+
+
+def pipeline() -> None:
+    # First we define our AppDef for the component, we set
+    echo_app = specs.AppDef(
+        name="test-dist",
+        roles=[
+            specs.Role(
+                name="dist-echo",
+                image="alpine",
+                entrypoint="/bin/echo",
+                args=["hello dist!"],
+                num_replicas=3,
+            ),
+        ],
+    )
+
+    # To convert the TorchX AppDef into a KFP container we use
+    # the resource_from_app adapter. This takes generates a KFP Kubernetes
+    # resource operator definition from the TorchX app def and instantiates it.
+    echo_container: kfp.dsl.BaseOp = resource_from_app(echo_app, queue="test")
+
+
+# %%
+# To generate the pipeline definition file we need to call into the KFP compiler
+# with our pipeline function.
+
+kfp.compiler.Compiler().compile(
+    pipeline_func=pipeline,
+    package_path="pipeline.yaml",
+)
+
+with open("pipeline.yaml", "rt") as f:
+    print(f.read())
+
+# %%
+# Once this has all run you should have a pipeline file (typically
+# pipeline.yaml) that you can upload to your KFP cluster via the UI or
+# a kfp.Client.
+#
+# See the
+# `KFP SDK Examples <https://www.kubeflow.org/docs/components/pipelines/tutorials/sdk-examples/#examples>`_
+# for more info on launching KFP pipelines.
+
+# %%
+# See the :ref:`Advanced KubeFlow Pipelines Example` for how to chain multiple
+# components together and use builtin components.

torchx/pipelines/kfp/adapter.py

@@ -22,6 +22,7 @@
     V1VolumeMount,
     V1EmptyDirVolumeSource,
 )
+from torchx.schedulers.kubernetes_scheduler import app_to_resource
 from torchx.specs import api
 from typing_extensions import Protocol
 
@@ -229,3 +230,41 @@ def container_from_app(
     """
     factory = component_from_app(app, ui_metadata)
     return factory(*args, **kwargs)
+
+
+def resource_from_app(
+    app: api.AppDef,
+    queue: str,
+) -> dsl.ResourceOp:
+    """
+    resource_from_app generates a KFP ResourceOp from the provided app that uses
+    the Volcano job scheduler on Kubernetes to run distributed apps. See
+    https://volcano.sh/en/docs/ for more info on Volcano and how to install.
+
+    Args:
+        app: The torchx AppDef to adapt.
+        queue: the Volcano queue to schedule the operator in.
+
+    >>> import kfp
+    >>> from torchx import specs
+    >>> from torchx.pipelines.kfp.adapter import resource_from_app
+    >>> app_def = specs.AppDef(
+    ...     name="trainer",
+    ...     roles=[specs.Role("trainer", image="foo:latest", num_replicas=3)],
+    ... )
+    >>> def pipeline():
+    ...     trainer = resource_from_app(app_def, queue="test")
+    ...     print(trainer)
+    >>> kfp.compiler.Compiler().compile(
+    ...     pipeline_func=pipeline,
+    ...     package_path="/tmp/pipeline.yaml",
+    ... )
+    {'ResourceOp': {... 'name': 'trainer-0', ... 'name': 'trainer-1', ... 'name': 'trainer-2', ...}}
+    """
+    return dsl.ResourceOp(
+        name=app.name,
+        action="create",
+        success_condition="status.state.phase = Completed",
+        failure_condition="status.state.phase = Failed",
+        k8s_resource=app_to_resource(app, queue),
+    )

torchx/schedulers/kubernetes_scheduler.py

@@ -164,6 +164,43 @@ def role_to_pod(name: str, role: Role) -> "V1Pod":
     )
 
 
+def app_to_resource(app: AppDef, queue: str) -> Dict[str, object]:
+    tasks = []
+    for i, role in enumerate(app.roles):
+        for replica_id in range(role.num_replicas):
+            values = macros.Values(
+                img_root="",
+                app_id=macros.app_id,
+                replica_id=str(replica_id),
+            )
+            name = f"{role.name}-{replica_id}"
+            replica_role = values.apply(role)
+            pod = role_to_pod(name, replica_role)
+            tasks.append(
+                {
+                    "replicas": 1,
+                    "name": name,
+                    "template": pod,
+                    "maxRetry": role.max_retries,
+                    "policies": RETRY_POLICIES[role.retry_policy],
+                }
+            )
+
+    job_retries = min(role.max_retries for role in app.roles)
+    resource: Dict[str, object] = {
+        "apiVersion": "batch.volcano.sh/v1alpha1",
+        "kind": "Job",
+        "metadata": {"generateName": f"{app.name}-"},
+        "spec": {
+            "schedulerName": "volcano",
+            "queue": queue,
+            "tasks": tasks,
+            "maxRetry": job_retries,
+        },
+    }
+    return resource
+
+
 @dataclass
 class KubernetesJob:
     resource: Dict[str, object]
@@ -237,39 +274,9 @@ def _submit_dryrun(
         self, app: AppDef, cfg: RunConfig
     ) -> AppDryRunInfo[KubernetesJob]:
         queue = cfg.get("queue")
-        tasks = []
-        for i, role in enumerate(app.roles):
-            for replica_id in range(role.num_replicas):
-                values = macros.Values(
-                    img_root="",
-                    app_id=macros.app_id,
-                    replica_id=str(replica_id),
-                )
-                name = f"{role.name}-{replica_id}"
-                replica_role = values.apply(role)
-                pod = role_to_pod(name, replica_role)
-                tasks.append(
-                    {
-                        "replicas": 1,
-                        "name": name,
-                        "template": pod,
-                        "maxRetry": role.max_retries,
-                        "policies": RETRY_POLICIES[role.retry_policy],
-                    }
-                )
-
-        job_retries = min(role.max_retries for role in app.roles)
-        resource: Dict[str, object] = {
-            "apiVersion": "batch.volcano.sh/v1alpha1",
-            "kind": "Job",
-            "metadata": {"generateName": f"{app.name}-"},
-            "spec": {
-                "schedulerName": "volcano",
-                "queue": queue,
-                "tasks": tasks,
-                "maxRetry": job_retries,
-            },
-        }
+        if not isinstance(queue, str):
+            raise TypeError(f"config value 'queue' must be a string, got {queue}")
+        resource = app_to_resource(app, queue)
         req = KubernetesJob(resource=resource)
         info = AppDryRunInfo(req, repr)
         info._app = app