Here’s what the graph looks like

I checked the GCS storage bucket used by Flyte, the file really doesn’t exist at

metadata/propeller/flytesnacks-development-algopipelinesflytedatapipeline-da0bfb4f/n10/data/0/dn19-dn0/0/futures.pb

This far, the path is correct:

metadata/propeller/flytesnacks-development-algopipelinesflytedatapipeline-da0bfb4f/n10/data/0

It does not have dn19-dn0 folder but it does have a dn19 folder and a dn0 folder there and they both have a folder named

0

with futures.pb

If it matters, I use flytekit version 1.6.2 and flyte-core v1.7.0. I’ll try upgrading my flytekit to v1.7.0 as well just in case

Missing file sounds like an assumption error

10 workflows is not high load

Can you just share the code remove logic

The workflow:

import os
import uuid

from algopipelines.flytedatapipeline.kubes import (
    slack_notify,
    parse_session_info,
    parse_takes_status,
    git_clone,
    should_run_fit,
    qt_texture_training,
    get_parameters,
    skip,
)

from algopipelines.flytedatapipeline.subworkflows.shape_fit_flow import shape_fit_flow
from algopipelines.flytedatapipeline.subworkflows.evaluation_flow import evaluation_flow
from flytekit import workflow, conditional

@workflow
def smplx_proccess_pipeline(dummy: bool):

    pipeline_params = get_parameters(dummy=dummy)
    parsed_session_info = parse_session_info(dummy=dummy)
    parsed_takes_status = parse_takes_status(dummy=dummy)

    slack_notify(dummy=dummy)

    run_fit = should_run_fit(dummy=dummy)

    (motion_capture_repo := git_clone(dummy=dummy).with_overrides(name="Clone CGMocap")) >> run_fit
    (core_algo_repo := git_clone(dummy=dummy).with_overrides(name="Clone CoreAlgo")) >> run_fit
    (evaluator_repo := git_clone(dummy=dummy).with_overrides(name="Clone Evaluators")) >> run_fit
    (fitting_repo := git_clone(dummy=dummy).with_overrides(name="Clone Fitting")) >> run_fit
    (common_algo_repo := git_clone(dummy=dummy).with_overrides(name="Clone CommonAlgo")) >> run_fit

    conditional_fit = conditional("dummy_condition_1").if_(run_fit.is_true()
    ).then(shape_fit_flow(
        dummy=dummy
    )).else_().then(skip())

    conditional_train_qt = conditional("should_train_qt").if_(dummy == True
    ).then(qt_texture_training(
        dummy=dummy
    )).else_().then(skip())

    conditional_evaluation = conditional("should_run_evaluation").if_(dummy == True
    ).then(evaluation_flow(
        dummy=dummy
    )).else_().then(skip())


    # Dependencies that were handled by flyte before dummying the workflow:

    # git_clone nodes are inputs of all of the conditionals
    # parsed_session_info is an input of all the conditionals
    # parsed_takes_status is an input of the evaluation conditional
    # pipeline_params is an input of all the conditionals

    # Explicit dependencies related to our specific logic:

    pipeline_params >> run_fit

    motion_capture_repo >> conditional_train_qt
    core_algo_repo >> conditional_train_qt
    common_algo_repo >> conditional_train_qt
    evaluator_repo >> conditional_train_qt
    fitting_repo >> conditional_train_qt

    conditional_fit >> conditional_train_qt
    conditional_fit >> parsed_takes_status
    conditional_train_qt >> conditional_evaluation

shape_fit_flow dynamic (used dynamic for looping over contents in a list input:

import os
import uuid

from algopipelines.flytedatapipeline.kubes import (
    download_video_folder,
    mocap_shape_process,
    upload_folder,
    upload_status_file,
    skip,
)
from algopipelines.flytedatapipeline.data_pipelines.fitting_scripts.smplx_shape_fitting import smplx_shape_fitting
from algopipelines.flytedatapipeline.data_pipelines.fitting_scripts.smplx_expression_fitting import smplx_expression_fitting
from algopipelines.flytedatapipeline.subworkflows.mocap_process_flow import mocap_process_flow

from algopipelines.datahandler.k8s import task_config
from flytekit import conditional, dynamic
from flytekit.types.directory import FlyteDirectory



@dynamic(**task_config(
    image='algopipelines-generic:v1',
    cpu='400m', memory='400Mi'))
def shape_fit_flow(
    dummy=bool
):

    shape_video = download_video_folder(dummy=dummy)

    shape_fitting_outputs_qt = smplx_shape_fitting(dummy=dummy)
    upload_folder(dummy=dummy).with_overrides(name='Upload Shape Fitting Outputs')

    shape_fitting_outputs_lite = smplx_shape_fitting(dummy=dummy)
    upload_folder(dummy=dummy).with_overrides(name='Upload Shape Fitting Outputs (Lite)')

    mocap_shape_process_outputs = mocap_shape_process(dummy=dummy)

    upload_folder(dummy=dummy).with_overrides(name='Upload Mocap Shape Process Outputs')

    upload_folder(dummy=dummy).with_overrides(name='Upload Shape Raw Data')

    for expression_path in ['dummy_path_1', "dummy_path_2", "...", "dummy_path_13"]:

        expression_video = download_video_folder(dummy=dummy)

        upload_folder(dummy=dummy).with_overrides(name="Upload Expression Raw Data")

        expression_fitting_outputs = smplx_expression_fitting(dummy=dummy)

        upload_status_file(dummy=dummy)

        upload_folder(dummy=dummy)

        conditional("should_run_mocap_processing").if_("dummy" == "dummy").then(
            mocap_process_flow(dummy-dummy)
        ).else_().then(skip())

mocap_process_flow dynamic: (used dynamic as workaround because some input strings were Promise(,,,), with dynamic it evaluates them properly)

from algopipelines.flytedatapipeline.kubes import (
    mocap_expression_process,
    upload_folder,
)

from algopipelines.datahandler.k8s import task_config
from flytekit import dynamic
from flytekit.types.directory import FlyteDirectory


@dynamic(**task_config(
    image='algopipelines-generic:v1',
    cpu='400m', memory='400Mi'))
def mocap_process_flow(dummy: bool):

    expression_process_outputs = mocap_expression_process(dummy=dummy)
    upload_folder(dummy=dummy)

evaluation_flow:

from algopipelines.flytedatapipeline.evaluators_kubes import (
    evaluation,
    download_evaluation_inputs,
)

from algopipelines.datahandler.k8s import task_config
from algopipelines.flytedatapipeline.kubes import (
    upload_status_file,
    upload_folder,
)

from flytekit import dynamic
from flytekit.types.directory import FlyteDirectory

@dynamic(**task_config(
    image='algopipelines-generic:v1',
    cpu='400m', memory='400Mi'))
def evaluation_flow(dummy: bool,):

    evaluation_inputs = download_evaluation_inputs(dummy=dummy)
    evaluation_output = evaluation(dummy=dummy)

    upload_status_file(dummy=dummy)
    upload_folder(dummy=dummy)

k8s.py (outputs kwargs for task decorators):

from typing import List
from flytekitplugins.pod import Pod
from kubernetes.client.models import (
    V1PodSpec,
    V1Container,
    V1EmptyDirVolumeSource,
    V1PodSpec,
    V1ResourceRequirements,
    V1Volume,
    V1VolumeMount,
    V1EnvVar,
    V1EnvFromSource,
    V1ConfigMapEnvSource,
    V1SecretEnvSource,
    V1Toleration,
)

def k8s_env(env_dict) -> List[V1EnvVar]:
    return [V1EnvVar(name=k, value=v) for k, v in env_dict.items()]

def k8s_env_from_configmap(cms) -> List[V1EnvFromSource]:
    return [V1EnvFromSource(config_map_ref=V1ConfigMapEnvSource(name=cm)) for cm in cms]

def k8s_env_from_secret(secrets) -> List[V1EnvFromSource]:
    return [V1EnvFromSource(secret_ref=V1SecretEnvSource(name=secret)) for secret in secrets]

def task_config(image: str, **kwargs):
    cache = kwargs.get("cache", False)
    primary_container_name = kwargs.get("primary_container_name", "primary")
    env = kwargs.get("env", {})
    env_configmaps = kwargs.get("env_configmaps", [])
    env_secrets = kwargs.get("env_secrets", [])
    node_pool = kwargs.get("node_pool", "default-pool")
    cpu_request = kwargs.get("cpu", kwargs.get("cpu_request", "1"))
    cpu_limit = kwargs.get("cpu", kwargs.get("cpu_limit", "1"))
    memory_request = kwargs.get("memory", kwargs.get("memory_request", "1Gi"))
    memory_limit = kwargs.get("memory", kwargs.get("memory_limit", "1Gi"))
    gpu = int(kwargs.get("gpu", 0))
    mount_shared_memory = kwargs.get("mount_shared_memory", False)

    # Hard-coded default environment variables for all tasks
    default_env = {
        "PYTHONUNBUFFERED": "1"
    }

    env.update(default_env)

    env_from_sources = k8s_env_from_configmap(env_configmaps) + k8s_env_from_secret(env_secrets)

    resource_requests = {"cpu": cpu_request, "memory": memory_request}
    resource_limits = {"cpu": cpu_limit, "memory": memory_limit}

    if gpu > 0:
        resource_requests["<http://nvidia.com/gpu|nvidia.com/gpu>"] = gpu
        resource_limits["<http://nvidia.com/gpu|nvidia.com/gpu>"] = gpu

    volumes = []
    volume_mounts = []

    if mount_shared_memory:
        dshm_volume = V1Volume(
            name="dshm",
            empty_dir=V1EmptyDirVolumeSource(medium="Memory")
        )
        volumes.append(dshm_volume)

        dshm_volume_mount = V1VolumeMount(
            mount_path="/dev/shm",
            name="dshm"
        )
        volume_mounts.append(dshm_volume_mount)

    pod = Pod(pod_spec=V1PodSpec(
        containers=[V1Container(
            name=primary_container_name,
            env=k8s_env(env),
            env_from=env_from_sources,
            resources=V1ResourceRequirements(
                requests=resource_requests,
                limits=resource_limits,
            ),
            volume_mounts=volume_mounts,
        )],
        volumes=volumes,
        node_selector={"<http://cloud.google.com/gke-nodepool|cloud.google.com/gke-nodepool>": node_pool},
        tolerations=[
            V1Toleration(
                key="nodepool",
                operator="Equal",
                value=node_pool,
                effect="NoSchedule",
            ),
            V1Toleration(
                key='<http://nvidia.com/gpu|nvidia.com/gpu>',
                operator='Exists',
                effect='NoSchedule',
            ),
        ],
    ))

    return {
        'task_config': pod,
        'container_image': image, # Must match pod spec's image
        'cache': cache,
        'cache_serialize': cache,
        'cache_version': "xyz", # Change this string to invalidate all existing cache, it can be set to  any string
    }

This should be good enough to create a similar graph of nodes, but if the problem is because of inputs/outputs, it will probably not be reproduced with this example. I used list, namedtuple, str, bool, int, flytedirectory, flytefile as my types for inputs/outputs

Cc @high-accountant-32689

@kind-kite-58745, continuing the investigation on this, you mentioned in an earlier message that the futures.pb files existed only in a slightly different path. For example, in that message you're saying that

metadata/propeller/flytesnacks-development-algopipelinesflytedatapipeline-da0bfb4f/n10/data/0/dn19/dn0/0/futures.pb

exists, but

metadata/propeller/flytesnacks-development-algopipelinesflytedatapipeline-da0bfb4f/n10/data/0/dn19-dn0/0/futures.pb

doesn't, correct? Do you have any other files in either path (e.g.: a file called

errors.pb

) ?

Update now that I have more context We use image streaming on GKE, because our container images need to contain a lot of heavy dependencies and can get above 10GB in size (the cudnn base itself is already 4GB). With image streaming, there’s lazy loading of the container images which lets the tasks start within seconds rather than minutes. It usually works well, but seems to not be able to keep with the pace in higher scale I recently tried to run 100 of our workflows in parallel, each has multiple gpu tasks running in parallel, which resulted in 200-1000 (kubernetes) nodes in total being up during these workflow executions. I was unable to reproduce this issue with 10 parallel workflow executions, it happened only a little bit with 20 wf executions, and with 100 the failure rate was around 80%. I wrapped the docker image with a custom entrypoint that has it sleep for 3 minutes before running anything. This gave GKE/GAR more time to load the container images, and now only 4% (4/100) executions failed. This narrows down the potential causes for this and other strange errors (such as random bus errors) enough that I feel confident saying it’s likely happening because of lazy loading of the container images, which affects the flytekit/pyflate running inside the container as well as the code itself that runs in the task. Right now I’m trying to figure out what can be checked to determine whether or not the container image has loaded enough to run flyte and our code, if I find such a thing then the next step will be to have my entrypoint check it every few seconds until it has loaded enough. If we find that stability can’t be guaranteed unless the entire image is loaded, then we will need to look into alternative solutions for container pulling, but there is no bug from Flyte’s side here.