Flyte enables production-grade orchestration for machine learning workflows and data processing created to accelerate local workflows to production.

Flyte

Hello Team!
I have some problems, I made a workflow that has one shelltask. This shelltask have pod task config.
But workflow gives errors that
```failed at Node[n0]. RuntimeExecutionError: failed during plugin execution, caused by: failed to execute
│  handle for plugin [sidecar]: [BadTaskSpecification] invalid TaskSpecification [&lt;nil&gt;], Err: [nil Struct Object passed]```
How can I handle it?

```  train = ShellTask(
⚠ name="train",
      debug=True,
      script="""
      set -ex
      NPROC_PER_NODE=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
      python -m torch.distributed.launch --nproc_per_node $NPROC_PER_NODE pocs/wav2vec2/flyte.py --cfg {inp&gt;
      """,
      inputs=kwtypes(cfg=str, save_directory=str),
      task_config=Pod(pod_spec=generate_gpu_pod_spec_for_task(), primary_container_name="primary"),
  )

⚠ @workflow
  def wf(cfg: str, save_directory: str) -&gt; None:
      train(cfg=cfg, save_directory=save_directory)```
my workflow

generate_gpu_pod_spec_for_task() method works on another tasks.

with `@task(task_config=Pod(…))` annotation, task’s config has `k8sPod` item, however, shelltask with pod task_config, task’s config has `container` item, not `k8sPod`

maybe this is missing for ShellTask, just like it is missing for ContainerTask:
<https://github.com/flyteorg/flyte/issues/3055>

<@U04KE24HE49> currently the `Pod` `task_config` is only supported in python task definitions. There has been some great conversation on the restrictions on this and how we might address it.

Basically, we decided to support a `pod_template` argument for all task definitions that will work exactly as you expected in your example above. We are tracking progress in <https://github.com/flyteorg/flyte/issues/3123|this issue>. The backend changes are close to being complete (hopefully this week) and then after completing the flytekit and testing end 2 end we will make this support available!