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

Flyte

our use case is that we cant just define our service in the task because we have dependencies that aren't compatible with flytekit
so we have to build our training service separately

have you looked at this doc? <https://docs.flyte.org/projects/cookbook/en/latest/auto_examples/kftensorflow_plugin/tf_mnist.html#run-distributed-tensorflow-training>
why is there no `TFJob`?

yes I have seen this, they use:

```TfJob(worker=Worker(replicas=1), ps=PS(replicas=1), chief=Chief(replicas=1)),```
This means flyte will run the code in the task in the image. However our code has conflicts with flytekit so we need to build a separate image for it

i think you need to add flytekit to the images you supply to chief and worker as well.

I did that but the TFJob spec that gets built has a command that tries to run pyflyte-execute and points to the serialized python function for the task

which I dont know how to inject into the image

alternatively is there an example anywhere that uses the `image`  attribute of `Chief`/`Ps` ?

i don't think so. <@U05FFAF4FC2> / <@USU6W5ATA> / <@U045124RRFX> any idea how to implement this?

it seems that when you provide an image to TfJob Chief/worker it doesn't actually package the task module into the image

Hi <@U05RVC90SLQ> did you examine the destination directory in your running pod’s container? Are you sure the task modules are not in there?

<@U05FFAF4FC2> did you have a chance to look at this?

still working on other issues, probably will take a look at friday.
You can take a look at this file.
<https://github.com/flyteorg/flytekit/blob/master/plugins/flytekit-kf-tensorflow/flytekitplugins/kftensorflow/task.py>

I don't think we currently support specifying image to TFJob Chief/Worker

If it is doable, you can create PR to support this feature.

<https://github.com/flyteorg/flyte/blob/master/flyteplugins/go/tasks/plugins/k8s/kfoperators/tensorflow/tensorflow.go>