TFX1.14.0 causing Google Cloud Dataflow jobs to fail
coocloud opened this issue · 27 comments
System information
- Have I specified the code to reproduce the issue: Yes
- Environment in which the code is executed: Google Cloud Dataflow
- TensorFlow version: 2.13.0
- TFX Version: 1.14.0
- Python version: 3.8.10
- Python dependencies: Docker Image
Describe the current behavior
When running the BigQueryExampleGen component on Google Cloud Dataflow using TFX1.14.0, the dataflow job gets stuck with the error:
Workflow failed. Causes: The Dataflow job appears to be stuck because no worker activity has been seen in the last 1h. Please check the worker logs in Stackdriver Logging. You can also get help with Cloud Dataflow at https://cloud.google.com/dataflow/support.
Describe the expected behavior
It should not fail/get stuck.
Standalone code to reproduce the issue
PIPELINE_IMAGE = "tensorflow/tfx:1.14.0"
DATAFLOW_BEAM_PIPELINE_ARGS = [
f"--project={GOOGLE_CLOUD_PROJECT}",
"--runner=DataflowRunner",
f"--temp_location={TEMP_LOCATION}",
f"--region={GOOGLE_CLOUD_REGION}",
"--disk_size_gb=50",
"--machine_type=e2-standard-8",
"--experiments=use_runner_v2",
f"--subnetwork={SUBNETWORK}",
f"‑‑experiments=use_sibling_sdk_workers",
f"--sdk_container_image={PIPELINE_IMAGE}",
f"--labels=group={GROUP}",
f"--labels=team={TEAM}",
f"--labels=project={PROJECT}",
"--job_name=test-tfx-1-14",
]
def run():
query = {
"train": "SELECT 1 as one",
"eval": "SELECT 1 as one",
"test": "SELECT 1 as one",
}
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern=query["train"]),
example_gen_pb2.Input.Split(name="eval", pattern=query["eval"]),
example_gen_pb2.Input.Split(name="test", pattern=query["test"]),
]
)
BQ_BEAM_ARGS = [
f"--project={GOOGLE_CLOUD_PROJECT}",
f"--temp_location={TEMP_LOCATION}",
]
example_gen = BigQueryExampleGen(
input_config=input_config
).with_beam_pipeline_args(DATAFLOW_BEAM_PIPELINE_ARGS)
metadata_config = (
tfx.orchestration.experimental.get_default_kubeflow_metadata_config()
)
pipeline_operator_funcs = get_default_pipeline_operator_funcs()
runner_config = tfx.orchestration.experimental.KubeflowDagRunnerConfig(
kubeflow_metadata_config=metadata_config,
tfx_image=PIPELINE_IMAGE,
pipeline_operator_funcs=pipeline_operator_funcs
)
pod_labels = {
"add-pod-env": "true",
tfx.orchestration.experimental.LABEL_KFP_SDK_ENV: "tfx-template",
}
tfx.orchestration.experimental.KubeflowDagRunner(
config=runner_config,
pod_labels_to_attach=pod_labels
).run(
pipeline=pipeline.Pipeline(
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
components=[example_gen],
beam_pipeline_args=BQ_BEAM_ARGS
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
Other info / logs
The job fails after 1hr, regardless of the machine type or query used.
Setting PIPELINE_IMAGE to tfx1.13.0 still fails, it currently only works on tfx.1.12.0
I also observed this going from TFX 1.12.0 to 1.14.0. My only Dataflow component is the Transform component, so it is the one that gets stuck.
Here are some metrics. There is no throughput:
CPU usage has a cyclical pattern:
It is not lack of memory: it is only using a fraction of the available memory.
I came across few troubleshooting steps to debug the issue further. Can you please follow the step as shown in Troubleshooting dataflow error and let us know what is causing this error. This will help us lot in finding the root cause of the issue. Thank you!
These are the errors in the logs
System logs:
- ima: Can not allocate sha384 (reason: -2)
Kubelet logs:
- "Error initializing dynamic plugin prober" err="error (re-)creating driver directory: mkdir /usr/libexec/kubernetes: read-only file system"
- "Skipping pod synchronization" err="[container runtime status check may not have completed yet, PLEG is not healthy: pleg has yet to be successful]"
Also seems like it's struggling to download the container image
@AnuarTB This is the Dataflow Job ID that failed
2023-10-24_02_28_52-1220281879361432238
This issue was previously reported by one of the TFX users and apologies for missing that. Dataflow struggles to fetch the TFX image as TFX image is of large size.
The solution for this issue is to set the flag --experiments=disable_worker_container_image_prepull in pipeline options. Ref: similar issue
Please try the above solution and let us know if you face any issues. Thank you!
@singhniraj08
I have added that flag but I am still getting the same error
Dataflow job id: 2023-11-17_04_23_23-6315535713304255245
Can you share your Dataflow job id?
Regarding 2023-11-17_04_23_23-6315535713304255245, it looks like the image is so big that you ran out of disk space. Try bumping up it up.
Increasing the disk space also didn't seem to fix the issue.
Job id: 2023-11-20_05_04_45-14348954509617130059
The size of the image is around 11GB so surely it should be enough?
It is not about the image size. If you check your worker-startup log, one error occurred:
2023/11/20 13:11:49 failed to create a virtual environment. If running on Ubuntu systems, you might need to install `python3-venv` package. To run the SDK process in default environment instead, set the environment variable `RUN_PYTHON_SDK_IN_DEFAULT_ENVIRONMENT=1`. In custom Docker images, you can do that with an `ENV` statement. Encountered error: python venv initialization failed: exit status 1
Any update on this. I am facing the exact same issue highlighted above.
check my comment in #6386 (comment). If you see that error, you can ssh to your container like docker run --rm -it --entrypoint=/bin/bash YOUR_CONTAINER_IMAGE and check whether venv is installed. or when building your container, use ENV to define RUN_PYTHON_SDK_IN_DEFAULT_ENVIRONMENT=1.
Thanks @liferoad : I got the exact same error so i build a custom image, which basically ran tfx 1.14.0 but added that ENV and it all worked fine.
Thank you!!!
@liferoad Thanks, my dataflow job seems to run successfully after adding that environment variable.
Closing this issue, since the issue is resolved for you. Please take a look into the answers provided above, feel free to reopen and post your comments(if you still have queries on this). Thank you!
@singhniraj08, should this environment variable not be added to the TFX base image before the issue is closed? Is the TFX base image not intended to be used to run TFX jobs (on Vertex AI or Kubeflow)? Those TFX jobs might reasonably include with Dataflow components.
@IzakMaraisTAL, Yes, It will make more sense to add the environment variable to TFX base image to avoid these issues in future. I have to make sure that it doesn't break any other scenarios where DockerFile is being used apart from Dataflow.
Reopening this issue. We will update this thread. Thank you for bringing this up!
I want to mention that Dataflow-side, it seems that when the job is cancelled after the 1-hour timeout, the pip install of the TFX package is still running with high CPU usage. I can reproduce this issue locally with
pyenv global 3.8.10 # issue also occurs on 3.10.8
mkdir /tmp/venv
python -m venv /tmp/venv
source /tmp/venv/bin/activate
pip install 'tfx==1.14.0'Which outputs logs such as
INFO: pip is looking at multiple versions of exceptiongroup to determine which version is compatible with other requirements. This could take a while.
INFO: pip is looking at multiple versions of anyio to determine which version is compatible with other requirements. This could take a while.
Collecting anyio>=3.1.0
Downloading anyio-4.0.0-py3-none-any.whl (83 kB)
|████████████████████████████████| 83 kB 2.3 MB/s
INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. If you want to abort this run, you can press Ctrl + C to do so. To improve how pip performs, tell us what happened here: https://pip.pypa.io/surveys/backtracking
Using cached anyio-3.7.1-py3-none-any.whl (80 kB)
Using cached anyio-3.7.0-py3-none-any.whl (80 kB)
Using cached anyio-3.6.2-py3-none-any.whl (80 kB)
Using cached anyio-3.6.1-py3-none-any.whl (80 kB)
Using cached anyio-3.6.0-py3-none-any.whl (80 kB)
Using cached anyio-3.5.0-py3-none-any.whl (79 kB)
INFO: pip is looking at multiple versions of anyio to determine which version is compatible with other requirements. This could take a while.
Using cached anyio-3.4.0-py3-none-any.whl (78 kB)
Using cached anyio-3.3.4-py3-none-any.whl (78 kB)
Using cached anyio-3.3.3-py3-none-any.whl (78 kB)
Using cached anyio-3.3.2-py3-none-any.whl (78 kB)
Using cached anyio-3.3.1-py3-none-any.whl (77 kB)
INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. If you want to abort this run, you can press Ctrl + C to do so. To improve how pip performs, tell us what happened here: https://pip.pypa.io/surveys/backtracking
Using cached anyio-3.3.0-py3-none-any.whl (77 kB)
Using cached anyio-3.2.1-py3-none-any.whl (75 kB)
Using cached anyio-3.2.0-py3-none-any.whl (75 kB)
Using cached anyio-3.1.0-py3-none-any.whl (74 kB)
INFO: pip is looking at multiple versions of jupyter-server to determine which version is compatible with other requirements. This could take a while.
Collecting jupyter-server<3,>=2.4.0
Using cached jupyter_server-2.10.1-py3-none-any.whl (378 kB)
...
It has been doing so for the last 20+ minutes.
I believe the Dataflow job fails because pip is unable to resolve the versions within the 1-hour time limit.
I tried the update the ENV variable in TFX dockerfile and build image but it takes forever to build because of #6468. TFX dependencies takes lot of time to install and results in installation failure. Once that issue is fixed, I will be able to integrate the environment variable in docker file and test it. Thanks.
@jonathan-lemos, Thank you for bringing this up. This should be fixed once we fix #6468 issue.
Hello @singhniraj08 and @liferoad ,
I'm still encountering the same problem.
Even after adding the "--experiments=disable_worker_container_image_prepull" and ENV RUN_PYTHON_SDK_IN_DEFAULT_ENVIRONMENT=1
My job id : 2024-01-28_15_08_34-3324575780147226127
Here is my code for docker image :
`FROM gcr.io/tfx-oss-public/tfx:1.14.0
ENV RUN_PYTHON_SDK_IN_DEFAULT_ENVIRONMENT=1
COPY requirementsfinal.txt requirements.txt
RUN sed -i 's/python3/python/g' /usr/bin/pip
RUN pip install -r requirements.txt
COPY src/ src/
ENV PYTHONPATH="/pipeline:${PYTHONPATH}"
`
And the rest is similar to the one
`
train_output_config = example_gen_pb2.Output(
split_config=example_gen_pb2.SplitConfig(
splits=[
example_gen_pb2.SplitConfig.Split(
name="train", hash_buckets=int(config.NUM_TRAIN_SPLITS)
),
example_gen_pb2.SplitConfig.Split(
name="eval", hash_buckets=int(config.NUM_EVAL_SPLITS)
),
]
)
)
# Train example generation
train_example_gen = tfx.extensions.google_cloud_big_query.BigQueryExampleGen(
query=train_sql_query,
output_config=train_output_config,
custom_config=json.dumps({})
).with_beam_pipeline_args(config.BEAM_DATAFLOW_PIPELINE_ARGS).with_id("TrainDataGen")
`
BEAM_DATAFLOW_PIPELINE_ARGS = [ f"--project={PROJECT}", f"--temp_location={os.path.join(GCS_LOCATION, 'temp')}", f"--region=us-east1", f"--runner={BEAM_RUNNER}", "--disk_size_gb=50", "--experiments=disable_worker_container_image_prepull", ]
for the runner
runner = tfx.orchestration.experimental.KubeflowV2DagRunner( config=tfx.orchestration.experimental.KubeflowV2DagRunnerConfig(default_image=config.TFX_IMAGE_URI), output_filename=PIPELINE_DEFINITION_FILE)
the logs
and worker logs
@IzakMaraisTAL and @coocloud can you share your config files or anything you have done differently to make it work, please !
This also didn't work for me the first time I tried it. Then I realised you also need to make sure your custom image is used by Dataflow by adding f"--sdk_container_image={PIPELINE_IMAGE}", to BEAM_DATAFLOW_PIPELINE_ARGS.
It worked !! Thank you @IzakMaraisTAL !