Enable running tfc.run() on notebook running from within a AI Platform hosted notebook.
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
Research direction
Start in tensorflow_cloud/core/preprocess.py at _get_colab_notebook_content() and trace how tfc.run() obtains notebook content. Reproduce the failure from an AI Platform hosted Jupyter notebook using the shown DockerConfig and entry_point settings. Done means the hosted-notebook case can run with the supplied requirements file and base image without relying on the unavailable Colab message interface.
Written by the indexing model from the issue text.
Description
Using AI Platform hosted notebooks, we created an Jupyter notebook with the model that we are were planning to train and saved it. We created a separate notebook in which we had our runner wrapping script similar to
import tensorflow_cloud as tfc
tfc.run(
docker_config=tfc.DockerConfig(
image_build_bucket="somebucket",
parent_image="gcr.io/xyz"),
entry_point="model.ipynb",
distribution_strategy="auto",
worker_count=5,
requirements_txt='requirements.txt',
chief_config=tfc.COMMON_MACHINE_CONFIGS["CPU"],
worker_config=tfc.COMMON_MACHINE_CONFIGS["CPU"],
job_labels={
"job": "kaggle_competition",
"team": "base_line",
},
stream_logs=False
)
The run fails with error
/opt/conda/lib/python3.7/site-packages/tensorflow_cloud/core/preprocess.py in _get_colab_notebook_content()
207 def _get_colab_notebook_content():
208 """Returns the colab notebook python code contents."""
--> 209 response = _message.blocking_request("get_ipynb",
210 request="",
211 timeout_sec=200)
AttributeError: 'NoneType' object has no attribute 'blocking_request'
Would be nice to add support for this case were all requirements and a proper base image are directly provided for the remote run.
- Dominant language
- Python
- Stars
- 383
- Forks
- 93
- Avg merge
- 1d 3h
- Merged PRs (30d)
- 1
Contributor guide
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