aws / aws/amazon-sagemaker-examples
tf-resnet-profiling-multi-gpu-multi-node-boto3.ipynb failed CI
- Dominant language
- Jupyter Notebook
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Description
Link to the notebook:
https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker-debugger/tensorflow_profiling/tf-resnet-profiling-multi-gpu-multi-node-boto3.ipynb
Error:
---------------------------------------------------------------------------
Exception encountered at "In [8]":
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
in
----> 1 from smdebug.profiler.analysis.notebook_utils.training_job import TrainingJob
2 tj = TrainingJob(training_job_name, region)
3 tj.wait_for_sys_profiling_data_to_be_available()
/usr/local/lib/python3.7/site-packages/smdebug/profiler/analysis/notebook_utils/__init__.py in
1 # Local
----> 2 from .metrics_histogram import MetricsHistogram
3 from .step_histogram import StepHistogram
4 from .training_job import TrainingJob
/usr/local/lib/python3.7/site-packages/smdebug/profiler/analysis/notebook_utils/metrics_histogram.py in
4 # Third Party
5 import numpy as np
----> 6 from bokeh.io import out
[...]
ModuleNotFoundError: No module named 'bokeh'
Contributor guide
Research direction
Open tf-resnet-profiling-multi-gpu-multi-node-boto3.ipynb and inspect cell In [8], beginning by reproducing the TrainingJob import in CI. Done means the notebook runs past that cell without the reported bokeh ModuleNotFoundError.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, jupyter-notebook, python
- Domain
- ci-cd, machine-learning
- Issue type
- Bug
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100