Comments (6)
A current workaround for this is to manually signal the output folder to tf.sh
via the -m
flag. For example, if the desired output folder is /data/models/output
,
./scripts/tf.sh -m /data/output /home/user/ml/ml4cvd/recipes.py --mode train --output_folder /data/models/output
./scripts/tf.sh -m /data/models/output /home/user/ml/ml4cvd/recipes.py \
--mode train \
--output_folder /data/models/output
would mount /data/models/output
as a shared volume in Docker and the results will be accessible after the run.
I agree that realizing that the results are gone only after the run is complete is a bit annoying. I'll see if we can at least release a warning when the user is running the risk of this happening. Also, saving output results outside the repo is a good practice, as it makes startup time faster, so we should encourage it by making it easier.
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Thanks for the workaround, I will try to use that for now!
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Paolo, in your example above, did you mean to suggest mounting the same directory as the user specifies for --output_folder
, e.g.:
./scripts/tf.sh -m /data/models/output /home/user/ml/ml4cvd/recipes.py \
--mode train \
--output_folder /data/models/output
rather than:
./scripts/tf.sh -m /data/output /home/user/ml/ml4cvd/recipes.py \
--mode train \
--output_folder /data/models/output
I did the former and it worked!
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You're absolutely right, Erik, sorry for the typo!
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Ok thanks -- I know it seems like a silly question but I know so little about Docker I wanted to be sure!
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@paolodi I am not sure exactly what changed, but I no longer have to use -m
flag to mount drives outside of the repo. Can this issue be closed?
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Related Issues (20)
- Type Checks in Conv Layer
- Tensorflow 2.2.0 errors HOT 1
- No OSX support for multithreading HOT 2
- p-values for bootstrapped performance comparison HOT 1
- Migrate to tf.data.Dataset and/or torch.utils.data.DataLoader HOT 1
- Support multi-dimensional TMAPs in output HOT 1
- Transformer Model Module
- Store MRI metadata in a DataFrame during hd5 production HOT 2
- ml4h install: vtk module is pinned to a version not supported in py3.8 HOT 4
- some tensormap key names need to be updated. HOT 1
- fix display of MRI images in plot_mri_tensor_as_animation HOT 2
- issues running the `mnist_survival_analysis_demo.ipynb` notebook HOT 3
- PCLR SavedModel different to PCLR.h5, unable to load PCLR.h5 HOT 2
- Jupyter, Welder failed to start after 10 minutes.
- missing function: slice
- Channels in mri_silhouettes
- PCLR_lead_I.h5
- droid docker image HOT 1
- Regarding the issue of DROID-LV prediction output values being too small HOT 1
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