Try HDF5 save format for faster model load about deepcell-imaging

[dchaley]

Results on local environment are very promising.

Before:

Loaded model in 12.3 s
Ran prediction in 1.25 s; success: True

After:

Loaded model in 0.84 s
Ran prediction in 1.19 s; success: True

This is the time to load the model from disk, not to fetch it from storage.

Once the PR is merged we can test on cloud.

[dchaley]

The new model is uploaded:

gs://genomics-data-public-central1/cellular-segmentation/vanvalenlab/deep-cell/vanvalenlab-tf-model-multiplex-downloaded-20230706/MultiplexSegmentation-resaved-20240710.h5

Its md5 hash is: 56b0f246081fe6b730ca74eab8a37d60
gs://genomics-data-public-central1/cellular-segmentation/vanvalenlab/deep-cell/vanvalenlab-tf-model-multiplex-downloaded-20230706/MultiplexSegmentation-resaved-20240710-md5.txt

It was generated like so:

model =  tf.keras.models.load_model("/Users/davidhaley/.keras/models/MultiplexSegmentation")
model.save("MultiplexSegmentation-resaved-20240710.h5")

It seems to be really that simple… but, see upcoming PR for caveats on loading the .h5 model.

[dchaley]

Cloud results 🎉

Before:

Reading model from /root/.keras/models/MultiplexSegmentation.
Loaded model in 8.99 s
Ran prediction in 2.82 s; success: True

After:

Loading model from: /root/.keras/models/MultiplexSegmentation-resaved-20240710.h5
Loaded model in 2.68 s
Ran prediction in 2.79 s; success: True

Summary of results:

Environment	Before	After	Diff
Macbook M3 Max Pro	12.3 s	0.84 s	-11.46 s (-93%)
n1-standard-8 w/ 1 T4 GPU	8.99 s	2.68 s	-6.31 s (-70%)
n1-standard-32 w/ 1 T4 GPU	8.21s	2.72 s	-5.49 s (-67%)

Of note, loading the model into memory used to take ~3x the time of predicting the 512x512 image. Now it's roughly the same.