BPNet-OSKN

import kipoi
model = kipoi.get_model('BPNet-OSKN')

pred = model.pipeline.predict_example()

# Download example dataloader kwargs
dl_kwargs = model.default_dataloader.download_example('example')
# Get the dataloader and instantiate it
dl = model.default_dataloader(**dl_kwargs)
# get a batch iterator
it = dl.batch_iter(batch_size=4)
# predict for a batch
batch = next(it)
model.predict_on_batch(batch['inputs'])

pred = model.pipeline.predict(dl_kwargs, batch_size=4)

library(reticulate)
kipoi <- import('kipoi')
model <- kipoi$get_model('BPNet-OSKN')

predictions <- model$pipeline$predict_example()

# Download example dataloader kwargs
dl_kwargs <- model$default_dataloader$download_example('example')
# Get the dataloader
dl <- model$default_dataloader(dl_kwargs)
# get a batch iterator
it <- dl$batch_iter(batch_size=4)
# predict for a batch
batch <- iter_next(it)
model$predict_on_batch(batch$inputs)

pred <- model$pipeline$predict(dl_kwargs, batch_size=4)

docker pull haimasree/kipoi-docker:bpnet-oskn

docker run -it haimasree/kipoi-docker:bpnet-oskn

docker run haimasree/kipoi-docker:bpnet-oskn kipoi test BPNet-OSKN --source=kipoi

# Create an example directory containing the data
mkdir -p $PWD/kipoi-example 
# You can replace $PWD/kipoi-example with a different absolute path containing the data 
docker run -v $PWD/kipoi-example:/app/ haimasree/kipoi-docker:bpnet-oskn \
kipoi get-example BPNet-OSKN -o /app/example 
docker run -v $PWD/kipoi-example:/app/ haimasree/kipoi-docker:bpnet-oskn \
kipoi predict BPNet-OSKN \
--dataloader_args='{'intervals_file': '/app/example/intervals_file', 'fasta_file': '/app/example/fasta_file'}' \
-o '/app/BPNet-OSKN.example_pred.tsv' 
# check the results
head $PWD/kipoi-example/BPNet-OSKN.example_pred.tsv