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kipoi

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Description

Kipoi (pronounce: kípi; from the Greek κήποι: gardens) is an API and a repository of ready-to-use trained models for genomics. It currently contains 2131 different models, covering canonical predictive tasks in transcriptional and post-transcriptional gene regulation. Kipoi's API is implemented as a python package (github.com/kipoi/kipoi) and it is also accessible from the command line or R.

Publisher

Kipoi

Latest Tag

v0.6.8-keras1.2

Modified

September 24, 2020

Compressed Size

1.14 GB

Multinode Support

No

Multi-Arch Support

No

v0.6.8-keras1.2 (Latest) Scan Results

No results available.

Kipoi

Kipoi is an API and a repository of trained models for application to genomics. The repository contains over 2000 different models, covering canonical predictive tasks in transcriptional and post-transcriptional gene regulation.

More information about Kipoi is available on the Kipoi webpage.

System Requirements

Before running the NGC Kipoi container, please ensure that your system meets the following requirements.

  • Pascal (sm60) or Volta (sm70) NVIDIA GPU(s)
  • CUDA >= 10.0 -or- 9.0
  • One of the following container runtimes

A Kipoi executable optimized for your system hardware will be chosen automatically at runtime.

Running Kipoi

Command Invocation

Examples

Kipoi provides models and example data through its API. To run Kipoi with the example data for the DeepSEA/predict model:

Download the example data

Use the Kipoi container to download the DeepSEA/predict example data:

Docker:

nvidia-docker run --rm -it --net host -v $(pwd):/host_pwd kipoi:v0.6.8-keras2 kipoi get-example DeepSEA/predict -o /host_pwd/example

Singularity:

$ singularity build kipoi_v0.6.8-keras2.simg docker://nvcr.io/hpc/kipoi:v0.6.8-keras2
$ singularity run --nv -B $(pwd):/host_pwd kipoi:v0.6.8_keras2.simg /bin/bash -c "source activate kipoi-py3-keras2 kipoi get-example DeepSEA/predict -o /host_pwd/example"

Run the Kipoi Container

Use the Kipoi container to run the DeepSEA/predict example:

Docker:

$ nvidia-docker run --rm -it --net host -v $(pwd):/host_pwd kipoi:v0.6.8-keras2 kipoi predict DeepSEA/predict --dataloader_args='{"intervals_file": "/host_pwd/example/intervals_file", "fasta_file": "/host_pwd/example/fasta_file"}' -o '/host_pwd/predict.tsv'

Singularity:

$ singularity run --nv -B $(pwd):/host_pwd kipoi_v0.6.8-keras2.simg /bin/bash -c "source activate kipoi-py3-keras2 && kipoi predict DeepSEA/predict --dataloader_args='{"intervals_file": "/host_pwd/example/intervals_file", "fasta_file": "/host_pwd/example/fasta_file"}' -o '/host_pwd/predict.tsv'"

More detailed instructions on using the NGC Microvolution container in Docker and Singularity can be found below.

Running with nvidia-docker

NGC supports the Docker runtime through the nvidia-docker plugin.

nvidia-docker Aliases

To simplify the examples below, define the following command alias. This may be set as an environment variable in a shell or batch script.

DOCKER will be used to launch processes within the NGC Microvolution container using the nvidia-docker runtime:

$ export DOCKER="nvidia-docker run --rm -it --net host -v $(pwd):/host_pwd nvcr.io/hpc/kipoi:v0.6.8-keras2"

Where:

  • DOCKER: alias used to store the base Docker command
  • run: specifies the mode of execution
  • --rm: makes the container instance ephemeral (does not save on exit)
  • -it: runs the container in an interactive tty shell
  • --net host: allows the container to use the host network devices (necessary to connect to the Kipoi API server)
  • -v $(pwd):/host_pwd: bind mounts the current working directory into the container at /host_pwd
  • nvcr.io/hpc/kipoi:v0.6.8-keras2: URI of the latest NGC Kipoi container

Local workstation with nvidia-docker

This mode of running is suitable for interactive execution from a local workstation containing one or more GPUs. There are no requirements other than those stated in the System Requirements section.

Command line execution with nvidia-docker

To launch Kipoi via nvidia-docker, use:

$ ${DOCKER} kipoi predict <model_name> --dataloader_args='{"intervals_file": "</path/to/intervals_file>", "fasta_file": "</path/to/fasta_file>"}' -o '</path/to/output_file.tsv>' -n <thread_count> --batch_size <batch_size>

Where:

  • DOCKER: alias used to store the base Docker command
  • <model_name>: the name of the Kipoi model to use
  • </path/to/intervals_file>: path to the intervals input file for Kipoi
  • </path/to/fasta_file>: path to the fasta input file for Kipoi
  • </path/to/output_file.tsv>: path of a file for Kipoi to write prediction results into
  • <thread_count>: specify the number of threads for Kipoi to use at runtime
  • <batch_size>: specify the number of input intervals for Kipoi to process simultaneously

Interactive shell with nvidia-docker

To start an interactive shell within the container environment, run /bin/bash in the container:

$ ${DOCKER} /bin/bash

Where:

  • DOCKER: alias used to store the base Docker command
  • /bin/bash: starts an interactive bash shell

To run Kipoi while using the interactive shell:

$ ${DOCKER} kipoi predict <model_name> --dataloader_args='{"intervals_file": "</path/to/intervals_file>", "fasta_file": "</path/to/fasta_file>"}' -o '</path/to/output_file.tsv>' -n <thread_count> --batch_size <batch_size>

Where:

  • DOCKER: alias used to store the base Docker command
  • <model_name>: the name of the Kipoi model to use
  • </path/to/intervals_file>: path to the intervals input file for Kipoi
  • </path/to/fasta_file>: path to the fasta input file for Kipoi
  • </path/to/output_file.tsv>: path of a file for Kipoi to write prediction results into
  • <thread_count>: specify the number of threads for Kipoi to use at runtime
  • <batch_size>: specify the number of input intervals for Kipoi to process simultaneously

Running with Singularity

Pull the image

Save the NGC Kipoi container as a local Singularity image file:

$ singularity build kipoi_v0.6.8-keras2.simg docker://nvcr.io/hpc/kipoi:v0.6.8-keras2

This command saves the container in the current directory as kipoi_v0.6.8-keras2.simg

Note: Singularity/2.x

In order to pull NGC images with singularity version 2.x and earlier, NGC container registry authentication credentials are required.

To set your NGC container registry authentication credentials:

$ export SINGULARITY_DOCKER_USERNAME='$oauthtoken'
$ export SINGULARITY_DOCKER_PASSWORD=<NVIDIA NGC Cloud Services API key>

More information describing how to obtain and use your NVIDIA NGC Cloud Services API key can be found here.

Once the local Singularity image has been pulled, the following modes of running are supported:

Singularity Aliases

To simplify the examples below, define the following command aliases. These may be set as environment variables in a shell or batch script.

Singularity alias

SINGULARITY will be used to launch processes within the NGC Kipoi container using the Singularity runtime:

$ export SINGULARITY="$(which singularity) run --nv -B $(pwd):/host_pwd kipoi_v0.6.8-keras2.simg"

Where:

  • run: specifies mode of execution
  • --nv: exposes the host GPU to the container
  • -B $(pwd):/host_pwd: bind mounts the current working directory in the container at /host_pwd
  • kipoi_v0.6.8-keras2.simg: path of the saved Singularity image file

Local workstation with Singularity

This mode of running is suitable for interactive execution from a local workstation containing one or more GPUs. There are no requirements other than those stated in the System Requirements section.

Command line

To launch Kipoi, use:

$ ${SINGULARITY} /bin/bash -c "source activate kipoi-py3-keras1.2 && kipoi predict <model_name> --dataloader_args='{"intervals_file": "</path/to/intervals_file>", "fasta_file": "</path/to/fasta_file>"}' -o '</path/to/output_file.tsv>' -n <thread_count> --batch_size <batch_size>"

Where:

  • source activate kipoi-py3-keras1.2: activates the appropriate Conda environment for keras/v1.2 runs of Kipoi
  • SINGULARITY: alias used to store the base Singularity command
  • <model_name>: the name of the Kipoi model to use
  • </path/to/intervals_file>: path to the intervals input file for Kipoi
  • </path/to/fasta_file>: path to the fasta input file for Kipoi
  • </path/to/output_file.tsv>: path of a file for Kipoi to write prediction results into
  • <thread_count>: specify the number of threads for Kipoi to use at runtime
  • <batch_size>: specify the number of input intervals for Kipoi to process simultaneously

Interactive shell

To invoke an interactive shell, run /bin/bash within the container:

$ ${SINGULARITY} /bin/bash

While Singularity provides an interactive shell via singularity shell, this invocation ignores container entrypoint scripts. Thus, the preferred method to access an interactive shell is via a singularity run command directed at /bin/bash.

To run a HOOMD-blue Python script while using the interactive shell:

$ ${SINGULARITY} /bin/bash -c "source activate kipoi-py3-keras1.2 && /bin/bash"

Where:

  • SINGULARITY: alias used to store the base Singularity command