Genomics Data Generation
Last updated
Last updated
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 2201 different models, covering canonical predictive tasks in transcriptional and post-transcriptional gene regulation. Kipoi's API is implemented as a python package and it is also accessible from the command line.
Running genomics model on Bacalhau
To get started, you need to install the Bacalhau client, see more information here
To run Genomics on Bacalhau we need to set up a Docker container. To do this, you'll need to create a Dockerfile and add your desired configuration. The Dockerfile is a text document that contains the commands that specify how the image will be built.
The docker build command builds Docker images from a Dockerfile.
Before running the command replace;
hub-user with your docker hub username, If you don’t have a docker hub account follow these instructions to create a Docker Account, and use the username of the account you created
repo-name with the name of the container, you can name it anything you want
tag this is not required but you can use the latest tag
In our case
Next, upload the image to the registry. This can be done by using the Docker hub username, repo name or tag.
After the repo image has been pushed to Docker Hub, we can now use the container for running on Bacalhau. To submit a job, run the following Bacalhau command:
When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.
Job status: You can check the status of the job using bacalhau list.
When it says Completed, that means the job is done, and we can get the results.
Job information: You can find out more information about your job by using bacalhau describe.
Job download: You can download your job results directly by using bacalhau get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in that directory.
After the download has finished you should see the following contents in the results directory
To view the file, run the following command:
