An experiment in running KnowEnG platform, pipelines, and IDE under Kubernetes.
- Docker (preferrably 1.10.x - 1.13.x)
- Mount propagation must be enabled
NOTE: On my Ubuntu AWS VM, I had to run the following command (or else the kubelet container would fail to start):
sudo mount --make-shared /git clone https://github.com/nds-org/ndslabs-knoweng
cd ndslabs-knowengTo quickly build up all of the pipeline images:
./compose.sh buildNOTE: If your Docker version differs, you may need to adjust the version in ./compose.sh
To push all images to DockerHub (required for multi-node cluster):
./compose.sh pushNOTE: You will need to docker login (and probably change the image/tags in the docker-compose.yml) before you can push
For some introductory slides to Kubernetes terminology, check out https://docs.google.com/presentation/d/1VDYrSlwLY_Efucq_n75m9Rf_euJIOIACh27BfOmh-ps/edit?usp=sharing
- Vagrant: https://github.com/kubernetes-incubator/kubespray/blob/master/docs/vagrant.md
- Terraform / Kubespray: https://github.com/BenGalewsky/kubespray
- ndslabs-deploy-tools (deprecated): https://github.com/nds-org/ndslabs-deploy-tools
To run a development Kubernetes cluster (via Docker):
./kube.shThis will start a series of Docker containers that allow you to run Kubernetes on a single machine!
NOTE: You'll need to manually add the path to the kubectl binary to your $PATH.
See https://github.com/kubernetes/minikube
See https://github.com/kubernetes-incubator/kubespray/blob/master/docs/vagrant.md
To run the KnowEnG platform, you need only kubectl access to a Kubernetes cluster.
To start up the platform in dev mode, with a Cloud9 IDE:
./knoweng.shFor an example of the running platform, see: knoweng.org/analyze
The ./knoweng.sh helper script does several things:
- Ensures that the user has a
basic-authsecret set up for Cloud9 to consume - Generates self-signed SSL certs if they are not found for the given ___domain
- Ensures that certificates have been imported as Kubernetes secrets
- Create ingress rules (referencing our TLS secrets) that will route
/ide.htmlto Cloud9, and route everything else to the KnowEnG Dashboard - Starts up a Pod running the Kubernetes NGINX Ingress Controller and its default-backend
- Starts up a Pod running the 4 containers comprising KnowEnG Dashboard
- Starts up a Pod running the Cloud9 IDE
To view the logs of an individual pod (where your work items are being executed):
root@knowdev2:/home/ubuntu/ndslabs-knoweng# kubectl get pods
NAME READY STATUS RESTARTS AGE
cloud9-984f9 1/1 Running 0 4h
default-http-backend-blrqv 1/1 Running 0 4h
nest-1442377537-3hhbf 4/4 Running 0 4h
nginx-ilb-rc-02fzw 1/1 Running 0 4h
# View the logs of a single-container Pod
kubectl logs -f nginx-ilb-rc-02fzw
# For multi-container pods, you must specify a container with -c
kubectl logs -f nest-1442377537-3hhbf -c flaskTo run the Data Cleanup pipeline:
kubectl create -f pipelines/dc.job.yamlTo run the Gene Prioritization pipeline:
kubectl create -f pipelines/gp.job.yamlTo run the Gene Set Characterization pipeline:
kubectl create -f pipelines/gsc.job.yamlTo run the Samples Clustering pipeline:
kubectl create -f pipelines/sc.job.yamlThis will create the Job objects on your Kubernetes cluster. Job objects themselves don't execute anything (and therefore don't keep logs), but they will spawn Pods (groups of containers) to execute the desired work item(s).
To view the status of your jobs and their pods:
core@nds842-master1 ~/ndslabs-knoweng $ kubectl get jobs,pods -a
NAME DESIRED SUCCESSFUL AGE
jobs/gp-test 1 1 1m
NAME READY STATUS RESTARTS AGE
po/gp-test-hzcq8 0/1 Completed 0 1mThis will list off all running jobs and their respectives pods (replicas).
NOTE: The -a flag tells to Kubernetes to include pods that have Completed in the returned list.
Use the pod name to check the log output:
# View the logs of a Pod spawned by a Job
kubectl logs -f gp-test-hzcq8 Check the contents of your shared storage to view output files:
core@nds842-node1 ~ $ du -h -a /var/glfs/global/jobs
512 /var/glfs/global/jobs/data-cleanup/results/TEST_1_gene_expression_UNMAPPED.tsv
0 /var/glfs/global/jobs/data-cleanup/results/TEST_1_gene_expression_MAP.tsv
1.0K /var/glfs/global/jobs/data-cleanup/results/log_gene_prioritization_pipeline.yml
5.5K /var/glfs/global/jobs/data-cleanup/results
9.5K /var/glfs/global/jobs/data-cleanup
512 /var/glfs/global/jobs/gene-prioritization/results/drug_A_bootstrap_net_correlation_pearson_Mon_03_Jul_2017_23_37_33.461636543_viz.tsv
512 /var/glfs/global/jobs/gene-prioritization/results/drug_B_bootstrap_net_correlation_pearson_Mon_03_Jul_2017_23_37_33.502016782_viz.tsv
512 /var/glfs/global/jobs/gene-prioritization/results/drug_C_bootstrap_net_correlation_pearson_Mon_03_Jul_2017_23_37_33.491072654_viz.tsv
512 /var/glfs/global/jobs/gene-prioritization/results/drug_E_bootstrap_net_correlation_pearson_Mon_03_Jul_2017_23_37_33.497779369_viz.tsv
512 /var/glfs/global/jobs/gene-prioritization/results/drug_D_bootstrap_net_correlation_pearson_Mon_03_Jul_2017_23_37_33.502823591_viz.tsv
512 /var/glfs/global/jobs/gene-prioritization/results/ranked_genes_per_phenotype_bootstrap_net_correlation_pearson_Mon_03_Jul_2017_23_37_33.781181335_download.tsv
512 /var/glfs/global/jobs/gene-prioritization/results/top_genes_per_phenotype_bootstrap_net_correlation_pearson_Mon_03_Jul_2017_23_37_33.786688327_download.tsv
7.5K /var/glfs/global/jobs/gene-prioritization/results
12K /var/glfs/global/jobs/gene-prioritization
512 /var/glfs/global/jobs/gene-set-characterization/results/net_path_ranked_by_property_Mon_03_Jul_2017_23_37_33.879306793.df
512 /var/glfs/global/jobs/gene-set-characterization/results/net_path_sorted_by_property_score_Mon_03_Jul_2017_23_37_33.885757923.df
512 /var/glfs/global/jobs/gene-set-characterization/results/net_path_droplist_Mon_03_Jul_2017_23_37_33.891902446.tsv
5.5K /var/glfs/global/jobs/gene-set-characterization/results
2.5K /var/glfs/global/jobs/gene-set-characterization/job-parameters.yml
12K /var/glfs/global/jobs/gene-set-characterization
365K /var/glfs/global/jobs/samples-clustering/results/consensus_matrix_cc_net_nmf_Mon_03_Jul_2017_23_39_04.368379831_viz.tsv
512 /var/glfs/global/jobs/samples-clustering/results/silhouette_average_cc_net_nmf_Mon_03_Jul_2017_23_39_04.496203184_viz.tsv
4.0K /var/glfs/global/jobs/samples-clustering/results/samples_label_by_cluster_cc_net_nmf_Mon_03_Jul_2017_23_39_04.502247810_viz.tsv
1.5K /var/glfs/global/jobs/samples-clustering/results/clustering_evaluation_result_Mon_03_Jul_2017_23_39_05.487591743.tsv
56M /var/glfs/global/jobs/samples-clustering/results/genes_by_samples_heatmap_cc_net_nmf_Mon_03_Jul_2017_23_39_09.115032196_viz.tsv
594K /var/glfs/global/jobs/samples-clustering/results/genes_averages_by_cluster_cc_net_nmf_Mon_03_Jul_2017_23_39_17.276435136_viz.tsv
404K /var/glfs/global/jobs/samples-clustering/results/genes_variance_cc_net_nmf_Mon_03_Jul_2017_23_39_17.396065711_viz.tsv
305K /var/glfs/global/jobs/samples-clustering/results/top_genes_by_cluster_cc_net_nmf_Mon_03_Jul_2017_23_39_17.440115690_download.tsv
58M /var/glfs/global/jobs/samples-clustering/results
4.5K /var/glfs/global/jobs/samples-clustering/job-parameters.yml
58M /var/glfs/global/jobs/samples-clustering
58M /var/glfs/global/jobsWe can see that no matter which node ran our jobs, all of our output files are available in one place.
NOTE: On multi-node clusters, you will need to SSH to a compute node or start a container mounting the shared storage. The shared storage is not currently mounted on the master.
Kubernetes leaves it up to the user to delete their own Job objects, which stick around indefinitely to ease debugging.
To delete a job (and trigger clean up of its corresponding pods):
kubectl delete jobs/gp-testTo delete all jobs:
kubectl delete jobs --allNOTE: Supposedly, Kubernetes can be configured to clean up completed/failed jobs automatically, but I have not yet experimented with this feature.