gxabm 2.11.0

Opinionated Benchmarking Automatation in Galaxy

Automated Benchmarking in Galaxy

An opinionated Python Bioblend script for automating benchmarking tasks in Galaxy.

Installation

It is recommended to install abm into its own virtual environment.

python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install gxabm

From Source

1. Clone the GitHub repository.

   git clone https://github.com/galaxyproject/gxabm.git
   cd gxabm
   

2. Create a virtual env and install the required libraries

   python3 -m venv .venv
   source .venv/bin/activate
   pip install --upgrade pip
   pip install -r requirements.txt
   

:bulb: The included setup.sh file can be sourced to both activate the virtual environment and create an alias so you do not need to type python3 abm.py or python3 -m abm all the time. The remainder of this document assumes that the setup.sh file has been sourced or abm has been installed from PyPI.

> source setup.sh
> abm workflow help

Setup

Prerequisites

To make full use of the abm program users will need to install:

1. kubectl (optional)
2. helm

The kubectl program is only required when bootstrapping a new Galaxy instance, in particular to obtain the Galaxy URL from the Kubernetes cluster (abm <cloud> kube url). Helm is used to update Galaxy's job configuration settings and is required to run any experiments.

Credentials

You will need an API key for every Galaxy instance you would like to intereact with. You will also need the kubeconfig file for each Kubernetes cluster. The abm script loads the Galaxy server URLs, API keys, and the location of the kubeconfig files from a Yaml configuration file that it expects to find in $HOME/.abm/profile.yml or .abm-profile.yml in the current directory. You can use the samples/profile.yml file as a starting point and it includes the URLs for all Galaxy instances we have used to date (December 22, 2021 as of this writing).

:bulb: It is now possible (>=2.0.0) to create Galaxy users and their API keys directly with abm.

abm <cloud> user create username [e-mail] password
abm <cloud> user key [e-mail]

Users will also need the kubeconfig files for each Kubernetes cluster. By default kubectl expects that all kubeconfigs are stored in a single configuration file located at $HOME/.kube/config. However, this is a system wide configuration making it difficult for two processes to operate on different Kubernetes clusters at the same time. Therefore the abm scripts expects each cluster to store it's configuration in its own kubeconfig file in a directory named $HOME/.kube/configs.

:warning: Creating users and their API keys requires that a master api key has been configured for Galaxy.

Usage

To get general usage information run the command:

> abm help

You can get information about a specific abm command with:

> abm workflow help

When running a command (i.e. not just printing help) you will need to specify the Galaxy instance to target as the first parameter:

> abm aws workflow list
> abm aws workflow run configs/paired-dna.yml

New In 2.0.0

Version 2.0.0 refactors the workflow and benchmark commands to eliminate any confusion between a Galaxy workflow and what abm referred to as a workflow.

Terms and Definitions

workflow
A Galaxy workflow. Workflows in abm are mangaged with the workflow sub-command. Workflows can not be run directly via the abm command, but are run through the benchmark or experiment commands.

benchmark
A benchmark consists of one or more workflows with their inputs and outputs defined in a YAML configuration file. See the Benchmark Configuration section for instructions on defining a benchmark.

experiment
An experiment consists of one or more benchmarks to be run on one or more cloud providers. Each experiment definition consists of:

1. The number of runs to be executed. Each benchmark will be executed this number of times.
2. The benchmarks to be executed
3. The cloud providers the benchmarks should be executed on
4. The job rule configurations to be used. The job rule configurations define the number of CPUs and amount of memory to be allocated to the tools being benchmarked.

See the Experiment Configuration section for instructions on defining an experiment.

Changes to Functionality

While the functionality in abm is the same, some functions have been moved to other sub-commands. In particular, the workflow translate, workflow validate, and workflow run command have been moved to the benchmark subcommand and the benchmark run and benchmark summarize commands have moved to the experiment subcommand.

1.x	2.x
workflow translate	benchmark translate
workflow validate	benchmark validate
workflow run	benchmark run
benchmark run	experiment run
benchmark summarize	experiment summarize

Instance Configuration

Before ABM can interact with the Galaxy cluster an entry for that cluster needs to be created in ABM's ~/.abm/profile.yml configuration file. Since the profile is just a YAML file it can be edited in any text editor to add the entry with the URL, API key, and KUBECONFIG location. Or we can use abm commands to create the entry.

abm config create cloud /path/to/kubeconfig                          (1)
abm config url cloud https://galaxy.url                              (2)
abm cloud user create username [e-mail] userpassword   (3)
key=$(abm cloud user apikey [e-mail])                  (4)
abm config key cloud $key                                            (5)
abm config show cloud

1. Creates a new entry for cloud in the ~/.abm/profile.yml file. The config create expects two parameters: the name of the cloud instance and the path to the kubeconfig file used by kubectl to intereact with the cluster. The name can be anything you want, and long as that name has not already been used. The kubeconfig will have been generated when the cluster was provisioned and how it is obtained will depend on the cloud provider and is beyond the scope of this document.
2. Sets the url field in the profile. The abm cloud kube url command can be used to determine Galaxy's URL, but see the caveats section for known problems. If the kube url command does not work you can also use kubectl get svc -n galaxy to find the ingress service name and kubectl describe svc -n galaxy service-name to find the ingress URL.
3. Creates a new user in the Galaxy instance. The email address should be specified in the Galaxy admin_users sections of the values.yml file used when installing Galaxy to the cluster. If the user is not an admin user then installing tools will fail.
4. Fetch the user's API key for that Galaxy instance and saves it to an environment variable
5. Save the API key to the profile configuration.

Benchmark Configuration

The runtime parameters for benchmarking runs are specified in a YAML configuration file. The configuration file can contain more than one runtime configuration specified as a YAML list. This file can be stored anywhere, but several examples are included in the config directory.

The YAML configuration for a single workflow looks like:

- workflow_id: d6d3c2119c4849e4
  output_history_base_name: RNA-seq
  reference_data:
    - name: Reference Transcript (FASTA)
      dataset_id: 50a269b7a99356aa
  runs:
    - history_name: 1
      inputs:
      - name: FASTQ RNA Dataset
        dataset_id: 28fa757e56346a34
    - history_name: 2
      inputs:
      - name: FASTQ RNA Dataset
        dataset_id: 1faa2d3b2ed5c436

• workflow_id
  The ID of the workflow to run.

• output_history_ base_name (optional)
  Name to use as the basis for histories created. If the output_history_base_name is not specified then the workflow_id is used.

• reference_data (optional)
  Input data that is the same for all benchmarking runs and only needs to be set once. See the section on inputs below for a description of the fields

• runs
  Input definitions for a benchmarking run. Each run defintion shoud contain:

  • history_name (optional)
    The name of the history created for the output. The final output history name is generated by concatenating the output_history_base_name from above and the history_name. If the history_name is not specified an incrementing integer counter is used.
  • inputs
    The one or more input datasets to the workflow. Each input specification consists of:
    1. name the input name as specified in the workflow editor
    2. dataset_id the History API ID as displayed in the workflow editor or with the abm history list command.

Experiment Configuration

Each experiment is defined by a YAML configuration file. Example experiments can be found in the experiments directory.

name: Benchmarking DNA
runs: 3
benchmark_confs:
  - benchmarks/dna-named.yml
cloud:
  - tacc1
  - tacc2
job_configs:
  - 4x8
  - 8x16

• name
  The name of the experiment. This value is not currently not used.
• runs
  The number of times each benchmark will be executed. Note a benchmark configuration may itself define more than one workflow execution.
• benchmark_confs
  The benchmark configurations to be execute during the experiment. These directories/files are expected to be relative to the current working directory.
• cloud
  The cloud providers, as defined in the profile.yml file, where the experiments will be run. The cloud provider instances must already have the workflows and history datasets uploaded and available for use. Use the bootstrap.py script to provision an instance for running experiements.
• job_configs
  The jobs.rules.container_mapper_rules files that define the CPU and memory resources allocated to tools. These files must be located in the rules directory.

Moving Workflows

Use the abm <cloud> workflow download and abm <cloud> workflow upload commands to transfer Galaxy workflows between Galaxy instances.

> abm cloud1 workflow download <workflow ID> /path/to/save/workflow.ga
> abm cloud2 workflow upload /path/to/save/workflow.ga

NOTE the name of the saved file (workflow.ga in the above example) is unrelated to the name of the workflow as it will appear in the Galaxy user interface or when listed with the workflow list command.

Moving Benchmarks

The benchmark translate and benchmark validate commands can be used when moving workflows and datasets between Galaxy instances. The benchmark translate command takes the path to a benchmark configuration file, translates the workflow and dataset ID values to their name as they appear in the Galaxy user interface, and writes the configuration to stdout. To save the translated workflow configuration, redirect the output to a file:

> abm aws benchmark translate config/rna-seq.yml > benchmarks/rna-seq-named.yml

Then use the benchmark validate command to ensure that the other Galaxy instance has the same workflow and datasets installed:

> abm gcp benchmark validate config/rna-seq-named.yml

Moving Histories

Exporting Histories

1. Ensure the history is publicly available (i.e. published) on the Galaxy instance. You can do this through the Galaxy user interface or via the abm history publish command:

$> abm cloud history publish <history id>

If you do not know the <history id> you can find it with abm cloud history list.

2. Export the history

$> abm cloud history export <history id>

Make note of the URL that is returned from the histroy export command as this is the URL to use to import the history to another Galaxy instance. Depending on the size of the datasets in the history it may take several hours for the history to be exported, during which time your computer terminal will be blocked. Use the [-n|--no-wait] option if you do not want history export to block until the export is complete.

$> abm cloud history export <history id> --no-wait

The history export command will return immediately and print the job ID for the export job. Use this job id to obtain the status of the job and determine when it has completed.

$> abm cloud job show <job id>

Once a history has been exported the first time, and as long it has not changed, running abm history export again simply print the URL and exit without re-exporting the history. This is useful when the --no-wait option was specified and we need to determine the URL to use for importing.

:bulb: A History should only be exported once and the URL re-used on new benchmarking instances as they are created. Use the lib/histories.yml file to record the URLs so they can be easily reused with the history import command.

Importing Histories

To import a history use the URL returned from the history export command.

$> abm dest history import URL

# For example
$> abm dest history import https://usegalaxy.org/history/export_archive?id=9198b7907edea3fa&jeha_id=02700395dbc14520

We can easily import histories defined in lib/histories.yml by specifying the YAML dictionary key name.

$> abm dest history import rna

Troubleshooting

Generate SSL/TLS certificates used by kubeadm. Use the --apiserver-cert-extra-sans parameter to list additional IP addresses that the certificates will be valid for.

> kubeadm init phase certs all --apiserver-advertise-address=0.0.0.0 --apiserver-cert-extra-sans=10.161.233.80,114.215.201.87

Future Work

• Run benchmarks/experiments in parallel when using more than one cloud provider.
• Integrate with the Galaxy Benchmarker
• Use as much as we can from Git-Gat

Contributing

Fork this repository and then create a working branch for yourself from the dev branch. All pull requests should target dev and not the master branch.

git clone https://github.com/ksuderman/bioblend-scripts.git
cd bioblend-scripts
git checkout -b my-branch

If you decide to work on one of the issues be sure to assign yourself to that issue to let others know the issue is taken.

Versioning

Use the included bump Python script to update the version number. The bump script behaves similarily to the bumpversion Python package without the version control integration.

bump major
bump minor
bump revision
bump build  

The bump build command is only valid for development versions, that is, a version number followed by a dash, followed some characters, followed some digits. For example 2.0.0-rc1 or 2.1.0-dev8. Use bump release to move from a development build to a release build.

Building and Deploying

make clean
make
make test-deploy
make deploy

The make test-deploy deploys artifacts to TestPyPI server and is intended for deploying and testing development builds. Development build should not be deployed to PyPI.

Caveats and Known Problems

The abm kube url command is intended to retrieve the URL needed to access the Galaxy instance on the Kubernetes cluster. However, there are a few issues that make this not so straight-forward:

• the name of the ingress controller is not consistant. Sometimes it is ingress-nginx-controller (AWS) and sometimes it is simply ingress-nginx (GCP)
• sometimes the instance is accessed via the hostname field (AWS) and sometimes the ip field
• the URL for the Galaxy instance may have an arbitrary path included, i.e. https://hostname or https://hostname/galaxy or https://hostname/something/galaxy