diff --git a/.gitignore b/.gitignore index ce9a353..e2c9558 100644 --- a/.gitignore +++ b/.gitignore @@ -22,7 +22,8 @@ test.log /docker-tests ~* /docs/source/_static/images/logo-square.png -/src/australianimagingservice_community/_version.py +/src/**/_version.py *.venv .mypy_cache .build* +.ipynb_checkpoints diff --git a/requirements.txt b/requirements.txt index 5038a3e..5e7380a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,2 @@ -arcana >=0.10.11 -arcana-xnat >= 0.3.6 -fileformats >= 0.8.6 -fileformats-extras >= 0.2.1 -fileformats-datascience >= 0.1.0 -fileformats-datascience-extras >= 0.1.1 -fileformats-medimage >= 0.4.4 -fileformats-medimage-extras >= 0.1.5 +pydra2app-xnat >=0.51 + diff --git a/specs/australian-imaging-service-community/au/edu/sydney/sydneyimaging/t1_preproc.yaml b/specs/australian-imaging-service-community/au/edu/sydney/sydneyimaging/t1_preproc.yaml index 9fced27..268e2cc 100644 --- a/specs/australian-imaging-service-community/au/edu/sydney/sydneyimaging/t1_preproc.yaml +++ b/specs/australian-imaging-service-community/au/edu/sydney/sydneyimaging/t1_preproc.yaml @@ -1,5 +1,5 @@ -arcana_spec_version: 1.0 # the version of the specification format used for this file -title: 'Preprocess T1-weighted MRI' # Short name for the pipeline referenced in the UI +title: "Preprocess T1-weighted MRI" # Short name for the pipeline referenced in the UI +schema_version: 1.0 # the version of the specification format used for this file version: # The version of Ubuntu's zip we are using package: "1.0" @@ -24,18 +24,19 @@ packages: neurodocker: mrtrix3: 3.0.2 command: - task: australianimagingservice.community.au.edu.sydney.sydneyimaging.t1_preproc:t1_preproc # Use the generic "shell-cmd" task - row_frequency: session # the pipeline is desgined to run on imaging "sessions" as opposed to "subjects" or "projects" - inputs: # List the inputs that are presented to end-user in UI + task: australianimagingservice.community.au.edu.sydney.sydneyimaging.t1_preproc:t1_preproc # Use the generic "shell-cmd" task + row_frequency: session # the pipeline is desgined to run on imaging "sessions" as opposed to "subjects" or "projects" + inputs: # List the inputs that are presented to end-user in UI example_input: - datatype: medimage/dicom-series # MIME-type or "MIME-like" format - help: "Example Input" # description of field presented in UI - outputs: # List the outputs generated by the pipeline + datatype: medimage/dicom-series # MIME-type or "MIME-like" format + help: "Example Input" # description of field presented in UI + outputs: # List the outputs generated by the pipeline example_output: - datatype: medimage/nifti-gz-x # MIME-type or "MIME-like" format - help: Example Output # description of field presented in UI - parameters: # Parameters exposed to user to UI + datatype: medimage/nifti-gz-x # MIME-type or "MIME-like" format + help: Example Output # description of field presented in UI + parameters: # Parameters exposed to user to UI example_param: - datatype: field/integer # Format of the field - help: an example parameter # description of field presented in UI - default: 99 # Default value, filled in on the UI + datatype: field/integer # Format of the field + help: an example parameter # description of field presented in UI + default: 99 # Default value, filled in on the UI + diff --git a/specs/australian-imaging-service-community/examples/bet.yaml b/specs/australian-imaging-service-community/examples/bet.yaml index bdf97d3..638e01c 100644 --- a/specs/australian-imaging-service-community/examples/bet.yaml +++ b/specs/australian-imaging-service-community/examples/bet.yaml @@ -1,5 +1,5 @@ -arcana_spec_version: 1.0 # the version of the specification format used for this file -title: 'Example BET pipeline' # Short name for the pipeline referenced in the UI +schema_version: 1.0 # the version of the specification format used for this file +title: "Example BET pipeline" # Short name for the pipeline referenced in the UI version: # Define a version for the pipeline in combination of the underlying package version (e.g. FSL) # and the "build" of the pipeline @@ -8,8 +8,8 @@ version: base_image: # Chose a NeuroDesk FSL container as the base name: vnmd/fsl_6.0.6.4 - package_manager: apt # It is not obvious from the base image what the package manager is - tag: '20230618' + package_manager: apt # It is not obvious from the base image what the package manager is + tag: "20230618" packages: # Install dependencies for DICOM->NIfTI conversion pip: @@ -22,29 +22,27 @@ authors: - name: Thomas G. Close email: thomas.close@sydney.edu.au docs: - info_url: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki # Link to external documentation for underlying tool - description: | # Description of tool for auto-docs + info_url: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki # Link to external documentation for underlying tool + description: | # Description of tool for auto-docs An example wrapping BET in a XNAT pipeline command: - task: arcana.common:shell_cmd # Use the generic "shell-cmd" task - row_frequency: session # the pipeline is desgined to run on imaging "sessions" as opposed to "subjects" or "projects" - inputs: # List the inputs that are presented to end-user in UI - T1w: - datatype: medimage/nifti-gz # MIME-type or "MIME-like" format - help: "T1-weighted anatomical scan" # description of field presented in UI - configuration: # Additional configuration parameters that are passed to the arcana.common:shell_cmd task in the "inputs" dict - position: -2 # Position of field when printed to command line call. Negative numbers are indexed from the end backwards - argstr: '' # prefix for field when printed to command line - column_defaults: - datatype: medimage/dicom-series # the default - outputs: # List the outputs generated by the pipeline + task: common:shell # Use the generic "shell-cmd" task + row_frequency: session # the pipeline is desgined to run on imaging "sessions" as opposed to "subjects" or "projects" + inputs: # List the inputs that are presented to end-user in UI + t1w: + datatype: medimage/nifti-gz # MIME-type or "MIME-like" format + help: "T1-weighted anatomical scan" # description of field presented in UI + configuration: # Additional configuration parameters that are passed to the pydra2app.common:shell task in the "inputs" dict + position: -2 # Position of field when printed to command line call. Negative numbers are indexed from the end backwards + argstr: "" # prefix for field when printed to command line + outputs: # List the outputs generated by the pipeline brain: - datatype: medimage/nifti-gz # MIME-type or "MIME-like" format - help: Brain-extracted data # description of field presented in UI - configuration: # Additional configuration parameters that are passed to the arcana.common:shell_cmd task in the "outputs" dict - position: -1 # Position of field when printed to command line call. Negative numbers are indexed from the end backwards - argstr: '' # prefix for field when printed to command line + datatype: medimage/nifti-gz # MIME-type or "MIME-like" format + help: Brain-extracted data # description of field presented in UI + configuration: # Additional configuration parameters that are passed to the pydra2app.common:shell task in the "outputs" dict + position: -1 # Position of field when printed to command line call. Negative numbers are indexed from the end backwards + argstr: "" # prefix for field when printed to command line parameters: {} - configuration: # Additional args passed to arcana.common:shell_cmd + configuration: # Additional args passed to pydra2app.common:shell name: bet executable: bet diff --git a/specs/australian-imaging-service-community/examples/zip.yaml b/specs/australian-imaging-service-community/examples/zip.yaml index daef1dd..a9b13e3 100644 --- a/specs/australian-imaging-service-community/examples/zip.yaml +++ b/specs/australian-imaging-service-community/examples/zip.yaml @@ -1,5 +1,7 @@ -arcana_spec_version: 1.0 # the version of the specification format used for this file -title: 'Zip up a directory' # Short name for the pipeline referenced in the UI +# The version of the specification format used for this file +schema_version: 1.0 +# Short name for the pipeline referenced in the UI +title: "Zips up a file or directory" version: # The version of Ubuntu's zip we are using package: "3.0" @@ -21,34 +23,53 @@ docs: packages: # Install the zip command in the Ubuntu image system: - - zip + - zip command: - task: arcana.common:shell_cmd # Use the generic "shell-cmd" task - row_frequency: session # the pipeline is desgined to run on imaging "sessions" as opposed to "subjects" or "projects" - inputs: # List the inputs that are presented to end-user in UI + # Use the generic "shell-command" task + task: common:shell + # the pipeline is desgined to run on imaging "sessions" as opposed to "subjects" or "projects" + row_frequency: session + # List the inputs that are presented to end-user in UI + inputs: to_zip: - datatype: generic/fs-object # MIME-type or "MIME-like" format - help: "Input file-system object to zip" # description of field presented in UI - configuration: # Additional configuration args that are passed to the arcana.common:shell_cmd task as part of the "inputs" dict - argstr: '' # Position of field when printed to command line call. Negative numbers are indexed from the end backwards - position: -1 # prefix for field when printed to command line - outputs: # List the outputs generated by the pipeline + # MIME-type or "MIME-like" format, generic/fs-object corresponds a file or directory + datatype: generic/fs-object + # description of field presented in UI + help: "Input file-system object to zip" + # Additional config args that are passed to the shell task as part of the "inputs" dict + configuration: + # Position of field on command line call. Negative numbers are indexed backwards from the end + argstr: "" + # prefix for field when printed to command line + position: -1 + # List the outputs generated by the pipeline + outputs: zipped: - datatype: application/zip # MIME-type or "MIME-like" format - help: Zipped FS Object # description of field presented in UI + # MIME-type or "MIME-like" format + datatype: application/zip + # description of field presented in UI + help: Zipped file-system Object + # Additional config args that are passed to the shell task as part of the "outputs" dict configuration: - argstr: '' # Position of field when printed to command line call. Negative numbers are indexed from the end backwards - position: -2 # prefix for field when printed to command line - parameters: # Parameters exposed to user to UI + # Position of field on command line call. Negative numbers are indexed backwards from the end + argstr: "" + # prefix for field when printed to command line + position: -2 + # Parameters exposed to user to UI + parameters: compression: - datatype: field/integer # Format of the field - help: the level of compression applied # description of field presented in UI - default: 5 # Default value, filled in on the UI - configuration: # Additional configuration parameter args that are passed to the arcana.common:shell_cmd task in the "parameters" dict + # Format of the field + datatype: field/integer + # description of field presented in UI + help: the level of compression applied + # Default value, filled in on the UI + default: 5 + # Additional config args that are passed to the shell task in the "parameters" dict + configuration: # string template for field when printed to command line. "{field-name}" are # replaced by the value provided to the field name argstr: -{compression} - configuration: # Additional args passed to arcana.common:shell_cmd - name: zip_dir + # Additional args passed to shell + configuration: + # the command to run executable: zip - \ No newline at end of file diff --git a/tests/test_bet.py b/tests/test_bet.py index 10016ec..9309148 100644 --- a/tests/test_bet.py +++ b/tests/test_bet.py @@ -1,13 +1,13 @@ - from pathlib import Path import tempfile -from arcana.common import Clinical +from frametree.common import Clinical from fileformats.medimage import NiftiGz -from arcana.common import DirTree +from frametree.common import DirTree from arcana.testing.data.blueprint import ( - TestDatasetBlueprint, FileSetEntryBlueprint as FileBP + TestDatasetBlueprint, + FileSetEntryBlueprint as FileBP, ) -from arcana.core.deploy.command import ContainerCommand +from pydra2app.core.command import ContainerCommand from medimages4tests.mri.neuro.t1w import get_image @@ -19,13 +19,13 @@ NiftiGz(get_image()).copy(source_dir, new_stem="t1w") bp = TestDatasetBlueprint( - hierarchy=["session"], - space=Clinical, - dim_lengths=[1, 1, 1], - entries=[ - FileBP(path="t1_weighted", datatype=NiftiGz, filenames=["t1w.nii.gz"]), - ], - ) + hierarchy=["session"], + space=Clinical, + dim_lengths=[1, 1, 1], + entries=[ + FileBP(path="t1_weighted", datatype=NiftiGz, filenames=["t1w.nii.gz"]), + ], +) work_dir = Path(tempfile.mkdtemp()) @@ -33,7 +33,7 @@ saved_dataset = bp.make_dataset(DirTree(), dataset_id, name="", source_data=source_dir) command_spec = ContainerCommand( - task="arcana.common:shell_cmd", + task="common:shell", row_frequency=Clinical.session, inputs=[ { @@ -43,7 +43,7 @@ "configuration": { "argstr": "", "position": -2, - } + }, }, ], outputs=[ @@ -54,12 +54,12 @@ "configuration": { "argstr": "", "position": -1, - } + }, } ], configuration={ "executable": "bet", - } + }, ) # Start generating the arguments for the CLI # Add source to loaded dataset diff --git a/tests/test_build.py b/tests/test_build.py index eb9dfd1..bda5014 100644 --- a/tests/test_build.py +++ b/tests/test_build.py @@ -1,14 +1,14 @@ from pathlib import Path from click.testing import CliRunner -from arcana.core.cli.deploy import make_app -from arcana.core.utils.misc import show_cli_trace +from pydra2app.core.cli import make +from frametree.core.utils import show_cli_trace PKG_PATH = Path(__file__).parent.parent.absolute() runner = CliRunner() results = runner.invoke( - make_app, + make, [ f"{PKG_PATH}/australian-imaging-service-community/", "xnat:XnatApp", diff --git a/tests/test_zip.py b/tests/test_zip.py index 842294a..56e48b4 100644 --- a/tests/test_zip.py +++ b/tests/test_zip.py @@ -1,28 +1,28 @@ - from pathlib import Path import tempfile import operator as op import functools from fileformats.generic import Directory from fileformats.application import Zip -from arcana.common import DirTree -from arcana.testing import TestDataSpace -from arcana.testing.data.blueprint import ( - TestDatasetBlueprint, FileSetEntryBlueprint as FileBP +from frametree.common import DirTree +from frametree.testing import TestDataSpace +from frametree.testing.blueprint import ( + TestDatasetBlueprint, + FileSetEntryBlueprint as FileBP, ) -from arcana.core.deploy.command import ContainerCommand +from pydra2app.core.command import ContainerCommand bp = TestDatasetBlueprint( - hierarchy=[ - "abcd" - ], # e.g. XNAT where session ID is unique in project but final layer is organised by timepoint - space=TestDataSpace, - dim_lengths=[1, 1, 1, 1], - entries=[ - FileBP(path="dir1", datatype=Directory, filenames=["dir"]), - ], - ) + hierarchy=[ + "abcd" + ], # e.g. XNAT where session ID is unique in project but final layer is organised by timepoint + space=TestDataSpace, + dim_lengths=[1, 1, 1, 1], + entries=[ + FileBP(path="dir1", datatype=Directory, filenames=["dir"]), + ], +) work_dir = Path(tempfile.mkdtemp()) @@ -30,7 +30,7 @@ saved_dataset = bp.make_dataset(DirTree(), dataset_id, name="") command_spec = ContainerCommand( - task="arcana.common:shell_cmd", + task="arcana.common:shell", row_frequency=bp.space.default(), inputs=[ { @@ -40,7 +40,7 @@ "configuration": { "argstr": "", "position": -1, - } + }, }, ], outputs=[ @@ -51,7 +51,7 @@ "configuration": { "argstr": "", "position": -2, - } + }, } ], parameters=[ @@ -67,7 +67,7 @@ ], configuration={ "executable": "zip", - } + }, ) # Start generating the arguments for the CLI # Add source to loaded dataset diff --git a/tutorial/ais-pipelines-tutorial.ipynb b/tutorial/ais-pipelines-tutorial.ipynb new file mode 100644 index 0000000..834af7f --- /dev/null +++ b/tutorial/ais-pipelines-tutorial.ipynb @@ -0,0 +1,1356 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7cce59cd", + "metadata": {}, + "source": [ + "# AIS Pipelines Tutorial" + ] + }, + { + "cell_type": "markdown", + "id": "5cf50801", + "metadata": {}, + "source": [ + "In this workshop you will learn how to design, deploy and run Australian Imaging Service pipelines\n", + "\n", + "##### Preparation\n", + "1. Explore the community pipelines repository\n", + "1. Start up a test XNAT instance to test the pipelines\n", + "1. Set up your Git/GitHub\n", + "\n", + "##### Build and deploy your first pipelines\n", + "\n", + "1. Build *Zip* pipeline from an existing example specification\n", + "1. Install, enable and launch the zip pipeline using the XNAT UI\n", + "1. Build *FSL BET* pipeline image\n", + "1. Install, enable and launch the BET pipeline using `pydra2app ext xnat (install|launch)-command`s\n", + "\n", + "##### Design a pipeline to run mri_convert\n", + "1. Create a new Git branch\n", + "1. Generate specification for *mri_convert* command\n", + "1. Build *mri_convert* pipeline\n", + "1. Install project-specific Freesurfer license\n", + "1. Test the *mri_convert* pipeline\n", + "1. Create a test pull-request on GitHub\n", + "\n", + "##### Design your own pipeline (if you have one in mind)\n", + "\n", + " Design, build and test your own pipeline using the methods demonstrated in previous sections\n" + ] + }, + { + "cell_type": "markdown", + "id": "138b2936-bc0a-4f98-bfe6-46183ddd2198", + "metadata": {}, + "source": [ + "## Preparation" + ] + }, + { + "cell_type": "markdown", + "id": "78fdb31d", + "metadata": {}, + "source": [ + "### Explore the community pipelines repository" + ] + }, + { + "cell_type": "markdown", + "id": "86d0bf23", + "metadata": {}, + "source": [ + "Examine the structure of the pipelines repository using the `tree` utility" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7de3909f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[01;34m.\u001b[0m\n", + "├── \u001b[00mLICENSE\u001b[0m\n", + "├── \u001b[00mREADME.md\u001b[0m\n", + "├── \u001b[00mcodecov.yml\u001b[0m\n", + "├── \u001b[01;34mdocs\u001b[0m\n", + "├── \u001b[00mrequirements.txt\u001b[0m\n", + "├── \u001b[01;34mspecs\u001b[0m\n", + "│   └── \u001b[01;34maustralian-imaging-service-community\u001b[0m\n", + "│   ├── \u001b[01;34mau\u001b[0m\n", + "│   │   └── \u001b[01;34medu\u001b[0m\n", + "│   │   └── \u001b[01;34msydney\u001b[0m\n", + "│   │   └── \u001b[01;34msydneyimaging\u001b[0m\n", + "│   │   └── \u001b[00mt1_preproc.yaml\u001b[0m\n", + "│   └── \u001b[01;34mexamples\u001b[0m\n", + "│   ├── \u001b[00mbet.yaml\u001b[0m\n", + "│   └── \u001b[00mzip.yaml\u001b[0m\n", + "├── \u001b[01;34msrc\u001b[0m\n", + "│   └── \u001b[01;34mau.edu.sydney.sydneyimaging\u001b[0m\n", + "│   ├── \u001b[00mREADME.md\u001b[0m\n", + "│   ├── \u001b[01;34maustralianimagingservice\u001b[0m\n", + "│   │   └── \u001b[01;34mcommunity\u001b[0m\n", + "│   │   └── \u001b[01;34mau\u001b[0m\n", + "│   │   └── \u001b[01;34medu\u001b[0m\n", + "│   │   └── \u001b[01;34msydney\u001b[0m\n", + "│   │   └── \u001b[01;34msydneyimaging\u001b[0m\n", + "│   │   ├── \u001b[00m__init__.py\u001b[0m\n", + "│   │   ├── \u001b[00mt1_preproc.py\u001b[0m\n", + "│   │   └── \u001b[01;34mtests\u001b[0m\n", + "│   │   └── \u001b[00mtest_t1_preproc.py\u001b[0m\n", + "│   └── \u001b[00mpyproject.toml\u001b[0m\n", + "├── \u001b[01;34mtests\u001b[0m\n", + "│   ├── \u001b[00mtest_bet.py\u001b[0m\n", + "│   ├── \u001b[00mtest_build.py\u001b[0m\n", + "│   ├── \u001b[00mtest_docs.py\u001b[0m\n", + "│   └── \u001b[00mtest_zip.py\u001b[0m\n", + "└── \u001b[01;34mtutorial\u001b[0m\n", + " ├── \u001b[00mais-pipelines-tutorial.ipynb\u001b[0m\n", + " ├── \u001b[00mrequirements.txt\u001b[0m\n", + " ├── \u001b[00mstart-up-script.sh\u001b[0m\n", + " └── \u001b[00mxnat4tests-config.yaml\u001b[0m\n", + "\n", + "20 directories, 20 files\n" + ] + } + ], + "source": [ + "cd ~/git/pipelines-community\n", + "tree . --gitignore" + ] + }, + { + "cell_type": "markdown", + "id": "521d8ef7", + "metadata": {}, + "source": [ + "Examine the layout of the *Zip* pipelines specification" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7fafd93b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# The version of the specification format used for this file\n", + "spec_version: 1.0\n", + "# Short name for the pipeline referenced in the UI\n", + "title: \"Zips up a file or directory\"\n", + "version:\n", + " # The version of Ubuntu's zip we are using\n", + " package: \"3.0\"\n", + "base_image:\n", + " # Pick a generic base image, in this case Ubuntu - Jammy (22.04LTS)\n", + " name: ubuntu\n", + " tag: jammy\n", + "authors:\n", + " # Authors of the pipeline. The first email will be considered to be the maintainer of\n", + " # the generated container images\n", + " - name: Thomas G. Close\n", + " email: thomas.close@sydney.edu.au\n", + "docs:\n", + " # Link to the external documentation for the tool\n", + " info_url: https://manpages.ubuntu.com/manpages/focal/man1/zip.1.html\n", + " # Description for auto-generated docss\n", + " description: |\n", + " This is a simple pipeline that zips up the given directory\n", + "packages:\n", + " # Install the zip command in the Ubuntu image\n", + " system:\n", + " - zip\n", + "command:\n", + " # Use the generic \"shell-command\" task\n", + " task: pydra2app.common:shell\n", + " # the pipeline is desgined to run on imaging \"sessions\" as opposed to \"subjects\" or \"projects\"\n", + " row_frequency: session\n", + " # List the inputs that are presented to end-user in UI\n", + " inputs:\n", + " to_zip:\n", + " # MIME-type or \"MIME-like\" format, generic/fs-object corresponds a file or directory\n", + " datatype: generic/fs-object\n", + " # description of field presented in UI\n", + " help: \"Input file-system object to zip\"\n", + " # Additional config args that are passed to the shell task as part of the \"inputs\" dict\n", + " configuration:\n", + " # Position of field on command line call. Negative numbers are indexed backwards from the end\n", + " argstr: \"\"\n", + " # prefix for field when printed to command line\n", + " position: -1\n", + " # List the outputs generated by the pipeline\n", + " outputs:\n", + " zipped:\n", + " # MIME-type or \"MIME-like\" format\n", + " datatype: application/zip\n", + " # description of field presented in UI\n", + " help: Zipped file-system Object\n", + " # Additional config args that are passed to the shell task as part of the \"outputs\" dict\n", + " configuration:\n", + " # Position of field on command line call. Negative numbers are indexed backwards from the end\n", + " argstr: \"\"\n", + " # prefix for field when printed to command line\n", + " position: -2\n", + " # Parameters exposed to user to UI\n", + " parameters:\n", + " compression:\n", + " # Format of the field\n", + " datatype: field/integer\n", + " # description of field presented in UI\n", + " help: the level of compression applied\n", + " # Default value, filled in on the UI\n", + " default: 5\n", + " # Additional config args that are passed to the shell task in the \"parameters\" dict\n", + " configuration:\n", + " # string template for field when printed to command line. \"{field-name}\" are\n", + " # replaced by the value provided to the field name\n", + " argstr: -{compression}\n", + " # Additional args passed to shell\n", + " configuration:\n", + " # the command to run\n", + " executable: zip\n" + ] + } + ], + "source": [ + "cat specs/australian-imaging-service-community/examples/zip.yaml" + ] + }, + { + "cell_type": "markdown", + "id": "9f943cc5", + "metadata": {}, + "source": [ + "### Start up a test XNAT instance to test the pipelines" + ] + }, + { + "cell_type": "markdown", + "id": "7173a999-9968-493e-94ff-a95f922dd5b7", + "metadata": {}, + "source": [ + "View the public key here so we can use it in subsequent steps while we wait for the test XNAT to start up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d3cde9f-ade9-4336-912f-e3f3673ee38d", + "metadata": {}, + "outputs": [], + "source": [ + "cat $HOME/.ssh/id_rsa.pub" + ] + }, + { + "cell_type": "markdown", + "id": "155ad9ba", + "metadata": {}, + "source": [ + "Start a test XNAT on your machine/VM using the `xnat4tests` package" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fefe84fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-06 18:12:25,419 - xnat4tests - INFO - Building xnat4tests in '/Users/tclose/.xnat4tests/build' directory\n", + "2024-08-06 18:14:05,134 - xnat4tests - INFO - Built xnat4tests successfully\n", + "2024-08-06 18:14:05,141 - xnat4tests - INFO - Did not find xnat4tests container, relaunching\n", + "2024-08-06 18:14:05,629 - xnat4tests - INFO - xnat4tests launched successfully\n", + "2024-08-06 18:14:05,629 - xnat4tests - INFO - Attempting to connect to http://localhost:8080\n", + "2024-08-06 18:14:05,629 - xnat4tests - INFO - Connecting to http://localhost:8080 as 'admin'\n", + "2024-08-06 18:14:10,638 - xnat4tests - INFO - Connecting to http://localhost:8080 as 'admin'\n", + "2024-08-06 18:14:15,647 - xnat4tests - INFO - Connecting to http://localhost:8080 as 'admin'\n", + "2024-08-06 18:15:45,805 - xnat4tests - INFO - Connected to http://localhost:8080 successfully\n", + "2024-08-06 18:15:45,878 - xnat4tests - INFO - Configuing docker server for container service\n" + ] + } + ], + "source": [ + "xnat4tests -c ~/git/pipelines-community/tutorial/xnat4tests-config.yaml start" + ] + }, + { + "cell_type": "markdown", + "id": "500f569d", + "metadata": {}, + "source": [ + "It will take XNAT a couple of minutes to boot up. Once the frame above completes successfully you will be able to navigate to http://localhost:8080 and login with username=`admin`, password=`admin`.\n", + "\n", + "Now we will add some open-source data from OpenNeuro to our XNAT in order to test the pipelines we will build" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "56a8b6fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-06 18:18:26,382 - xnat4tests - INFO - Connecting to http://localhost:8080 as 'admin'\n", + "2024-08-06 18:18:27,331 - xnat4tests - INFO - Connecting to http://localhost:8080 as 'admin'\n", + "2024-08-06 18:18:33,253 - xnat4tests - INFO - Connecting to http://localhost:8080 as 'admin'\n", + "2024-08-06 18:18:37,026 - xnat4tests - INFO - Connecting to http://localhost:8080 as 'admin'\n", + "2024-08-06 18:18:37,778 - xnat4tests - INFO - Connecting to http://localhost:8080 as 'admin'\n", + "2024-08-06 18:18:40,868 - xnat4tests - INFO - Connecting to http://localhost:8080 as 'admin'\n" + ] + } + ], + "source": [ + "xnat4tests -c ~/git/pipelines-community/tutorial/xnat4tests-config.yaml add-data simple-dir\n", + "xnat4tests -c ~/git/pipelines-community/tutorial/xnat4tests-config.yaml add-data openneuro-t1w" + ] + }, + { + "cell_type": "markdown", + "id": "48c963ec-b6be-46d0-8a3b-e0ce584e3df1", + "metadata": {}, + "source": [ + "### Set up your Git/GitHub" + ] + }, + { + "cell_type": "markdown", + "id": "03e833e5-abd6-4087-b290-5fdd35b9a37c", + "metadata": {}, + "source": [ + "Fork your own copy of the community pipelines repo\n", + "\n", + "1. Navigate to https://github.com\n", + "1. Create a GitHub user account (if you don't have one already)\n", + "1. Add the SSH key generated in the first step to your GitHub account under `Settings>SSH and GPG keys` (Settings are accessed by clicking your avatar in the top right hand corner)\n", + "1. Fork the https://github.com/Australian-Imaging-Service/pipelines-community into your GitHub user account" + ] + }, + { + "cell_type": "markdown", + "id": "526c4c67-2d47-494e-ba26-6bd632c95896", + "metadata": {}, + "source": [ + "Set the origin of the repository to your fork" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "2f910886-c303-4bb8-8efb-55c8d39ea126", + "metadata": {}, + "outputs": [], + "source": [ + "git remote rename origin upstream\n", + "git remote add origin git@github.com:\"\"/pipelines-community.git" + ] + }, + { + "cell_type": "markdown", + "id": "d20e62f0-b04c-42fa-841a-5a3ea2554ee0", + "metadata": {}, + "source": [ + "Update the repository with the latest changes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ea96efe-5e97-4189-812e-2db76f1301fe", + "metadata": {}, + "outputs": [], + "source": [ + "git pull upstream" + ] + }, + { + "cell_type": "markdown", + "id": "e4042467-5d45-4180-9b15-1c6348424d29", + "metadata": {}, + "source": [ + "Configure your local Git user" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c891a9d-d20d-4d75-9a71-addd70adc7d1", + "metadata": {}, + "outputs": [], + "source": [ + "git config --global user.name \"Your Name\"\n", + "git config --global user.email \"youremail@example.com\"" + ] + }, + { + "cell_type": "markdown", + "id": "a3aafaa2", + "metadata": {}, + "source": [ + "## Build and deploy your first pipelines" + ] + }, + { + "cell_type": "markdown", + "id": "180e8f7d", + "metadata": {}, + "source": [ + "### Build *Zip* pipeline from an existing example specification" + ] + }, + { + "cell_type": "markdown", + "id": "9d6dd189", + "metadata": {}, + "source": [ + "Checkout the help for the `pydra2app make` command we will use to build the pipeline from the specification" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3a6a38f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Usage: pydra2app make [OPTIONS] TARGET SPEC_PATH\n", + "\n", + " Construct and build a docker image containing a pipeline to be run on data\n", + " stored in a data repository or structure (e.g. XNAT Container Service\n", + " Pipeline or BIDS App)\n", + "\n", + " TARGET is the type of image to build. For standard images just the pydra2app\n", + " sub-package is required (e.g. 'xnat' or 'common'). However, specific App\n", + " subclasses can be specified using : format,\n", + " e.g. pydra2app.xnat:XnatApp\n", + "\n", + " SPEC_PATH is the file system path to the specification to build, or\n", + " directory containing multiple specifications\n", + "\n", + "Options:\n", + " --registry TEXT The Docker registry to deploy the pipeline\n", + " to\n", + " --build-dir PATH Specify the directory to build the Docker\n", + " image in. Defaults to `.build` in the\n", + " directory containing the YAML specification\n", + " --release \n", + " Name of the release for the package as a\n", + " whole (i.e. for all pipelines)\n", + " --tag-latest / --dont-tag-latest\n", + " whether to tag the release as the \"latest\"\n", + " or not\n", + " --save-manifest PATH File path at which to save the build\n", + " manifest\n", + " --logfile PATH Log output to file instead of stdout\n", + " --loglevel TEXT The level to display logs at\n", + " --use-local-packages / --dont-use-local-packages\n", + " Use locally installed Python packages,\n", + " instead of pulling them down from PyPI\n", + " --install-extras TEXT Install extras to use when installing\n", + " Pydra2App inside the container image.\n", + " Typically only used in tests to provide\n", + " 'test' extra\n", + " --use-test-config / --dont-use-test-config\n", + " Build the image so that it can be run in\n", + " Pydra2App's test configuration (only for\n", + " internal use)\n", + " --raise-errors / --log-errors Raise exceptions instead of logging failures\n", + " --generate-only / --build Just create the build directory and\n", + " dockerfile\n", + " --license \n", + " Licenses provided at build time to be stored\n", + " in the image (instead of downloaded at\n", + " runtime)\n", + " --license-to-download TEXT Specify licenses that are not provided at\n", + " runtime and instead downloaded from the data\n", + " store at runtime in order to satisfy their\n", + " conditions\n", + " --check-registry / --dont-check-registry\n", + " Check the registry to see if an existing\n", + " image with the same tag is present, and if\n", + " so whether the specification matches (and\n", + " can be skipped) or not (raise an error)\n", + " --push / --dont-push push built images to registry\n", + " --clean-up / --dont-clean-up Remove built images after they are pushed to\n", + " the registry\n", + " --spec-root PATH The root path to consider the specs to be\n", + " relative to, defaults to CWD\n", + " -s, --source-package PATH Path to a local Python package to be\n", + " included in the image. Needs to have a\n", + " package definition that can be built into a\n", + " source distribution and the name of the\n", + " directory needs to match that of the package\n", + " to be installed. Multiple packages can be\n", + " specified by repeating the option.\n", + " -e, --export-file \n", + " Path to be exported from the Docker build\n", + " directory for convenience. Multiple files\n", + " can be specified by repeating the option.\n", + " --help Show this message and exit.\n" + ] + } + ], + "source": [ + "pydra2app make --help" + ] + }, + { + "cell_type": "markdown", + "id": "e5f1977e", + "metadata": {}, + "source": [ + "Run the `pydra2app make` command to build the Zip pipeline from its specification\n", + "\n", + "Notes:\n", + "\n", + "* The name of the built image is taken from its relative file-system path, so we pass the `--spec-root` option to specify where this path should start from\n", + "* The `--for-localhost` is required to be able to run the pipeline using the test XNAT repository, i.e. it is only used in development not production\n", + "* We export the generated `xnat_command.json` file from the build directory, so we can easily copy it into the XNAT UI in subsequent steps" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1d824073", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - Building sdist...\n", + "INFO - Building sdist...\n", + "INFO - Dockerfile for 'australian-imaging-service-community/examples.zip:3.0' generated at specs/australian-imaging-service-community/examples/.build-zip/Dockerfile\n", + "INFO - Successfully built docker image australian-imaging-service-community/examples.zip:3.0\n", + "australian-imaging-service-community/examples.zip:3.0\n", + "INFO - Successfully built australian-imaging-service-community/examples.zip:3.0 pipeline\n" + ] + } + ], + "source": [ + "pydra2app make xnat \\\n", + "./specs/australian-imaging-service-community/examples/zip.yaml \\\n", + "--spec-root ./specs \\\n", + "--for-localhost \\\n", + "--export-file xnat_command.json ~/zip-xnat-command.json" + ] + }, + { + "cell_type": "markdown", + "id": "0c723085", + "metadata": {}, + "source": [ + "**NOTES:**\n", + "* `--spec-root` the name and organisation given to the generated image is based on the file path to the specification file, the spec root specifies where this path should be relative to (if not provided it is the current working directory\n", + "* `--for-localhost` is required when running the containers on a test XNAT server installed on the localhost\n", + "* `--export-file` exports generated files from the build directory to a location they can be accessed more conveniently\n", + "\n", + "We can take a look at the *XNAT command JSON* that is generated by the make process. This is the specification that\n", + "tells XNAT how to run the pipeline within the image" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "fa298a80", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"name\": \"examples.zip\",\n", + " \"description\": \"examples.zip 3.0: Zips up a file or directory\",\n", + " \"label\": \"examples.zip\",\n", + " \"schema-version\": \"1.0\",\n", + " \"image\": \"australian-imaging-service-community/examples.zip:3.0\",\n", + " \"index\": \"docker.io\",\n", + " \"datatype\": \"docker\",\n", + " \"override-entrypoint\": true,\n", + " \"mounts\": [\n", + " {\n", + " \"name\": \"in\",\n", + " \"writable\": false,\n", + " \"path\": \"/input\"\n", + " },\n", + " {\n", + " \"name\": \"out\",\n", + " \"writable\": true,\n", + " \"path\": \"/output\"\n", + " },\n", + " {\n", + " \"name\": \"work\",\n", + " \"writable\": true,\n", + " \"path\": \"/work\"\n", + " }\n", + " ],\n", + " \"ports\": {},\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"to_zip\",\n", + " \"description\": \"Match resource (application/x-fs-object) [SCAN-TYPE]: Input file-system object to zip \",\n", + " \"type\": \"string\",\n", + " \"default-value\": \"\",\n", + " \"required\": false,\n", + " \"user-settable\": true,\n", + " \"replacement-key\": \"[TO_ZIP_INPUT]\"\n", + " },\n", + " {\n", + " \"name\": \"compression\",\n", + " \"description\": \"Parameter (): the level of compression applied\",\n", + " \"type\": \"number\",\n", + " \"default-value\": 5,\n", + " \"required\": false,\n", + " \"user-settable\": true,\n", + " \"replacement-key\": \"[COMPRESSION_PARAM]\"\n", + " },\n", + " {\n", + " \"name\": \"Pydra2App_flags\",\n", + " \"description\": \"Flags passed to `run-pydra2app-pipeline` command\",\n", + " \"type\": \"string\",\n", + " \"default-value\": \"--plugin serial --work /wl --dataset-name default --loglevel info --export-work /work\",\n", + " \"required\": false,\n", + " \"user-settable\": true,\n", + " \"replacement-key\": \"#PYDRA2APP_FLAGS#\"\n", + " },\n", + " {\n", + " \"name\": \"PROJECT_ID\",\n", + " \"description\": \"Project ID\",\n", + " \"type\": \"string\",\n", + " \"required\": true,\n", + " \"user-settable\": false,\n", + " \"replacement-key\": \"[PROJECT_ID]\"\n", + " },\n", + " {\n", + " \"name\": \"SESSION_LABEL\",\n", + " \"description\": \"Imaging session label\",\n", + " \"type\": \"string\",\n", + " \"required\": true,\n", + " \"user-settable\": false,\n", + " \"replacement-key\": \"[SESSION_LABEL]\"\n", + " },\n", + " {\n", + " \"name\": \"SUBJECT_LABEL\",\n", + " \"description\": \"Subject label\",\n", + " \"type\": \"string\",\n", + " \"required\": true,\n", + " \"user-settable\": false,\n", + " \"replacement-key\": \"[SUBJECT_LABEL]\"\n", + " }\n", + " ],\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"zipped\",\n", + " \"description\": \"zipped (application/zip)\",\n", + " \"required\": true,\n", + " \"mount\": \"out\",\n", + " \"path\": \"zipped.zip\",\n", + " \"glob\": null\n", + " }\n", + " ],\n", + " \"xnat\": [\n", + " {\n", + " \"name\": \"examples.zip\",\n", + " \"description\": \"Zips up a file or directory\",\n", + " \"contexts\": [\n", + " \"xnat:imageSessionData\"\n", + " ],\n", + " \"external-inputs\": [\n", + " {\n", + " \"name\": \"SESSION\",\n", + " \"description\": \"Imaging session\",\n", + " \"type\": \"Session\",\n", + " \"source\": null,\n", + " \"default-value\": null,\n", + " \"required\": true,\n", + " \"replacement-key\": null,\n", + " \"sensitive\": null,\n", + " \"provides-value-for-command-input\": null,\n", + " \"provides-files-for-command-mount\": \"in\",\n", + " \"via-setup-command\": null,\n", + " \"user-settable\": false,\n", + " \"load-children\": true\n", + " }\n", + " ],\n", + " \"derived-inputs\": [\n", + " {\n", + " \"name\": \"__SESSION_LABEL__\",\n", + " \"type\": \"string\",\n", + " \"derived-from-wrapper-input\": \"SESSION\",\n", + " \"derived-from-xnat-object-property\": \"label\",\n", + " \"provides-value-for-command-input\": \"SESSION_LABEL\",\n", + " \"user-settable\": false\n", + " },\n", + " {\n", + " \"name\": \"__SUBJECT_ID__\",\n", + " \"type\": \"string\",\n", + " \"derived-from-wrapper-input\": \"SESSION\",\n", + " \"derived-from-xnat-object-property\": \"subject-id\",\n", + " \"provides-value-for-command-input\": \"SUBJECT_LABEL\",\n", + " \"user-settable\": false\n", + " },\n", + " {\n", + " \"name\": \"__PROJECT_ID__\",\n", + " \"type\": \"string\",\n", + " \"derived-from-wrapper-input\": \"SESSION\",\n", + " \"derived-from-xnat-object-property\": \"project-id\",\n", + " \"provides-value-for-command-input\": \"PROJECT_ID\",\n", + " \"user-settable\": false\n", + " }\n", + " ],\n", + " \"output-handlers\": [\n", + " {\n", + " \"name\": \"zipped-resource\",\n", + " \"accepts-command-output\": \"zipped\",\n", + " \"via-wrapup-command\": null,\n", + " \"as-a-child-of\": \"SESSION\",\n", + " \"type\": \"Resource\",\n", + " \"label\": \"zipped\",\n", + " \"format\": \"application/zip\"\n", + " }\n", + " ]\n", + " }\n", + " ],\n", + " \"command-line\": \"conda run --no-capture-output -n pydra2app pydra2app ext xnat cs-entrypoint xnat-cs//[PROJECT_ID] --input to_zip '[TO_ZIP_INPUT]' --output zipped 'zipped' --parameter compression '[COMPRESSION_PARAM]' --dataset-hierarchy subject,session --ids [SESSION_LABEL] #PYDRA2APP_FLAGS#\"\n", + "}\n" + ] + } + ], + "source": [ + "cat ~/zip-xnat-command.json" + ] + }, + { + "cell_type": "markdown", + "id": "84498233", + "metadata": {}, + "source": [ + "### Install, enable and launch the zip pipeline using the XNAT UI" + ] + }, + { + "attachments": { + "Screen%20Shot%202024-08-05%20at%2012.59.37%20pm.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "ddcaf447", + "metadata": {}, + "source": [ + "##### Install the pipeline\n", + "1. Navigate to http://localhost:8080\n", + "1. Login with default XNAT credentials: username=`admin` password=`admin`\n", + "1. Click on the `Administer` menu item in top ribbon and select `Plugin Settings` from the drop-down menu\n", + "1. Select `Images & Commands` under the `Container Service` heading from the left-hand menu\n", + "1. Click the `New Command` button and copy and paste the command JSON generated by the build process in the dialog that opens (replacing the `{}` that is already there) and click `Save Command`\n", + "\n", + "##### Enable the pipeline globally\n", + "1. Select `Command Configurations` item from the left-hand menu under `Container Service`\n", + "1. Toggle the `Enabled` switch next to the Zip pipeline\n", + "\n", + "NB: This enables the pipeline globally, but users still cannot launch the pipeline at this stage. It still needs to be enabled for each project it is to be run on by a project owner.\n", + "\n", + "##### Enable the pipeline for a specific project\n", + "1. Navigate back to the home screen by clicking the XNAT logo in the top-left corner\n", + "1. Select the `SIMPLE_DIR` project\n", + "1. Select `Project Settings` from the bottom of the right-hand actions menu\n", + "1. Select `Configure Commands` from the left-hand menu\n", + "1. Toggle the `Enabled` switch next to the Zip pipeline\n", + "\n", + "##### Launch the pipeline\n", + "1. Navigate back to project home by clicking the `SIMPLE_DIR` breadcrumb\n", + "1. Select either one of the two subjects\n", + "1. Select the MR session\n", + "1. Click on the `Run containers` from the bottom of the right-hand side actions menu and select \"Zip up a file or directory\"\n", + "1. In the `To_zip` field of the dialog that opens up enter `a-directory` to select the scan that is to be zipped and click `Run Container`\n", + "\n", + "##### Check the status of the pipeline\n", + "1. In the `History` panel click the `Reload` button and you should see the pipeline status\n", + "1. Select the \"eye\" image that appears to the right of the status when you hover over it to view details of the workflow status\n", + "1. If the workflow has failed (after a successful launch), you can view the output and error logs by clicking the `View StdOut.log` and `View StdErr.log` buttons at the bottom of the page.\n", + "1. (Advanced) to access the working directory of the command in order to debug anything that has gone wrong, look up the `container-host-path` of the `work` mount listed under `container mounts` (see image below)\n", + "1. Select `Manage Files` from the right-hand side Actions menu to view the generated zip file\n", + "\n", + "\n", + "![View workflow status.png](attachment:Screen%20Shot%202024-08-05%20at%2012.59.37%20pm.png)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "066e2361", + "metadata": {}, + "source": [ + "### Build FSL BET pipeline image" + ] + }, + { + "cell_type": "markdown", + "id": "42f982e3", + "metadata": {}, + "source": [ + "Build the example BET specification this time including the licence you just downloaded into the image (as this is permitted by the licence conditions)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "03db8871", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - Building sdist...\n", + "INFO - Building sdist...\n", + "INFO - Dockerfile for 'australian-imaging-service-community/examples.bet:6.0.6.4-1' generated at specs/australian-imaging-service-community/examples/.build-bet/Dockerfile\n", + "INFO - Successfully built docker image australian-imaging-service-community/examples.bet:6.0.6.4-1\n", + "australian-imaging-service-community/examples.bet:6.0.6.4-1\n", + "INFO - Successfully built australian-imaging-service-community/examples.bet:6.0.6.4-1 pipeline\n" + ] + } + ], + "source": [ + "pydra2app make xnat \\\n", + "./specs/australian-imaging-service-community/examples/bet.yaml \\\n", + "--spec-root ./specs \\\n", + "--for-localhost" + ] + }, + { + "cell_type": "markdown", + "id": "a9a122ff", + "metadata": {}, + "source": [ + "### Install, enable and launch the BET pipeline using `pydra2app ext xnat (install|launch)-command`s" + ] + }, + { + "cell_type": "markdown", + "id": "80933b50", + "metadata": {}, + "source": [ + "For convenience (primarily during testing), I have created a couple of commands to install and launch pipelines via the CLI" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7cc01126", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved alias/token for 'http://localhost:8080' XNAT in '/Users/tclose/.pydra2app_xnat_user_token.json' file, please ensure the file is secure\n" + ] + } + ], + "source": [ + "pydra2app ext xnat save-token \\\n", + "--server http://localhost:8080 \\\n", + "--user admin \\\n", + "--password admin" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3c57c1fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading existing alias/token pair from '/Users/tclose/.pydra2app_xnat_user_token.json\n", + "INFO - Deleted existing command 'examples.bet'\n", + "Successfully installed the 'australian-imaging-service-community/examples.bet:6.0.6.4-1' pipeline on 'http://localhost:8080'\n" + ] + } + ], + "source": [ + "pydra2app ext xnat install-command \\\n", + "australian-imaging-service-community/examples.bet:6.0.6.4-1 \\\n", + "--enable \\\n", + "--enable-project OPENNEURO_T1W \\\n", + "--replace-existing" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "cf75564c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading existing alias/token pair from '/Users/tclose/.pydra2app_xnat_user_token.json\n", + "Successfully launched the 'examples.bet' pipeline on 'subject01_MR01' session in 'OPENNEURO_T1W' project on 'http://localhost:8080'\n" + ] + } + ], + "source": [ + "pydra2app ext xnat launch-command \\\n", + "examples.bet \\\n", + "OPENNEURO_T1W \\\n", + "subject01_MR01 \\\n", + "--input t1w t1w" + ] + }, + { + "cell_type": "markdown", + "id": "5b850362", + "metadata": {}, + "source": [ + "## Design a pipeline to run mri_convert" + ] + }, + { + "cell_type": "markdown", + "id": "6ce5d0b8", + "metadata": {}, + "source": [ + "### Create a new Git branch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc0399d9", + "metadata": {}, + "outputs": [], + "source": [ + "git checkout -b my-mri-convert" + ] + }, + { + "cell_type": "markdown", + "id": "26d6902c", + "metadata": {}, + "source": [ + "### Generate specification for *mri_convert* command" + ] + }, + { + "cell_type": "markdown", + "id": "981bbfac", + "metadata": {}, + "source": [ + "Using the `pydra2app bootstrap` command we can generate a YAML specification for mri_synthstrip that we can edit later.\n", + "\n", + "**NOTE:** You will need change the `\"name-of-your-institution-goes-here\"` and `\"name-of-your-group-goes-here\"` placeholders to appropriate values" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "17b98c06", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Usage: pydra2app [OPTIONS] COMMAND [ARGS]...\n", + "Try 'pydra2app --help' for help.\n", + "\n", + "Error: No such command 'bootstrap'.\n", + "bash: --base-image-name: command not found\n" + ] + }, + { + "ename": "", + "evalue": "127", + "output_type": "error", + "traceback": [] + } + ], + "source": [ + "export INSTITUTION_NAME=\"name-of-your-institution-goes-here\" # e.g. \"sydney\" for The University of Sydney\n", + "export GROUP_NAME=\"name-of-your-group-goes-here\" # e.g. \"sydneyimaging\" for Sydney Imaging\n", + "export AUTHORS_NAME=\"Your name goes here\"\n", + "export AUTHORS_EMAIL=\"your.email@goes.here\"\n", + "\n", + "pydra2app bootstrap \\\n", + "./specs/australian-image-service-community/au/edu/${INSITUTION_NAME}/${GROUP_NAME}/mri_convert.yaml \\\n", + "--authors-name ${AUTHORS_NAME} \\\n", + "--authors-email ${AUTHORS_EMAIL} \\\n", + "--version 0.1\n", + "--base-image-name vnmd/freesurfer_7.1.1 \\\n", + "--packages-pip fileformats-medimage-extras \\\n", + "--packages-neurodocker dcm2niix v1.0.20201102 \\\n", + "--command-task shell \\\n", + "--inputs head \"datatype=medimage/nifti-gz,position=-2,argstr=''\" \\\n", + "--outputs brain \"datatype=medimage/nifti-gz,position=-1,argstr=''\" \\\n", + "--configuration executable mri_convert \\\n", + "--version-package 7.1.1 \\\n", + "--title \"MRI Convert\" \\\n", + "--licenses freesurfer \"destination=/opt/freesurfer/license.txt,info_url=https://surfer.nmr.mgh.harvard.edu/registration.html\"" + ] + }, + { + "cell_type": "markdown", + "id": "06916664", + "metadata": {}, + "source": [ + "You can view the generated YAML specification and make any edits that are required." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e0451e1a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bash: name-of-your-institution-goes-here: No such file or directory\n" + ] + }, + { + "ename": "", + "evalue": "1", + "output_type": "error", + "traceback": [] + } + ], + "source": [ + "cat ./specs/australian-image-service-community/au/edu/${INSITUTION_NAME}/${GROUP_NAME}/mri_convert.yaml" + ] + }, + { + "cell_type": "markdown", + "id": "c47caf9b", + "metadata": {}, + "source": [ + "### Build the `mri_convert` pipeline" + ] + }, + { + "cell_type": "markdown", + "id": "1f8616c7", + "metadata": {}, + "source": [ + "Build the newly created pipeline specification" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d6d655fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bash: name-of-your-institution-goes-here: No such file or directory\n" + ] + }, + { + "ename": "", + "evalue": "1", + "output_type": "error", + "traceback": [] + } + ], + "source": [ + "pydra2app make xnat \\\n", + "./specs/australian-image-service-community/au/edu/${INSITUTION_NAME}/${GROUP_NAME}/mri_convert.yaml \\\n", + "--spec-root ./specs \\\n", + "--for-localhost" + ] + }, + { + "cell_type": "markdown", + "id": "c454bd86", + "metadata": {}, + "source": [ + "### Install a project-specific Freesurfer licence using FrameTree\n", + "\n", + "Download the Freesurfer licence file from Discord or request your own at https://surfer.nmr.mgh.harvard.edu/registration.html\n", + "\n", + "First, create a local reference to the test XNAT server" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1af18b4b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/tclose/.pyenv/versions/frametree-test/bin/frametree\", line 8, in \n", + " sys.exit(cli())\n", + " ^^^^^\n", + " File \"/Users/tclose/.pyenv/versions/3.11.8/envs/frametree-test/lib/python3.11/site-packages/click/core.py\", line 1157, in __call__\n", + " return self.main(*args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/tclose/.pyenv/versions/3.11.8/envs/frametree-test/lib/python3.11/site-packages/click/core.py\", line 1078, in main\n", + " rv = self.invoke(ctx)\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"/Users/tclose/.pyenv/versions/3.11.8/envs/frametree-test/lib/python3.11/site-packages/click/core.py\", line 1688, in invoke\n", + " return _process_result(sub_ctx.command.invoke(sub_ctx))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/tclose/.pyenv/versions/3.11.8/envs/frametree-test/lib/python3.11/site-packages/click/core.py\", line 1688, in invoke\n", + " return _process_result(sub_ctx.command.invoke(sub_ctx))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/tclose/.pyenv/versions/3.11.8/envs/frametree-test/lib/python3.11/site-packages/click/core.py\", line 1434, in invoke\n", + " return ctx.invoke(self.callback, **ctx.params)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/tclose/.pyenv/versions/3.11.8/envs/frametree-test/lib/python3.11/site-packages/click/core.py\", line 783, in invoke\n", + " return __callback(*args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/tclose/git/workflows/frametree/frametree/core/cli/store.py\", line 84, in add\n", + " store_cls = ClassResolver(DataStore)(type)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/tclose/git/workflows/frametree/frametree/core/serialize.py\", line 86, in __call__\n", + " klass = self.fromstr(class_str, subpkg=True, pkg=self.package)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/tclose/git/workflows/frametree/frametree/core/serialize.py\", line 135, in fromstr\n", + " raise ValueError(\n", + "ValueError: Class location 'test-xnat' should contain a ':' unless it is in the builtins module\n" + ] + }, + { + "ename": "", + "evalue": "1", + "output_type": "error", + "traceback": [] + } + ], + "source": [ + "frametree store add xnat test-xnat --server http://localhost:8080 --user admin --password admin" + ] + }, + { + "cell_type": "markdown", + "id": "24db59c0", + "metadata": {}, + "source": [ + "Create a default dataset on the Open Neuro T1w project" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "93fcea4c", + "metadata": {}, + "outputs": [], + "source": [ + "frametree dataset define test-xnat//OPENNEURO_T1W" + ] + }, + { + "cell_type": "markdown", + "id": "c0d492e4", + "metadata": {}, + "source": [ + "Install the freesurfer license into the OPENNEURO_T1W dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "50108c2d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[38;2;0;255;0m100%\u001b[39m of 1.8 KiB |################################| 3.2 MiB/s Time: 0:00:00\n", + "/var/folders/mz/yn83q2fd3s758w1j75d2nnw80000gn/T/tmpfsd1l7oi/OPENNEURO_T1W/resources/__frametree__/files/_.json\n", + "INFO:frametree:Put freesurfer_LICENSE@ into dataset:None row via API access\n", + "INFO:frametree.core.cli.dataset:Successfully installed 'freesurfer' license for '' dataset on test-xnat store\n" + ] + } + ], + "source": [ + "frametree dataset install-license freesurfer ~/freesurfer-license.txt test-xnat//OPENNEURO_T1W" + ] + }, + { + "cell_type": "markdown", + "id": "1066677e", + "metadata": {}, + "source": [ + "### Test the new pipeline" + ] + }, + { + "cell_type": "markdown", + "id": "11a56e4e", + "metadata": {}, + "source": [ + "Install and launch your newly created pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1a44525", + "metadata": {}, + "outputs": [], + "source": [ + "pydra2app ext xnat install-command \\\n", + "australian-image-service-community.${INSITUTION_NAME}.${GROUP_NAME}.mri_convert:0.1 \\\n", + "http://localhost:8080 \\\n", + "--enable \\\n", + "--enable-project OPENNEURO_T1W \\\n", + "--replace-existing\n", + "\n", + "pydra2app ext xnat launch-command \\\n", + "australian-image-service-community.au.edu.${INSITUTION_NAME}.${GROUP_NAME}.mri_convert:0.1 \\\n", + "http://localhost:8080 \\\n", + "OPENNEURO_T1W \\\n", + "subject01_MR01 \\\n", + "--input t1w t1w" + ] + }, + { + "cell_type": "markdown", + "id": "779803ec-756d-4369-b763-3e1a90045fbc", + "metadata": {}, + "source": [ + "### Create a test pull-request on GitHub" + ] + }, + { + "cell_type": "markdown", + "id": "2ecf3933-1360-42a4-acf1-eacefea46502", + "metadata": {}, + "source": [ + "Commit and your changes" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "686d7741-a77e-43da-b175-237f3ba9bef5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[detached HEAD e6aff42] added specification for Freesurfer's mri_convert\n", + " 1 file changed, 32 insertions(+), 7 deletions(-)\n" + ] + } + ], + "source": [ + "git commit -am\"added specification for Freesurfer's mri_convert\"\n", + "git push" + ] + }, + { + "cell_type": "markdown", + "id": "dba5c217", + "metadata": {}, + "source": [ + "Create the pull-request on GitHub\n", + "\n", + "1. Navigate to your fork of the AIS community pipelines repo, https://github.com/your-github-username/pipelines-community\n", + "1. Select \"Pull requests\" in the top ribbon\n", + "1. Click the \"New pull request\" button\n", + "1. Select \"base:main\" <- \"your-fork:my-mri-convert\" from the drop-down lists\n", + "1. Click \"Create pull request\"\n", + "\n", + "This will then start the process for the pipeline to be accepted and deployed\n", + "\n", + "1. Maintainers of AIS Community Pipelines repository (i.e. Arkiev and myself) will be notified that you wish to add your pipeline to the community repository.\n", + "1. The repository maintainers (RM) will review your proposed pipeline for security issues\n", + "1. RM will potentially request some changes to your specification\n", + "1. RM accept your pipeline and merge your pull request\n", + "1. The pipeline is built using the continuous integration and deployment actions running on GitHub\n", + "1. Checks for newly pipelines are run periodically to pull the latest versions of the pipelines to your local node (although this is not setup at every node yet)" + ] + }, + { + "cell_type": "markdown", + "id": "2f6c108d", + "metadata": {}, + "source": [ + "## Design your own pipeline\n", + "1. Create and switch to a new Git branch (you will notice that your mri-convert changes will disappear)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fea2b35-755c-4bef-94b0-4434b40e5abb", + "metadata": {}, + "outputs": [], + "source": [ + "git checkout main\n", + "git checkout -b my-own-pipeline" + ] + }, + { + "cell_type": "markdown", + "id": "030a88e8-dd59-4de3-8411-58b042135109", + "metadata": {}, + "source": [ + "2. Bootstrap your new specification\n", + "1. Build your specifcation\n", + "1. Test your specification\n", + "1. Create a pull-request on GitHub to add your pipeline to the central repository, https://github.com/Australian-Imaging-Service/pipelines-community" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8672c17e-186a-409f-846f-220844c7eeb3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Bash", + "language": "bash", + "name": "bash" + }, + "language_info": { + "codemirror_mode": "shell", + "file_extension": ".sh", + "mimetype": "text/x-sh", + "name": "bash" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorial/requirements.txt b/tutorial/requirements.txt new file mode 100644 index 0000000..c23a3a8 --- /dev/null +++ b/tutorial/requirements.txt @@ -0,0 +1,5 @@ +pydra2app-xnat >=0.5 +xnat4tests +jupyter +bash_kernel + diff --git a/tutorial/start-up-script.sh b/tutorial/start-up-script.sh new file mode 100644 index 0000000..145fde1 --- /dev/null +++ b/tutorial/start-up-script.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash + +ssh-keygen -t rsa -N "" -f $HOME/.ssh/id_rsa.pub + +# Clone the pipelines-community repository +mkdir -p ~/git +if [ ! -d ~/git/pipelines-community ]; then + git clone https://github.com/Australian-Imaging-Service/pipelines-community.git ~/git/pipelines-community +fi +pushd ~/git/pipelines-community + +# Update the pipelines-community repository +git pull + +# Install the pipelines-community repository +pip install -r ./tutorial/requirements.txt + +# Pre-build/pull the required XNAT docker images to save time +xnat4tests -c ./tutorial/xnat4tests-config.yaml start --with-data openneuro-t1w +xnat4tests -c ./tutorial/xnat4tests-config.yaml stop +pydra2app make xnat ./specs/australian-imaging-service-community/examples/zip.yaml --spec-root ./specs --for-localhost +pydra2app make xnat ./specs/australian-imaging-service-community/examples/bet.yaml --spec-root ./specs --for-localhost +docker pull vnmd/freesurfer_7.1.1 +popd \ No newline at end of file diff --git a/tutorial/xnat4tests-config.yaml b/tutorial/xnat4tests-config.yaml new file mode 100644 index 0000000..2e7c6a2 --- /dev/null +++ b/tutorial/xnat4tests-config.yaml @@ -0,0 +1,4 @@ +build_args: + xnat_cs_plugin_version: 3.4.3 + xnat_version: 1.8.10.1 +xnat_root_dir: /Users/tclose/xnat4tests-root # /home/ubuntu/xnat4tests-root