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Merge pull request #17170 from thepineapplepirate/qiskit_in_galaxy
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Qiskit in galaxy
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@bgruening
bgruening authored Dec 12, 2023
2 parents 0628bdd + 48417b0 commit 26ffc86
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<tool id="interactive_tool_qiskit_jupyter_notebook" tool_type="interactive" name="Qiskit Jupyter notebook" version="0.0.1" profile="22.01">
<description>interactive tool</description>
<requirements>
<container type="docker">thepineapplepirate/qiskit_galaxy:1.0.0</container>
<!-- files for this docker image can be found at https://github.com/thepineapplepirate/qiskit-jupyter-galaxy_docker.git -->
</requirements>
<entry_points>
<entry_point name="Qiskit Jupyter Notebook" requires_domain="True">
<port>8888</port>
<url>ipython/lab</url>
</entry_point>
</entry_points>
<environment_variables>
<environment_variable name="HISTORY_ID">$__history_id__</environment_variable>
<environment_variable name="REMOTE_HOST">$__galaxy_url__</environment_variable>
<environment_variable name="GALAXY_WEB_PORT">8080</environment_variable>
<environment_variable name="GALAXY_URL">$__galaxy_url__</environment_variable>
<environment_variable name="DISABLE_AUTH">true</environment_variable>
<environment_variable name="API_KEY" inject="api_key"/>
</environment_variables>
<configfiles>
<inputs name="inputs" filename="galaxy_inputs.json" data_style="staging_path_and_source_path"/>
<configfile name="staging_script" filename="staging_script.py"><![CDATA[
import json, os, shutil
import os.path
galaxy_inputs = "galaxy_inputs.json"
galaxy_inputs_raw = "galaxy_inputs_raw.json"
base_staging_path = "jupyter"
base_data_staging_path = "galaxy_inputs"
data_staging_path = os.path.join(base_staging_path, base_data_staging_path)
os.makedirs(data_staging_path, exist_ok=True)
shutil.copyfile(galaxy_inputs, os.path.join(data_staging_path, galaxy_inputs_raw))
galaxy_input_dict = {}
for user_input in json.load(open(galaxy_inputs, "r")).get("user_inputs", []):
input_values = user_input['input_type']['input_value']
input_name = user_input["input_name"]
assert input_name not in galaxy_input_dict, ValueError("Each name can only be used once, but a duplicate was found: %s" % (input_name))
if 'dataset' in user_input['input_type']['input_type_selector']:
input_values2 = []
if not isinstance(input_values, list):
input_values = [input_values]
for input_value_dict in input_values:
path, filename = os.path.split(input_value_dict['staging_path'])
input_value_dict2 = {"path": os.path.join(base_data_staging_path, input_name, path, filename), "metadata_files": []}
## TODO: enable passing in all metadata values; ideally just handed off from original json creation
path = os.path.join(data_staging_path, input_name, path)
os.makedirs(path, exist_ok=True)
try:
os.symlink(input_value_dict['source_path'], os.path.join(path, filename))
except FileExistsError:
pass
for metadata_value_dict in input_value_dict['metadata_files']:
path, filename = os.path.split(metadata_value_dict['staging_path'])
input_value_dict2["metadata_files"].append(os.path.join(base_data_staging_path, input_name, path, filename))
path = os.path.join(data_staging_path, input_name, path)
os.makedirs(path, exist_ok=True)
try:
os.symlink(metadata_value_dict['source_path'], os.path.join(path, filename))
except FileExistsError:
pass
input_values2.append(input_value_dict2)
input_values = input_values2
galaxy_input_dict[input_name] = input_values
with open(os.path.join(data_staging_path, galaxy_inputs), "w") as fh:
json.dump(galaxy_input_dict, fh)
]]>
</configfile>
<configfile name="galaxy_input_startup_script"><![CDATA[
import json, os
import shutil
import galaxy_ie_helpers
from galaxy_ie_helpers import get
from galaxy_ie_helpers import put
from galaxy_ie_helpers import get_galaxy_connection
from bioblend.galaxy.histories import HistoryClient
hid = os.environ.get('DATASET_HID', None)
HISTORY_ID = history_id = os.environ['HISTORY_ID']
if hid not in ('None', None):
galaxy_ie_helpers.get(int(hid))
shutil.copy('/import/%s' % hid, '/import/ipython_galaxy_notebook.ipynb')
additional_ids = os.environ.get("ADDITIONAL_IDS", "")
if additional_ids:
gi = galaxy_ie_helpers.get_galaxy_connection(history_id=history_id, obj=False)
hc = HistoryClient(gi)
history = hc.show_history(history_id, contents=True)
additional_ids = additional_ids.split(",")
for hda in history:
if hda["id"] in additional_ids:
galaxy_ie_helpers.get(int(hda["hid"]))
try:
GALAXY_INPUTS = json.load(open(os.path.join(os.environ.get("GALAXY_WORKING_DIR", ""), "jupyter", "galaxy_inputs", "galaxy_inputs.json")))
except FileNotFoundError:
GALAXY_INPUTS = {}
]]>
</configfile>
<configfile name="qiskitrc"><![CDATA[
[ibmq]
token = $__user__.extra_preferences.get('ibmq_account|token', "TOKEN_NOT_SET")
url = $__user__.extra_preferences.get('ibmq_account|url', "https://auth.quantum-computing.ibm.com/api")
verify = True
]]>
</configfile>
</configfiles>
<command><![CDATA[
python staging_script.py &&
export GALAXY_WORKING_DIR=`pwd` &&
mkdir -p ./jupyter/outputs/collection &&
mkdir -p ./jupyter/galaxy_inputs &&
## change into the directory where the notebooks are located
cd ./jupyter/ &&
export HOME=/home/jovyan/ &&
export PATH=/home/jovyan/.local/bin:\$PATH &&
cp '${galaxy_input_startup_script}' /home/jovyan/.ipython/profile_default/startup/01-load.py &&
mkdir -p /home/jovyan/.qiskit && cp '${qiskitrc}' /home/jovyan/.qiskit/qiskitrc &&
mkdir -p Qiskit_Resources &&
ln -s /home/jovyan/qiskit Qiskit_Resources/ &&
#set $output_notebook_name = 'qiskit_galaxy_notebook.ipynb'
#if $mode.mode_select == 'scratch':
## copy default notebook
cp '$__tool_directory__/default_notebook.ipynb' ${output_notebook_name} &&
jupyter trust ${output_notebook_name} &&
jupyter lab --allow-root --no-browser --ServerApp.quit_button=True &&
#else:
#import re
#set $cleaned_name = re.sub('[^\w\-\.]', '_', str($mode.ipynb.element_identifier))
#if not $cleaned_name.endswith(".ipynb"):
#set $cleaned_name = "%s.ipynb" % $cleaned_name
#end if
cp '$mode.ipynb' '${cleaned_name}' &&
jupyter trust '${cleaned_name}' &&
#if $mode.run_it:
jupyter nbconvert --to notebook --execute --output '${output_notebook_name}' --allow-errors '${cleaned_name}' &&
#else:
#set $output_notebook_name = $cleaned_name
jupyter lab --allow-root --no-browser --ServerApp.quit_button=True &&
#end if
#end if
cp '${output_notebook_name}' '$jupyter_notebook'
]]>
</command>
<inputs>
<conditional name="mode">
<param name="mode_select" type="select" label="Do you already have a notebook?" help="If not, no problem we will provide you with a default one.">
<option value="scratch">Start with a fresh notebook</option>
<option value="previous">Load a previous notebook</option>
</param>
<when value="scratch"/>
<when value="previous">
<param name="ipynb" type="data" format="ipynb" label="IPython Notebook"/>
<param name="run_it" type="boolean" truevalue="true" falsevalue="false" label="Execute notebook and return a new one." help="This option is useful in workflows when you just want to execute a notebook and not dive into the webfrontend."/>
</when>
</conditional>
<repeat name="user_inputs" title="User inputs">
<param name="input_name" type="text" value="" label="Name for parameter" optional="False" help="Required. ASCII letters and numbers only.">
<validator type="empty_field" message="Name is required"/>
<sanitizer>
<valid initial="string.ascii_letters,string.digits"/>
</sanitizer>
</param>
<param name="description" type="text" label="Additional optional description" optional="true"/>
<conditional name="input_type">
<param name="input_type_selector" type="select" label="Choose the input type">
<option value="dataset" selected="true">Dataset</option>
<option value="dataset_multiple">Multiple datasets</option>
<option value="dataset_collection">Dataset collection</option>
<option value="dataset_collection_list">Dataset collection (list)</option>
<option value="dataset_collection_paired">Dataset collection (paired)</option>
<option value="dataset_collection_list_paired">Dataset collection (list:paired)</option>
<option value="text">Text</option>
<option value="integer">Integer</option>
<option value="float">Floating point</option>
<option value="boolean">Boolean</option>
<option value="color">Color selector</option>
<option value="dataset_optional" selected="true">Optional Dataset</option>
<option value="dataset_multiple_optional">Optional Multiple datasets</option>
<option value="dataset_collection_optional">Optional Dataset collection</option>
<option value="dataset_collection_list_optional">Optional Dataset collection (list)</option>
<option value="dataset_collection_paired_optional">Optional Dataset collection (paired)</option>
<option value="dataset_collection_list_paired_optional">Optional Dataset collection (list:paired)</option>
<option value="text_optional">Optional Text</option>
<option value="integer_optional">Optional Integer</option>
<option value="float_optional">Optional Floating point</option>
<option value="boolean_optional">Optional Boolean</option>
<option value="color_optional">Optional Color selector</option>
</param>
<when value="dataset">
<param name="input_value" type="data" format="data" label="Select value" multiple="false" optional="false"/>
</when>
<when value="dataset_multiple">
<param name="input_value" type="data" format="data" label="Select value" multiple="true" optional="false"/>
</when>
<when value="dataset_collection">
<param name="input_value" type="data_collection" format="data" label="Select value" optional="false"/>
</when>
<when value="dataset_collection_list">
<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="list" optional="false"/>
</when>
<when value="dataset_collection_paired">
<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="paired" optional="false"/>
</when>
<when value="dataset_collection_list_paired">
<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="list:paired" optional="false"/>
</when>
<when value="text">
<param name="input_value" type="text" value="" label="Select value" optional="false"/>
</when>
<when value="integer">
<param name="input_value" type="integer" value="" label="Select value" optional="false"/>
</when>
<when value="float">
<param name="input_value" type="float" value="" label="Select value" optional="false"/>
</when>
<when value="boolean">
<param name="input_value" type="boolean" truevalue="true" falsevalue="false" label="Select value" optional="false"/>
</when>
<when value="color">
<param name="input_value" type="color" label="Select value" optional="false"/>
</when>
<when value="dataset_optional">
<param name="input_value" type="data" format="data" label="Select value" multiple="false" optional="true"/>
</when>
<when value="dataset_multiple_optional">
<param name="input_value" type="data" format="data" label="Select value" multiple="true" optional="true"/>
</when>
<when value="dataset_collection_optional">
<param name="input_value" type="data_collection" format="data" label="Select value" optional="true"/>
</when>
<when value="dataset_collection_list_optional">
<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="list" optional="true"/>
</when>
<when value="dataset_collection_paired_optional">
<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="paired" optional="true"/>
</when>
<when value="dataset_collection_list_paired_optional">
<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="list:paired" optional="true"/>
</when>
<when value="text_optional">
<param name="input_value" type="text" value="" label="Select value" optional="true"/>
</when>
<when value="integer_optional">
<param name="input_value" type="integer" value="" label="Select value" optional="true"/>
</when>
<when value="float_optional">
<param name="input_value" type="float" value="" label="Select value" optional="true"/>
</when>
<when value="boolean_optional">
<param name="input_value" type="boolean" truevalue="true" falsevalue="false" label="Select value" optional="true"/>
</when>
<when value="color_optional">
<param name="input_value" type="color" label="Select value" optional="true"/>
</when>
</conditional>
</repeat>
</inputs>
<outputs>
<data name="jupyter_notebook" format="ipynb" label="Executed JupyTool Notebook"/>
<data name="output_dataset" format="data">
<discover_datasets pattern="__designation_and_ext__" directory="jupyter/outputs" visible="true" assign_primary_output="true"/>
</data>
<collection name="output_collection" type="list" label="JupyTool output collection">
<discover_datasets pattern="__designation_and_ext__" directory="jupyter/outputs/collection" visible="false"/>
</collection>
</outputs>
<tests>
<test expect_num_outputs="1">
<param name="mode" value="previous"/>
<param name="ipynb" value="test.ipynb"/>
<param name="run_it" value="true"/>
<output name="jupyter_notebook" file="test.ipynb" ftype="ipynb"/>
</test>
</tests>
<help><![CDATA[
Welcome to the **Qiskit JupyTool**! This tool incorporates the entire Qiskit stack, including tutorials and supplemental resources. This tool is an extension
of the standard **JupyTool**, so a full functionality of Jupyter lab is also included. You can create, run, and share custom Galaxy tools based upon Jupyter Notebooks.
Qiskit is a python based, open-source software stack for quantum computing on IBM quantum systems. When run, the Jupyter lab instance includes directories
for many resources and relevant tutorials, including the introductory materials for building quantum circuits, to using quantum algorithms like Variational Quantum Eigensolvers (VQE),
Quantum Approximate Optimization Algorithm (QAOA), as well as Grover's algorithm. The associated docker image contains most relevant Qiskit modules including:
qiskit-aer, qiskit-dynamics, qiskit-experiments, qiskit-finance, qiskit-ibm-experiment, qiskit-ibm-provider, qiskit-ibm-runtime, qiskit-ibmq-provider,
qiskit-machine-learning, qiskit-nature, qiskit-optimization, qiskit_research, qiskit-terra, qiskit-xyz2pdb. So, this tool provides the necessary resources for
beginners and experienced users of Qiskit alike. Users can readily run their scripts with quantum simulators on the backend (qasm, aer, etc.), or run on actual quantum hardware. For the latter, users
need to have an account with IBM quantum lab and pass their signature API token by pasting it in their user preferences on this Galaxy instance (User -> preferences -> manage information). Instructions for
specifying the quantum backend can be found at https://qiskit.org/ecosystem/ibm-runtime/how_to/backends.html.
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations,
visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization,
machine learning, and much more.
Galaxy offers you the use of Jupyter Notebooks directly in Galaxy accessing and interacting with Galaxy datasets as you like. A very common use-case is to
do the heavy lifting and data reduction steps in Galaxy and the plotting and more `interactive` part on smaller datasets in Jupyter.
You can start with a new Jupyter notebook from scratch or load an already existing one, e.g. from your colleague and execute it on your dataset.
You can specify any number of user-defined inputs using the repeat input, providing `name` value, selecting the type of input, and then providing values.
You can make the Qiskit JupyTool reusable in a workflow, by allowing the user to specify input values for the defined input blocks.
Inputs can be accessed by `name` from the automatically provided `GALAXY_INPUTS` dictionary.
Outputs can be written automatically to the user's history by writing to the `outputs` directory for one individual file or to the `outputs/collection` directory for multiple files.
Using collection tools, you can parse out the individual elements from the collection, as needed.
For backwards compatibility, you can import data into the notebook via a predefined `get()` function and write results back to Galaxy with a `put()` function.
.. image:: https://upload.wikimedia.org/wikipedia/commons/5/51/Qiskit-Logo.svg
]]></help>
</tool>

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