Job request: 3140
- Organisation:
- Workspace:
- sro-measures-cam-poc
- ID:
- 5dlmf2e3dypovgu6
This page shows the technical details of what happened when the authorised researcher Chris Yeung requested one or more actions to be run against real patient data in the project, within a secure environment.
By cross-referencing the list of jobs with the
pipeline section below, you can infer what
security level
various outputs were written to. Researchers can never directly
view outputs marked as
highly_sensitive
;
they can only request that code runs against them. Outputs
marked as
moderately_sensitive
can be viewed by an approved researcher by logging into a highly
secure environment. Only outputs marked as
moderately_sensitive
can be requested for release to the public, via a controlled
output review service.
Jobs
-
- Job identifier:
-
hvp4b54uecjaq2ds
Pipeline
Show project.yaml
version: '3.0'
expectations:
population_size: 1000
actions:
generate_study_population:
run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2019-01-01 to 2021-02-01 by month" --output-dir=output
# run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2019-01-01 to 2020-02-01 by month" --output-dir=output
outputs:
highly_sensitive:
cohort: output/input_*.csv
generate_study_population_practice_count:
run: cohortextractor:latest generate_cohort --study-definition study_definition_practice_count --output-dir=output
outputs:
highly_sensitive:
cohort: output/input_practice_count.csv
generate_measures:
run: cohortextractor:latest generate_measures --study-definition study_definition --output-dir=output
needs: [generate_study_population]
outputs:
moderately_sensitive:
measure_csv: output/measure_*.csv
get_practice_count:
run: python:latest python analysis/get_practice_count.py
needs: [generate_study_population_practice_count]
outputs:
moderately_sensitive:
text: output/practice_count.json
get_patient_count:
run: python:latest python analysis/get_patients_counts.py
needs: [generate_study_population]
outputs:
moderately_sensitive:
text: output/patient_count.json
generate_notebook:
run: jupyter:latest jupyter nbconvert /workspace/notebooks/sentinel_measures.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input
needs: [generate_measures, get_practice_count, get_patient_count]
outputs:
moderately_sensitive:
notebook: output/sentinel_measures.html
# generate_notebook_practice:
# run: jupyter:latest jupyter nbconvert /workspace/notebooks/sentinel_measures_by_practice.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input
# needs: [generate_measures, get_practice_count, get_patient_count]
# outputs:
# moderately_sensitive:
# notebook: output/sentinel_measures_by_practice.html
# csvs: output/*_check.csv
get_event_summary:
run: python:latest python analysis/get_event_summary.py
needs: [generate_study_population]
outputs:
moderately_sensitive:
text: output/*_event_summary.csv
Timeline
-
Created:
-
Started:
-
Finished:
-
Runtime: 00:02:25
These timestamps are generated and stored using the UTC timezone on the GRAPHNET backend.
Job information
- Status
-
Failed
- Backend
- GRAPHNET
- Workspace
- sro-measures-cam-poc
- Requested by
- Chris Yeung
- Branch
- graphnet
- Force run dependencies
- No
- Git commit hash
- 070516c
- Requested actions
-
-
generate_study_population
-
Code comparison
Compare the code used in this Job Request