retry educational example

.gitlab-ci.yml

-stages:          # List of stages for jobs, and their order of execution
-  - build
-  - test
-  - deploy
-
-cache: &global_cache
-  # key: $CI_COMMIT_REF_SLUG
-  key: $CI_COMMIT_SHA
-  paths:
-    - bin
-  policy: pull-push
-
-build-job:       # This job runs in the build stage, which runs first.
-  stage: build
-  tags: 
-    - cloud
-  script:
-    - echo "Compiling the code..."
-    - echo "Compile complete."
-    - mkdir -p bin
-    - cp /usr/bin/rev bin
-
-unit-test-job:   # This job runs in the test stage.
-  stage: test    # It only starts when the job in the build stage completes successfully.
-  tags: 
-    - cloud-gpu
-  cache:
-    # inherit all global cache settings
-    <<: *global_cache
-    # override the policy
-    policy: pull
-  script:
-    - echo "Running unit tests... This will take about 60 seconds." | bin/rev
-
-lint-test-job:   # This job also runs in the test stage.
-  stage: test    # It can run at the same time as unit-test-job (in parallel).
-  tags: 
-    - cloud
-  cache:
-    # inherit all global cache settings
-    <<: *global_cache
-    # override the policy
-    policy: pull
-  script:
-    - echo "Linting code... This will take about 10 seconds." | bin/rev
-
-deploy-job:      # This job runs in the deploy stage.
-  stage: deploy  # It only runs when *both* jobs in the test stage complete successfully.
-  tags: 
-    - cloud
-  cache:
-    # inherit all global cache settings
-    <<: *global_cache
-    # override the policy
-    policy: pull
-  script:
-    - echo "Deploying application..."
-    - echo "Application successfully deployed." | bin/rev
+test_python_numpy:
+    image: python:3.9
+    tags: - docker
+    cache:
+        key: "python39_pip_cache_20220731"
+        paths:
+            - .cache/pip
+    variables:
+        PIP_CACHE_DIR: "$CI_PROJECT_DIR/.cache/pip"
+    before_script:
+        - pip install numpy==1.22.4
+    script:
+        - >
+          python -c
+          "
+              import numpy as np
+              A = np.random.rand(32,32)
+              B = np.linalg.inv(A)
+              C = A @ B
+              assert(np.allclose(C.diagonal(), np.ones(32)))
+          "