Add example workflows

example_workflows/curl_external/workflow_template.yaml

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+metadata:
+  name: example-curl-external
+  namespace: argo-workflows
+spec:
+  serviceAccountName: argo-workflow
+  entrypoint: curl
+  arguments:
+    parameters:
+      - name: url
+        value: https://ipinfo.io
+  templates:
+    - name: curl
+      inputs:
+        parameters:
+          - name: url
+            value: "{{workflow.parameters.url}}"
+      container:
+        name: main
+        image: alpine/curl
+        command:
+          - curl
+        args:
+          - "-s"
+          - "{{inputs.parameters.url}}"
+  ttlStrategy:
+    secondsAfterCompletion: 300
+  podGC:
+    strategy: OnPodCompletion
+    deleteDelayDuration: 300

example_workflows/input_and_output_artifacts/generate_plots_no_txt.R

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+library(tidyverse)
+setwd("/tmp/test/")
+head(mpg)
+tmp <-
+  ggplot(mpg, aes(x = hwy, y = cty)) +
+  geom_point() +
+  geom_smooth()
+tmp_boxplot <-
+  ggplot(mpg, aes(x = class, y = hwy)) +
+  geom_boxplot() +
+  theme_classic()
+ggsave("/tmp/routput/test.png", plot = tmp, device = "png")
+ggsave("/tmp/routput/test_boxplot.png", plot = tmp_boxplot, device = "png")

example_workflows/input_and_output_artifacts/input_and_output_artifacts.yaml

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+metadata:
+  name: input-and-output-artifact-s3
+  namespace: argo-workflows
+spec:
+  templates:
+    - name: s3-input-and-output-example
+      inputs:
+        artifacts:
+          - name: rscript
+            path: /tmp/test/generate_plots_no_txt.R
+            s3:
+              key: generate_plots_no_txt.R
+      outputs:
+        artifacts:
+          - name: routput
+            path: /tmp/routput
+            s3:
+              key: /plot_gen.tgz
+      container:
+        image: rocker/tidyverse:latest
+        command:
+          - sh
+          - -c
+        args:
+          - mkdir /tmp/routput; Rscript /tmp/test/generate_plots_no_txt.R
+  entrypoint: s3-input-and-output-example
+  serviceAccountName: argo-workflow

example_workflows/python_population_analysis/generate_csv.py

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+#!/usr/bin/env python3
+from csv import DictWriter
+
+from faker import Faker
+
+Faker.seed(36903)
+fake = Faker(["en_GB", "fr_FR", "de_DE"])
+
+population = [fake.profile() for _ in range(5000)]
+
+field_names = population[0].keys()
+with open("./population.csv", "w", newline="") as csvfile:
+    writer = DictWriter(csvfile, field_names)
+
+    writer.writeheader()
+    for profile in population:
+        writer.writerow(profile)

example_workflows/python_population_analysis/requirements.txt

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+pandas[performance, plot]

example_workflows/python_population_analysis/requirements_generate.txt

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+faker

example_workflows/python_population_analysis/script.py

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+#!/usr/bin/env python3
+import re
+from decimal import Decimal
+from enum import Enum, unique
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+@unique
+class BloodGroup(Enum):
+    a_negative = "A-"
+    a_positive = "A+"
+    ab_negative = "AB-"
+    ab_positive = "AB+"
+    b_negative = "B-"
+    b_positive = "B+"
+    o_negative = "O-"
+    o_positive = "O+"
+
+
+re_location = re.compile(r"\(Decimal\('(.*)'\), Decimal\('(.*)'\)\)")
+
+
+def convert_location(location: str) -> tuple[Decimal, Decimal]:
+    match = re_location.match(location)
+    return (Decimal(match.group(1)), Decimal(match.group(2)))
+
+
+df = pd.read_csv(
+    "./population.csv",
+    converters={
+        "blood_group": BloodGroup,
+        "current_location": convert_location,
+        "website": eval,
+    },
+    dtype={
+        "job": str,
+        "company": str,
+        "ssn": str,
+        "residence": str,
+        "username": str,
+        "address": str,
+        "mail": str,
+    },
+    parse_dates=["birthdate"],
+    header=0,
+)
+
+ax = df["blood_group"].value_counts().plot.pie()
+ax.get_figure().savefig("./blood_group.png")
+
+plt.clf()
+
+now = np.datetime64("now")
+df["age"] = df["birthdate"].apply(lambda x: int((now - x).days / 365.25))
+ax = df["age"].plot.hist()
+ax.get_figure().savefig("./age.png")