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Merge pull request #13 from ryanlovett/code-cells
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Convert code cells to markdown code-cells.
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@ryanlovett
ryanlovett authored Oct 8, 2024
2 parents 8344f68 + d4e22cc commit 9c6cd1c
Showing 1 changed file with 55 additions and 56 deletions.
111 changes: 55 additions & 56 deletions workflow/calculate-compute-cost.ipynb
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"cells": [
{
"cell_type": "markdown",
"id": "d3e5b963-b7e9-4ee1-8732-a56da4dc8149",
"id": "0d5aa25a-939c-44b3-9781-9ac9161ea629",
"metadata": {},
"source": [
"# GCP cloud cost simulator widget\n",
"\n",
"\n",
"This interactive widget allows you to calculate the cost for running CPU heavy workloads in standard virtual machine vs CPU optimized machine"
"---\n",
"title: GCP cloud cost simulator widget\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "92feae99-edd5-4d23-bbac-0693a4e77087",
"cell_type": "markdown",
"id": "8e1ab363-88e7-49a5-9690-b2625e955d0c",
"metadata": {},
"outputs": [],
"source": [
"Enable computation on this page ⏻, then execute it ►, to display an interactive calculator. It can show you the cost for running CPU heavy workloads in standard virtual machines versus CPU optimized machines."
]
},
{
"cell_type": "markdown",
"id": "f7ef6b11-dfb6-402c-b439-b753b647aa70",
"metadata": {},
"source": [
"```{code-cell} python\n",
":tags: remove-input\n",
"import sys\n",
"\n",
"# Check if running inside JupyterLite (Pyodide)\n",
"if \"pyodide\" in sys.modules:\n",
" import micropip\n",
" # Install ipywidgets in JupyterLite\n",
" await micropip.install(\"ipywidgets\")"
" await micropip.install(\"ipywidgets\")\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "aeeb8fae-5f5f-4d2e-b344-f6e4d2ab0140",
"cell_type": "markdown",
"id": "33edc772-b562-4f23-baf6-58b492df5cfd",
"metadata": {},
"outputs": [],
"source": [
"```{code-cell} python\n",
":tags: remove-input\n",
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"from ipywidgets import Layout"
"from ipywidgets import Layout\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "46998d91-adbd-4ddf-b7ef-478db83ab347",
"cell_type": "markdown",
"id": "f052e711-5bc5-4b30-9fad-78d04dad882c",
"metadata": {},
"outputs": [],
"source": [
"```{code-cell} python\n",
":tags: remove-input\n",
"# Pricing data for the latest instance costs in the Iowa Central region (us-central1)\n",
"NODE_PRICING = {\n",
" 'n2-highcpu-32': 27.93, # Latest price for n2-highcpu-32\n",
" 'n2-highcpu-96': 83.73, # Latest price for n2-highcpu-96\n",
" 'n2-highmem-32': 50.31, # Latest price for n2-highmem-32\n",
"}"
"}\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "63660908-e767-484c-80e6-aadd8dd20f9c",
"cell_type": "markdown",
"id": "2bb13dc7-d842-4e6a-8a4a-7e26acfa3048",
"metadata": {},
"outputs": [],
"source": [
"```{code-cell} python\n",
":tags: remove-input\n",
"# Function to calculate the total number of nodes and total cost\n",
"def calculate_total_cost(node_type, num_cpus, num_students, num_days):\n",
" # Extract the number of CPUs in the node from the node type (e.g., 32 or 96)\n",
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" total_nodes, total_cost = calculate_total_cost(node_type, num_cpus, num_students, num_days)\n",
" \n",
" # Show result in text box\n",
" result_text.value = f\"Total nodes allocated: {total_nodes:.2f}\\nTotal cost for {num_days} days: ${total_cost:.2f}\""
" result_text.value = f\"Total nodes allocated: {total_nodes:.2f}\\nTotal cost for {num_days} days: ${total_cost:.2f}\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d4ea709b-8bc3-437b-8aee-09a4fdbc815c",
"cell_type": "markdown",
"id": "7fab467f-78c3-44f8-81e0-24882de1e969",
"metadata": {},
"outputs": [],
"source": [
"```{code-cell} python\n",
":tags: remove-input\n",
"# Widget elements\n",
"node_type_dropdown = widgets.Dropdown(\n",
" options=['n2-highcpu-32', 'n2-highcpu-96', 'n2-highmem-32'],\n",
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" description='Number of Days:',\n",
" style={'description_width': 'auto'},\n",
" layout=Layout(width='50%', padding='1em')\n",
")"
")\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "851f2d83-ad88-46fc-b74e-d390b627398e",
"cell_type": "markdown",
"id": "1ae0d62f-694d-4bca-9623-fc8031c27679",
"metadata": {},
"outputs": [],
"source": [
"```{code-cell} python\n",
":tags: remove-input\n",
"calculate_button = widgets.Button(\n",
" description='Calculate Total Cost',\n",
" button_style='success',\n",
Expand All @@ -166,33 +178,20 @@
" input_columns,\n",
" #calculate_button,\n",
" result_text\n",
"], layout=Layout(padding='1em'))"
"], layout=Layout(padding='1em'))\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "77d7aee6-3179-4557-928c-18186b40ce18",
"cell_type": "markdown",
"id": "36f11156-c283-429e-98b0-a641912425ff",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c3c8eddd397422faf311f576d5ceadd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(VBox(children=(Dropdown(description='Node Type:', layout=Layout(padding='1em', width='50%'), op…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"```{code-cell} python\n",
":tags: remove-input\n",
"# Display the entire widget setup\n",
"display(final_layout)"
"display(final_layout)\n",
"```"
]
},
{
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
"version": "3.12.6"
}
},
"nbformat": 4,
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