init tracker

Dockerfile

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+FROM python:3.12
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /src
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /src
+COPY --chown=user . /src
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from config import Config as cfg
+
+import dash
+from dash import dcc, html
+from dash.dependencies import Input, Output
+
+import json 
+
+import pandas as pd
+
+import plotly.graph_objects as go
+
+from utils import flatten_json
+
+# Load data
+with open(cfg.data_path, "r") as f:
+    data = f.read()
+    data = json.loads(data)
+
+flattened_data = [flatten_json(d) for d in data]
+df = pd.DataFrame(flattened_data)
+
+def get_info(idx:int, df):
+    name        = df.loc[idx, "name"] 
+    country     = df.loc[idx, "location_country"]
+    n_gpus      = df.loc[idx, "capacity_n_GPUs"]
+    gpu_type    = df.loc[idx, "capacity_GPU_type"]
+    addr        = df.loc[idx, "location_address"]
+    last_update = df.loc[idx, "last_update"]
+    capacity    = df.loc[idx, "capacity_pflops_performance"]
+    url    = df.loc[idx, "contact_info_website"]
+    info = dcc.Markdown(
+        f""" 
+            {country} 
+            {name} - **{str(n_gpus)}x{gpu_type}**
+            Overall Performance: **{capacity} petaFLOPS**
+            ({last_update})
+            {addr}
+            Website: {url}
+        """,
+        style={"white-space": "pre"}
+        )
+    return info
+
+# Map
+fig = go.Figure(data=go.Choropleth(
+    locations=df['location_CN'], 
+    z = df['capacity_pflops_performance'], 
+    zmax=10,
+    zmin=0,
+    locationmode = 'ISO-3',
+    colorscale = 'reds',
+    # text=df["info"],
+    colorbar=dict(
+            title='Compute capability (petaFLOPS)', # Title for your colorbar
+            orientation='h',     # Set orientation to horizontal
+            thickness=5,        # Adjust thickness as needed
+        )
+))
+
+fig.update_layout(
+    geo_scope='africa',
+    height=400,
+    #margin={"r": 0, "t": 0, "l": 0, "b": 0},
+    margin={"r": 0, "t": 0, "b": 0},
+    autosize=True,
+    # paper_bgcolor='beige' # Background color of the entire figure
+    dragmode=False
+
+)
+fig.update_geos(
+    landcolor="white",  # Sets the color of land areas
+    oceancolor="lightblue", # Sets the color of ocean areas
+    subunitcolor="darkgray", # Sets the color of country subdivisions
+    lakecolor="lightblue",      # Set lake color to blue
+    rivercolor="lightblue",
+    showlakes=True,        
+    showrivers=True,        # Make lakes visible
+    showland=True,
+    showocean=True,
+    showsubunits=True,
+    resolution=50,
+)
+    
+# App
+
+external_stylesheets = ["https://fonts.googleapis.com/css2?family=Tahoma&display=swap"]
+app = dash.Dash("Africa HPC Tracker", external_stylesheets=external_stylesheets)
+server = app.server
+
+TITLE_COLOR = "#095C73"
+DETAILS_COLOR = "#0F718E"
+
+app.layout = html.Div(
+    id="parent_div",
+    children=[
+        html.H1(
+            id='header-text',
+            children='Africa HPC Tracker',
+            style={'color':TITLE_COLOR}
+        ),
+        html.Div(
+            id='hover-info',
+            style={'color':DETAILS_COLOR}
+        ),
+        dcc.Graph(
+            id='hpc-tracker',
+            figure=fig            
+        ),
+
+    ],
+    style={
+        'textAlign'     : 'center', 
+        'font-family'   : 'Arial, sans-serif',
+    }
+)
+
+# Callbacks
+
+@app.callback(
+    Output('hover-info', 'children'),
+    Input('hpc-tracker', 'hoverData'))
+
+def display_hover_data(hoverData):
+    if hoverData:
+        # Access hovered point data and display or modify other elements
+        idx = hoverData['points'][0]['pointIndex']
+        return get_info(idx, df)
+    return "Hover over a region to display the details"
+
+if __name__ == '__main__':
+    app.run(
+        host=cfg.HOST, 
+        port=cfg.PORT,
+        debug=cfg.IS_DEBUG,
+        dev_tools_ui=cfg.IS_DEBUG
+    )

config.py

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+
+class Config:
+    data_path   = "data.json"
+    HOST        = "0.0.0.0"
+    PORT        = 8050
+    IS_DEBUG    = True

data.json

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+[{
+  "name": "Taouey",
+  "location": {
+    "country": "Senegal",
+    "CN": "SEN",
+    "city": "Diamniadio",
+    "coordinates": {"lat": 14.70721512041391, "lng": -17.419260299665588},
+    "region": "West",
+    "address": "Cité du Savoir, Diamniadio, Dakar"
+
+  },
+  "type": "National HPC",
+  "status": "Live",
+  "capacity": {
+    "GPU_type": "V100", 
+    "n_GPUs": 48,
+    "pflops_performance": 0.5376
+  },
+  "contact_info": {
+    "website": "https://cineri.sn/"
+  },
+  "last_update": "2019"
+},
+{
+  "name": "Toubkal (GPU Cluster)",
+  "location": {
+    "country": "Morocco",
+    "CN": "MAR",
+    "city": "Ben Guerir",
+    "coordinates": {"lat": 32.217507573027476, "lng": -7.938512605642216},
+    "region": "North",
+    "address": "Mohammed VI Polytechnic University, Ben Guerir 43150"
+  },
+  "type": "National HPC",
+  "status": "Live",
+  "capacity": {
+    "GPU_type": "A100",
+    "n_GPUs": 20,
+    "pflops_performance": 6.24
+  },
+  "contact_info": {
+    "website": "https://toubkal.um6p.ma/"
+  },
+  "last_update": "2021"
+},
+{
+  "name": "CNCCI",
+  "location": {
+    "country": "Côte d'Ivoire",
+    "city": "Abidjan",
+    "CN": "CIV",
+    "coordinates": {"lat": 5.358842210111155, "lng": -3.902231013281939},
+    "region": "West",
+    "address": "Pôle Scientifique et d'Innovation de Bingerville (PSI), Abidjan"
+
+  },
+  "type": "National HPC",
+  "status": "Live",
+  "capacity": {
+    "GPU_type": "K80",
+    "n_GPUs": 24,
+    "pflops_performance": 0.3226
+  },
+  "contact_info": {
+    "website": "https://cncci.edu.ci/cncci/"
+  },
+  "last_update": "2018"
+},
+{
+  "name": "Lengau",
+  "location": {
+    "country": "South Africa",
+    "CN": "ZAF",
+    "city": "Cape Town",
+    "coordinates": {"lat": -33.92584, "lng": 18.42322},
+    "region": "South",
+    "address": "CSIR Campus, 15 Lower Hope Street, Rosebank, Cape Town"
+
+  },
+  "type": "National HPC",
+  "status": "Live",
+  "capacity": {
+    "GPU_type": "V100",
+    "n_GPUs": 36,
+    "pflops_performance": 1.029
+  },
+  "contact_info": {
+    "website": "https://www.chpc.ac.za/"
+  },
+  "last_update": "2016"
+},
+{
+  "name": "DGX A100",
+  "location": {
+    "country": "Tunisia",
+    "city": "Tunis",
+    "CN": "TUN",
+    "coordinates": {"lat": 36.89913191678015, "lng": 10.18927862098627},
+    "region": "North",
+    "address": "ESPRIT School of Business, Campus ESB Lot N°46, Z.I Chotrana II, 2088, Ariana"
+
+  },
+  "type": "Private Workstation",
+  "status": "Live",
+  "capacity": {
+    "GPU_type": "A100",
+    "n_GPUs": 8,
+    "pflops_performance": 5
+  },
+  "contact_info": {
+    "website": "https://www.esb.tn/en"
+  },
+  "last_update": "2024"
+}
+]

requirements.txt

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+dash
+plotly
+pandas
+uvicorn
+huggingface_hub

utils.py

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+# Flatten the JSON data
+def flatten_json(y):
+    out = {}
+
+    def flatten(x, name=''):
+        if type(x) is dict:
+            for a in x:
+                flatten(x[a], f"{name}{a}_")
+        elif type(x) is list:
+            for i, a in enumerate(x):
+                flatten(a, f"{name}{i}_")
+        else:
+            out[name[:-1]] = x
+
+    flatten(y)
+    return out