Copy files from folding dashboard

api.py

@@ -0,0 +1,147 @@
+
+import atexit
+import datetime
+
+from flask import Flask, request, jsonify
+from apscheduler.schedulers.background import BackgroundScheduler
+
+import utils
+
+app = Flask(__name__)
+
+
+# Global variables (saves time on loading data)
+state_vars = None
+reload_timestamp = datetime.datetime.now().strftime('%D %T')
+
+
+def load_data():
+    """
+    Reload the state variables
+    """
+    global state_vars, reload_timestamp
+    state_vars = utils.load_state_vars()
+
+    reload_timestamp = datetime.datetime.now().strftime('%D %T')
+
+    print(f'Reloaded data at {reload_timestamp}')
+
+
+def start_scheduler():
+    scheduler = BackgroundScheduler()
+    scheduler.add_job(func=load_data, trigger="interval", seconds=60*30)
+    scheduler.start()
+
+    # Shut down the scheduler when exiting the app
+    atexit.register(lambda: scheduler.shutdown())
+
+
+@app.route('/', methods=['GET'])
+def home():
+    return "Welcome to the Bittensor Protein Folding Leaderboard API!"
+
+
+@app.route('/updated', methods=['GET'])
+def updated():
+    return reload_timestamp
+
+
+@app.route('/data', methods=['GET'])
+@app.route('/data/<period>', methods=['GET'])
+def data(period=None):
+    """
+    Get the productivity metrics
+    """
+    assert period in ('24h', None), f"Invalid period: {period}. Must be '24h' or None."
+    df = state_vars["dataframe_24h"] if period == '24h' else state_vars["dataframe"]
+    return jsonify(
+        df.astype(str).to_dict(orient='records')
+    )
+
+@app.route('/productivity', methods=['GET'])
+@app.route('/productivity/<period>', methods=['GET'])
+def productivity_metrics(period=None):
+    """
+    Get the productivity metrics
+    """
+
+    assert period in ('24h', None), f"Invalid period: {period}. Must be '24h' or None."
+    df = state_vars["dataframe_24h"] if period == '24h' else state_vars["dataframe"]
+    return jsonify(
+        utils.get_productivity(df)
+    )
+
+
+@app.route('/throughput', methods=['GET'])
+@app.route('/throughput/<period>', methods=['GET'])
+def throughput_metrics(period=None):
+    """
+    Get the throughput metrics
+    """
+    assert period in ('24h', None), f"Invalid period: {period}. Must be '24h' or None."
+    df = state_vars["dataframe_24h"] if period == '24h' else state_vars["dataframe"]
+    return jsonify(utils.get_data_transferred(df))
+
+
+@app.route('/metagraph', methods=['GET'])
+def metagraph():
+    """
+    Get the metagraph data
+    Returns:
+    - metagraph_data: List of dicts (from pandas DataFrame)
+    """
+
+    df_m = state_vars["metagraph"]
+
+    return jsonify(
+        df_m.to_dict(orient='records')
+    )
+
+@app.route('/leaderboard', methods=['GET'])
+@app.route('/leaderboard/<entity>', methods=['GET'])
+@app.route('/leaderboard/<entity>/<ntop>', methods=['GET'])
+def leaderboard(entity='identity',ntop=10):
+    """
+    Get the leaderboard data
+    Returns:
+    - leaderboard_data: List of dicts (from pandas DataFrame)
+    """
+
+    assert entity in utils.ENTITY_CHOICES, f"Invalid entity choice: {entity}"
+
+    df_miners = utils.get_leaderboard(
+        state_vars["metagraph"],
+        ntop=int(ntop),
+        entity_choice=entity
+        )
+
+    return jsonify(
+        df_miners.to_dict(orient='records')
+    )
+
+@app.route('/validator', methods=['GET'])
+def validator():
+    """
+    Get the validator data
+    Returns:
+    - validator_data: List of dicts (from pandas DataFrame)
+    """
+    df_m = state_vars["metagraph"]
+    df_validators = df_m.loc[df_m.validator_trust > 0]
+
+    return jsonify(
+        df_validators.to_dict(orient='records')
+    )
+
+
+if __name__ == '__main__':
+
+    load_data()
+    start_scheduler()
+
+    app.run(host='0.0.0.0', port=5001, debug=True)
+    
+
+    # to test locally
+    # curl -X GET http://0.0.0.0:5001/data
+    

app.py

@@ -0,0 +1,140 @@
+import time
+import pandas as pd
+import streamlit as st
+import plotly.express as px
+
+import utils
+
+_ = """
+Proteins folded (delta 24hr)
+Current proteins folding (24hr)
+Average time to fold trend
+Refolded proteins (group by run id and pdb id and get unique)
+Simulation duration distribution
+"""
+
+UPDATE_INTERVAL = 3600
+
+
+st.title('Folding Subnet Dashboard')
+st.markdown('<br>', unsafe_allow_html=True)
+
+# reload data periodically
+df = utils.build_data(time.time()//UPDATE_INTERVAL)
+st.toast(f'Loaded {len(df)} runs')
+
+# TODO: fix the factor for 24 hours ago
+runs_alive_24h_ago = (df.last_event_at > pd.Timestamp.now() - pd.Timedelta('1d'))
+df_24h = df.loc[runs_alive_24h_ago]
+# correction factor to account for the fact that the data straddles the 24h boundary
+# correction factor is based on the fraction of the run which occurred in the last 24h
+# factor = (df_24h.last_event_at - pd.Timestamp.now() + pd.Timedelta('1d')) / pd.Timedelta('1d')
+
+
+#### ------ PRODUCTIVITY ------
+
+# Overview of productivity
+st.subheader('Productivity overview')
+st.info('Productivity metrics show how many proteins have been folded, which is the primary goal of the subnet. Metrics are estimated using weights and biases data combined with heuristics.')
+
+productivity = utils.get_productivity(df)
+productivity_24h = utils.get_productivity(df_24h)
+
+
+m1, m2, m3 = st.columns(3)
+m1.metric('Unique proteins folded', f'{productivity.get("unique_folded"):,.0f}', delta=f'{productivity_24h.get("unique_folded"):,.0f} (24h)')
+m2.metric('Total proteins folded', f'{productivity.get("total_simulations"):,.0f}', delta=f'{productivity_24h.get("total_simulations"):,.0f} (24h)')
+m3.metric('Total simulation steps', f'{productivity.get("total_md_steps"):,.0f}', delta=f'{productivity_24h.get("total_md_steps"):,.0f} (24h)')
+
+st.markdown('<br>', unsafe_allow_html=True)
+
+time_binned_data = df.set_index('last_event_at').groupby(pd.Grouper(freq='12h'))
+
+PROD_CHOICES = {
+    'Unique proteins folded': 'unique_pdbs',
+    'Total simulations': 'total_pdbs',
+    'Total simulation steps': 'total_md_steps',
+}
+prod_choice_label = st.radio('Select productivity metric', list(PROD_CHOICES.keys()), index=0, horizontal=True)
+prod_choice = PROD_CHOICES[prod_choice_label]
+steps_running_total = time_binned_data[prod_choice].sum().cumsum()
+st.plotly_chart(
+    # add fillgradient to make it easier to see the trend
+    px.area(steps_running_total, y=prod_choice, 
+            labels={'last_event_at':'', prod_choice: prod_choice_label},
+    ).update_traces(fill='tozeroy'),
+    use_container_width=True,
+)
+
+st.markdown('<br>', unsafe_allow_html=True)
+
+
+#### ------ THROUGHPUT ------
+st.subheader('Throughput overview')
+
+st.info('Throughput metrics show the total amount of data sent and received by the validators. This is a measure of the network activity and the amount of data that is being processed by the subnet.')
+
+MEM_UNIT = 'GB' #st.radio('Select memory unit', ['TB','GB', 'MB'], index=0, horizontal=True)
+
+data_transferred = utils.get_data_transferred(df,unit=MEM_UNIT)
+data_transferred_24h = utils.get_data_transferred(df_24h, unit=MEM_UNIT)
+
+m1, m2, m3 = st.columns(3)
+m1.metric(f'Total sent data ({MEM_UNIT})', f'{data_transferred.get("sent"):,.0f}', delta=f'{data_transferred_24h.get("sent"):,.0f} (24h)')
+m2.metric(f'Total received data ({MEM_UNIT})', f'{data_transferred.get("received"):,.0f}', delta=f'{data_transferred_24h.get("received"):,.0f} (24h)')
+m3.metric(f'Total transferred data ({MEM_UNIT})', f'{data_transferred.get("total"):,.0f}', delta=f'{data_transferred_24h.get("total"):,.0f} (24h)')
+
+
+IO_CHOICES = {'total_data_sent':'Sent', 'total_data_received':'Received'}
+io_running_total = time_binned_data[list(IO_CHOICES.keys())].sum().rename(columns=IO_CHOICES).cumsum().melt(ignore_index=False)
+io_running_total['value'] = io_running_total['value'].apply(utils.convert_unit, args=(utils.BASE_UNITS, MEM_UNIT))
+
+st.plotly_chart(
+    px.area(io_running_total, y='value', color='variable',
+            labels={'last_event_at':'', 'value': f'Data transferred ({MEM_UNIT})', 'variable':'Direction'},
+    ),
+    use_container_width=True,
+)
+
+st.markdown('<br>', unsafe_allow_html=True)
+
+
+#### ------ LEADERBOARD ------
+
+st.subheader('Leaderboard')
+st.info('The leaderboard shows the top miners by incentive.')
+m1, m2 = st.columns(2)
+ntop = m1.slider('Number of top miners to display', value=10, min_value=3, max_value=50, step=1)
+entity_choice = m2.radio('Select entity', utils.ENTITY_CHOICES, index=0, horizontal=True)
+
+df_m = utils.get_metagraph(time.time()//UPDATE_INTERVAL)
+df_miners = utils.get_leaderboard(df_m, ntop=ntop, entity_choice=entity_choice)
+
+# hide colorbar and don't show y axis
+st.plotly_chart(
+    px.bar(df_miners, x='I', color='I', hover_name=entity_choice, text=entity_choice if ntop < 20 else None,
+            labels={'I':'Incentive', 'trust':'Trust', 'stake':'Stake', '_index':'Rank'},
+    ).update_layout(coloraxis_showscale=False, yaxis_visible=False),
+    use_container_width=True,
+)
+
+
+with st.expander('Show raw metagraph data'):
+    st.dataframe(df_m)
+
+st.markdown('<br>', unsafe_allow_html=True)
+
+
+#### ------ LOGGED RUNS ------
+
+st.subheader('Logged runs')
+st.info('The timeline shows the creation and last event time of each run.')
+st.plotly_chart(
+    px.timeline(df, x_start='created_at', x_end='last_event_at', y='username', color='state',
+                labels={'created_at':'Created at', 'last_event_at':'Last event at', 'username':''},
+                ),
+    use_container_width=True
+)
+
+with st.expander('Show raw run data'):
+    st.dataframe(df)