import pandas as pd import streamlit as st import wandb from dashboard_utils.bubbles import get_global_metrics, get_new_bubble_data, get_leaderboard from dashboard_utils.main_metrics import get_main_metrics from streamlit_observable import observable import time import requests import streamlit as st from streamlit_lottie import st_lottie def load_lottieurl(url: str): r = requests.get(url) if r.status_code != 200: return None return r.json() # Only need to set these here as we are add controls outside of Hydralit, to customise a run Hydralit! st.set_page_config(page_title="Dashboard", layout="wide") st.markdown("

Dashboard

", unsafe_allow_html=True) key_figures_margin_left, key_figures_c1, key_figures_c2, key_figures_c3, key_figures_margin_right = st.columns( (2, 1, 1, 1, 2) ) chart_c1, chart_c2 = st.columns((3, 2)) lottie_url_loading = "https://assets5.lottiefiles.com/packages/lf20_OdNgAj.json" lottie_loading = load_lottieurl(lottie_url_loading) with key_figures_c1: st.caption("\# of contributing users") placeholder_key_figures_c1 = st.empty() with placeholder_key_figures_c1: st_lottie(lottie_loading, height=100, key="loading_key_figure_c1") with key_figures_c2: st.caption("\# active users") placeholder_key_figures_c2 = st.empty() with placeholder_key_figures_c2: st_lottie(lottie_loading, height=100, key="loading_key_figure_c2") with key_figures_c3: st.caption("Total runtime") placeholder_key_figures_c3 = st.empty() with placeholder_key_figures_c3: st_lottie(lottie_loading, height=100, key="loading_key_figure_c3") with chart_c1: st.subheader("Metrics over time") st.caption("Training Loss") placeholder_chart_c1_1 = st.empty() with placeholder_chart_c1_1: st_lottie(lottie_loading, height=100, key="loading_c1_1") st.caption("Number of alive runs over time") placeholder_chart_c1_2 = st.empty() with placeholder_chart_c1_2: st_lottie(lottie_loading, height=100, key="loading_c1_2") st.caption("Number of steps") placeholder_chart_c1_3 = st.empty() with placeholder_chart_c1_3: st_lottie(lottie_loading, height=100, key="loading_c1_3") with chart_c2: st.subheader("Global metrics") st.caption("Collaborative training participants") placeholder_chart_c2_1 = st.empty() with placeholder_chart_c2_1: st_lottie(lottie_loading, height=100, key="loading_c2_1") st.write("Chart showing participants of the collaborative-training. Circle radius is relative to the total number of " "processed batches, the circle is greyed if the participant is not active. Every purple square represents an " "active device, darker color corresponds to higher performance.") st.caption("Leaderboard") placeholder_chart_c2_3 = st.empty() with placeholder_chart_c2_3: st_lottie(lottie_loading, height=100, key="loading_c2_2") wandb.login(anonymous="must") steps, dates, losses, alive_peers = get_main_metrics() source = pd.DataFrame({"steps": steps, "loss": losses, "alive participants": alive_peers, "date": dates}) placeholder_chart_c1_1.vega_lite_chart( source, { "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "description": "Training Loss", "mark": {"type": "line", "point": {"tooltip": True, "filled": False, "strokeOpacity": 0}}, "encoding": {"x": {"field": "date", "type": "temporal"}, "y": {"field": "loss", "type": "quantitative"}}, "config": {"axisX": {"labelAngle": -40}}, }, use_container_width=True, ) placeholder_chart_c1_2.vega_lite_chart( source, { "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "description": "Alive participants", "mark": {"type": "line", "point": {"tooltip": True, "filled": False, "strokeOpacity": 0}}, "encoding": { "x": {"field": "date", "type": "temporal"}, "y": {"field": "alive participants", "type": "quantitative"}, }, "config": {"axisX": {"labelAngle": -40}}, }, use_container_width=True, ) placeholder_chart_c1_3.vega_lite_chart( source, { "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "description": "Training Loss", "mark": {"type": "line", "point": {"tooltip": True, "filled": False, "strokeOpacity": 0}}, "encoding": {"x": {"field": "date", "type": "temporal"}, "y": {"field": "steps", "type": "quantitative"}}, "config": {"axisX": {"labelAngle": -40}}, }, use_container_width=True, ) serialized_data, profiles = get_new_bubble_data() df_leaderboard = get_leaderboard(serialized_data) observable( "_", notebook="d/9ae236a507f54046", # "@huggingface/participants-bubbles-chart", targets=["c_noaws"], redefine={"serializedData": serialized_data, "profileSimple": profiles, "width": 0}, ) placeholder_chart_c2_3.dataframe(df_leaderboard[["User", "Total time contributed"]]) global_metrics = get_global_metrics(serialized_data) placeholder_key_figures_c1.write(f"{global_metrics['num_contributing_users']}", unsafe_allow_html=True) placeholder_key_figures_c2.write(f"{global_metrics['num_active_users']}", unsafe_allow_html=True) placeholder_key_figures_c3.write(f"{global_metrics['total_runtime']}", unsafe_allow_html=True) with placeholder_chart_c2_1: observable( "Participants", notebook="d/9ae236a507f54046", # "@huggingface/participants-bubbles-chart", targets=["c_noaws"], redefine={"serializedData": serialized_data, "profileSimple": profiles}, )