import streamlit as st from dashboard_utils.bubbles import get_new_bubble_data from dashboard_utils.main_metrics import get_main_metrics from streamlit_observable import observable def draw_current_progress(): source = get_main_metrics() st.vega_lite_chart( source, { "height": 200, "title": "Training DALLE with volunteers. Updated every few minutes during NeurIPS.", "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "description": "Current training progress", "encoding": {"x": {"field": "wall time", "type": "temporal"}}, "config": {"axisX": {"labelAngle": -40}}, "resolve": {"scale": {"y": "independent"}}, "layer": [ { "mark": {"type": "line", "point": {"tooltip": True, "filled": False, "strokeOpacity": 0}, "color": "#85A9C5"}, "encoding": { "y": {"field": "training loss", "type": "quantitative", "axis": {"titleColor": "#85A9C5"}}}, }, { "mark": {"type": "line", "point": {"tooltip": True, "filled": False, "strokeOpacity": 0.0}, "color": "#85C5A6", "opacity": 0.5}, "encoding": { "y": {"field": "active participants", "type": "quantitative", "axis": {"titleColor": "#85C5A6"}}}, }, ], }, use_container_width=True, ) def draw_participant_bubbles(): with st.expander("Who's training?", expanded=False): st.markdown("### Collaborative training participants\n(may take a few seconds to load)") serialized_data, profiles = get_new_bubble_data() observable( "Participants", notebook="d/9ae236a507f54046", # "@huggingface/participants-bubbles-chart", targets=["c_noaws"], redefine={"serializedData": serialized_data, "profileSimple": profiles}, )