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import streamlit as st | |
from persist import persist, load_widget_state | |
global variable_output | |
def main(): | |
cs_body() | |
def cs_body(): | |
st.markdown('# Training Details') | |
st.write("Provide an overview of the Training Data and Training Procedure for this model") | |
left, middle, right = st.columns([2,1,7]) | |
with left: | |
st.write("\n") | |
st.write("\n") | |
st.markdown('## Training Data:') | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
with middle: | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.markdown(' \n ## Training Procedure') | |
with left: | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.markdown('#### Preprocessing:') | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.markdown('#### Speeds, Sizes, Time:') | |
with right: | |
#soutput_jinja = parse_into_jinja_markdown() | |
st.text_area("", help ="Ideally this links to a Dataset Card.", key=persist("training_Data")) | |
#st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.write("\n") | |
st.text_area("",key=persist("model_preprocessing")) | |
st.text_area("", help = "This section provides information about throughput, start/end time, checkpoint size if relevant, etc.", key=persist("Speeds_Sizes_Times")) | |
if __name__ == '__main__': | |
load_widget_state() | |
main() |