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| import os | |
| import re | |
| import pandas as pd | |
| import streamlit as st | |
| import opendashboards.utils.utils as utils | |
| def load_runs(project, filters, min_steps=10): | |
| runs = [] | |
| msg = st.empty() | |
| for run in utils.get_runs(project, filters, api_key=st.secrets['WANDB_API_KEY']): | |
| step = run.summary.get('_step',0) | |
| if step < min_steps: | |
| msg.warning(f'Skipped run `{run.name}` because it contains {step} events (<{min_steps})') | |
| continue | |
| duration = run.summary.get('_runtime') | |
| end_time = run.summary.get('_timestamp') | |
| # extract values for selected tags | |
| rules = {'hotkey': re.compile('^[0-9a-z]{48}$',re.IGNORECASE), 'version': re.compile('^\\d\.\\d+\.\\d+$'), 'spec_version': re.compile('\\d{4}$')} | |
| tags = {k: tag for k, rule in rules.items() for tag in run.tags if rule.match(tag)} | |
| # include bool flag for remaining tags | |
| tags.update({k: k in run.tags for k in ('mock','custom_gating_model','nsfw_filter','outsource_scoring','disable_set_weights')}) | |
| runs.append({ | |
| 'state': run.state, | |
| 'num_steps': step, | |
| 'entity': run.entity, | |
| 'id': run.id, | |
| 'name': run.name, | |
| 'project': run.project, | |
| 'url': run.url, | |
| 'path': os.path.join(run.entity, run.project, run.id), | |
| 'start_time': pd.to_datetime(end_time-duration, unit="s"), | |
| 'end_time': pd.to_datetime(end_time, unit="s"), | |
| 'duration': pd.to_datetime(duration, unit="s"), | |
| **tags | |
| }) | |
| msg.empty() | |
| return pd.DataFrame(runs).astype({'state': 'category', 'hotkey': 'category', 'version': 'category', 'spec_version': 'category'}) | |
| def load_data(selected_runs, load=True, save=False): | |
| frames = [] | |
| n_events = 0 | |
| successful = 0 | |
| progress = st.progress(0, 'Loading data') | |
| info = st.empty() | |
| if not os.path.exists('data/'): | |
| os.makedirs('data/') | |
| for i, idx in enumerate(selected_runs.index): | |
| run = selected_runs.loc[idx] | |
| prog_msg = f'Loading data {i/len(selected_runs)*100:.0f}% ({successful}/{len(selected_runs)} runs, {n_events} events)' | |
| file_path = os.path.join('data',f'history-{run.id}.csv') | |
| if load and os.path.exists(file_path): | |
| progress.progress(i/len(selected_runs),f'{prog_msg}... **reading** `{file_path}`') | |
| try: | |
| df = utils.load_data(file_path) | |
| except Exception as e: | |
| info.warning(f'Failed to load history from `{file_path}`') | |
| st.exception(e) | |
| continue | |
| else: | |
| progress.progress(i/len(selected_runs),f'{prog_msg}... **downloading** `{run.path}`') | |
| try: | |
| # Download the history from wandb | |
| df = utils.download_data(run.path) | |
| # Add metadata to the dataframe | |
| df.assign(**run.to_dict()) | |
| if save and run.state != 'running': | |
| df.to_csv(file_path, index=False) | |
| # st.info(f'Saved history to {file_path}') | |
| except Exception as e: | |
| info.warning(f'Failed to download history for `{run.path}`') | |
| st.exception(e) | |
| continue | |
| frames.append(df) | |
| n_events += df.shape[0] | |
| successful += 1 | |
| progress.empty() | |
| if not frames: | |
| info.error('No data loaded') | |
| st.stop() | |
| # Remove rows which contain chain weights as it messes up schema | |
| return pd.concat(frames) | |