| import argparse,csv,os |
| import matplotlib.pyplot as plt |
| p=argparse.ArgumentParser(); p.add_argument('--run',default='outputs/real_preliminary'); p.add_argument('--out',default='publish/plots'); a=p.parse_args(); os.makedirs(a.out,exist_ok=True) |
| rows=list(csv.DictReader(open(os.path.join(a.run,'leaderboard.csv')))); names=[r['model'] for r in rows] |
| for metric,title in [('validation_loss','Held-out validation loss'),('tokens_per_second','Training throughput (tokens/s)')]: |
| if not rows or metric not in rows[0]: continue |
| vals=[float(r[metric]) for r in rows]; plt.figure(figsize=(8,4.5)); plt.bar(names,vals); plt.title(title); plt.xticks(rotation=30,ha='right'); plt.tight_layout(); plt.savefig(os.path.join(a.out,metric+'.png'),dpi=160); plt.close() |
| plt.figure(figsize=(8,4.5)) |
| for name in names: |
| fn=os.path.join(a.run,name,'history.csv') |
| if os.path.exists(fn): |
| h=list(csv.DictReader(open(fn))); plt.plot([int(x['step']) for x in h],[float(x['train_loss']) for x in h],label=name) |
| plt.xlabel('step'); plt.ylabel('training loss'); plt.title('Training curves'); plt.legend(fontsize=8); plt.tight_layout(); plt.savefig(os.path.join(a.out,'training_curves.png'),dpi=160); plt.close() |
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