code/make_pretty_dataset.py

import os
import json
from huggingface_hub import HfApi
import glob
from datetime import datetime
from datasets import Dataset

TOKEN = os.environ.get("HF_WRITE_TOKEN")
API = HfApi(token=TOKEN)
REPO_ID = "AIEnergyScore/results_debug" #"meg/calculate_carbon_runs"
UPLOAD_REPO_ID = 'meg/HUGS_energy'

output_directory = API.snapshot_download(repo_id=REPO_ID, repo_type='dataset')
print(output_directory)
#runs_dir = glob.glob(f"{output_directory}/*")
#print(runs_dir)
dataset_results = []
for task in ['text_generation']:
    org_dirs = glob.glob(f"{output_directory}/{task}/*") #runs/{task}/*")
    print(org_dirs)
    for org_dir in org_dirs:
        org = org_dir.split("/")[-1]
        model_dirs = glob.glob(f"{org_dir}/*")
        print(model_dirs)
        for model_dir in model_dirs:
            model = model_dir.split("/")[-1]
            model_runs = glob.glob(f"{model_dir}/*")
            dates = [dir.split("/")[-1] for dir in model_runs]
            try:
                # Sort dates as dates
                sorted_dates = sorted(
                    [datetime.strptime(date, '%Y-%m-%d-%H-%M-%S') for date in
                     dates])
                # Convert back to string format
                sorted_dates_str = [date.strftime('%Y-%m-%d-%H-%M-%S') for date in
                                    sorted_dates]
                last_date = sorted_dates_str[-1]
                most_recent_run = f"{model_dir}/{last_date}"
                print(most_recent_run)
                try:
                    benchmark_report = json.loads(open(f"{most_recent_run}/benchmark_report.json", "rb+").read())
                    print(benchmark_report)
                    prefill_data = benchmark_report['prefill']
                    prefill_energy = prefill_data['energy']
                    prefill_efficiency = prefill_data['efficiency']
                    decode_data = benchmark_report['decode']
                    decode_energy = decode_data['energy']
                    decode_efficiency = decode_data['efficiency']
                    preprocess_data = benchmark_report['preprocess']
                    preprocess_energy = preprocess_data['energy']
                    preprocess_efficiency = preprocess_data['efficiency']
                    dataset_results += [{'task':task, 'org':org, 'model':model, 'hardware':'a10g-large',
                                         'date':last_date, 'prefill':{'energy':prefill_energy,
                                                                    'efficency':prefill_efficiency},
                                                            'decode':{'energy':decode_energy, 'efficiency':decode_efficiency},
                                                            'preprocess': {'energy':preprocess_energy, 'efficiency': preprocess_efficiency}},]

                except FileNotFoundError:
                    error_report = open(f"{most_recent_run}/error.log", "rb+").read()
                    print(error_report)
            except ValueError:
                # Not a directory with a timestamp.
                continue

hub_dataset_results = Dataset.from_list(dataset_results)
print(hub_dataset_results)
hub_dataset_results.push_to_hub(UPLOAD_REPO_ID, token=TOKEN)