Create code/make_pretty_dataset.py

code/make_pretty_dataset.py

@@ -0,0 +1,67 @@
+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)