added all the progress bars for each worker

pull_requests.py

@@ -1,5 +1,6 @@
 from collections import defaultdict
-import argparse, os, subprocess, docker, uuid, concurrent.futures
+import argparse, os, subprocess, docker, uuid
+from concurrent.futures import wait, FIRST_COMPLETED, ProcessPoolExecutor
 from github.Commit import Commit
 from github.ContentFile import ContentFile
 from github.PullRequest import PullRequest
@@ -214,6 +215,7 @@ def process_pull(
     repos_dir: str,
     archive_destination: str,
     cache: dict[str, dict[int, DatasetEntry]] = {},
+    show_progress: bool = True,
 ):
     if pr.number in cache.get(repo.full_name, set()):
         dataset.entries.append(cache[repo.full_name][pr.number])
@@ -279,7 +281,9 @@ def process_pull(
         ),
     ]
 
-    pbar = tqdm(total=len(setup_steps) + 6, desc="Processing PR", leave=False)
+    pbar = tqdm(
+        total=len(setup_steps) + 6, desc="Processing PR", leave=False, disable=not show_progress
+    )
     for message, action in setup_steps:
         pbar.set_postfix(
             {
@@ -361,6 +365,8 @@ def process_repo(
     repos_dir: str,
     archive_destination: str,
     cache: dict[str, dict[int, DatasetEntry]] = {},
+    position: int = 1,
+    show_progress: bool = True,
 ):
     repo = g.get_repo(repo_name)
     already_seen_prs = set()
@@ -370,7 +376,9 @@ def process_repo(
     prs = repo.get_pulls(state="closed")
 
     n_good_prs = 0
-    with tqdm(total=prs.totalCount, desc="Processing prs", leave=False) as pbar:
+    with tqdm(
+        total=prs.totalCount, desc=f"Processing prs of {repo_name}", leave=False, position=position
+    ) as pbar:
         for pr in prs:
             pbar.set_postfix({"pr": pr.number, "# new good found": n_good_prs})
             try:
@@ -378,8 +386,10 @@ def process_repo(
                     continue
 
                 n_good_prs += 1
-                process_pull(repo, pr, dataset, repos_dir, archive_destination, cache)
+                process_pull(
-                dataset.to_json(args.output)
+                    repo, pr, dataset, repos_dir, archive_destination, cache, show_progress
+                )
+                # dataset.to_json(args.output)
             except Exception as e:
                 tqdm.write(f"[ERROR] PR #{pr.number} in {repo.full_name}. {type(e)}: {e}")
             finally:
@@ -388,20 +398,25 @@ def process_repo(
 
 # Wrapper to run in each worker process
 def process_repo_worker(
-    repo_name: str, repos_dir: str, archive_destination: str, cache: dict
+    repo_name: str, repos_dir: str, archive_destination: str, cache: dict, position: int
 ) -> list:
-    # Initialize GitHub and Docker clients in each process
-    token = os.environ.get("GITHUB_AUTH_TOKEN_CRAB")
-    g_worker = Github(token, seconds_between_requests=0)
-    docker_client_worker = docker.from_env()
-
     # Local dataset to collect entries for this repo
     local_dataset = Dataset()
 
     # Call the existing process_repo, but passing the local GitHub and Docker clients
     # You may need to modify process_repo to accept g and docker_client as parameters
-    process_repo(repo_name, local_dataset, repos_dir, archive_destination, cache)
+    try:
-    return local_dataset.entries
+        process_repo(
+            repo_name,
+            local_dataset,
+            repos_dir,
+            archive_destination,
+            cache,
+            position=position,
+            show_progress=False,
+        )
+    finally:
+        return local_dataset.entries
 
 
 def process_repos_parallel(
@@ -409,6 +424,7 @@ def process_repos_parallel(
     dataset: Dataset,
     repos_dir: str,
     archive_destination: str,
+    n_workers: int,
     cache: dict[str, dict[int, DatasetEntry]] = {},
 ):
     """
@@ -421,29 +437,62 @@ def process_repos_parallel(
         archive_destination: Directory for archives
         cache: Optional cache of previously processed PR entries
     """
+    for pr2entry in tqdm(cache.values(), desc="Adding cache in dataset"):
+        dataset.entries.extend(pr2entry.values())
+    dataset.to_json(args.output)
+
     repo_names = df["name"]
-
+    free_positions = list(range(1, n_workers + 1))
-    # Use all CPUs; adjust max_workers as needed
+    repo_names_iter = iter(repo_names)
-    with concurrent.futures.ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:
+    future_to_repo = {}
+    with tqdm(
+        total=len(repo_names),
+        desc="Processing repos",
+    ) as outer_pb, ProcessPoolExecutor(max_workers=n_workers) as executor:
         # Map each repo to a future
-        future_to_repo = {
-            executor.submit(process_repo_worker, name, repos_dir, archive_destination, cache): name
-            for name in repo_names
-        }
 
-        # Iterate as repos complete
+        for _ in range(n_workers):
-        for future in tqdm(
-            concurrent.futures.as_completed(future_to_repo),
-            total=len(future_to_repo),
-            desc="Processing repos",
-        ):
-            repo_name = future_to_repo[future]
             try:
-                entries = future.result()
+                name = next(repo_names_iter)
-                dataset.entries.extend(entries)
+            except StopIteration:
-                dataset.to_json(args.output)
+                break
-            except Exception as e:
+            pos = free_positions.pop(0)
-                tqdm.write(f"[ERROR] Repo {repo_name}: {e}")
+            fut = executor.submit(
+                process_repo_worker, name, repos_dir, archive_destination, cache, pos
+            )
+            future_to_repo[fut] = (name, pos)
+
+        try:
+            while future_to_repo:
+                done, _ = wait(future_to_repo, return_when=FIRST_COMPLETED)
+                for fut in done:
+                    repo_finished, pos = future_to_repo.pop(fut)
+                    entries = fut.result()
+                    dataset.entries.extend(entries)
+                    dataset.to_json(args.output)
+                    outer_pb.update(1)
+
+                    try:
+                        name = next(repo_names_iter)
+                    except StopIteration:
+                        # no more tasks: free the slot
+                        free_positions.append(pos)
+                    else:
+                        new_fut = executor.submit(
+                            process_repo_worker, name, repos_dir, archive_destination, cache, pos
+                        )
+                        future_to_repo[new_fut] = (name, pos)
+        except KeyboardInterrupt:
+            print("Saving all the entries up until now")
+            # any futures that happen to be done but not yet popped:
+            for fut in list(future_to_repo):
+                if fut.done():
+                    try:
+                        dataset.entries.extend(fut.result())
+                    except Exception:
+                        pass
+            # re-raise so the top‐level finally block still runs
+            raise
 
 
 def process_repos(
@@ -539,6 +588,12 @@ if __name__ == "__main__":
         action="store_true",
         help="Caches GitHub API requests in a SQLite file using 'requests_cache' (see optional-requirements.txt). Useful for faster reruns if the script crashes or you’re tweaking it. Might produce stale data.",
     )
+    parser.add_argument(
+        "--max-workers",
+        metavar="N_WORKERS",
+        type=int,
+        help="Parallelize the processing of the repos with the given number of workers. If not given, the script is monothreaded",
+    )
 
     args = parser.parse_args()
 
@@ -577,6 +632,18 @@ if __name__ == "__main__":
 
     dataset = Dataset()
     try:
-        process_repos(df, dataset, args.repos, args.archive_destination, cache)
+        if args.max_workers is not None:
+            process_repos_parallel(
+                df, dataset, args.repos, args.archive_destination, args.max_workers, cache
+            )
+        else:
+            process_repos(df, dataset, args.repos, args.archive_destination, cache)
     finally:
+        print("")
+        print("")
+        print("")
+        print("")
+        print("")
+        print("")
+        print(f"Writing dataset to {args.output}")
         dataset.to_json(args.output)