Removes the parallelism in file convertion (during download)

- Parallelism increases memory usage (each process has to convert a 10Go
  file on average, meaning 20Go per process, so could be 100Go for 5
  workers)
- Parallelism might not speed the process up (Looking into it).
  Since everything should be limited by disk speed, making concurrent
  shouldn't really help.

Updated the timings from ETA to elapsed (since ETA was imprecise
anyway).
Also using `datetime` directly to get simpler code IMO.

server/text_generation_server/utils/convert.py

@@ -1,5 +1,6 @@
 import concurrent
 import time
+import datetime
 import torch
 
 from concurrent.futures import ThreadPoolExecutor
@@ -78,17 +79,9 @@ def convert_file(pt_file: Path, st_file: Path):
 def convert_files(pt_files: List[Path], st_files: List[Path]):
     assert len(pt_files) == len(st_files)
 
-    executor = ThreadPoolExecutor(max_workers=5)
-    futures = [
-        executor.submit(convert_file, pt_file=pt_file, st_file=st_file)
-        for pt_file, st_file in zip(pt_files, st_files)
-    ]
-
+    N = len(pt_files)
     # We do this instead of using tqdm because we want to parse the logs with the launcher
-    start_time = time.time()
-    for i, future in enumerate(concurrent.futures.as_completed(futures)):
-        elapsed = timedelta(seconds=int(time.time() - start_time))
-        remaining = len(futures) - (i + 1)
-        eta = (elapsed / (i + 1)) * remaining if remaining > 0 else 0
-
-        logger.info(f"Convert: [{i + 1}/{len(futures)}] -- ETA: {eta}")
+    start = datetime.datetime.now()
+    for i, (pt_file, sf_file) in enumerate(zip(pt_files, st_files)):
+        elapsed = datetime.datetime.now() - start
+        logger.info(f"Convert: [{i + 1}/{N}] -- Took: {elapsed}")