1.2.5 autotune

HISTORY.md

@@ -1,5 +1,9 @@
 # History
 
+1.2.4 (2026-04-24)
+------------------
+* Support `mps` (MacOS) with `phold autotune`
+
 1.2.4 (2026-04-22)
 ------------------
 * Bugfix to ensure ProstT5 confidence outputs are correctly output when annotating multiple contigs/genomes with `phold run` or `phold predict`

pyproject.toml

@@ -5,7 +5,7 @@ requires = ["setuptools>=61.0", "wheel>=0.37.1"]
 [project]
 # https://packaging.python.org/en/latest/specifications/declaring-project-metadata/
 name = "phold"
-version = "1.2.4" # change VERSION too
+version = "1.2.5" # change VERSION too
 description = "Phage Annotations using Protein Structures"
 readme = "README.md"
 requires-python = ">=3.8, <4"

src/phold/features/autotune.py

@@ -44,6 +44,14 @@ def sample_probe_sequences(seqs, n=5000, seed=0):
 
     return sampled
 
+
+def device_synchronize(device: torch.device):
+    if device.type == "cuda":
+        torch.cuda.synchronize(device)
+    elif device.type == "mps":
+        torch.mps.synchronize()
+    # CPU and others: no-op
+
 def autotune_batching_real_data(
     model_dir,
     model_name,
@@ -67,6 +75,7 @@ def autotune_batching_real_data(
 
     if cpu is True:
         device = torch.device("cpu")
+        logger.error("You are running phold autotune with CPU, which is not supported. Please use --batch_size 1")
     else:
         # check for NVIDIA/cuda
         if torch.cuda.is_available():
@@ -76,6 +85,7 @@ def autotune_batching_real_data(
             device = torch.device("mps")
         else:
             device = torch.device("cpu")
+            logger.error("You are running phold autotune with CPU, which is not supported. Please use --batch_size 1")
 
 
     while bs <= max_bs:
@@ -108,11 +118,12 @@ def autotune_batching_real_data(
                 inputs = {k: v.to(device) for k, v in inputs.items()}
 
                 # timing
-                torch.cuda.synchronize()
+                device_synchronize(device)
                 t0 = time.perf_counter()
                 with torch.no_grad():
                     _ = model(**inputs)
-                torch.cuda.synchronize()
+                device_synchronize(device)
+
 
                 total_time += time.perf_counter() - t0
 

src/phold/features/predict_3Di.py

@@ -141,9 +141,8 @@ def get_T5_model(
         localfile = False
         logger.info("ProstT5 not found. Downloading ProstT5 from Hugging Face")
     try:
-        # suppress warning and progress bar
+        # suppress warning
         logging.set_verbosity_error()
-        utils.logging.disable_progress_bar()
         model = T5EncoderModel.from_pretrained(
             model_name,
             cache_dir=f"{model_dir}/",
@@ -155,9 +154,7 @@ def get_T5_model(
         logger.warning("Download from Hugging Face failed. Trying backup from Zenodo.")
         logdir = f"{model_dir}/logdir"
         download_zenodo_prostT5(model_dir, logdir, threads)
-
         logging.set_verbosity_error()
-        utils.logging.disable_progress_bar()
 
         model = T5EncoderModel.from_pretrained(
             model_name,

src/phold/utils/VERSION

@@ -1 +1 @@
-1.2.4
+1.2.5