Fix/remove logging

.github/workflows/mergeCi.yaml

@@ -27,7 +27,7 @@ jobs:
         run: docker-compose --env-file .env.ci exec develop koina_lint.sh --fail-under=6
 
       - name: Testing
-        run: docker-compose --env-file .env.ci exec develop koina_test.sh
+        run: docker-compose --env-file .env.ci exec develop koina_test.sh --check
 
       - name: Shutdown
         if: always()

koina_format.sh

@@ -1,3 +1,3 @@
 #!/bin/bash
 cd /workspace/koina/
-poetry run -C clients/python black . --check
+poetry run -C clients/python black . $@

models/AlphaPept/AlphaPept_Preprocess_instrument_types/1/model.py

@@ -7,7 +7,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = json.loads(args["model_config"])

models/AlphaPept/AlphaPept_ccs_generic/1/model.py

@@ -18,7 +18,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = model_config = json.loads(args["model_config"])

models/AlphaPept/AlphaPept_ccs_generic_postprocess/1/model.py

@@ -8,7 +8,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = json.loads(args["model_config"])

models/AlphaPept/AlphaPept_ms2_generic/1/model.py

@@ -18,7 +18,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = model_config = json.loads(args["model_config"])

models/AlphaPept/AlphaPept_ms2_generic_postprocess/1/model.py

@@ -9,7 +9,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = json.loads(args["model_config"])

models/AlphaPept/AlphaPept_rt_generic/1/model.py

@@ -18,7 +18,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = model_config = json.loads(args["model_config"])

models/AlphaPept/AlphaPept_rt_generic_postprocess/1/model.py

@@ -8,7 +8,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = json.loads(args["model_config"])

models/Deeplc/Deeplc_Preprocess_onehot/1/model.py

@@ -56,7 +56,7 @@ def initialize(self, args):
     def execute(self, requests):
         peptide_in_str = []
         responses = []
-        logger = pb_utils.Logger
+
         for request in requests:
             peptide_in = pb_utils.get_input_tensor_by_name(request, "peptide_sequences")
             peptides_ = peptide_in.as_numpy().tolist()

models/Generic/fragment_mz/1/model.py

@@ -41,7 +41,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.frag_mz_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = model_config = json.loads(args["model_config"])

models/Prosit/Prosit_2019_intensity_postprocess/1/model.py

@@ -20,7 +20,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = json.loads(args["model_config"])

models/Prosit/Prosit_2019_irt_postprocess/1/model.py

@@ -6,7 +6,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = json.loads(args["model_config"])

models/Prosit/Prosit_Preprocess_fragmentation_types/1/model.py

@@ -7,7 +7,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = json.loads(args["model_config"])

models/Prosit/Prosit_Preprocess_peptide/1/model.py

@@ -25,7 +25,7 @@ def initialize(self, args):
     def execute(self, requests):
         peptide_in_str = []
         responses = []
-        logger = pb_utils.Logger
+
         for request in requests:
             peptide_in = pb_utils.get_input_tensor_by_name(request, "peptide_sequences")
             peptides_ = peptide_in.as_numpy().tolist()

models/Prosit/Prosit_Preprocess_peptide/1/sequence_conversion.py

@@ -64,7 +64,6 @@ def split_modstring(sequence, r_pattern):
 
 def character_to_array(character):
     array = np.zeros((1, SEQ_LEN), dtype=np.uint8)
-    logger = pb_utils.Logger
 
     generator_sequence_numeric = parse_modstrings(
         [character], alphabet=ALPHABET, translate=True, filter=False

models/Prosit/Prosit_Preprocess_peptide_2020_TMT/1/model.py

@@ -15,7 +15,7 @@ def initialize(self, args):
     def execute(self, requests):
         peptide_in_str = []
         responses = []
-        logger = pb_utils.Logger
+
         for request in requests:
             peptide_in = pb_utils.get_input_tensor_by_name(request, "peptide_sequences")
             peptides_ = peptide_in.as_numpy().tolist()

models/Prosit/XL_Prosit_2023_intensity_postprocess/1/model.py

@@ -27,15 +27,13 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = json.loads(args["model_config"])
         output0_config = pb_utils.get_output_config_by_name(model_config, "intensities")
         self.output_dtype = pb_utils.triton_string_to_numpy(output0_config["data_type"])
 
     def execute(self, requests):
-        logger = pb_utils.Logger
         responses = []
         for request in requests:
             peptide_in = (

models/Prosit/XL_Prosit_Helper_annotation/1/model.py

@@ -143,7 +143,6 @@ def initialize(self, args):
         self.output_dtype = pb_utils.triton_string_to_numpy(output0_config["data_type"])
 
     def execute(self, requests):
-        logger = pb_utils.Logger
         responses = []
         annotation = np.empty((0, 348))
 

models/Prosit/XL_Prosit_Preprocess_peptide_CMS2/1/model.py

@@ -34,7 +34,6 @@ def initialize(self, args):
     def execute(self, requests):
         peptide_in_str = []
         responses = []
-        logger = pb_utils.Logger
         for request in requests:
             peptide_in_1 = pb_utils.get_input_tensor_by_name(
                 request, "peptide_sequences_1"
@@ -47,17 +46,13 @@ def execute(self, requests):
             peptides_2 = peptide_in_2.as_numpy().tolist()
             peptide_in_1_list = [x[0].decode("utf-8") for x in peptides_1]
             peptide_in_2_list = [x[0].decode("utf-8") for x in peptides_2]
-            logger.log_info(str(peptide_in_1_list))
-            logger.log_info(str(peptide_in_2_list))
 
             sequences_1 = np.asarray(
                 [character_to_array(seq).flatten() for seq in peptide_in_1_list]
             )
             sequences_2 = np.asarray(
                 [character_to_array(seq).flatten() for seq in peptide_in_2_list]
             )
-            logger.log_info(str(sequences_1))
-            logger.log_info(str(sequences_2))
 
             t_1 = pb_utils.Tensor(
                 "peptides_in_1:0", sequences_1.astype(self.output_dtype)

models/Prosit/XL_Prosit_Preprocess_peptide_CMS2/1/sequence_conversion.py

@@ -64,9 +64,6 @@ def split_modstring(sequence, r_pattern):
 
 def character_to_array(character):
     array = np.zeros((1, SEQ_LEN), dtype=np.uint8)
-    logger = pb_utils.Logger
-
-    logger.log_info(str(character))
     generator_sequence_numeric = parse_modstrings(
         [character], alphabet=ALPHABET, translate=True, filter=True
     )

models/Prosit/XL_Prosit_Preprocess_peptide_CMS3/1/model.py

@@ -25,17 +25,14 @@ def initialize(self, args):
     def execute(self, requests):
         peptide_in_str = []
         responses = []
-        logger = pb_utils.Logger
         for request in requests:
             peptide_in = pb_utils.get_input_tensor_by_name(request, "peptide_sequences")
             peptides_ = peptide_in.as_numpy().tolist()
             peptide_in_list = [x[0].decode("utf-8") for x in peptides_]
-            logger.log_info(str(peptide_in_list))
 
             sequences = np.asarray(
                 [character_to_array(seq).flatten() for seq in peptide_in_list]
             )
-            logger.log_info(str(sequences))
 
             t = pb_utils.Tensor("peptides_in:0", sequences.astype(self.output_dtype))
 

models/Prosit/XL_Prosit_Preprocess_peptide_CMS3/1/sequence_conversion.py

@@ -64,9 +64,6 @@ def split_modstring(sequence, r_pattern):
 
 def character_to_array(character):
     array = np.zeros((1, SEQ_LEN), dtype=np.uint8)
-    logger = pb_utils.Logger
-
-    logger.log_info(str(character))
     generator_sequence_numeric = parse_modstrings(
         [character], alphabet=ALPHABET, translate=True, filter=True
     )

models/ms2pip/ms2pip_fragmentmz_wrapper/1/model.py

@@ -19,7 +19,6 @@ class TritonPythonModel:
     def __init__(self):
         super().__init__()
         self.output_dtype = None
-        self.logger = pb_utils.Logger
 
     def initialize(self, args):
         model_config = json.loads(args["model_config"])

models/ms2pip/ms2pip_postprocess_single_model/1/model.py

@@ -12,7 +12,6 @@ def initialize(self, args):
         self.output_dtype = pb_utils.triton_string_to_numpy(output0_config["data_type"])
 
     def execute(self, requests):
-        logger = pb_utils.Logger
         responses = []
         for request in requests:
             peptide_in = pb_utils.get_input_tensor_by_name(request, "raw_intensities")

models/ms2pip/ms2pip_preprocess/1/model.py

@@ -16,7 +16,6 @@ def initialize(self, args):
     def execute(self, requests):
         responses = []
 
-        logger = pb_utils.Logger
         ## every request is up to abatch_size
         for request in requests:
             peptide_in = pb_utils.get_input_tensor_by_name(request, "proforma")

models/start.py

@@ -106,9 +106,10 @@ def find_and_download():
             "--allow-cpu-metrics=true",
             "--allow-gpu-metrics=true",
             "--metrics-port=8502",
-            "--log-info=true",
-            "--log-warning=true",
-            "--log-error=true",
+            "--log-info=false",
+            "--log-warning=false",
+            "--log-error=false",
+            "--log-verbose=0",
             "--rate-limit",
             "execution_count",
             "--cuda-memory-pool-byte-size",