Add cache and build name to status report

src/mlagility/analysis/analysis.py

@@ -89,7 +89,11 @@ def explore_invocation(
     # Update status to "computing"
     invocation_info.status_message = "Computing..."
     invocation_info.status_message_color = printing.Colors.OKBLUE
-    status.update(tracer_args.models_found)
+
+    build_name = filesystem.get_build_name(
+        tracer_args.script_name, tracer_args.labels, invocation_info.hash
+    )
+    status.update(tracer_args.models_found, build_name, tracer_args.cache_dir)
 
     # Get a copy of the keyword arguments
     args, kwargs = model_inputs
@@ -117,10 +121,6 @@ def explore_invocation(
                 inputs[all_args[i]] = args[i]
     invocation_info.inputs = inputs
 
-    build_name = filesystem.get_build_name(
-        tracer_args.script_name, tracer_args.labels, invocation_info.hash
-    )
-
     # Save model labels
     tracer_args.labels["class"] = [f"{type(model_info.model).__name__}"]
     labels.save_to_cache(tracer_args.cache_dir, build_name, tracer_args.labels)
@@ -188,7 +188,7 @@ def explore_invocation(
         # Ensure that stdout is not being forwarded before updating status
         if hasattr(sys.stdout, "terminal"):
             sys.stdout = sys.stdout.terminal
-        status.update(tracer_args.models_found)
+        status.update(tracer_args.models_found, build_name, tracer_args.cache_dir)
 
         if tracer_args.device == "groq":
             import groqflow.common.build as groq_build
@@ -510,7 +510,10 @@ def forward_spy(*args, **kwargs):
                 # Ensure that groqit() doesn't interfere with our execution count
                 model_info.executed = 1
 
-            status.update(tracer_args.models_found)
+            build_name = filesystem.get_build_name(
+                tracer_args.script_name, tracer_args.labels, invocation_info.hash
+            )
+            status.update(tracer_args.models_found, build_name, tracer_args.cache_dir)
 
             # Turn tracing on again after computing the outputs
             sys.setprofile(tracer)

src/mlagility/analysis/status.py

@@ -4,8 +4,9 @@
 import onnxflow.common.build as build
 from mlagility.analysis.util import ModelInfo
 
-
-def update(models_found: Dict[str, ModelInfo]) -> None:
+def update(models_found: Dict[str, ModelInfo],
+           build_name: str,
+           cache_dir: str) -> None:
     """
     Prints all models and submodels found
     """
@@ -16,11 +17,13 @@ def update(models_found: Dict[str, ModelInfo]) -> None:
         "\nModels discovered during profiling:\n",
         c=printing.Colors.BOLD,
     )
-    recursive_print(models_found, None, None, [])
+    recursive_print(models_found, build_name, cache_dir, None, None, [])
 
 
 def recursive_print(
     models_found: Dict[str, ModelInfo],
+    build_name: str,
+    cache_dir: str,
     parent_model_hash: Union[str, None] = None,
     parent_invocation_hash: Union[str, None] = None,
     script_names_visited: List[str] = False,
@@ -50,6 +53,8 @@ def recursive_print(
 
                 print_invocation(
                     model_info,
+                    build_name,
+                    cache_dir,
                     invocation_hash,
                     print_file_name,
                     invocation_idx=invocation_idx,
@@ -63,6 +68,8 @@ def recursive_print(
 
                 recursive_print(
                     models_found,
+                    build_name,
+                    cache_dir,
                     parent_model_hash=model_hash,
                     parent_invocation_hash=invocation_hash,
                     script_names_visited=script_names_visited,
@@ -71,6 +78,8 @@ def recursive_print(
 
 def print_invocation(
     model_info: ModelInfo,
+    build_name: str,
+    cache_dir: str,
     invocation_hash: Union[str, None],
     print_file_name: bool = False,
     invocation_idx: int = 0,
@@ -133,6 +142,7 @@ def print_invocation(
 
     print(f"{ident}\tInput Shape:\t{input_shape}")
     print(f"{ident}\tHash:\t\t" + invocation_hash)
+    print(f"{ident}\tBuild dir:\t" + cache_dir + '/' + build_name)
 
     # Print benchit results if benchit was run
     if unique_invocation.performance:

src/mlagility/api/report.py

@@ -2,23 +2,23 @@
 APIs to help with programmatically parsing the MLAgility report
 """
 
-from typing import Dict
+from typing import Dict, List
 import pandas as pd
 
-
-def get_dict(report_csv: str, column: str) -> Dict[str, str]:
+def get_dict(report_csv: str, columns: List[str]) -> Dict[str, Dict[str, str]]:
     """
-    Returns a dictionary where the keys are model names and the values are the values of
-    a given column in a report.csv file.
-
+    Returns a dictionary where the keys are model names and the values are dictionaries. 
+    Each dictionary represents a model with column names as keys and their corresponding values.
     args:
      - report_csv: path to a report.csv file generated by benchit
-     - column: name of a column in the report.csv file whose values will be used to
+     - columns: list of column names in the report.csv file whose values will be used to
         populate the dictionary
     """
 
-    # Open the MLAgility report as a dataframe
+    # Load the MLAgility report as a dataframe
     dataframe = pd.read_csv(report_csv)
 
-    # Convert to dictionary
-    return pd.Series(dataframe[column].values, index=dataframe.model_name).to_dict()
+    # Create a nested dictionary with model_name as keys and another dictionary of {column: value} pairs as values
+    result = {row[0]: row[1].to_dict() for row in dataframe.set_index("model_name")[columns].iterrows()}
+
+    return result

test/cli.py

@@ -19,7 +19,7 @@
 from mlagility.cli.cli import main as benchitcli
 import mlagility.cli.report as report
 import mlagility.api.report as report_api
-from mlagility.common import filesystem
+import mlagility.common.filesystem as filesystem
 import mlagility.api.ortmodel as ortmodel
 import mlagility.api.trtmodel as trtmodel
 import onnxflow.common.build as build
@@ -436,11 +436,11 @@ def test_004_cli_report(self):
         ), f"onnx_converted must be True, got {linear_summary['onnx_converted']}"
 
         # Make sure the report.get_dict() API works
-        result_dict = report_api.get_dict(summary_csv_path, "onnx_exported")
-        for result in result_dict.values():
+        result_dict = report_api.get_dict(summary_csv_path, ["onnx_exported"])
+        for model_name, result in result_dict.items():
             # All of the models should have exported to ONNX, so the "onnx_exported" value
             # should be True for all of them
-            assert result
+            assert result.get("onnx_exported") is True
 
     def test_005_cli_list(self):
         # NOTE: this is not a unit test, it relies on other command