Better naming for Stats methods and members

docs/contribute.md

@@ -87,7 +87,7 @@ To add a runtime to a plugin:
     - `"RuntimeClass": <class_name>`, where `<class_name>` is a unique name for a Python class that inherits `BaseRT` and implements the runtime.
       - For example, `"RuntimeClass": ExampleRT` implements the `example` runtime.
       - The interface for the runtime class is defined in [Runtime Class](#runtime-class) below.
-    - (Optional) `"status_stats": List[str]`: a list of keys from the build stats that should be printed out at the end of benchmarking in the CLI's `Status` output. These keys, and corresponding values, must be set in the runtime class using `self.stats.add_build_stat(key, value)`.
+    - (Optional) `"status_stats": List[str]`: a list of keys from the build stats that should be printed out at the end of benchmarking in the CLI's `Status` output. These keys, and corresponding values, must be set in the runtime class using `self.stats.save_model_eval_stat(key, value)`.
     - (Optional) `"requirement_check": Callable`: a callable that runs before each benchmark. This may be used to check whether the device selected is available and functional before each benchmarking run. Exceptions raised during this callable will halt the benchmark of all selected files.
 
 1. Populate the package with the following files (see [Plugin Directory Layout](#plugin-directory-layout)):

examples/cli/plugins/example_rt/turnkeyml_plugin_example_rt/runtime.py

@@ -51,8 +51,8 @@ def benchmark(self) -> MeasuredPerformance:
 
         # Assign values to the stats that will be printed
         # out by the CLI when status is reported
-        self.stats.add_build_stat("magic_perf_points", 42)
-        self.stats.add_build_stat("super_runtime_points", 100)
+        self.stats.save_model_eval_stat("magic_perf_points", 42)
+        self.stats.save_model_eval_stat("super_runtime_points", 100)
 
         return MeasuredPerformance(
             mean_latency=self.mean_latency,

src/turnkeyml/analyze/script.py

@@ -152,40 +152,40 @@ def explore_invocation(
         invocation_info.stats_keys = []
 
     # Create an ID for the build stats by combining the device and runtime.
-    # We don't need more info in the stats_id because changes to benchmark_model()
+    # We don't need more info in the evaluation_id because changes to benchmark_model()
     # arguments (e.g., sequence) will trigger a rebuild, which is intended to replace the
     # build stats so long as the device and runtime have not changed.
-    stats_id = f"{tracer_args.device}_{selected_runtime}"
+    evaluation_id = f"{tracer_args.device}_{selected_runtime}"
 
     stats = fs.Stats(
         tracer_args.cache_dir,
         build_name,
-        stats_id,
+        evaluation_id,
     )
     invocation_info.stats = stats
 
     # Stats that apply to the model, regardless of build
-    stats.save_stat(
+    stats.save_model_stat(
         fs.Keys.HASH,
         model_info.hash,
     )
-    stats.save_stat(
+    stats.save_model_stat(
         fs.Keys.MODEL_NAME,
         tracer_args.script_name,
     )
-    stats.save_stat(
+    stats.save_model_stat(
         fs.Keys.PARAMETERS,
         model_info.params,
     )
     if model_info.model_type != build.ModelType.ONNX_FILE:
-        stats.save_stat(fs.Keys.CLASS, type(model_info.model).__name__)
+        stats.save_model_stat(fs.Keys.CLASS, type(model_info.model).__name__)
     if fs.Keys.AUTHOR in tracer_args.labels:
-        stats.save_stat(fs.Keys.AUTHOR, tracer_args.labels[fs.Keys.AUTHOR][0])
+        stats.save_model_stat(fs.Keys.AUTHOR, tracer_args.labels[fs.Keys.AUTHOR][0])
     if fs.Keys.TASK in tracer_args.labels:
-        stats.save_stat(fs.Keys.TASK, tracer_args.labels[fs.Keys.TASK][0])
+        stats.save_model_stat(fs.Keys.TASK, tracer_args.labels[fs.Keys.TASK][0])
 
     # Save all of the lables in one place
-    stats.save_stat(fs.Keys.LABELS, tracer_args.labels)
+    stats.save_model_stat(fs.Keys.LABELS, tracer_args.labels)
 
     # If the input script is a built-in TurnkeyML model, make a note of
     # which one
@@ -203,18 +203,18 @@ def explore_invocation(
             fs.MODELS_DIR,
             f"https://github.com/onnx/turnkeyml/tree/{git_hash}/models",
         ).replace("\\", "/")
-        stats.save_stat(fs.Keys.MODEL_SCRIPT, relative_path)
+        stats.save_model_stat(fs.Keys.MODEL_SCRIPT, relative_path)
 
     # Build-specific stats
-    stats.add_build_stat(
+    stats.save_model_eval_stat(
         fs.Keys.DEVICE_TYPE,
         tracer_args.device,
     )
-    stats.add_build_stat(
+    stats.save_model_eval_stat(
         fs.Keys.RUNTIME,
         selected_runtime,
     )
-    stats.add_build_stat(
+    stats.save_model_eval_stat(
         fs.Keys.ITERATIONS,
         tracer_args.iterations,
     )
@@ -233,12 +233,14 @@ def explore_invocation(
             # we will try to catch the exception and note it in the stats.
             # If a concluded build still has a status of "running", this means
             # there was an uncaught exception.
-            stats.add_build_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.RUNNING)
+            stats.save_model_eval_stat(
+                fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.RUNNING
+            )
 
             perf = benchmark_model(
                 model_info.model,
                 inputs,
-                stats_id=stats_id,
+                evaluation_id=evaluation_id,
                 device=tracer_args.device,
                 runtime=selected_runtime,
                 build_name=build_name,
@@ -263,7 +265,7 @@ def explore_invocation(
         invocation_info.status_message = f"Build Error: {e}"
         invocation_info.status_message_color = printing.Colors.WARNING
 
-        stats.add_build_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.FAILED)
+        stats.save_model_eval_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.FAILED)
 
         _store_traceback(invocation_info)
 
@@ -275,22 +277,22 @@ def explore_invocation(
         )
         invocation_info.status_message_color = printing.Colors.WARNING
 
-        stats.add_build_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.KILLED)
+        stats.save_model_eval_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.KILLED)
 
     except exp.ArgError as e:
         # ArgError indicates that some argument to benchmark_model() was
         # illegal. In that case we want to halt execution so that users can
         # fix their arguments.
 
-        stats.add_build_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.FAILED)
+        stats.save_model_eval_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.FAILED)
 
         raise e
 
     except exp.Error as e:
         invocation_info.status_message = f"Error: {e}."
         invocation_info.status_message_color = printing.Colors.WARNING
 
-        stats.add_build_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.FAILED)
+        stats.save_model_eval_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.FAILED)
 
         _store_traceback(invocation_info)
 
@@ -300,19 +302,21 @@ def explore_invocation(
         invocation_info.status_message = f"Unknown turnkey error: {e}"
         invocation_info.status_message_color = printing.Colors.WARNING
 
-        stats.add_build_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.FAILED)
+        stats.save_model_eval_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.FAILED)
 
         _store_traceback(invocation_info)
     else:
         # If there was no exception then we consider the build to be a success
-        stats.add_build_stat(fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.SUCCESSFUL)
+        stats.save_model_eval_stat(
+            fs.Keys.BENCHMARK_STATUS, fs.BenchmarkStatus.SUCCESSFUL
+        )
 
     finally:
         # Ensure that stdout/stderr is not being forwarded before updating status
         util.stop_logger_forward()
 
         system_info = build.get_system_info()
-        stats.save_stat(
+        stats.save_model_stat(
             fs.Keys.SYSTEM_INFO,
             system_info,
         )
@@ -324,11 +328,11 @@ def explore_invocation(
 
             # ONNX stats that we want to save into the build's turnkey_stats.yaml file
             # so that they can be easily accessed by the report command later
-            if fs.Keys.ONNX_FILE in stats.build_stats.keys():
+            if fs.Keys.ONNX_FILE in stats.evaluation_stats.keys():
                 # Just in case the ONNX file was generated on a different machine:
                 # strip the state's cache dir, then prepend the current cache dir
                 final_onnx_file = fs.rebase_cache_dir(
-                    stats.build_stats[fs.Keys.ONNX_FILE],
+                    stats.evaluation_stats[fs.Keys.ONNX_FILE],
                     build_name,
                     tracer_args.cache_dir,
                 )
@@ -337,22 +341,22 @@ def explore_invocation(
                 onnx_model_info = util.populate_onnx_model_info(final_onnx_file)
                 onnx_input_dimensions = util.onnx_input_dimensions(final_onnx_file)
 
-                stats.save_stat(
+                stats.save_model_stat(
                     fs.Keys.ONNX_OPS_COUNTER,
                     onnx_ops_counter,
                 )
-                stats.save_stat(
+                stats.save_model_stat(
                     fs.Keys.ONNX_MODEL_INFO,
                     onnx_model_info,
                 )
-                stats.save_stat(
+                stats.save_model_stat(
                     fs.Keys.ONNX_INPUT_DIMENSIONS,
                     onnx_input_dimensions,
                 )
 
             if perf:
                 for key, value in vars(perf).items():
-                    stats.add_build_stat(
+                    stats.save_model_eval_stat(
                         key=key,
                         value=value,
                     )

src/turnkeyml/analyze/status.py

@@ -198,7 +198,7 @@ def print_invocation(
         if unique_invocation.stats_keys is not None:
             for key in unique_invocation.stats_keys:
                 nice_key = _pretty_print_key(key)
-                value = unique_invocation.stats.build_stats[key]
+                value = unique_invocation.stats.evaluation_stats[key]
                 printing.logn(f"{ident}\t\t\t{nice_key}:\t{value}")
         print()
     else:

src/turnkeyml/build/export.py

@@ -181,8 +181,10 @@ def fire(self, state: build.State):
         if check_model(output_path, success_msg, fail_msg):
             state.intermediate_results = [output_path]
 
-            stats = fs.Stats(state.cache_dir, state.config.build_name, state.stats_id)
-            stats.add_build_stat(
+            stats = fs.Stats(
+                state.cache_dir, state.config.build_name, state.evaluation_id
+            )
+            stats.save_model_eval_stat(
                 fs.Keys.ONNX_FILE,
                 output_path,
             )
@@ -307,8 +309,10 @@ def fire(self, state: build.State):
         if check_model(output_path, success_msg, fail_msg):
             state.intermediate_results = [output_path]
 
-            stats = fs.Stats(state.cache_dir, state.config.build_name, state.stats_id)
-            stats.add_build_stat(
+            stats = fs.Stats(
+                state.cache_dir, state.config.build_name, state.evaluation_id
+            )
+            stats.save_model_eval_stat(
                 fs.Keys.ONNX_FILE,
                 output_path,
             )
@@ -428,8 +432,10 @@ def fire(self, state: build.State):
         if check_model(output_path, success_msg, fail_msg):
             state.intermediate_results = [output_path]
 
-            stats = fs.Stats(state.cache_dir, state.config.build_name, state.stats_id)
-            stats.add_build_stat(
+            stats = fs.Stats(
+                state.cache_dir, state.config.build_name, state.evaluation_id
+            )
+            stats.save_model_eval_stat(
                 fs.Keys.ONNX_FILE,
                 output_path,
             )
@@ -492,8 +498,10 @@ def fire(self, state: build.State):
         if check_model(output_path, success_msg, fail_msg):
             state.intermediate_results = [output_path]
 
-            stats = fs.Stats(state.cache_dir, state.config.build_name, state.stats_id)
-            stats.add_build_stat(
+            stats = fs.Stats(
+                state.cache_dir, state.config.build_name, state.evaluation_id
+            )
+            stats.save_model_eval_stat(
                 fs.Keys.ONNX_FILE,
                 output_path,
             )
@@ -596,8 +604,10 @@ def fire(self, state: build.State):
         if check_model(output_path, success_msg, fail_msg):
             state.intermediate_results = [output_path]
 
-            stats = fs.Stats(state.cache_dir, state.config.build_name, state.stats_id)
-            stats.add_build_stat(
+            stats = fs.Stats(
+                state.cache_dir, state.config.build_name, state.evaluation_id
+            )
+            stats.save_model_eval_stat(
                 fs.Keys.ONNX_FILE,
                 output_path,
             )

src/turnkeyml/build/hummingbird.py

@@ -217,9 +217,8 @@ def fire(self, state: build.State):
         np.save(state.original_inputs_file, state.inputs)
 
         state.intermediate_results = [output_path]
-        stats = fs.Stats(state.cache_dir, state.config.build_name)
-        stats.add_sub_stat(
-            state.stats_id,
+        stats = fs.Stats(state.cache_dir, state.config.build_name, state.evaluation_id)
+        stats.save_model_eval_stat(
             fs.Keys.ONNX_FILE,
             output_path,
         )

src/turnkeyml/build/ignition.py

@@ -250,7 +250,7 @@ def _rebuild_if_needed(
 
 def load_or_make_state(
     config: build.Config,
-    stats_id: str,
+    evaluation_id: str,
     cache_dir: str,
     rebuild: str,
     model_type: build.ModelType,
@@ -274,7 +274,7 @@ def load_or_make_state(
         "inputs": inputs,
         "monitor": monitor,
         "rebuild": rebuild,
-        "stats_id": stats_id,
+        "evaluation_id": evaluation_id,
         "cache_dir": cache_dir,
         "config": config,
         "model_type": model_type,

src/turnkeyml/build/stage.py

@@ -273,8 +273,8 @@ def launch(self, state: build.State) -> build.State:
             raise exp.Error(msg)
 
         # Collect telemetry for the build
-        stats = fs.Stats(state.cache_dir, state.config.build_name, state.stats_id)
-        stats.add_build_stat(
+        stats = fs.Stats(state.cache_dir, state.config.build_name, state.evaluation_id)
+        stats.save_model_eval_stat(
             fs.Keys.ALL_BUILD_STAGES,
             self.get_names(),
         )
@@ -292,7 +292,7 @@ def launch(self, state: build.State) -> build.State:
                 # Collect telemetry about the stage
                 execution_time = time.time() - start_time
 
-                stats.add_build_sub_stat(
+                stats.save_model_eval_sub_stat(
                     parent_key=fs.Keys.COMPLETED_BUILD_STAGES,
                     key=stage.unique_name,
                     value=execution_time,

src/turnkeyml/build_api.py

@@ -11,7 +11,7 @@ def build_model(
     model: build.UnionValidModelInstanceTypes = None,
     inputs: Optional[Dict[str, Any]] = None,
     build_name: Optional[str] = None,
-    stats_id: Optional[str] = "build",
+    evaluation_id: Optional[str] = "build",
     cache_dir: str = filesystem.DEFAULT_CACHE_DIR,
     monitor: Optional[bool] = None,
     rebuild: Optional[str] = None,
@@ -30,7 +30,7 @@ def build_model(
         build_name: Unique name for the model that will be
             used to store the ONNX file and build state on disk. Defaults to the
             name of the file that calls build_model().
-        stats_id: Unique name for build statistics that should persist across multiple
+        evaluation_id: Unique name for evaluation statistics that should persist across multiple
             builds of the same model.
         cache_dir: Directory to use as the cache for this build. Output files
             from this build will be stored at cache_dir/build_name/
@@ -94,7 +94,7 @@ def build_model(
     # Get the state of the model from the cache if a valid build is available
     state = ignition.load_or_make_state(
         config=config,
-        stats_id=stats_id,
+        evaluation_id=evaluation_id,
         cache_dir=parsed_cache_dir,
         rebuild=rebuild or build.DEFAULT_REBUILD_POLICY,
         model_type=model_type,