add isolate_from_data and train_from_data, with refactor entrypoint (tekton prerequisite)

cmd/README.md

@@ -2,63 +2,6 @@
 
 Use kepler model server function as a standalone docker container.
 
-```
-usage: main.py [-h] [-i INPUT] [-o OUTPUT] [-s SERVER] [--interval INTERVAL] [--step STEP] [--metric-prefix METRIC_PREFIX] [-p PIPELINE_NAME] [--extractor EXTRACTOR] [--isolator ISOLATOR] [--profile PROFILE] [--target-hints TARGET_HINTS] [--bg-hints BG_HINTS]
-               [-e ENERGY_SOURCE] [--abs-trainers ABS_TRAINERS] [--dyn-trainers DYN_TRAINERS] [--benchmark BENCHMARK] [-ot OUTPUT_TYPE] [-fg FEATURE_GROUP] [--model-name MODEL_NAME] [--target-data TARGET_DATA] [--scenario SCENARIO] [--id ID] [--version VERSION]
-               [--publisher PUBLISHER] [--include-raw INCLUDE_RAW]
-               command
-
-Kepler model server entrypoint
-
-positional arguments:
-  command               The command to execute.
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -i INPUT, --input INPUT
-                        Specify input file/folder name.
-  -o OUTPUT, --output OUTPUT
-                        Specify output file/folder name
-  -s SERVER, --server SERVER
-                        Specify prometheus server.
-  --interval INTERVAL   Specify query interval.
-  --step STEP           Specify query step.
-  --metric-prefix METRIC_PREFIX
-                        Specify metrix prefix to filter.
-  -p PIPELINE_NAME, --pipeline-name PIPELINE_NAME
-                        Specify pipeline name.
-  --extractor EXTRACTOR
-                        Specify extractor name (default, smooth).
-  --isolator ISOLATOR   Specify isolator name (none, min, profile, trainer).
-  --profile PROFILE     Specify profile input (required for trainer and profile isolator).
-  --target-hints TARGET_HINTS
-                        Specify dynamic workload container name hints (used by TrainIsolator)
-  --bg-hints BG_HINTS   Specify background workload container name hints (used by TrainIsolator)
-  -e ENERGY_SOURCE, --energy-source ENERGY_SOURCE
-                        Specify energy source.
-  --abs-trainers ABS_TRAINERS
-                        Specify trainer names (use comma(,) as delimiter).
-  --dyn-trainers DYN_TRAINERS
-                        Specify trainer names (use comma(,) as delimiter).
-  --benchmark BENCHMARK
-                        Specify benchmark file name.
-  -ot OUTPUT_TYPE, --output-type OUTPUT_TYPE
-                        Specify output type (AbsPower or DynPower) for energy estimation.
-  -fg FEATURE_GROUP, --feature-group FEATURE_GROUP
-                        Specify target feature group for energy estimation.
-  --model-name MODEL_NAME
-                        Specify target model name for energy estimation.
-  --target-data TARGET_DATA
-                        Speficy target plot data (preprocess, estimate)
-  --scenario SCENARIO   Speficy scenario
-  --id ID               specify machine id
-  --version VERSION     Specify model server version.
-  --publisher PUBLISHER
-                        Specify github account of model publisher
-  --include-raw INCLUDE_RAW
-                        Include raw query data
-```
-
 ## Get started
 
 1. Deploy Kepler with Prometheus in the cluster exporting prometheus to port `:9090`

cmd/cmd_plot.py

@@ -0,0 +1,113 @@
+import os
+import sys
+
+cur_path = os.path.join(os.path.dirname(__file__), '.')
+sys.path.append(cur_path)
+src_path = os.path.join(os.path.dirname(__file__), '..', 'src')
+sys.path.append(src_path)
+
+from util.prom_types import TIMESTAMP_COL
+from util import PowerSourceMap
+
+
+def ts_plot(data, cols, title, output_folder, name, labels=None, subtitles=None, ylabel=None):
+    plot_height = 3
+    plot_width = 10
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+    sns.set(font_scale=1.2)
+    fig, axes = plt.subplots(len(cols), 1, figsize=(plot_width, len(cols)*plot_height))
+    for i in range(0, len(cols)):
+        if len(cols) == 1:
+            ax = axes
+        else:
+            ax = axes[i]
+        if isinstance(cols[i], list):
+            # multiple lines
+            for j in range(0, len(cols[i])):
+                sns.lineplot(data=data, x=TIMESTAMP_COL, y=cols[i][j], ax=ax, label=labels[j])
+            ax.set_title(subtitles[i])
+        else:
+            sns.lineplot(data=data, x=TIMESTAMP_COL, y=cols[i], ax=ax)
+            ax.set_title(cols[i])
+        if ylabel is not None:
+            ax.set_ylabel(ylabel)
+    plt.suptitle(title, x=0.5, y=0.99)
+    plt.tight_layout()
+    filename = os.path.join(output_folder, name + ".png")
+    fig.savefig(filename)
+    plt.close()
+
+def feature_power_plot(data, model_id, output_type, energy_source, feature_cols, actual_power_cols, predicted_power_cols, output_folder, name):
+    plot_height = 5
+    plot_width = 5
+
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+    sns.set(font_scale=1.2)
+    row_num = len(feature_cols)
+    col_num = len(actual_power_cols)
+    width = max(10, col_num*plot_width)
+    fig, axes = plt.subplots(row_num, col_num, figsize=(width, row_num*plot_height))
+    for xi in range(0, row_num):
+        feature_col = feature_cols[xi]
+        for yi in range(0, col_num):
+            if row_num == 1:
+                if col_num == 1:
+                    ax = axes
+                else:
+                    ax = axes[yi]
+            else:
+                if col_num == 1:
+                    ax = axes[xi]
+                else:
+                    ax = axes[xi][yi]
+            sorted_data = data.sort_values(by=[feature_col])
+            sns.scatterplot(data=sorted_data, x=feature_col, y=actual_power_cols[yi], ax=ax, label="actual")
+            sns.lineplot(data=sorted_data, x=feature_col, y=predicted_power_cols[yi], ax=ax, label="predicted", color='C1')
+            if xi == 0:
+                ax.set_title(actual_power_cols[yi])
+            if yi == 0:
+                ax.set_ylabel("Power (W)")
+    title = "{} {} prediction correlation \n by {}".format(energy_source, output_type, model_id)
+    plt.suptitle(title, x=0.5, y=0.99)
+    plt.tight_layout()
+    filename = os.path.join(output_folder, name + ".png")
+    fig.savefig(filename)
+    plt.close()
+
+def summary_plot(args, energy_source, summary_df, output_folder, name):
+    if len(summary_df) == 0:
+        print("no summary data to plot")
+        return
+
+    plot_height = 3
+    plot_width = 20
+
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+    sns.set(font_scale=1.2)
+
+    energy_components = PowerSourceMap[energy_source]
+    col_num = len(energy_components)
+    fig, axes = plt.subplots(col_num, 1, figsize=(plot_width, plot_height*col_num))
+    for i in range(0, col_num):
+        component = energy_components[i]
+        data = summary_df[(summary_df["energy_source"]==energy_source) & (summary_df["energy_component"]==component)]
+        data = data.sort_values(by=["Feature Group", "MAE"])
+        if col_num == 1:
+            ax = axes
+        else:
+            ax = axes[i]
+        sns.barplot(data=data, x="Feature Group", y="MAE", hue="Model", ax=ax)
+        ax.set_title(component)
+        ax.set_ylabel("MAE (Watt)")
+        ax.set_ylim((0, 100))
+        if i < col_num-1:
+            ax.set_xlabel("")
+        ax.legend(bbox_to_anchor=(1.05, 1.05))
+    plt.suptitle("{} {} error".format(energy_source, args.output_type))
+    plt.tight_layout()
+    filename = os.path.join(output_folder, name + ".png")
+    fig.savefig(filename)
+    plt.close()