Fix paths in profiler report (#131)

* changed path in profiler report

Author: NRauschmayr

smdebug/profiler/analysis/rules/docker/profiler_report.ipynb

@@ -136,7 +136,7 @@
    "source": [
     "def load_report(rule_name):\n",
     "    try:\n",
-    "        report = json.load(open('/opt/ml/processing/outputs/profiler-reports/'+rule_name+'.json'))\n",
+    "        report = json.load(open('/opt/ml/processing/output/rule/profiler-reports/'+rule_name+'.json'))\n",
     "        return report\n",
     "    except FileNotFoundError:\n",
     "        print (rule_name + ' not triggered')"
@@ -207,68 +207,58 @@
    },
    "outputs": [],
    "source": [
-    "text =  \"\"\"The following table gives a summary about the training job. The table includes information about when the training job started and ended, how much time initialization, training loop and finalization took.\"\"\"\n",
-    "if len(job_statistics) > 0:\n",
-    "    df = pd.DataFrame.from_dict(job_statistics, orient='index')\n",
-    "    start_time = us_since_epoch_to_human_readable_time(report['Details']['job_start'] * 1000000)\n",
-    "    date = datetime.datetime.strptime(start_time, '%Y-%m-%dT%H:%M:%S:%f')\n",
-    "    day = date.date().strftime(\"%m/%d/%Y\")\n",
-    "    hour = date.time().strftime(\"%H:%M:%S\")\n",
-    "    duration = job_duration_in_seconds\n",
-    "    text = f\"\"\"{text} \\n Your training job started on {day} at {hour} and ran for {duration} seconds.\"\"\"\n",
-    "     \n",
-    "    #pretty_print(df)\n",
-    "    if \"first\" in report['Details'][\"step_num\"] and \"last\" in report['Details'][\"step_num\"]:\n",
-    "        if finalization_perc  < 0:\n",
-    "            job_statistics[\"Finalization%\"]  = 0\n",
-    "        if training_loop_perc < 0:\n",
-    "            job_statistics[\"Training loop\"] = 0\n",
-    "        if initialization_perc < 0:\n",
-    "            job_statistics[\"Initialization\"] = 0\n",
+    "if report:\n",
+    "    text =  \"\"\"The following table gives a summary about the training job. The table includes information about when the training job started and ended, how much time initialization, training loop and finalization took.\"\"\"\n",
+    "    if len(job_statistics) > 0:\n",
+    "        df = pd.DataFrame.from_dict(job_statistics, orient='index')\n",
+    "        start_time = us_since_epoch_to_human_readable_time(report['Details']['job_start'] * 1000000)\n",
+    "        date = datetime.datetime.strptime(start_time, '%Y-%m-%dT%H:%M:%S:%f')\n",
+    "        day = date.date().strftime(\"%m/%d/%Y\")\n",
+    "        hour = date.time().strftime(\"%H:%M:%S\")\n",
+    "        duration = job_duration_in_seconds\n",
+    "        text = f\"\"\"{text} \\n Your training job started on {day} at {hour} and ran for {duration} seconds.\"\"\"\n",
+    "\n",
+    "        #pretty_print(df)\n",
+    "        if \"first\" in report['Details'][\"step_num\"] and \"last\" in report['Details'][\"step_num\"]:\n",
+    "            if finalization_perc  < 0:\n",
+    "                job_statistics[\"Finalization%\"]  = 0\n",
+    "            if training_loop_perc < 0:\n",
+    "                job_statistics[\"Training loop\"] = 0\n",
+    "            if initialization_perc < 0:\n",
+    "                job_statistics[\"Initialization\"] = 0\n",
+    "        else:\n",
+    "            text = f\"\"\"{text} \\n Your training job started on {day} at {hour} and ran for {duration} seconds.\"\"\"\n",
+    "            \n",
+    "    if len(job_statistics) > 0:\n",
+    "        df2 = df.reset_index()\n",
+    "        df2.columns = [\"0\", \"1\"]\n",
+    "        source = ColumnDataSource(data=df2)\n",
+    "        columns = [TableColumn(field='0', title=\"\"),\n",
+    "                   TableColumn(field='1', title=\"Job Statistics\"),]\n",
+    "        table = DataTable(source=source, columns=columns, width=450, height=380)\n",
+    "\n",
+    "    plot = None\n",
+    "\n",
+    "    if \"Initialization\" in job_statistics:\n",
+    "        piechart_data = {}\n",
+    "        piechart_data[\"Initialization\"] = initialization_perc  \n",
+    "        piechart_data[\"Training loop\"]  = training_loop_perc\n",
+    "        piechart_data[\"Finalization\"]  = finalization_perc \n",
+    "\n",
+    "        plot = create_piechart(piechart_data, \n",
+    "                               height=350,\n",
+    "                               width=500,\n",
+    "                               x1=0.15,\n",
+    "                               x2=0.15,\n",
+    "                               radius=0.15, \n",
+    "                               toolbar_location=None)\n",
+    "\n",
+    "    if plot != None:\n",
+    "        paragraph = Paragraph(text=f\"\"\"{text}\"\"\", width = 800)\n",
+    "        show(column(paragraph, row(table, plot)))\n",
     "    else:\n",
-    "        text = f\"\"\"{text} \\n Your training job started on {day} at {hour} and ran for {duration} seconds.\"\"\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "if len(job_statistics) > 0:\n",
-    "    df2 = df.reset_index()\n",
-    "    df2.columns = [\"0\", \"1\"]\n",
-    "    source = ColumnDataSource(data=df2)\n",
-    "    columns = [TableColumn(field='0', title=\"\"),\n",
-    "               TableColumn(field='1', title=\"Job Statistics\"),]\n",
-    "    table = DataTable(source=source, columns=columns, width=450, height=380)\n",
-    "\n",
-    "plot = None\n",
-    "    \n",
-    "if \"Initialization\" in job_statistics:\n",
-    "    piechart_data = {}\n",
-    "    piechart_data[\"Initialization\"] = initialization_perc  \n",
-    "    piechart_data[\"Training loop\"]  = training_loop_perc\n",
-    "    piechart_data[\"Finalization\"]  = finalization_perc \n",
-    "\n",
-    "    plot = create_piechart(piechart_data, \n",
-    "                           height=350,\n",
-    "                           width=500,\n",
-    "                           x1=0.15,\n",
-    "                           x2=0.15,\n",
-    "                           radius=0.15, \n",
-    "                           toolbar_location=None)\n",
-    "\n",
-    "if plot != None:\n",
-    "    paragraph = Paragraph(text=f\"\"\"{text}\"\"\", width = 800)\n",
-    "    show(column(paragraph, row(table, plot)))\n",
-    "else:\n",
-    "    paragraph = Paragraph(text=f\"\"\"{text}. No step information was profiled from your training job. The time spent on initialization and finalization cannot be computed.\"\"\" , width = 800)\n",
-    "    show(column(paragraph, row(table)))"
+    "        paragraph = Paragraph(text=f\"\"\"{text}. No step information was profiled from your training job. The time spent on initialization and finalization cannot be computed.\"\"\" , width = 800)\n",
+    "        show(column(paragraph, row(table)))"
    ]
   },
   {
@@ -372,22 +362,6 @@
     "    show(column( text1, text2, row(table)))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "def display_image(image_name):\n",
-    "    files = glob.glob('/opt/ml/processing/outputs/profiler-reports/' + image_name)\n",
-    "    for filename in files:\n",
-    "        display(Image(filename=filename))"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -696,7 +670,7 @@
    },
    "outputs": [],
    "source": [
-    "files = glob.glob('/opt/ml/processing/outputs/profiler-reports/*json')\n",
+    "files = glob.glob('/opt/ml/processing/output/rule/profiler-reports/*json')\n",
     "summary = {}\n",
     "for i in files:\n",
     "    rule_name = i.split('/')[-1].replace('.json','')\n",

smdebug/profiler/analysis/rules/docker/rule_evaluation.py

@@ -22,7 +22,8 @@
 from smdebug.rules.rule_invoker import invoke_rule
 from smdebug.trials import create_trial
 
-os.mkdir("/opt/ml/processing/outputs/profiler-reports/")
+os.mkdir("/opt/ml/processing/output/")
+os.mkdir("/opt/ml/processing/output/rule/")
 os.mkdir("/opt/ml/processing/outputs/.sagemaker-ignore")
 os.mkdir("/opt/ml/code/")
 os.system("cp profiler_report.ipynb /opt/ml/code/")
@@ -42,7 +43,7 @@ def run_rule(rule_obj):
 
 # path to profiler data
 profiler_path = os.environ["S3_PATH"]
-trial = create_trial(profiler_path, profiler=True)
+trial = create_trial(profiler_path, profiler=True, output_dir="/opt/ml/processing/output/rule")
 
 if "TRIGGER_ALL" in os.environ:
     # create list of rules
@@ -93,6 +94,8 @@ def run_rule(rule_obj):
     trial,
     create_html=True,
     nb_full_path="profiler_report.ipynb",
-    output_full_path="/opt/ml/processing/outputs/profiler-report.ipynb",
+    output_full_path="/opt/ml/processing/output/rule/profiler-report.ipynb",
 )
 rule._plot_visualization(last_found_step=0)
+
+os.system("cp -r /opt/ml/processing/output/rule/profiler-reports/ /opt/ml/processing/outputs/")