!488 1. add the limitation of the number of tag in tensor visualization;...

!488 1. add the limitation of the number of tag in tensor visualization; update the max step per tensor tag to 20; 3. support query one train_job in the interface of train_jobs.
Merge pull request !488 from wangshuide/wsd0727_r06

mindinsight/backend/datavisual/task_manager_api.py

@@ -66,12 +66,13 @@ def query_train_jobs():
     """Query train jobs."""
     offset = request.args.get("offset", default=0)
     limit = request.args.get("limit", default=10)
+    train_id = get_train_id(request)
 
     offset = Validation.check_offset(offset=offset)
     limit = Validation.check_limit(limit, min_value=1, max_value=SummaryWatcher.MAX_SUMMARY_DIR_COUNT)
 
     processor = TrainTaskManager(DATA_MANAGER)
-    total, train_jobs = processor.query_train_jobs(offset, limit)
+    total, train_jobs = processor.query_train_jobs(offset, limit, train_id)
 
     return jsonify({
         'name': os.path.basename(os.path.realpath(settings.SUMMARY_BASE_DIR)),

mindinsight/conf/constants.py

@@ -84,9 +84,10 @@ MAX_TAG_SIZE_PER_EVENTS_DATA = 300
 DEFAULT_STEP_SIZES_PER_TAG = 500
 
 MAX_GRAPH_TAG_SIZE = 10
+MAX_TENSOR_TAG_SIZE = 6
 MAX_IMAGE_STEP_SIZE_PER_TAG = 10
 MAX_SCALAR_STEP_SIZE_PER_TAG = 1000
 MAX_GRAPH_STEP_SIZE_PER_TAG = 1
 MAX_HISTOGRAM_STEP_SIZE_PER_TAG = 50
-MAX_TENSOR_STEP_SIZE_PER_TAG = 50
+MAX_TENSOR_STEP_SIZE_PER_TAG = 20
 MAX_TENSOR_RESPONSE_DATA_SIZE = 100000

mindinsight/datavisual/data_transform/events_data.py

@@ -35,6 +35,7 @@ CONFIG = {
     'max_tag_sizes_per_plugin':
         {
             PluginNameEnum.GRAPH.value: settings.MAX_GRAPH_TAG_SIZE,
+            PluginNameEnum.TENSOR.value: settings.MAX_TENSOR_TAG_SIZE
         },
     'max_step_sizes_per_tag':
         {

mindinsight/datavisual/processors/train_task_manager.py

@@ -83,17 +83,24 @@ class TrainTaskManager(BaseProcessor):
             plugins=plugins
         )
 
-    def query_train_jobs(self, offset=0, limit=10):
+    def query_train_jobs(self, offset=0, limit=10, request_train_id=None):
         """
         Query train jobs.
 
         Args:
             offset (int): Specify page number. Default is 0.
             limit (int): Specify page size. Default is 10.
+            request_train_id (str): Specify train id. Default is None.
 
         Returns:
             tuple, return quantity of total train jobs and list of train jobs specified by offset and limit.
         """
+        if request_train_id is not None:
+            train_job_item = self._get_train_job_item(request_train_id)
+            if train_job_item is None:
+                return 0, []
+            return 1, [train_job_item]
+
         brief_cache = self._data_manager.get_brief_cache()
         brief_train_jobs = list(brief_cache.get_train_jobs().values())
         brief_train_jobs.sort(key=lambda x: x.basic_info.update_time, reverse=True)
@@ -106,37 +113,52 @@ class TrainTaskManager(BaseProcessor):
         train_ids = [train_job.basic_info.train_id for train_job in brief_train_jobs[start:end]]
 
         for train_id in train_ids:
-            try:
-                train_job = self._data_manager.get_train_job(train_id)
-            except exceptions.TrainJobNotExistError:
-                logger.warning('Train job %s not existed', train_id)
+            train_job_item = self._get_train_job_item(train_id)
+            if train_job_item is None:
                 continue
-
-            basic_info = train_job.get_basic_info()
-            train_job_item = dict(
-                train_id=basic_info.train_id,
-                relative_path=basic_info.train_id,
-                create_time=basic_info.create_time.strftime('%Y-%m-%d %H:%M:%S'),
-                update_time=basic_info.update_time.strftime('%Y-%m-%d %H:%M:%S'),
-                profiler_dir=basic_info.profiler_dir,
-                cache_status=train_job.cache_status.value,
-            )
-
-            if train_job.cache_status == CacheStatus.CACHED:
-                plugins = self.get_plugins(train_id)
-            else:
-                plugins = dict(plugins={
-                    'graph': [],
-                    'scalar': [],
-                    'image': [],
-                    'histogram': [],
-                })
-
-            train_job_item.update(plugins)
             train_jobs.append(train_job_item)
 
         return total, train_jobs
 
+    def _get_train_job_item(self, train_id):
+        """
+        Get train job item.
+
+        Args:
+            train_id (str): Specify train id.
+
+        Returns:
+            dict, a dict of train job item.
+        """
+        try:
+            train_job = self._data_manager.get_train_job(train_id)
+        except exceptions.TrainJobNotExistError:
+            logger.warning('Train job %s not existed', train_id)
+            return None
+
+        basic_info = train_job.get_basic_info()
+        train_job_item = dict(
+            train_id=basic_info.train_id,
+            relative_path=basic_info.train_id,
+            create_time=basic_info.create_time.strftime('%Y-%m-%d %H:%M:%S'),
+            update_time=basic_info.update_time.strftime('%Y-%m-%d %H:%M:%S'),
+            profiler_dir=basic_info.profiler_dir,
+            cache_status=train_job.cache_status.value,
+        )
+
+        if train_job.cache_status == CacheStatus.CACHED:
+            plugins = self.get_plugins(train_id)
+        else:
+            plugins = dict(plugins={
+                'graph': [],
+                'scalar': [],
+                'image': [],
+                'histogram': [],
+            })
+
+        train_job_item.update(plugins)
+        return train_job_item
+
     def cache_train_jobs(self, train_ids):
         """
         Cache train jobs.