No dashboards are active for the current data set

I am trying detecting objects using tensorflow and Google colab. The steps is given in the link below: https://medium.com/swlh/tensorflow-2-object-detection-api-with-google-colab-b2af171e81cc

When I came to the step starting tensorboard, I’m facing :

 No dashboards are active for the current data set. 

After two steps, training the model, now I’m facing a lot warnings and eventually an error:

Traceback (most recent call last): File "model_main_tf2.py", line 113, in <module> tf.compat.v1.app.run() File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/platform/app.py", line 40, in run _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef) File "/usr/local/lib/python3.7/dist-packages/absl/app.py", line 303, in run _run_main(main, args) File "/usr/local/lib/python3.7/dist-packages/absl/app.py", line 251, in _run_main sys.exit(main(argv)) File "model_main_tf2.py", line 110, in main record_summaries=FLAGS.record_summaries) File "/usr/local/lib/python3.7/dist-packages/object_detection-0.1-py3.7.egg/object_detection/model_lib_v2.py", line 639, in train_loop loss = _dist_train_step(train_input_iter) File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py", line 828, in __call__ result = self._call(*args, **kwds) File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py", line 888, in _call return self._stateless_fn(*args, **kwds) File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py", line 2943, in __call__ filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py", line 1919, in _call_flat ctx, args, cancellation_manager=cancellation_manager)) File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py", line 560, in call ctx=ctx) File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py", line 60, in quick_execute inputs, attrs, num_outputs) tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found. (0) Resource exhausted: OOM when allocating tensor with shape[100,51150] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [[node Loss/Compare_9/IOU/Intersection/Minimum_1 (defined at /local/lib/python3.7/dist-packages/object_detection-0.1-py3.7.egg/object_detection/core/box_list_ops.py:257) ]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. [[Func/Loss/localization_loss_1/write_summary/summary_cond/then/_0/input/_71/_348]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. (1) Resource exhausted: OOM when allocating tensor with shape[100,51150] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [[node Loss/Compare_9/IOU/Intersection/Minimum_1 (defined at /local/lib/python3.7/dist-packages/object_detection-0.1-py3.7.egg/object_detection/core/box_list_ops.py:257) ]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. 0 successful operations. 0 derived errors ignored. [Op:__inference__dist_train_step_51563] Errors may have originated from an input operation. Input Source operations connected to node Loss/Compare_9/IOU/Intersection/Minimum_1: Loss/Compare_9/IOU/Intersection/split (defined at /local/lib/python3.7/dist-packages/object_detection-0.1-py3.7.egg/object_detection/core/box_list_ops.py:250) Input Source operations connected to node Loss/Compare_9/IOU/Intersection/Minimum_1: Loss/Compare_9/IOU/Intersection/split (defined at /local/lib/python3.7/dist-packages/object_detection-0.1-py3.7.egg/object_detection/core/box_list_ops.py:250) Function call stack: _dist_train_step -> _dist_train_step 

How can I handle these issues? And tensorboard plays important role? Or it activating tensorboard plays an important part or it just is something like optional?

Thx in advance for your answers.