Add FastAI example, disable binding if tensorboard is loaded (assume TensorBoradLogger will be used)

examples/frameworks/fastai/fastai_with_tensorboard.py

@@ -0,0 +1,23 @@
+# TRAINS - Fastai with Tensorboard example code, automatic logging the model and Tensorboard outputs
+#
+
+from fastai.callbacks.tensorboard import LearnerTensorboardWriter
+from fastai.vision import *  # Quick access to computer vision functionality
+
+from trains import Task
+
+task = Task.init(project_name="example", task_name="fastai with tensorboard callback")
+
+path = untar_data(URLs.MNIST_SAMPLE)
+
+data = ImageDataBunch.from_folder(path, ds_tfms=(rand_pad(2, 28), []), bs=64)
+data.normalize(imagenet_stats)
+
+learn = cnn_learner(data, models.resnet18, metrics=accuracy)
+tboard_path = Path("data/tensorboard/project1")
+learn.callback_fns.append(
+    partial(LearnerTensorboardWriter, base_dir=tboard_path, name="run0")
+)
+
+accuracy(*learn.get_preds())
+learn.fit_one_cycle(6, 0.01)

examples/frameworks/fastai/requirements.txt

@@ -0,0 +1 @@
+fastai

trains/backend_interface/task/task.py

@@ -780,14 +780,14 @@ class Task(IdObjectBase, AccessMixin, SetupUploadMixin):
         support parameter descriptions (the result is a dictionary of key-value pairs).
         :param backwards_compatibility: If True (default) parameters without section name
             (API version < 2.9, trains-server < 0.16) will be at dict root level.
-            If False, parameters without section name, will be nested under "general/" key.
+            If False, parameters without section name, will be nested under "Args/" key.
         :return: dict of the task parameters, all flattened to key/value.
             Different sections with key prefix "section/"
         """
         if not Session.check_min_api_version('2.9'):
             return self._get_task_property('execution.parameters')
 
-        # API will makes sure we get old parameters with type legacy on top level (instead of nested in General)
+        # API will makes sure we get old parameters with type legacy on top level (instead of nested in Args)
         parameters = dict()
         hyperparams = self._get_task_property('hyperparams') or {}
         if not backwards_compatibility:
@@ -877,7 +877,7 @@ class Task(IdObjectBase, AccessMixin, SetupUploadMixin):
                 # build nested dict from flat parameters dict:
                 org_hyperparams = self.data.hyperparams or {}
                 hyperparams = dict()
-                # if the task is a legacy task, we should put everything back under General/key with legacy type
+                # if the task is a legacy task, we should put everything back under Args/key with legacy type
                 legacy_name = self._legacy_parameters_section_name
                 org_legacy_section = org_hyperparams.get(legacy_name, dict())
 

trains/binding/frameworks/fastai_bind.py

@@ -20,6 +20,10 @@ class PatchFastai(object):
 
     @staticmethod
     def _patch_model_callback():
+        # if you have tensroboard, we assume you use TesnorboardLogger, which we catch, so no need to patch.
+        if "tensorboard" in sys.modules:
+            return
+
         if "fastai" in sys.modules:
             try:
                 from fastai.basic_train import Recorder