Docs fixes

README.md

@@ -120,7 +120,7 @@ First, finetune:
 ```python
 # import our libraries
 import flash
-from flash import download_data
+from flash.data.utils import download_data
 from flash.vision import ImageClassificationData, ImageClassifier
 
 # 1. Download the data
@@ -170,7 +170,7 @@ Flash has an Image embedding task to encodes an image into a vector of image fea
   <summary>View example</summary>
 
 ```python
-from flash.core.data import download_data
+from flash.data.utils import download_data
 from flash.vision import ImageEmbedder
 
 # 1. Download the data
@@ -197,7 +197,7 @@ Flash has a Summarization task to sum up text from a larger article into a short
 ```python
 # import our libraries
 import flash
-from flash import download_data
+from flash.data.utils import download_data
 from flash.text import SummarizationData, SummarizationTask
 
 # 1. Download the data
@@ -244,7 +244,7 @@ To illustrate, say we want to build a model to predict if a passenger survived o
 # import our libraries
 from torchmetrics.classification import Accuracy, Precision, Recall
 import flash
-from flash import download_data
+from flash.data.utils import download_data
 from flash.tabular import TabularClassifier, TabularData
 
 # 1. Download the data
@@ -295,7 +295,7 @@ To illustrate, say we want to build a model on a tiny coco dataset.
 ```python
 # import our libraries
 import flash
-from flash.core.data import download_data
+from flash.data.utils import download_data
 from flash.vision import ObjectDetectionData, ObjectDetector
 
 # 1. Download the data

docs/source/general/finetuning.rst

@@ -52,7 +52,7 @@ Here are the steps in code
 .. code-block:: python
 
     import flash
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.vision import ImageClassificationData, ImageClassifier
 
     # 1. download and organize the data

docs/source/general/predictions.rst

@@ -12,11 +12,11 @@ Predict on a single sample of data
 
 You can pass in a sample of data (image file path, a string of text, etc) to the :func:`~flash.core.model.Task.predict` method.
 
-	
+
 .. code-block:: python
 
 	from flash import Trainer
-	from flash.core.data import download_data
+	from flash.data.utils import download_data
 	from flash.vision import ImageClassificationData, ImageClassifier
 
 
@@ -37,7 +37,7 @@ Predict on a csv file
 
 .. code-block:: python
 
-	from flash.core.data import download_data
+	from flash.data.utils import download_data
 	from flash.tabular import TabularClassifier
 
 	# 1. Download the data
@@ -51,5 +51,3 @@ Predict on a csv file
 	# 3. Generate predictions from a csv file! Who would survive?
 	predictions = model.predict("data/titanic/titanic.csv")
 	print(predictions)
-
-

docs/source/general/training.rst

@@ -11,7 +11,7 @@ Some Flash tasks have been pretrained on large data sets. To accelerate your tra
 .. code-block:: python
 
     import flash
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.vision import ImageClassificationData, ImageClassifier
 
     # 1. download and organize the data
@@ -48,7 +48,7 @@ Flash tasks supports many advanced training functionalities out-of-the-box, such
 
     # train on 1 GPU
     flash.Trainer(gpus=1)
-    
+
 * Training on multiple GPUs
 
 .. code-block:: python
@@ -60,7 +60,7 @@ Flash tasks supports many advanced training functionalities out-of-the-box, such
 
     # train on gpu 1, 3, 5 (3 gpus total)
     flash.Trainer(gpus=[1, 3, 5])
-    
+
 * Using mixed precision training
 
 .. code-block:: python

docs/source/quickstart.rst

@@ -15,7 +15,7 @@ For getting started with Deep Learning
 
 Easy to learn
 ^^^^^^^^^^^^^
-If you are just getting started with deep learning, Flash offers common deep learning tasks you can use out-of-the-box in a few lines of code, no math, fancy nn.Modules or research experience required! 
+If you are just getting started with deep learning, Flash offers common deep learning tasks you can use out-of-the-box in a few lines of code, no math, fancy nn.Modules or research experience required!
 
 Easy to scale
 ^^^^^^^^^^^^^
@@ -70,7 +70,7 @@ You can install flash using pip or conda:
 Tasks
 =====
 
-Flash is comprised of a collection of Tasks. The Flash tasks are laser-focused objects designed to solve a well-defined type of problem, using state-of-the-art methods. 
+Flash is comprised of a collection of Tasks. The Flash tasks are laser-focused objects designed to solve a well-defined type of problem, using state-of-the-art methods.
 
 The Flash tasks contain all the relevant information to solve the task at hand- the number of class labels you want to predict, number of columns in your dataset, as well as details on the model architecture used such as loss function, optimizers, etc.
 
@@ -137,7 +137,7 @@ Here's an example of finetuning.
 .. code-block:: python
 
     import flash
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.vision import ImageClassificationData, ImageClassifier
 
     # 1. Download the data

docs/source/reference/image_classification.rst

@@ -25,7 +25,7 @@ Use the :class:`~flash.vision.ImageClassifier` pretrained model for inference on
 
     # import our libraries
     from flash import Trainer
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.vision import ImageClassificationData, ImageClassifier
 
     # 1. Download the data
@@ -90,7 +90,7 @@ Now all we need is three lines of code to build to train our task!
 .. code-block:: python
 
     import flash
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.vision import ImageClassificationData, ImageClassifier
 
     # 1. Download the data

docs/source/reference/image_embedder.rst

@@ -54,7 +54,7 @@ To tailor this image embedder to your dataset, finetune first.
 .. code-block:: python
 
     import flash
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.vision import ImageClassificationData, ImageEmbedder
 
     # 1. Download the data

docs/source/reference/object_detection.rst

@@ -75,7 +75,7 @@ To tailor the object detector to your dataset, you would need to have it in `COC
 .. code-block:: python
 
     import flash
-    from flash.core.data import download_data
+    from flash.data.utils import download_data
     from flash.vision import ObjectDetectionData, ObjectDetector
 
     # 1. Download the data

docs/source/reference/summarization.rst

@@ -60,7 +60,7 @@ Or on a given dataset, use :class:`~flash.core.trainer.Trainer` `predict` method
 
     # import our libraries
     from flash import Trainer
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.text import SummarizationData, SummarizationTask
 
     # 1. Download data
@@ -104,7 +104,7 @@ All we need is three lines of code to train our model!
 
     # import our libraries
     import flash
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.text import SummarizationData, SummarizationTask
 
     # 1. Download data

docs/source/reference/tabular_classification.rst

@@ -45,7 +45,7 @@ Next, we create the :class:`~flash.tabular.TabularClassifier` task, using the Da
 .. code-block:: python
 
     import flash
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.tabular import TabularClassifier, TabularData
     from torchmetrics.classification import Accuracy, Precision, Recall
 
@@ -92,7 +92,7 @@ You can make predcitions on a pretrained model, that has already been trained fo
 .. code-block:: python
 
 
-    from flash.core.data import download_data
+    from flash.data.utils import download_data
     from flash.tabular import TabularClassifier
 
     # 1. Download the data
@@ -113,7 +113,7 @@ Or you can finetune your own model and use that for prediction:
 .. code-block:: python
 
     import flash
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.tabular import TabularClassifier, TabularData
 
     # 1. Load the data

docs/source/reference/text_classification.rst

@@ -24,7 +24,7 @@ Use the :class:`~flash.text.classification.model.TextClassifier` pretrained mode
 
     from pytorch_lightning import Trainer
 
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.text import TextClassificationData, TextClassifier
 
 
@@ -77,7 +77,7 @@ All we need is three lines of code to train our model!
 .. code-block:: python
 
     import flash
-    from flash.core.data import download_data
+    from flash.data.utils import download_data
     from flash.text import TextClassificationData, TextClassifier
 
     # 1. Download the data

docs/source/reference/translation.rst

@@ -42,7 +42,7 @@ Or on a given dataset, use :class:`~flash.core.trainer.Trainer` `predict` method
 
     # import our libraries
     from flash import Trainer
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.text import TranslationData, TranslationTask
 
     # 1. Download data
@@ -86,7 +86,7 @@ All we need is three lines of code to train our model! By default, we use a `mBA
 
     # import our libraries
     import flash
-    from flash import download_data
+    from flash.data.utils import download_data
     from flash.text import TranslationData, TranslationTask
 
     # 1. Download data

flash_examples/finetuning/summarization.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 import torch
 
-import flash
-from flash import download_data, Trainer
+from flash import Trainer
+from flash.data.utils import download_data
 from flash.text import SummarizationData, SummarizationTask
 
 # 1. Download the data
@@ -33,7 +33,7 @@
 model = SummarizationTask()
 
 # 4. Create the trainer. Run once on data
-trainer = flash.Trainer(gpus=int(torch.cuda.is_available()), fast_dev_run=True)
+trainer = Trainer(gpus=int(torch.cuda.is_available()), fast_dev_run=True)
 
 # 5. Fine-tune the model
 trainer.finetune(model, datamodule=datamodule)

flash_examples/finetuning/translation.py

@@ -14,7 +14,7 @@
 import torch
 
 import flash
-from flash import download_data
+from flash.data.utils import download_data
 from flash.text import TranslationData, TranslationTask
 
 # 1. Download the data

flash_examples/predict/translation.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 from pytorch_lightning import Trainer
 
-from flash import download_data
+from flash.data.utils import download_data
 from flash.text import TranslationData, TranslationTask
 
 # 1. Download the data