
# DataFlow

### What is DataFlow

DataFlow is a library to build Python iterators for efficient data loading.

**Definition**: A DataFlow is something that has a `get_data()` generator method,
which yields `datapoints`.
A datapoint is a **list** of Python objects which are called the `components` of a datapoint.

**Example**: to train on MNIST dataset, you may need a DataFlow with a `get_data()` method
that yields datapoints (lists) of two components:
a numpy array of shape (64, 28, 28), and an array of shape (64,).

### Composition of DataFlow
One good thing about having a standard interface is to be able to provide
the greatest code reusability.
There are a lot of existing DataFlow utilities in tensorpack, which you can use to compose
complex DataFlow with a long data pipeline. A common pipeline usually
would __read from disk (or other sources), apply transformations, group into batches,
prefetch data__, etc. A simple example is as the following:

````python
# a DataFlow you implement to produce [tensor1, tensor2, ..] lists from whatever sources:
df = MyDataFlow(dir='/my/data', shuffle=True)
# resize the image component of each datapoint
df = AugmentImageComponent(df, [imgaug.Resize((225, 225))])
# group data into batches of size 128
df = BatchData(df, 128)
# start 3 processes to run the dataflow in parallel
df = PrefetchDataZMQ(df, 3)
````
You can find more complicated DataFlow in the [ResNet training script](../examples/ResNet/imagenet_utils.py)
with all the data preprocessing.

Unless you are working with standard data types (image folders, LMDB, etc),
you would usually want to write the source DataFlow (`MyDataFlow` in the above example) for your data format.
See [another tutorial](extend/dataflow.html)
for simple instructions on writing a DataFlow.
Once you have the source reader, all the [existing DataFlows](../modules/dataflow.html) are ready for you to complete
the rest of the data pipeline.

### Why DataFlow

1. It's easy: write everything in pure Python, and reuse existing utilities.
	 On the contrary, writing data loaders in TF operators is usually painful, and performance is hard to tune.
	 See more discussions in [Python Reader or TF Reader](input-source.html#python-reader-or-tf-reader).
2. It's fast: see [Efficient DataFlow](efficient-dataflow.html)
	on how to build a fast DataFlow with parallelism.
	If you're using DataFlow with tensorpack, also see [Input Pipeline tutorial](input-source.html)
	on how tensorpack further accelerates data loading in the graph.

Nevertheless, tensorpack support data loading with native TF operators / TF datasets as well.

### Use DataFlow (outside Tensorpack)
tensorpack `InputSource` interface works with DataFlow out-of-the-box.
If you use DataFlow in some custom code, call `reset_state()` first to initialize it,
and then use the generator however you like:
```python
df = SomeDataFlow()

df.reset_state()
generator = df.get_data()
for dp in generator:
	# dp is now a list. do whatever
```

DataFlow is __independent__ of both tensorpack and TensorFlow.
To `import tensorpack.dataflow`, you don't even have to install TensorFlow.
You can simply use it as a data processing pipeline and plug it into any other frameworks.

