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https://github.com/MachineLearningSystem/KungFu

Fast and Adaptive Distributed Machine Learning for TensorFlow, PyTorch and MindSpore.
https://github.com/MachineLearningSystem/KungFu

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Fast and Adaptive Distributed Machine Learning for TensorFlow, PyTorch and MindSpore.

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# KungFu



Making adaptive distributed machine learning easy and efficient.



[![Build Status](https://travis-ci.com/lsds/KungFu.svg?branch=master)](https://travis-ci.com/lsds/KungFu)

[![Documentation Status](https://readthedocs.org/projects/kungfu/badge/?version=latest)](https://kungfu.readthedocs.io/en/latest/?badge=latest)



## Features



KungFu aims to help users achieve *fast* and *adaptive* distributed machine learning with *minimal* efforts. This is important because a machine learning system must cope with growing complex models and increasingly complicated deployment environments, making it

difficult to constantly deliver high performance with an *empirical* configuration.

To address this, KungFu provides the following unique features:



* Simplicity: KungFu permits distributed training by adding minimal code in your training program. KungFu is also simple to install and run. It does not require extra deployment like parameter servers and heavy dependencies like MPI in Horovod.

* Adaptable distributed training: KungFu provides useful advanced [distributed optimizers](srcs/python/kungfu/tensorflow/optimizers/__init__.py) such as

communication-efficient ``PairAveragingOptimizer`` and hyper-parameter-robust ``SynchronousAveragingOptimizer`` to help you address the cases in which conventional Synchronous SGD does not scale. See [Optimizers](https://github.com/lsds/KungFu#choosing-the-right-optimizer) for how to choose the right KungFu optimizer for your training scenario.

* Online monitoring and control: KungFu aims to support [distributed SGD metrics](srcs/python/kungfu/tensorflow/optimizers/sync_sgd.py) such as [gradient noise scale](https://openai.com/blog/science-of-ai/) to help understand the training process with low overhead.

KungFu further provides control operators such as ``barrier`` and ``resize_cluster`` to help reconfigure training online, even in response to monitored metrics.

* Fast and scalable: KungFu has a decentralized architecture, an non-blocking runtime, and high-performance implementations of communication, monitoring and control operators. Check out its performance in [Benchmark](https://github.com/lsds/KungFu#benchmark).



We have been using KungFu for scaling out different deep learning models such as ResNet, DenseNet, OpenPose, BERT, CycleGAN and Alpha Zero. Check out their [examples](https://github.com/lsds/KungFu#examples).



## Usage



KungFu currently support TensorFlow and Keras. To scale out your TensorFlow program, for example, you need to make two changes:



1. Wrap your ``tf.train.optimizer`` in KungFu's ``SynchronousSGDOptimizer``, ``SynchronousAveragingOptimizer``, or ``PairAveragingOptimizer``.



2. Ensure all workers start with consistent states by broadcasting a worker's initial global variables.



```python

import tensorflow as tf



# Build model...

loss = ...

opt = tf.train.AdamOptimizer(0.01)



# KungFu Step 1: Wrap tf.optimizer in KungFu optimizers

from kungfu.tensorflow.optimizers import SynchronousSGDOptimizer

opt = SynchronousSGDOptimizer(opt)



# Make training operation

train_op = opt.minimize(loss)



# Train your model

with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())



    # KungFu Step 2: ensure distributed workers start with consistent states

    from kungfu.tensorflow.initializer import BroadcastGlobalVariablesOp

    sess.run(BroadcastGlobalVariablesOp())



    for step in range(10):

        sess.run(train_op)

```



You can find more details in the [Documentation](https://kungfu.readthedocs.io/en/latest/?badge=latest), for example, for how to use KungFu with [Session](examples/tf1_mnist_session.py), [TensorFlow Keras](examples/tf1_mnist_keras.py), [Estimator](examples/tf1_mnist_estimator.py), and [GradientTape](examples/tf2_mnist_gradient_tape.py) in TensorFlow 1 and 2.

For KungFu with Keras, check out [here](examples/keras_mnist.py).



## Install



KungFu is implemented in Go and C++.

Currently, it has a Python binding for TensorFlow (including v1 and v2) and Keras (assuming you use TensorFlow as the backend).



KungFu for TensorFlow requires [Python 3](https://www.python.org/downloads/), [CMake 3.5+](https://cmake.org/install/), and [Golang 1.13+](https://golang.org/dl/).

KungFu has been tested with [TensorFlow](https://www.tensorflow.org/install/pip#older-versions-of-tensorflow) 1.12, 1.13, 1.15 and 2.0.0.

KungFu has a known installation issue with TensorFlow 1.14.

Assuming you have the above pre-requites, you can install KungFu as follows:



```bash

git clone https://github.com/lsds/KungFu.git

cd KungFu

pip3 install --no-index -U --user .

```



KungFu provides ``kungfu-run`` to launch a KungFu process on a multi-GPU server.

In a cluster, we need to launch ``kungfu-run`` on each node.



```bash

# Show the help of kungfu-run

kungfu-run -help

```



You can use KungFu with Docker. Check out the docker files for [GPU](docker/Dockerfile.tf-gpu) and [CPU](docker/Dockerfile.tf-cpu) machines.



## Run



We show how to run a KungFu program using a MNIST example.

Download the MNIST dataset ([script](scripts/download-mnist.sh)) first and then run the following training script:



```bash

# Train a Single Layer Perception (SLP) model for the MNIST dataset using 4 CPUs for 10 data epochs.

kungfu-run -np 4 python3 examples/tf1_mnist_session.py --data-dir=./mnist

```



You can run this example on two machines (assuming each with 8 GPUs) using the below command (NOTE: this command must be called on each machine):



```bash

# Assume the machines have NIC eth0 and their IPs are 192.168.0.1 and 192.168.0.2.

# Assume NUM_GPU_SLOTS=8, NUM_GPUS=16

kungfu-run -np $NUM_GPUS \

    -H 192.168.0.1:$NUM_GPU_SLOTS,192.168.0.2:$NUM_GPU_SLOTS -nic eth0 \

    python3 examples/tf1_mnist_session.py  --data-dir=./mnist

```



``kungfu-run`` use the ``nic`` option to infer its IP and thus its role in the cluster.



## Examples



We have been using KungFu in training

different kinds of AI models.

The following are representative examples:



* ***ImageNet***: KungFu can speed up the training

of ResNet, VGG, DenseNet and others for ImageNet.

Check out this in an [ImageNet benchmark suite](https://github.com/luomai/benchmarks/tree/cnn_tf_v1.12_compatible_kungfu/scripts/tf_cnn_benchmarks#running-kungfu) extended from the [TensorFlow benchmark](https://github.com/luomai/benchmarks/tree/cnn_tf_v1.12_compatible_kungfu).



* ***Pose estimation***: Pose estimation models such as [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) are often batch-size sensitive.

We used KungFu in

a popular [OpenPose implementation](https://github.com/tensorlayer/openpose-plus) and improved time-to-accuracy

using the model averaging optimizer which preserves the merits of small batch size.



* ***Natural language processing***:

We have an [example](https://github.com/luomai/bert) that shows how you can use few lines to enable distributed training for the Google BERT model.



* ***Adversarial learning***:

Adversarial learning trains multiple networks in parallel and prefer using small batches for training.

KungFu thus become an attractive option, because of its minimal changes to GAN programs

and its optimizers that decouple batch size and system parallelism.

See the [CycleGAN example](https://github.com/tensorlayer/cyclegan).



* ***Reinforcement learning***:

We are working on an Alpha Zero distributed training example and will release it soon.



## Choosing the right optimizer



KungFu aims to help users decrease the

time to reach a desired accuracy (time-to-accuracy)

through scaling.

There are two major ways to improve time-to-accuracy in KungFu:



* Synchronous SGD: Adopt parallel workers to improve the estimation of gradients, and

reach a minima quickly using an increased learning rate.

* Model Averaging: Adopt parallel workers to explore the solution space and collaborate through averaging diverged models in order

to find a good minima quickly.



***Synchronous SGD***:

Synchronous SGD (S-SGD) is implemented as ``SynchronousSGDOptimizer`` in KungFu, equivalent to

the DistributedOptimizer in Horovod.

The use of S-SGD, however, poses scalability and accuracy challenges.

Scalability-wise, all S-SGD workers must exchange all gradients per iteration, making

them hard to deal with limited bandwidth and stragglers;

(ii) accuracy-wise, S-SGD *couples* training batch size with the number of workers,

enforcing users to use large batch sizes, which can adversely

affect the generality of a trained model (see [paper](https://arxiv.org/abs/1609.04836)).

To compensate the loss in generality, users must explore various [methods](https://research.fb.com/wp-content/uploads/2017/06/imagenet1kin1h5.pdf)

for tuning hyper-parameters.



***Model averaging***:

Model averaging is implemented as ``SynchronousAveragingOptimizer`` and

``PairAveragingOptimizer`` in KungFu.

The former realizes the hyper-parameter-robust [SMA](http://www.vldb.org/pvldb/vol12/p1399-koliousis.pdf)

algorithm; while the latter implements the [AD-PSGD](https://arxiv.org/abs/1710.06952) algorithm

which reduces bandwidth consumption and tolerates stragglers.

In model averaging, each worker trains its local

model using SGD, and average

its model with peers to speed up the search for minima.

Model averaging algorithms have a convergence guarantee (see [EA-SGD paper](https://arxiv.org/abs/1412.6651))

and can converge fast with DL models (see [Lookahead paper](https://arxiv.org/abs/1907.08610)).

A useful property of model averaging is: it decouples

batch size with system parallelism, often making

it *hyper-parameter robust*. We find

this property valuable

as DL users often find it hard and expensive to

tune synchronous SGD at scale.



***Convergence evaluation***:

We have tested KungFu optimizers using ResNet-50 and ResNet-101 for ImageNet.

When using 8 V100, all KungFu optimizers can reach the target 75% accuracy,

the same as the baseline Horovod.

When using 16 V100, Horovod and ``SynchronousSGDOptimizer`` suffer from

the increased batch size and their accuracy drop to 59% while

``SynchronousAveragingOptimizer`` and ``PairAveragingOptimizer`` still

reach the target 75%.

All these tests use a per-GPU batch size as 64 and [hyper-parameters](https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks#getting-started)

suggested by the TensorFlow benchmark authors.



## Benchmark



We benchmark KungFu in a cluster that has 16 V100 GPUs hosted by 2 DGX-1 machines.

The machines are interconnected by a 100 Gbps network. We measure the training throughput of ResNet-50, VGG16 and InceptionV3. These models represent different kinds of training workloads.



In the ***synchronous training*** case, we compare KungFu (``SynchronousSGDOptimizer``) with [Horovod](https://github.com/horovod/horovod) (0.16.1). Horovod uses OpenMPI 4.0.0. We evaluate the spectrum of batch size (from 256 to 4096) commonly used by S-SGD users.

This batch size is evenly shared by the 16 GPUs.

KungFu outperforms Horovod on all tested models, in particular with small batch sizes which significantly raise the

frequency of synchronization.



![sync](benchmarks/system/result/sync-scalability.svg)



In the ***asynchronous training*** case, we compare KungFu (``PairAveragingOptimizer``) with TensorFlow parameter servers (1.13.1). We uses the same range of batch sizes as above. KungFu exhibits better scalability as well.



![async](benchmarks/system/result/async-scalability.svg)



We have also run the same benchmark in a 16-server cluster (each has a P100).

KungFu exhibits better scalability in this communication-challenging environment,

and we thus only report the 16 V100 result here. You can find the benchmark scripts [here](benchmarks/system/).



## Development



KungFu is designed with extensibility in mind.

It has a low-level API and a modular architecture, making

it suitable for implementing new distributed training algorithms.

Check out the developer [guideline](CONTRIBUTING.md) for more information.