利用Kubernetes和Helm進行高效的超參數調優 頂 原 薦

Author: [email protected]

摘要：本文將討論Hyperparameter調優在落地時面臨的問題，以及如何利用Kubernetes+Helm解決這些問題。

Hyperparameter Sweep面臨的問題

在進行Hyperparameter Sweep的時候，我們需要根據許多不同的超參數組合進行不同的訓練，爲同一模型進行多次訓練需要消耗大量計算資源或者耗費大量時間。

• 如果根據不同的超參數並行進行訓練，這需要大量計算資源。
• 如果在固定計算資源上順序進行所有不同超參數組合對應的訓練，這需要花費大量時間完成所有組合對應的訓練。

因此在落地時中，大多數人通過非常有限的幾次手動微調他們的超參數就挑選一個相對最優的組合。

Kubernetes+Helm是利器

通過Kubernetes與Helm，您可以非常輕鬆地探索非常大的超參數空間，同時最大化集羣的利用率，從而優化成本。

Helm使我們能夠將應用程序打包到chart中並輕鬆地對其進行參數化。在Hyperparameter Sweep時，我們可以利用Helm chart values的配置，在template中生成對應的TFJobs進行訓練部署，同時chart中還可以部署一個TensorBoard實例來監控所有這些TFJobs，這樣我們就可以快速比較我們所有的超參數組合訓練的結果，對那些訓練效果不好的超參數組合，我們可以儘早刪除對應的訓練任務，這無疑會大幅的節省集羣的計算資源，從而降低成本。

利用Kubernetes+Helm進行Hyperparameter Sweep Demo

Helm Chart

我們將通過Azure/kubeflow-labs/hyperparam-sweep中的例子進行Demo。

首先通過以下Dockerfile製作訓練的鏡像：

FROM tensorflow/tensorflow:1.7.0-gpu
COPY requirements.txt /app/requirements.txt
WORKDIR /app
RUN mkdir ./output
RUN mkdir ./logs
RUN mkdir ./checkpoints
RUN pip install -r requirements.txt
COPY ./* /app/

ENTRYPOINT [ "python", "/app/main.py" ]

其中main.py訓練腳本內容如下：

import click
import tensorflow as tf
import numpy as np
from skimage.data import astronaut
from scipy.misc import imresize, imsave, imread

img = imread('./starry.jpg')
img = imresize(img, (100, 100))
save_dir = 'output'
epochs = 2000


def linear_layer(X, layer_size, layer_name):
    with tf.variable_scope(layer_name):
        W = tf.Variable(tf.random_uniform([X.get_shape().as_list()[1], layer_size], dtype=tf.float32), name='W')
        b = tf.Variable(tf.zeros([layer_size]), name='b')
        return tf.nn.relu(tf.matmul(X, W) + b)

@click.command()
@click.option("--learning-rate", default=0.01) 
@click.option("--hidden-layers", default=7)
@click.option("--logdir")
def main(learning_rate, hidden_layers, logdir='./logs/1'):
    X = tf.placeholder(dtype=tf.float32, shape=(None, 2), name='X') 
    y = tf.placeholder(dtype=tf.float32, shape=(None, 3), name='y')
    current_input = X
    for layer_id in range(hidden_layers):
        h = linear_layer(current_input, 20, 'layer{}'.format(layer_id))
        current_input = h

    y_pred = linear_layer(current_input, 3, 'output')

    #loss will be distance between predicted and true RGB
    loss = tf.reduce_mean(tf.reduce_sum(tf.squared_difference(y, y_pred), 1))
    tf.summary.scalar('loss', loss)

    train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss)
    merged_summary_op = tf.summary.merge_all()  

    res_img = tf.cast(tf.clip_by_value(tf.reshape(y_pred, (1,) + img.shape), 0, 255), tf.uint8)
    img_summary = tf.summary.image('out', res_img, max_outputs=1)
    
    xs, ys = get_data(img)

    with tf.Session() as sess:
        tf.global_variables_initializer().run()  
        train_writer = tf.summary.FileWriter(logdir + '/train', sess.graph)
        test_writer = tf.summary.FileWriter(logdir + '/test')
        batch_size = 50
        for i in range(epochs):
            # Get a random sampling of the dataset
            idxs = np.random.permutation(range(len(xs)))
            # The number of batches we have to iterate over
            n_batches = len(idxs) // batch_size
            # Now iterate over our stochastic minibatches:
            for batch_i in range(n_batches):
                batch_idxs = idxs[batch_i * batch_size: (batch_i + 1) * batch_size]
                sess.run([train_op, loss, merged_summary_op], feed_dict={X: xs[batch_idxs], y: ys[batch_idxs]})
                if batch_i % 100 == 0:
                    c, summary = sess.run([loss, merged_summary_op], feed_dict={X: xs[batch_idxs], y: ys[batch_idxs]})
                    train_writer.add_summary(summary, (i * n_batches * batch_size) + batch_i)
                    print("epoch {}, (l2) loss {}".format(i, c))           

            if i % 10 == 0:
                img_summary_res = sess.run(img_summary, feed_dict={X: xs, y: ys})
                test_writer.add_summary(img_summary_res, i * n_batches * batch_size)

def get_data(img):
    xs = []
    ys = []
    for row_i in range(img.shape[0]):
        for col_i in range(img.shape[1]):
            xs.append([row_i, col_i])
            ys.append(img[row_i, col_i])

    xs = (xs - np.mean(xs)) / np.std(xs)
    return xs, np.array(ys)

if __name__ == "__main__":
    main()

• docker build製作鏡像時，會將根目錄下的starry.jpg圖片打包進去供main.py讀取。
• main.py使用基於Andrej Karpathy's Image painting demo的模型，這個模型的目標是繪製一個儘可能接近原作的新圖片，文森特梵高的“星夜”。

在Helm chart values.yaml中配置如下：

image：ritazh / tf-paint：gpu 
useGPU：true 
hyperParamValues：
   learningRate：
    - 0.001 
    - 0.01 
    - 0.1 
  hiddenLayers：
    - 5 
    - 6 
    - 7

• image: 配置訓練任務對應的docker image，就是前面您製作的鏡像。
• useGPU: bool值，默認true表示將使用gpu進行訓練，如果是false，則需要您製作鏡像時使用tensorflow/tensorflow:1.7.0 base image。
• hyperParamValues: 超參數們的配置，在這裏我們只配置了learningRate, hiddenLayers兩個超參數。

Helm chart中主要是TFJob對應的定義、Tensorboard的Deployment及其Service的定義：

# First we copy the values of values.yaml in variable to make it easier to access them
{{- $lrlist := .Values.hyperParamValues.learningRate -}}
{{- $nblayerslist := .Values.hyperParamValues.hiddenLayers -}}
{{- $image := .Values.image -}}
{{- $useGPU := .Values.useGPU -}}
{{- $chartname := .Chart.Name -}}
{{- $chartversion := .Chart.Version -}}

# Then we loop over every value of $lrlist (learning rate) and $nblayerslist (hidden layer depth)
# This will result in create 1 TFJob for every pair of learning rate and hidden layer depth
{{- range $i, $lr := $lrlist }}
{{- range $j, $nblayers := $nblayerslist }}
apiVersion: kubeflow.org/v1alpha1
kind: TFJob # Each one of our trainings will be a separate TFJob
metadata:
  name: module8-tf-paint-{{ $i }}-{{ $j }} # We give a unique name to each training
  labels:
    chart: "{{ $chartname }}-{{ $chartversion | replace "+" "_" }}"
spec: 
  replicaSpecs:
    - template:
        spec:
          restartPolicy: OnFailure
          containers:
            - name: tensorflow
              image: {{ $image }} 
              env:
              - name: LC_ALL
                value: C.UTF-8
              args:
                # Here we pass a unique learning rate and hidden layer count to each instance.
                # We also put the values between quotes to avoid potential formatting issues
                - --learning-rate  
                - {{ $lr | quote }}
                - --hidden-layers
                - {{ $nblayers | quote }}
                - --logdir
                - /tmp/tensorflow/tf-paint-lr{{ $lr }}-d-{{ $nblayers }} # We save the summaries in a different directory
{{ if $useGPU }}  # We only want to request GPUs if we asked for it in values.yaml with useGPU
              resources:
                limits:
                  nvidia.com/gpu: 1
{{ end }}
              volumeMounts:
              - mountPath: /tmp/tensorflow
                subPath: module8-tf-paint # As usual we want to save everything in a separate subdirectory 
                name: azurefile
          volumes:
            - name: azurefile
              persistentVolumeClaim:
                claimName: azurefile
---
{{- end }}
{{- end }}
# We only want one instance running for all our jobs, and not 1 per job.
apiVersion: v1
kind: Service
metadata:
  labels:
    app: tensorboard
  name: module8-tensorboard
spec:
  ports:
  - port: 80
    targetPort: 6006
  selector:
    app: tensorboard
  type: LoadBalancer
---
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  labels:
    app: tensorboard
  name: module8-tensorboard
spec:
  template:
    metadata:
      labels:
        app: tensorboard
    spec:
      volumes:
        - name: azurefile
          persistentVolumeClaim:
            claimName: azurefile      
      containers:
      - name: tensorboard
        command:
          - /usr/local/bin/tensorboard
          - --logdir=/tmp/tensorflow
          - --host=0.0.0.0
        image: tensorflow/tensorflow
        ports:
        - containerPort: 6006
        volumeMounts:
        - mountPath: /tmp/tensorflow
          subPath: module8-tf-paint
          name: azurefile 

按照上面的超參數配置，在helm install時，9個超參數組合會產生9個TFJob，對應我們指定的3個learningRate和3個hiddenLayers所有組合。

main.py訓練腳本有3個參數：

argument	description	default value
--learning-rate	Learning rate value	0.001
--hidden-layers	Number of hidden layers in our network.	4
--log-dir	Path to save TensorFlow's summaries	None

Helm Install

執行helm install命令即可輕鬆完成所有不同超參數組合對應的訓練部署，這裏我們只使用了單機訓練，您也可以使用分佈式訓練。

helm install .

NAME:   telling-buffalo
LAST DEPLOYED: 
NAMESPACE: tfworkflow
STATUS: DEPLOYED

RESOURCES:
==> v1/Service
NAME                 TYPE          CLUSTER-IP    EXTERNAL-IP  PORT(S)       AGE
module8-tensorboard  LoadBalancer  10.0.142.217  <pending>    80:30896/TCP  1s

==> v1beta1/Deployment
NAME                 DESIRED  CURRENT  UP-TO-DATE  AVAILABLE  AGE
module8-tensorboard  1        1        1           0          1s

==> v1alpha1/TFJob
NAME                  AGE
module8-tf-paint-0-0  1s
module8-tf-paint-1-0  1s
module8-tf-paint-1-1  1s
module8-tf-paint-2-1  1s
module8-tf-paint-2-2  1s
module8-tf-paint-0-1  1s
module8-tf-paint-0-2  1s
module8-tf-paint-1-2  1s
module8-tf-paint-2-0  0s

==> v1/Pod(related)
NAME                                  READY  STATUS             RESTARTS  AGE
module8-tensorboard-7ccb598cdd-6vg7h   0/1    ContainerCreating  0         1s

部署chart後，查看已創建的Pods，您應該看到對應的一些列Pods，以及監視所有窗格的單個TensorBoard實例：

$ kubectl get pods

NAME                                      READY     STATUS    RESTARTS   AGE
module8-tensorboard-7ccb598cdd-6vg7h       1/1       Running   0          16s
module8-tf-paint-0-0-master-juc5-0-hw5cm   0/1       Pending   0          4s
module8-tf-paint-0-1-master-pu49-0-jp06r   1/1       Running   0          14s
module8-tf-paint-0-2-master-awhs-0-gfra0   0/1       Pending   0          6s
module8-tf-paint-1-0-master-5tfm-0-dhhhv   1/1       Running   0          16s
module8-tf-paint-1-1-master-be91-0-zw4gk   1/1       Running   0          16s
module8-tf-paint-1-2-master-r2nd-0-zhws1   0/1       Pending   0          7s
module8-tf-paint-2-0-master-7w37-0-ff0w9   0/1       Pending   0          13s
module8-tf-paint-2-1-master-260j-0-l4o7r   0/1       Pending   0          10s
module8-tf-paint-2-2-master-jtjb-0-5l84q   0/1       Pending   0          9s

注意：由於羣集中可用的GPU資源，某些pod正在等待處理。如果羣集中有3個GPU，則在給定時間最多只能有3個TFJob(每個TFJob請求了一塊gpu)並行訓練。

通過TensorBoard儘早識別最優的超參數組合

TensorBoard Service也會在Helm install執行時自動完成創建，您可以使用該Service的External-IP連接到TensorBoard。

$ kubectl get service

NAME                                 TYPE           CLUSTER-IP     EXTERNAL-IP      PORT(S)             AGE
module8-tensorboard                  LoadBalancer   10.0.142.217   <PUBLIC IP>     80:30896/TCP        5m

通過瀏覽器訪問TensorBoard的Public IP地址，你會看到類似如下的頁面（TensorBoard需要一點時間才能顯示圖像。）

在這裏我們可以看到一些超參數對應的模型比其他模型表現更好。例如，所有learning rate爲0.1對應的模型全部產生全黑圖像，模型效果極差。幾分鐘後，我們可以看到兩個表現最好的超參數組合是：

• hidden layers = 5，learning rate = 0.01
• hidden layers = 7，learning rate = 0.001

此時，我們可以立刻Kill掉其他表現差的模型訓練，釋放寶貴的gpu資源。

總結

通過本文簡單利用Helm進行Hyperparameter Sweep的使用方法介紹，希望能幫助大家更高效的進行超參數調優。

參考

• https://github.com/Azure/kubeflow-labs/tree/master/8-hyperparam-sweep