terrytangyuan

# This example demonstrates the ability to memoize a template.
# Cache can be configured in configmap. For example, if the cache
# ConfigMap looks like the following, then you should expect the node output
# retrieved from the cache to be "different world".
# Note that you also need to update `creationTimestamp` in
# `data.hello` to be within the `memoize.maxAge` if `maxAge` is specified in the workflow spec.
# 
#---
apiVersion: v1
data:

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# Hello World
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: hello-world-
spec:
  entrypoint: whalesay
  templates:
  - name: whalesay
    container:

terrytangyuan

Sys.setenv("GITHUB_TOKEN"="")

author <- "terrytangyuan"
from <- "2020-03-30"
until <- "2020-09-10"
orgs <- c("couler-proj", "argoproj", "kubeflow")
pr_info <- list()

for (org in orgs) {
  pr_info[[org]] <- list(

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You accept and agree to the following terms and conditions for Your present and future Contributions submitted to Ant Group. Except for the license granted herein to Ant Group and recipients of documentation and software distributed by Ant Group, You reserve all right, title, and interes

terrytangyuan

# --------------------------------------------------------------------------------
# airflow

def create_dag(dag_id,
               schedule,
               dag_number,
               default_args):

    def hello_world_py(*args):
        print('Hello World')

terrytangyuan

apiVersion: kubeflow.org/v1alpha2
kind: MPIJob
metadata:
  name: tensorflow-benchmarks-imagenet
spec:
  slotsPerWorker: 8
  cleanPodPolicy: Running
  mpiReplicaSpecs:
    Launcher:
      replicas: 1

terrytangyuan

apiVersion: kubeflow.org/v1alpha2
kind: MPIJob
metadata:
  name: tensorflow-benchmarks
spec:
  slotsPerWorker: 1
  cleanPodPolicy: Running
  mpiReplicaSpecs:
    Launcher:
      replicas: 1

terrytangyuan

"""An example of training Keras model with multi-worker strategies."""
import tensorflow as tf


dataset = tf.data.Dataset.from_tensor_slices((x, y))

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(16, activation='relu', input_shape=(10,)))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

terrytangyuan

# Keras

from keras.layers import Dense, Activation

model.add(Dense(output_dim=64, input_dim=100))
mode.add(Activation('relu'))
model.add(Dense(output_dim=10))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])