Tensorflow job with specified estimator
You can also use high-level TensorFlow API tf.estimator.Estimator class
for running Distributed TensorFlow with good modularity by using Arena
.
1. You need to create /data
in the NFS Server, and prepare mnist data
.
$ arenamkdir -p /nfs
$ mount -t nfs -o vers=4.0 NFS_SERVER_IP:/ /nfs
$ mkdir -p /data
$ cd /data
$ wget https://raw.githubusercontent.com/cheyang/tensorflow-sample-code/master/data/t10k-images-idx3-ubyte.gz
$ wget https://raw.githubusercontent.com/cheyang/tensorflow-sample-code/master/data/t10k-labels-idx1-ubyte.gz
$ wget https://raw.githubusercontent.com/cheyang/tensorflow-sample-code/master/data/train-images-idx3-ubyte.gz
$ wget https://raw.githubusercontent.com/cheyang/tensorflow-sample-code/master/data/train-labels-idx1-ubyte.gz
$ cd /
$ umount /nfs
- Create Persistent Volume. Moidfy
NFS_SERVER_IP
to yours.
$ cat nfs-pv.yaml
apiVersion: v1
kind: PersistentVolume
metadata:
name: tfdata
labels:
tfdata: nas-mnist
spec:
persistentVolumeReclaimPolicy: Retain
capacity:
storage: 10Gi
accessModes:
- ReadWriteMany
nfs:
server: NFS_SERVER_IP
path: "/data"
$ kubectl create -f nfs-pv.yaml
3. Create Persistent Volume Claim.
$ cat nfs-pvc.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: tfdata
annotations:
description: "this is the mnist demo"
owner: Tom
spec:
accessModes:
- ReadWriteMany
resources:
requests:
storage: 5Gi
selector:
matchLabels:
tfdata: nas-mnist
$ kubectl create -f nfs-pvc.yaml
Note
suggest to add description
and owner
4. Check the data volume.
$ arena data list
NAME ACCESSMODE DESCRIPTION OWNER AGE
tfdata ReadWriteMany this is for mnist demo myteam 43d
5. To run a distributed Tensorflow Training, you need to specify:
- GPUs of each worker (Include chief and evaluator)
- Enable chief (required)
- Enable Evaluator (optional)
- The number of workers (required)
- The number of PS (required)
- The docker image of worker and master (required)
- The docker image of PS (required)
- The Port of Chief (default is 22221)
- The Port of Worker (default is 22222)
- The Port of PS (default is 22223)
The following command is an example. In this example, it defines 1 chief worker, 1 workers, 1 PS and 1 evaluator, and each worker has 1 GPU. The source code of worker and PS are located in git, and the tensorboard are enabled.
$ arena submit tf --name=tf-estimator \
--gpus=1 \
--workers=1 \
--chief \
--evaluator \
--data=tfdata:/data/mnist \
--logdir=/data/mnist/models \
--worker-image=tensorflow/tensorflow:1.9.0-devel-gpu \
--sync-mode=git \
--sync-source=https://github.com/cheyang/models.git \
--ps=1 \
--ps-image=tensorflow/tensorflow:1.9.0-devel \
--tensorboard \
"bash code/models/dist_mnist_estimator.sh --data_dir=/data/mnist/MNIST_data --model_dir=/data/mnist/models"
configmap/tf-estimator-tfjob created
configmap/tf-estimator-tfjob labeled
service/tf-estimator-tensorboard created
deployment.extensions/tf-estimator-tensorboard created
tfjob.kubeflow.org/tf-estimator created
INFO[0001] The Job tf-estimator has been submitted successfully
INFO[0001] You can run `arena get tf-estimator --type tfjob` to check the job status
Note
--data
specifies the data volume to mount to all the tasks of the job, like<name_of_datasource>:<mount_point_on_job>
. In this example, the data volume istfdata
, and the target directory is/data/mnist
.
6. From the logs, we have found the training is started.
$ arena logs tf-estimator
2018-09-27T00:37:01.576672145Z 2018-09-27 00:37:01.576562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:chief/replica:0/task:0/device:GPU:0 with 15123 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:08.0, compute capability: 6.0)
2018-09-27T00:37:01.578669608Z 2018-09-27 00:37:01.578523: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:215] Initialize GrpcChannelCache for job chief -> {0 -> localhost:22222}
2018-09-27T00:37:01.578685739Z 2018-09-27 00:37:01.578550: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:215] Initialize GrpcChannelCache for job ps -> {0 -> tf-estimator-tfjob-ps-0:22223}
2018-09-27T00:37:01.578705274Z 2018-09-27 00:37:01.578562: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:215] Initialize GrpcChannelCache for job worker -> {0 -> tf-estimator-tfjob-worker-0:22222}
2018-09-27T00:37:01.579637826Z 2018-09-27 00:37:01.579454: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:334] Started server with target: grpc://localhost:22222
2018-09-27T00:37:01.701520696Z I0927 00:37:01.701258 140281586534144 tf_logging.py:115] Calling model_fn.
2018-09-27T00:37:02.172552485Z I0927 00:37:02.172167 140281586534144 tf_logging.py:115] Done calling model_fn.
2018-09-27T00:37:02.173930978Z I0927 00:37:02.173732 140281586534144 tf_logging.py:115] Create CheckpointSaverHook.
2018-09-27T00:37:02.431259294Z I0927 00:37:02.430984 140281586534144 tf_logging.py:115] Graph was finalized.
2018-09-27T00:37:02.4472109Z 2018-09-27 00:37:02.447018: I tensorflow/core/distributed_runtime/master_session.cc:1150] Start master session b0a6d2587e64ebef with config: allow_soft_placement: true graph_options { rewrite_options { meta_optimizer_iterations: ONE } }
...
2018-09-27T00:37:33.250440133Z I0927 00:37:33.250036 140281586534144 tf_logging.py:115] global_step/sec: 21.8175
2018-09-27T00:37:33.253100942Z I0927 00:37:33.252873 140281586534144 tf_logging.py:115] loss = 0.09276967, step = 500 (4.583 sec)
2018-09-27T00:37:37.764446795Z I0927 00:37:37.764101 140281586534144 tf_logging.py:115] Saving checkpoints for 600 into /data/mnist/models/model.ckpt.
2018-09-27T00:37:38.064104604Z I0927 00:37:38.063472 140281586534144 tf_logging.py:115] Loss for final step: 0.24215397.
7. Check the training status and tensorboard.
$ arena get tf-estimator
NAME STATUS TRAINER AGE INSTANCE NODE
tf-estimator SUCCEEDED TFJOB 5h tf-estimator-tfjob-chief-0 N/A
tf-estimator RUNNING TFJOB 5h tf-estimator-tfjob-evaluator-0 192.168.1.120
tf-estimator RUNNING TFJOB 5h tf-estimator-tfjob-ps-0 192.168.1.119
tf-estimator RUNNING TFJOB 5h tf-estimator-tfjob-worker-0 192.168.1.118
Your tensorboard will be available on:
192.168.1.117:31366
8. Check the tensorboard from 192.168.1.117:31366 in this sample.