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README.md

Testing Distributed Runtime in TensorFlow

This folder containers tools and test suites for the GRPC-based distributed runtime in TensorFlow.

There are three general modes of testing:

1) Launch a docker container and run parameters servers and workers as separate processes therein.

For example:

./local_test.sh

By default, local_test.sh runs the MNIST-with-replicas model as a test. However, you can use the --model_name flag to run the tf-learn/wide&deep cesnsu model:

./local_test.sh --model_name CENSUS_WIDENDEEP

You can test specify version of TensorFlow:

./local_test.sh ${whl_file_url}

For example, you can find these TensorFlow python package URLs from here for Ubuntu.

2) Launch a remote k8s cluster on Google Kubernetes Engine (GKE) and run the test suite on it

For example:

export TF_DIST_GCLOUD_PROJECT="tensorflow-testing"
export TF_DIST_GCLOUD_COMPUTE_ZONE="us-central1-f"
export TF_DIST_CONTAINER_CLUSTER="test-cluster-1"
export TF_DIST_GCLOUD_KEY_FILE="/var/gcloud-secrets/my-gcloud-key.json"
./remote_test.sh

Here you specify the Google Compute Engine (GCE) project, compute zone and container cluster with the first three environment variables, in that order. The environment variable "TF_DIST_GCLOUD_KEY_FILE_DIR" is a directory in which the JSON service account key file named "tensorflow-testing.json" is located. You can use the flag "--setup-cluster-only" to perform only the cluster setup step and skip the testing step:

./remote_test.sh --setup_cluster_only

3) Run the test suite on an existing k8s TensorFlow cluster

For example:

export TF_DIST_GRPC_SERVER_URL="grpc://11.22.33.44:2222"
./remote_test.sh

The IP address above is a dummy example. Such a cluster may have been set up using the command described at the end of the previous section.

Asynchronous and synchronous parameter updates

There are two modes for the coordination of the parameters from multiple workers: asynchronous and synchronous.

In the asynchronous mode, the parameter updates (gradients) from the workers are applied to the parameters without any explicit coordination. This is the default mode in the tests.

In the synchronous mode, a certain number of parameter updates are aggregated from the model replicas before the update is applied to the model parameters. To use this mode, do:

# For remote testing
./remote_test.sh --sync_replicas

# For local testing
./local_test.sh --sync_replicas

Specifying the number of workers

You can specify the number of workers by using the --num-workers option flag, e.g.,

# For remote testing
./remote_test.sh --num_workers 4

# For local testing
./local_test.sh --num_workers 4

Building the GRPC server Docker image

To build the Docker image for a test server of TensorFlow distributed runtime, run:

./build_server.sh <docker_image_name>

Using the GRPC server Docker image To launch a container as a TensorFlow GRPC server, do as the following example:

docker run tensorflow/tf_grpc_server --cluster_spec="worker|localhost:2222;foo:2222,ps|bar:2222;qux:2222" --job_name=worker --task_id=0

Generating configuration file for TensorFlow k8s clusters

The script at "scripts/k8s_tensorflow.py" can be used to generate yaml configuration files for a TensorFlow k8s cluster consisting of a number of workers and parameter servers. For example:

scripts/k8s_tensorflow.py \
    --num_workers 2 \
    --num_parameter_servers 2 \
    --grpc_port 2222 \
    --request_load_balancer true \
    --docker_image "tensorflow/tf_grpc_server" \
    > tf-k8s-with-lb.yaml

The yaml configuration file generated in the previous step can be used to a create a k8s cluster running the specified numbers of worker and parameter servers. For example:

kubectl create -f tf-k8s-with-lb.yaml

See Kubernetes kubectl documentation for more details.