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11 changes: 11 additions & 0 deletions Dockerfile
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# The Google Cloud Platform Python runtime is based on Debian Jessie
# You can read more about the runtime at:
# https://github.com/GoogleCloudPlatform/python-runtime
FROM gcr.io/google_appengine/python

RUN apt-get update
RUN apt-get -y install python3 python3-pip python3-dev build-essential

COPY requirements.txt /app/
RUN pip3 install --requirement /app/requirements.txt
COPY . /app/
203 changes: 203 additions & 0 deletions LICENSE
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121 changes: 121 additions & 0 deletions README.md
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# Run Evolution Strategies on Google Kubernetes Engine

## Introduction

Evolution Strategies (ES) performs iterative optimization with a large population of trials that are usually distributedly conducted.
[Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine/) (GKE) serves as a good platform for ES.
We hope the instructions and code here serves as a quickstart for researchers to run their ES experiments on GKE.
Please refer to the blog [here] for more information about the repository.
You are also strongly recommended to read this [blog](http://blog.otoro.net/2017/10/29/visual-evolution-strategies/) that provides excellent explanations if you want to know more about ES.


![Learning time comparison in BipedalWalkerHardcore](https://storage.googleapis.com/gcp_blog/img/bipedal_time_comparison.png)
![Learning time comparison in MinitaurLocomotion](https://storage.googleapis.com/gcp_blog/img/minitaur_time_comparison.png)

## How to use the code

The ES algorithms we provide as samples are Parameter-exploring Policy Gradients (PEPG) and Covariance Matrix Adaptation (CMA).
You can play with them in Google Brain's [Minitaur Locomotion](https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet/gym/pybullet_envs/minitaur/envs) and OpenAI's [BipedalWalkerHardcore-v2](https://github.com/openai/gym/wiki/Leaderboard#bipedalwalkerhardcore-v2).
You can also easily extend the code here to add your ES algorithms or change the configs to try the algorithms in your own environments.

### Run the demos on GKE

#### 1. Before you begin

You need a cluster on Google Cloud Platform (GCP) to run the demos, follow the instructions [here](https://cloud.google.com/kubernetes-engine/docs/how-to/creating-a-cluster) to create yours.
We use the following commands / configs to create our cluster, feel free to change these to suit your needs.
```Bash
GCLOUD_PROJECT={your-project-id}

gcloud container clusters create es-cluster \
--zone=us-central1-a \
--machine-type=n1-standard-64 \
--max-nodes=20 \
--min-nodes=17 \
--num-nodes=17 \
--enable-autoscaling \
--project ${GCLOUD_PROJECT}
```

#### 2. Follow the instructions to deploy a demo

The following command builds a container image for you.
You need to generate a new image if you have changed the code, remember to change the image version number when you do so.
```Bash
gcloud builds submit \
--tag gcr.io/${GCLOUD_PROJECT}/es-on-gke:1.0 . \
--timeout 3600 \
--project ${GCLOUD_PROJECT}
```

When the container image is built, edit `yaml/deploy_workers.yaml` and `yaml/deploy_master.yaml` to
* replace the `spec.template.spec.containers.image` with the one you just built
* change the `--config` parameter in `spec.template.spec.containers.command` to the environment you want to run

Replace `${GCLOUD_PROJECT}` in the following 2 yaml files with your project ID,
then start the ES workers and the ES master:
```Bash
# Run these commands to start workers.
sed "s/\${GCLOUD_PROJECT}/${GCLOUD_PROJECT}/g" yaml/deploy_workers_bipedal.yaml > workers.yaml
kubectl apply -f workers.yaml

# When all the workers are running, run these command to start the master.
sed "s/\${GCLOUD_PROJECT}/${GCLOUD_PROJECT}/g" yaml/deploy_master_bipedal.yaml > master.yaml
kubectl apply -f master.yaml
```
After a while you should be able to see your pods started in GCP console:
![Pod started](https://storage.googleapis.com/gcp_blog/img/start_master_workers.png)

That's all! ES should be training in your specified environment on GKE now.

#### 3. Check training progress and results

We provide 3 ways for you to check the training progress:
1. **Stackdriver** In GCP console, clicking GKE's Workloads page gives your detailed status report of your pods.
Go to the details of the `es-master-pod` and you can find "Container logs" there that will direct you to the Stackdriver's logging where you can see training and test rewards.
2. **HTTP Server** In our code, we start a simple HTTP server in the master to make training logs easily accessible to you.
You can access by checking the endpoint in `es-master-service` located in GKE's Services page. (The server may need some time to start up.)
3. **Kubectl** Finally you can use the *kubectl* command to fetch logs and models.
The following commands serve as examples.

```bash
POD_NAME=$(kubectl get pod | grep es-master | awk '{print $1}')

# Download reward vs time plot.
kubectl cp $POD_NAME:/var/log/es/log/reward_vs_time.png $HOME/
# Download reward vs iteration plot.
kubectl cp $POD_NAME:/var/log/es/log/reward_vs_iteration.png $HOME/
# Download best model so far.
kubectl cp $POD_NAME:/var/log/es/log/best_model.npz $HOME/
# Download model at iteration 1000.
kubectl cp $POD_NAME:/var/log/es/log/model_1000.npz $HOME/
# Download all test scores.
kubectl cp $POD_NAME:/var/log/es/log/scores.csv $HOME/
```


### Run the demos locally

As a debugging process, both training and test can be run locally.
Use `train_local.sh` and `test.py`, and add proper options to do so.
```Bash
# train locally
bash ./train_local.sh -c {path-to-config-file} -n {number-of-workers}

# test locally
python test.py --logdir={path-to-log-directory}
```

### Clean up

When the tasks are done, you can download all the training logs and models:
```bash
# ssh into the container
kubectl exec -it $POD_NAME /bin/bash
# In the container, make a tar ball of logs and then exit
tar -cvf log.tar /var/log/es && exit
# Download the tar ball
kubectl cp $POD_NAME:/app/log.tar $HOME/
```

Finally, if you don't need to run any tasks, don't forget to [delete the cluster](https://cloud.google.com/dataproc/docs/guides/manage-cluster).
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