doframework 0.1.0

Testing framework for data-driven decision-optimization algorithms.

DOFramework

doframework is a testing framework for data-driven decision-optimization algorithms. It integrates easily with the user's data-driven decision-optimization application (written in Python).

doframework randomly generates optimization problems (f,O,D,x*):

• f is continuous piece-wise linear functions defined over a domain in d-dimensional space (d>1),
• O is a region in dom(f) defined by linear constraints,
• D = (X,y) is a dataset derived for f,
• x* is the true optimum of f in O.

The testing framework feeds the constraints and the data (O,D) into the user's algorithm, and collects its predicted optimum. The algorithm's predicted optimal value can then be conpared to the true optimal value f(x*). By comparing the two over many optimization problems, doframework produces a performance profile for data-driven decision-optimization algorithms.

Design

doframework was designed for optimal cloud distribution following an event-driven approach.

doframework was built on top of ray for cloud distribution and rayvens for event driven management.

Requirements

doframework was written for Python version >= 3.9.0.

doframework can run either locally or remotely. For optimal performance, run it on a Kubernetes cluster. Cloud configuration is currently available for OpenShift clusters.

The framework relies on Cloud Object Storage (COS) to interact with simulation products.

Configs

The user provides COS specifications in the form of a configs.yaml.

The configs.yaml includes the list of source and target bucket names (under s3:buckets). Credentials are added under designated fields.

s3:
    buckets:
        inputs: '<inputs-bucket>'
        inputs_dest: '<inputs-dest-bucket>'
        objectives: '<objectives-bucket>'
        objectives_dest: '<objectives-dest-bucket>'
        data: '<data-bucket>'
        data_dest: '<data-dest-bucket>'
        solutions: '<solutions-bucket>'
    aws_secret_access_key: 'xxxx'
    aws_access_key_id: 'xxxx'
    endpoint_url: 'https://xxx.xxx.xxx'
    region: 'xx-xxxx'

The buckets must be distinct.

Install

To run doframework locally, install with

$ pip install doframework

To run doframework on an OpenShift cluster, cd into your project's folder and run the setup bash script doframework-setup.sh. Make sure to log into your cluster first (see OpenShift Login below).

The setup script doframework-setup.sh will generate the cluster configuration file doframework.yaml in your project's folder.

$ cd <user_project_folder>
$ doframework-setup.sh

To run doframework on a KiND cluster, run the setup bash script with the --kind option.

$ cd <user_project_folder>
$ doframework-setup.sh --kind

Inputs

The application will run end to end and produce results once input.json files are uploaded to <inputs_bucket>. The input.json files provide meta data for the random genration of optimization problems.

Here is an example of an input file (see input samples input_basic.json and input_all.json under ./inputs).

{     
    "f": {
        "vertices": {
            "num": 7,
            "range": [[5.0,20.0],[0.0,10.0]],
        },
        "values": {
            "range": [0.0,5.0]
        },
    },
    "omega" : {
        "ratio": 0.8,
        "scale": 0.01
    },
    "data" : {
        "N": 750,
        "noise": 0.01,
        "policy_num": 2,
        "scale": 0.4
    },
    "input_file_name": "input.json"
}

f:vertices:num: number of vertices in the piece-wise linear graph of f.
f:vertices:range: f domain will be inside this box range.
f:values:range: range of f values.
omega:ratio: vol(O) / vol(dom(f)) >= ratio.
omega:scale: max jitter in sampling feasibility regions (as a ratio of f domain diameter).
data:N: number of data points to sample.
data:noise: response variable $y$ noise.
data:policy_num: number of Gaussians in Gaussian mix distribution of data.
data:scale: max STD of Gaussians in Gaussian mix distribution of data (as a ratio of f domain diameter).

The jupyter notebook ./notebooks/inputs.ipynb allows you to automatically generate input files and upload them to <inputs_bucket>.

It's a good idea to start experimenting on low dimensional problems.

Outputs

doframework produces three types of files as experiment byproducts:

• objective.json: containing information on (f,O,x*)
• data.csv: containing the dataset the algorithm accepts as input
• solution.json: containing the algorithm's predicted optimum

Find sample files under ./outputs/

Test

Run the setup bash script doframework-setup.sh with the --example flag to generate the test script doframework_example.py in your project folder.

$ cd <user_project_folder>
$ doframework-setup.sh --example

Then run the test script locally

$ python doframework_example.py --configs configs.yaml

Make sure to upload input json files to <inputs_bucket>.

Adaptations

You have the option to adapt doframework.yaml to fit your application.

Using the option --project-dir will allow you to mount your application and pip install . it on cluster nodes.

$ doframework-setup.sh --project-dir <relative_dir_path>

Using the option --project-requirements will allow you to specify application requirements and pip install -r then on cluster nodes.

$ doframework-setup.sh --project-requirements <relative_dir_path>

Using the option --project-dep will allow you to specify application dependencies and apt-get install -y them on cluster nodes.

$ doframework-setup.sh --project-dep <dep>

Run

The testing framework is invoked within the user's application.

The user's model will be integrated into the testing framework, when it is decorated with doframework.resolve.

doframework supports the following inputs to the model:

data: 2D np.array with features X = data[ : , :-1] and response variable y = data[ : ,-1].
constraints: linear constraints as a 2D numpy array A. A data point x satisfies the constraints when A[ : , :-1]*x + A[ : ,-1] <= 0.
lower_bound: lower bound per feature variable.
upper_bound: upper bound per feature variable.
init_value: optional initial value.

A doframework experiment runs with doframework.run(). The run() utility accepts the decorated model and a relative path to the configs.yaml. It also accepts the keyword arguments:

objectives: number of objective targets to generate per input file.
datasets: number of datasets to generate per objective target.
feasibility_regions: number of feasibility regions to generate per objective and dataset.
distribute: True to run distributively, False to run sequentially.
logger: True to see logs, False otherwise.
after_idle_for: stop running when event stream is idle after this many seconds.
alg_num_cpus: number of CPUs provisioned for user model per worker.

Here is an example user application.

import doframework as dof

@dof.resolve
def model(data: np.array, constraints: np.array, **kwargs):
    ...    
    return optimal_arg, optimal_val, regression_model

if __name__ == '__main__':

    dof.run(model, 'configs.yaml', objectives=5, datasets=3)

KiND

A KiND cluster simulates a Kubernetes cluster without the need to dockerize. It still requires Docker installed and configured.

Here's how to set up KiND on a Mac. First install Go.

$ export GOPATH="${HOME}/.go"
$ export GOROOT="$(brew --prefix golang)/libexec"
$ export PATH="$PATH:${GOPATH}/bin:${GOROOT}/bin"
$ test -d "${GOPATH}" || mkdir "${GOPATH}"
$ test -d "${GOPATH}/src/github.com" || mkdir -p "${GOPATH}/src/github.com"
$ brew install go

Now install [KiND]

$ brew install kind

Set up the cluster by running the bash script doframework-setup.sh. The --skip flag will tell the script not to generate doframework.yaml again.

$ cd <user_project_folder>
$ doframework-setup.sh --kind  --skip

Run your application module.py on the [KiND] cluster with.

$ ray submit doframework.yaml module.py

At this point you may encounter

RuntimeError: Head node of cluster (rayvens-cluster) not found!

This is typically a resource allocation issue. To investigate this issue, make sure you have the kubectl CLI installed. Look for the rayvens-cluster-head node

$ kubectl get pods -n ray
$ kubectl describe pod rayvens-cluster-head-xxsfh -n ray

The decsribe command will spit out something like

Events:
  Type     Reason            Age                 From               Message
  ----     ------            ----                ----               -------
  Warning  FailedScheduling  24s (x13 over 11m)  default-scheduler  0/1 nodes are available: 1 Insufficient memory.

One quick way to resolve the issue is to go to the Docker Desktop and look under Resources -> Advanced. Play with the number of CPUs / Memory GBs, but don't go crazy, otherwise your machine will start running really ... really ... s-l-o-w.

Any resource allocation changes to doframework.yaml can be updated with

$ ray up doframework.yaml --no-config-cache --yes

Once you're done with KiND, clean up

$ kind delete cluster
$ docker stop registry
$ docker rm registry

OpenShift

This assumes you've already set up your cluster. It assumes you have kubectl, ibmcloud and oc CLI installed.

Log into IBM cloud services and follow the instructions.

$ ibmcloud login --sso

Generate a token for your openshift cluster (good for 24HR). Go to https://cloud.ibm.com (make sure your connection has access rights). Click on OpenShift Web Console (top right). Click on IAM#user and look for Copy Login Command. Copy the login command (oc login ...).

$ oc login --token=shaxxx~xxxx --server=https://xxx.xx-xx.xx.cloud.ibm.com:xxxxx

If you haven't already, define a new ray project [done once].

$ oc new-project ray

If you have already defined the "ray" project, you'll find it under oc projects.

Upload input.json file(s) to your <inputs_bucket>.

Run the bash script doframework-setup.sh to establish the ray cluster. Use the --skip flag to skip generating a new doframework.yaml file.

$ cd <user_project_folder>
$ doframework-setup.sh --skip

Otherwise, in case you are familiar with ray, run instead

$ ray up doframework.yaml --no-config-cache --yes

Submit your application to the ray cluster

$ ray submit doframework.yaml module.py

Changing cluster resource allocation is done through doframework.yaml. Change max_workers to the max CPUs you have available [but keep max_workers of head node at 0]. You can play with resources: requests: cpu, memory and resources: limits: cpu, memory for the head and worker nodes.

After introducing changes to doframework.yaml, update with

$ ray up doframework.yaml --no-config-cache --yes

To see the ray dashboard, open a separate terminal and run

$ oc -n ray port-forward service/rayvens-cluster-head 8265:8265

In your browser, connect to http://localhost:8265.

Some useful health-check commands: check the status of ray pods

$ kubectl get pods -n ray

Check status of the ray head node

$ kubectl describe pod rayvens-cluster-head-xxxxx -n ray

Monitor autoscaling with

$ ray exec doframework.yaml 'tail -n 100 -f /tmp/ray/session_latest/logs/monitor*'

Connect to a terminal on the head node

$ ray attach doframework.yaml
$ ...
$ exit

Get a remote shell to the cluster manually (find head node ID with kubectl describe)

$ kubectl -n ray exec -it rayvens-cluster-head-z97wc -- bash

Shutdown the ray cluster with

$ ray down -y doframework.yaml

CPLEX

In case your application relies on a solver, such as CPLEX, you will need to mount it onto cluster nodes, if you wish to run your application on a cluster.

To allow for a silent installation of CPLEX, create a installer.properties file under your project folder. Add the following lines to your installer.properties file:

INSTALLER_UI=silent
LICENSE_ACCEPTED=true
USER_INSTALL_DIR=/home/ray
INSTALLER_LOCALE=en

Add the following to your doframework.yaml file

file_mounts:
    {
        ...,
        "/home/ray/cplex.bin": "/path/to/ILOG_COS_20.10_LINUX_X86_64.bin",
        ...,
        "/home/ray/installer.properties": "./installer.properties"
    }
file_mounts_sync_continuously: false   
setup_commands:
    - chmod u+x /home/ray/cplex.bin
    - sudo bash /home/ray/cplex.bin -f "/home/ray/installer.properties"
    - echo 'export PATH="$PATH:/home/ray/cplex/bin/x86-64_linux"' >> ~/.bashrc
head_setup_commands:
    ...

Make sure you are mounting the Linux OS ILOG_COS_XX.XX_LINUX_X86_64.bin binary.

Now update your ray cluster

$ ray up doframework.yaml --no-config-cache --yes 

OpenShift Login

This assumes you have ibmcloud and oc CLI set up. Log into IBM cloud services and follow the instructions.

$ ibmcloud login --sso

Generate a token for your openshift cluster (good for 24hrs). Go to https://cloud.ibm.com (make sure your web connection has access rights). Click on OpenShift Web Console (top right). Click on your IAM#user and look for Copy Login Command. Copy the login command [oc login ...]. Now run it

$ oc login --token=shaxxx~xxxx --server=https://xxx.xx-xx.xx.cloud.ibm.com:xxxxx

Issues

Timing

Timing can be a delicate issue when running a doframework experiment. Ray workers may get throttled by too many tasks, which reduces the compute resources per task, effectively choking that worker.

One way to tackle this is to ray submit the application when the <inputs_bucket> is empty and then upload new input.json files at controlled time intervals. Finding the optimal rate may involve some trial and error.

Idle

When an experiment goes idle, or it does not go through full cycle, this may have to do with after_idle_for.

The after_idle_for time window should be sufficiently large for simulation products to make it through to the next stage. This is especially true when optimization problem dimensions are higher, or when your algorithm takes longer.

Autoscaling on OpenShift

If you're having problems with scaling, for instance, the application is only running on the head node, you can start by checking the ray logs with

$ ray exec doframework.yaml 'tail -n 100 -f /tmp/ray/session_latest/logs/monitor*'

or just the error logs

$ ray exec doframework.yaml 'tail -n 100 -f /tmp/ray/session_latest/logs/monitor.err'

You may see an error

mkdir: cannot create directory '/home/ray/..': Permission denied

The problem is that OpenShift generates worker nodes on random user ids which do not have permissions for file mounts. To fix the permissions issue, run

$ oc adm policy add-scc-to-group anyuid system:authenticated

---

Consumed Uploads

When files magically disappear when you upload them to the COS buckets, it may be that some kamel processes are still running, consuming any uploaded file.

You may be able to identify these kamel processes as source-type processes with

$ kubectl get all

To delete, use

$ kamel delete source-data source-inputs 

If that doesn't work, try shutting down ray.

---

SSH Unavailable

Running the bash script doframework-setup.sh, or the ray up command, you may encounter the following

Error from server (BadRequest): pod rayvens-cluster-head-mcsfh does not have a host assigned
    SSH still not available (Exit Status 1): kubectl -n ray exec -it rayvens-cluster-head-mcsfh -- bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && (uptime)', retrying in 5 seconds.

Just wait. Eventually it'll go through.

---

Login

When running doframework-setup.sh, you may see

--- creating namespace
error: You must be logged in to the server (Unauthorized)
--- installing Kamel operator
error: You must be logged in to the server (Unauthorized)
error: failed to create clusterrolebinding: Unauthorized
error: You must be logged in to the server (Unauthorized)

This is an indication that you haven't logged into your cluster (see login instructions above).

The doframework.yaml was generated, though!

---

rayvens Image Update

Any updates to the rayvens image you wish to make can be editted in doframework.yaml under containers: ... image: quay.io/ibm/rayvens:0.X.X.

---

Nothing Generated

If you only see kamel subprocesses after hitting ray submit, it's likely you haven't uploaded input.json files to <inputs_bucket>. You can upload then now -- no need to stop the experiment.

---

RayOutOfMemoryError

You may run into insufficient memory errors such as RayOutOfMemoryError: More than 95% of the memory on node rayvens-cluster-head-xxxxx is used.

Make sure you have enough memory on your cluster and increase memory allowance in doframework.yaml under resources:requests:memory: and resources:limits:memory:.