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Orchestrate a tracked Training Run

In this part of the docs, we will walk through how to execute a training job on a Kubernetes cluster using SigOpt Orchestrate. SigOpt Orchestrate should now be connected to a Kubernetes cluster of your choice.

Set Up Back to Top

If you haven't connected to a cluster yet, you can launch a cluster on AWS, connect to an existing Kubernetes cluster, or connect to an existing, shared K8s cluster.

Then, test whether or not you are connected to a cluster with SigOpt Orchestrate by running:

orchestrate cluster test

SigOpt Orchestrate will output:

Successfully connected to kubernetes cluster: tiny-cluster

If you're using a custom Kubernetes cluster, you will need to install plugins to get the controller image working:

orchestrate cluster install-plugins

SigOpt Orchestrate works when all of the files for your model are located in the same folder. So, please create an example directory (mkdir), and then change directories (cd) into that directory:

mkdir example && cd example

Then auto-generate templates for a Dockerfile and an SigOpt Orchestrate Configuration YAML file

orchestrate init

Next, you will create some files and put them in this example directory.

Dockerfile: Define your model environment Back to Top

For the tutorial, we'll be using a very simple Dockerfile. For instructions on how to specify more requirements see our guide on Dockerfiles. Please copy and paste the following snippet into the autogenerated file named Dockerfile.

FROM python:3.9

RUN pip install --no-cache-dir sigopt

RUN pip install --no-cache-dir scipy==1.7.1
RUN pip install --no-cache-dir scikit-learn==0.24.2
RUN pip install --no-cache-dir numpy==1.21.2

COPY . /sigopt
WORKDIR /sigopt

Define a Model Back to Top

This code defines a simple SGDClassifier model that measures accuracy classifying labels for the Iris flower dataset. Copy and paste the snippet below to a file titled model.py. Note the snippet below uses SigOpt's Runs to track model attributes.

# model.py

# SGDClassifier example written to run with SigOpt Orchestrate

# You'll use the SigOpt Training Runs API to communicate with SigOpt Orchestrate
# while your model is running on the cluster.
import sigopt


# These packages will need to be installed in order to run your model.
# To do this, define a requirements.txt file, and provide instructions
from sklearn import datasets
from sklearn.linear_model  import SGDClassifier
from sklearn.model_selection import cross_val_score
import numpy


# Here, we're using the standard Iris flower dataset:
# https://en.wikipedia.org/wiki/Iris_flower_data_set
def load_data():
  iris = datasets.load_iris()
  return (iris.data, iris.target)


# SigOpt Orchestrate handles the interaction with the SigOpt API.
# Each time this file is executed on the cluster, SigOpt Orchestrate
# will automatically create a Suggestion and populate new
# hyperparameter assignments.
def evaluate_model(X, y):
  # SigOpt Training Runs reads new assignments for all of the parameters
  # that you define in your experiment configuration file. If you did not
  # define a parameter in your experiment configuration file, this function
  # will fall back to the provided default value.
  classifier = SGDClassifier(
    loss=sigopt.get_parameter('loss', default='log'),
    penalty=sigopt.get_parameter('penalty', default='elasticnet'),
    alpha=10**sigopt.get_parameter('log_alpha', -4),
    l1_ratio=sigopt.get_parameter('l1_ratio', 0.15),
    max_iter=sigopt.get_parameter('max_iter', default=1000),
    tol=sigopt.get_parameter('tol', default=0.001),
  )
  cv_accuracies = cross_val_score(classifier, X, y, cv=5)
  return (numpy.mean(cv_accuracies), numpy.std(cv_accuracies))


# Each execution of model.py should represent one evaluation of your model.
# When this file is run, it loads data, evaluates the model using assignments
if __name__ == "__main__":
  (X, y) = load_data()
  (mean, std) = evaluate_model(X=X, y=y)
  print('Accuracy: {} +/- {}'.format(mean, std))
  sigopt.log
  sigopt.log_metric('accuracy', mean, std)

Notes on implementing your model Back to Top

When your model runs on a node in the cluster it can use all of the CPUs on that node with multithreading. This is good for performance if your model is the only process running on the node, but in many cases it will need to share those CPUs with other processes (ex. other model runs). For this reason it is a good idea to limit the number of threads that your model library can create in conjunction with the amount of cpu specified in your resources_per_model. This varies by implementation, but some common libraries are listed below:

Numpy

Threads spawned by Numpy can be configured with environment variables, which can be set in your Dockerfile:

ENV MKL_NUM_THREADS=N
ENV NUMEXPR_NUM_THREADS=N
ENV OMP_NUM_THREADS=N

Tensorflow/Keras

Can be configured in the Tensorflow module, see: https://www.tensorflow.org/api_docs/python/tf/config/threading

PyTorch

Can be configured in the PyTorch module, see: https://pytorch.org/docs/stable/generated/torch.set_num_threads.html

Create an Orchestrate Configuration File Back to Top

Here's a sample SigOpt Orchestrate configuration file that specifies a training run for the model.py specified above on one CPU.

Please copy and paste the following to the file named orchestrate.yml.

resources_per_model:
  requests:
    cpu: 0.5
    memory: 512Mi
  limits:
    cpu: 1
    memory: 512Mi
# We don't need any GPUs for this example, so we'll leave this commented out
#  gpus: 1

# Choose a descriptive name for your model
name: Orchestrate SGD Classifier (python)

# Here, we run the model
run: python model.py

# SigOpt Orchestrate creates a container for your model. Since we're using an AWS
# cluster, it's easy to securely store the model in the Amazon Elastic Container Registry.
# Choose a descriptive and unique name for each new experiment configuration file.
image: orchestrate/sgd-classifier

Execute Back to Top

So far, SigOpt Orchestrate is connected to your cluster, the Dockerfile defines your model requirements, and you've updated the SigOpt Orchestrate configuration file. Now is a good time to test that you can create your run and verify that your model code works in the cluster.

orchestrate test-run

Once you are confident that your runs will finish you can kick one off in the background and continue your experimentation.

orchestrate run

Monitor Back to Top

You can monitor the status of SigOpt Orchestrate Runs from the command line using the run name or the Run ID.

orchestrate status run/99999
run/99999:
    Run Name: run-jwc5fyyr
    Link: https://app.sigopt.com/run/99999
    State: failed
    Experiment link: https://app.sigopt.com/experiment/111111
    Suggestion id: 42613040
    Observation id: 28531050
    Pod phase: Deleted
    Node name: ip-111-11-11-111.us-west-2.compute.internal
    Follow logs: orchestrate kubectl logs pod/run-jwc5fyyr -f

The status will include a command that you can run in your terminal to follow the logs as they are generated by your code.

You can see all of the activity on your cluster with the following command:

You are currently connected to the cluster: test-cluster
Experiments: 1 total
    Experiment 374876: 45 runs
        Succeeded: 41 runs
        Pending: 3 runs
            run-868o4gou	Pending
            run-shb0yvxd	Pending
            run-t8co05nt	Pending
    Running: 1 runs
            run-dba7gdlc	Running
Nodes: 1 total
    ip-111-11-11-111.us-west-2.compute.internal:
        cpu:
            Allocatable: 1.93 CPU
            Requests: 250.00 mCPU, 12.95 %
            Limits: 250.00 mCPU, 12.95 %
        memory:
            Allocatable: 7.44 GB
            Requests: 1.07 GB, 14.44 %
            Limits: 1.07 GB, 14.44 %
orchestrate cluster status

Monitor progress in the web app Back to Top

You can monitor training run progress onhttps://app.sigopt.com/run/[id].

At the top of the page under the training run name, you’ll find the status of the run. Once the run is completed, the Performance and Metric sections will fill in.

Stop Back to Top

You can stop an in progress run and mark it as failed on SigOpt website by archiving it.

orchestrate stop --run <run-id>
orchestrate stop --run run/<run-id>
orchestrate stop --run <pod-name>