Using the Kubeflow Pipelines Benchmark Scripts
This guide explains the Kubeflow Pipelines benchmark scripts and demonstrates how to use them to collect basic performance data of a given Kubeflow Pipelines instance.
About the Kubeflow Pipelines benchmark script
The Kubeflow Pipelines benchmark scripts simulate typical workloads and record performance metrics, such as server latencies and pipeline run durations. To simulate a typical workload, the benchmark script uploads a pipeline manifest file to a Kubeflow Pipelines instance as a pipeline or a pipeline version, and creates multiple runs simultaneously.
You can specify the pipelines manifest used in the benchmark script, or you can use one of the preloaded samples pipelines. For example, the preloaded samples in Kubeflow pipelines can be used. Moreover, it is also a good practice to use a pipeline manifest that is representative of your particular use case. For example, if your Kubeflow Pipelines cluster is mainly used for pipelines of image recognition tasks, then it would be desirable to use an image recognition pipeline in the benchmark scripts.
After a proper pipeline is chosen, the benchmark scripts will run it multiple times simultaneously as mentioned before. Among all the operations that the Kubeflow Pipelines can perform, running a pipeline is arguably the most unpredictable and costly one. Other operations, such as creating a pipeline (version) or creating an experiment, usually induce a predictable and moderate cost. For example, creating a pipeline version will introduce a new row in the pipeline versions table and a new file in minio server. The new file’s size depends on the pipeline version’s manifest. If we exclude the rare case of an extremely large manifest and assume an average sized manifest for each created pipeline version, the total cost of creating a pipeline version grows linearly with the number of pipeline versions. However, on the other hand, the cost of running a pipeline or a pipeline version involves much more uncertainty and sometimes quite a high cost. A pipeline or a pipeline version can have arbitrary components and hence running a pipeline or a pipeline version can incur arbitrary time and space complexities. For example, a step in a pipeline can use a customized container image which performs a super expensive training task. In addition, the runs in a Kubeflow Pipelines instance also consume more DB space than pipelines, pipeline versions, experiments, etc. Therefore, in order to understand the performance and scalability pain points in a Kubeflow Pipelines instance, it is more effective to focus on the run operation in workloads.
Prerequisites for running benchmark scripts
To run the provided benchmark scripts, you need the following:
- A Jupyter notebook environment with access to the Kubeflow Pipelines API on your Kubeflow Pipelines cluster. For example, you must be able to call the CREATE, GET, DELETE, and LIST methods of the pipeline, pipeline version, run, job, and experiment services from your Jupyter notebook environment.
- A Kubeflow Pipelines cluster. If you do not have a Kubeflow Pipelines cluster, learn more about your options for installing Kubeflow Pipelines.
- A pipeline manifest. For example, this guide uses the taxi_updated_pool.yaml pipeline manifest file.
One way of setting up everything and running a benchmark script is shown below as an example.
Running the benchmark scripts
Use the following instructions to run the benchmark script on your Kubeflow Pipelines cluster.
Download the the run_service_api.ipynb benchmark script in your Jupyter notebook environment.
Open run_service_api.ipynb in the local Jupyter notebook. This benchmark script:
- Creates a new pipeline.
- Uses the default pipeline version of this pipeline to create multiple runs.
- Records the number of successful runs.
- Records the duration of each of the successful runs.
- Records the latency of CREATE, GET, DELETE.
- Cleans up the pipeline and its default pipeline version, the experiment and the runs.
In the benchmark script, enter the correct values for host, pipeline_file_url, num_runs, run_status_polling_interval_sec in the benchmark script.
host: The URL of the API server in your Kubeflow Pipelines cluster.
pipeline_file_url: The URL of the pipeline manifest file to use in your benchmark. taxi_updated_pool.yaml is used in this example. This example pipeline makes use of nodeSelector to explicitly schedule the runs of this pipeline onto the node pool named
pool-1. If you use the taxi_updated_pool.yaml pipeline manifest in your benchmarks, ensure that a node pool named
pool-1exists in your cluster.
NOTE: Do not use the value ‘https://storage.cloud.google.com/ml-pipeline/sample-benchmark/taxi_updated_pool.yaml' when running your benchmarks. Addresses that start with ‘storage.cloud.google.com’ incur a redirect that doesn’t work well with Kubeflow Pipelines.
num_runs: Specifies how many runs will be created in the benchmark script, and is a direct indicator of the simulated workload.
NOTE: Running the benchmark script on a cloud service incurs charges due to the consumption of cloud resources.
run_status_polling_interval_sec: Sets the time interval between two adjacent pollings of the run status. When a run reaches the success status, the run duration is recorded. Only the durations of successful runs are recorded.
After the parameters are properly set, you can run the benchmark script in the notebook.
The following snapshot shows the results of running the benchmark script run_service_api.ipynb using the taxi_updated_pool.yaml pipeline manifest for 50 runs on a Kubernetes cluster with two node pools. Each node pool has three nodes of machine type ‘n1-standard-8’.
Interpreting the results
In the above example output, there are two types of plots. One is the distribution plot, for latency and duration measurement; the other is the count plot, for counting succeeded and failed runs. The reading of those plots is in general straightforward.
In a count plot, the x-axis represents the possible run status: success or fail; and the y-axis shows how many runs fall into certain status respectively.
In a distribution plot, both histogram plot and rug plot are shown. In addition, it is also possible to show KDE (Kernel Density Estimate) plot. If a KDE plot is desirable, use ‘kde=True’ in the distplot() method.
Tuning with different configurations
The above example shows one performance report via running the benchmark script. In fact, there are multiple ways of tuning the pipeline and/or the Kubernetes clusters to acquire performance reports. The common ways are trying with
Different cluster sizes/zones, different numbers of pools in a cluster, different numbers of nodes in each pool, different node configurations (for example, RAM and disk configuration for each node, CPU or GPU configuration for each node)
Different number of runs
Different sizes/complexities of a pipeline, for example, the number of steps in a pipeline
Different running configurations, for example, specifying the node pools for running components or pipelines
Limitations and future work
When the benchmark script is tuned to generate a moderate workload, for example, 50 runs in the above example, the latency and run duration measurements can be made properly. However, it is also interesting to see how a Kubeflow Pipelines instance behaves or breaks under some extremely heavy workloads, or in other words, to probe the Kubeflow Pipelines instance. The example benchmark script can be used for that purpose as well. In that case, the measurement plots of the benchmark script are no longer the expected output. Instead, the errors and error logs provide information on the performance and scalability of the Kubeflow Pipelines deployment. With them, bugs and pain points can be discovered and then fixed. Moreover, when probing the Kubeflow Pipelines instance with extreme workloads, it will be really helpful to add internal monitoring to the server code to track server performance. For example, in the future, it would be desirable to use Prometheus to track and visualize metrics for Kubeflow Pipelines servers.
The internal performance monitoring inside the servers is complementary to the performance measurement from the client-side. When the example benchmark script measures the latencies from the client-side, the resulting measurements depend on both the Kubeflow Pipelines instance and the network transmission. On the other hand, the internal monitoring focuses on the actual processing cost inside the server given certain requests. Therefore, having both the client-side measurements and server side monitoring are useful in profiling accurately the performance and scalability of Kubeflow Pipelines.
If you run into any issues with the benchmark script and have any suggestions in profiling the performance and scalability of Kubeflow Pipelines, open an issue with us.
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