Dataflow ML Starter Project

Summary

This repo contains a simple Beam RunInference project, which demonstrates how to run this Beam pipeline using DirectRunner to develop and test and launch the production job using DataflowRunner on either CPUs or GPUs. It can be served as a boilerplate to create a new Dataflow ML project.

This is not an officially supported Google product.

Prerequisites

conda
git
make
docker
gcloud
python3-venv

sudo apt-get update
sudo apt-get install -y python3-venv git make time wget

Install Docker on Debian: https://docs.docker.com/engine/install/debian/ Without sudo,

sudo groupadd docker
sudo usermod -aG docker $USER
newgrp docker

Directory structure

.
├── LICENSE
├── .env.template           <- A configuration template file to define environment-specific variables
├── Makefile                <- Makefile with commands and type `make` to get the command list
├── README.md               <- The top-level README for developers using this project
├── data                    <- Any data for local development and testing
│   └── openimage_10.txt    <- A sample test data that contains the gcs file path for each image
├── pyproject.toml          <- The TOML format Python project configuration file
├── requirements.dev.txt    <- Packages for the development such as `pytest`
├── requirements.prod.txt   <- Packages for the production environment and produces `requirements.txt`
├── scripts                 <- utility bash scripts
├── setup.py                <- Used in `python setup.py sdist` to create the multi-file python package
├── src                     <- Source code for use in this project
│   ├── __init__.py         <- Makes src a Python module
│   ├── config.py           <- `pydantic` model classes to define sources, sinks, and models
│   ├── pipeline.py         <- Builds the Beam RunInference pipeline
│   └── run.py              <- A run module to parse the command options and run the Beam pipeline
├── tensor_rt.Dockerfile    <- A Dockerfile to create a customer container with TensorRT
└── tests                   <- Tests to cover local developments
    └── test_pipeline.py

User Guide

This process is only tested on GCE VMs with Debian.

Step 1: Clone this repo and edit .env

git clone https://github.com/liferoad/df-ml-starter.git
cd df-ml-starter
cp .env.template .env

Use your editor to fill in the information in the .env file.

If you want to try other pytorch models under gs://apache-beam-ml/models/,

gsutil ls gs://apache-beam-ml/models/

you need to edit config.py to add more model names.

It is highly recommended to run through this guide once using mobilenet_v2 for image classification.

All the useful actions can be triggered using make:

$ make

  make targets:

     check-beam                Check whether Beam is installed on GPU using VM with Custom Container
     check-pipeline            Check whether the Beam pipeline can run on GPU using VM with Custom Container and DirectRunner
     check-tf-gpu              Check whether Tensorflow works on GPU using VM with Custom Container
     check-torch-gpu           Check whether PyTorch works on GPU using VM with Custom Container
     clean                     Remove virtual environment, downloaded models, etc
     clean-lite                Remove pycache files, pytest files, etc
     create-vm                 Create a VM with GPU to test the docker image
     delete-vm                 Delete a VM
     docker                    Build a custom docker image and push it to Artifact Registry
     format                    Run formatter on source code
     help                      Print this help
     init                      Init virtual environment
     init-venv                 Create virtual environment in venv folder
     lint                      Run linter on source code
     run-df-cpu                Run a Dataflow job with CPUs and without Custom Container
     run-df-gpu                Run a Dataflow job using the custom container with GPUs
     run-direct                Run a local test with DirectRunner
     test                      Run tests

Pipeline Details

This project contains a simple RunInference Beam pipeline,

Read the GCS file that contains image GCS paths (beam.io.ReadFromText) ->
Pre-process the input image, run a Pytorch or Tensorflow image classification model, post-process the results -->
Write all predictions back to the GCS output file

The input image data is created from the ImageNet images.

The entire code flows in this way:

.env defines the environment variables such as Torch or TF models, model name, Dockerfile template, etc.
Makefile reads these environment variables from .env and based on the make targets, it can run tests, build docker images, run Dataflow jobs with CPUs or GPUs.
run.py is called by theMakefile targets to parse the input arguments and set ModelConfig, SourceConfig, and SinkConfig defined in config.py, then calls build_pipeline from pipeline.py to build the final Beam pipeline

To customize the pipeline, modify build_pipeline in pipeline.py. It defines how to read the image data from TextIO, pre-process the images, score them, post-process the predictions, and at last save the results using TextIO.

config.py contains a set of pydantic models to specify the configurations for sources, sinks, and models and validate them. Users can easily add more Pytorch classification models. Here contains more examples.

`.env` Details

Most of options are configured by the .env file. Below is one example to use the Pytorch mobilenet_v2 model for image classification:

################################################################################
### PYTHON SDK SETTINGS
################################################################################
PYTHON_VERSION=3.10
BEAM_VERSION=2.48.0
DOCKERFILE_TEMPLATE=pytorch_gpu.Dockerfile
DOCKER_CREDENTIAL_REGISTRIES="us-docker.pkg.dev"
################################################################################
### GCP SETTINGS
################################################################################
PROJECT_ID=apache-beam-testing
REGION=us-central1
DISK_SIZE_GB=50
MACHINE_TYPE=n1-standard-2
VM_NAME=beam-ml-starter-gpu-1
################################################################################
### DATAFLOW JOB SETTINGS
################################################################################
STAGING_LOCATION=gs://temp-storage-for-perf-tests/loadtests
TEMP_LOCATION=gs://temp-storage-for-perf-tests/loadtests
CUSTOM_CONTAINER_IMAGE=us-docker.pkg.dev/apache-beam-testing/xqhu/pytorch_gpu:latest
SERVICE_OPTIONS="worker_accelerator=type:nvidia-tesla-t4;count:1;install-nvidia-driver"
################################################################################
### DATAFLOW JOB MODEL SETTINGS
################################################################################
MODEL_STATE_DICT_PATH="gs://apache-beam-ml/models/torchvision.models.mobilenet_v2.pth"
MODEL_NAME=mobilenet_v2
################################################################################
### DATAFLOW JOB INPUT&OUTPUT SETTINGS
################################################################################
INPUT_DATA="gs://apache-beam-ml/testing/inputs/openimage_50k_benchmark.txt"
OUTPUT_DATA="gs://temp-storage-for-end-to-end-tests/torch/result_gpu_xqhu.txt"

Most of options are intuitive. DOCKERFILE_TEMPLATE provides the Dockerfile template that will be used to build the custom container. CUSTOM_CONTAINER_IMAGE is the Docker image storage location. In default, we use GPUs (i.e., T4) with the custom container defined by SERVICE_OPTIONS for this Dataflow job. MODEL_STATE_DICT_PATH and MODEL_NAME defines the Pytorch model information. For this Beam pipeline, we use the GCS buckets for input and output data.

Custom container

We provide three Dockerfile templates as examples to show how to build a custom container:

Name	Description
tensor_rt.Dockerfile	TensorRT + Python 3.8
pytorch_gpu.Dockerfile	Pytorch with GPUs + Python 3.10
tensorflow_gpu.Dockerfile	Tensorflow with GPUs + Python 3.8

Note You should keep your local Python environment same as the one defined in Dockerfile. These Dockerfile examples should be customized based on your project requirements.

Step 2: Initialize a venv for your project

make init
source venv/bin/activate

Note you must make sure the base Python version matches the version defined in .env. The base python can be configured using conda, e.g.,

conda create --name py38 python=3.8
conda activate py38

If anything goes wrong, you can rebuild the venv,

make clean
make init

To check the venv is created correctly,

make test

Step 3: Test the Beam pipeline using DirectRunner

DirectRunner provides the local way to validate whether your Beam pipeline works correctly,

make run-direct

Step 4: Run the Beam pipeline using DataflowRunner

To run a Dataflow job using CPUs without a custom container, try this:

make run-df-cpu

When using resnet101 to score 50k images, the job took ~30m and cost ~1.4$ with resnet101. For mobilenet_v2, it cost 0.5$ with ~22m. Note the cost and time depends on your job settings and the regions.

Build Custom Container with GPU supports

Running Dataflow GPU jobs needs to build a custom container,

make docker

The final docker image will be pushed to Artifact Registry. For this guide, we use tensor_rt.Dockerfile to demonstrate how to build the container to run the inference on GPUs with TensorRT. Note given the base image issue for TensorRT, only Python 3.8 should be used when running GPUs. You can follow this doc to create other GPU containers.

Test Custom Container using GCE VM

It is highly recommended to test your custom container locally before running it with Dataflow,

make create-vm

This creates a GCE VM with a T4 GPU and installs nvidia driver. It will take a few minutes. Now using this VM allows you to test whether the docker container is built correctly,

# check whether Beam is installed in Custom Container
make check-beam
# check whether Tensorflow can use GPUs in Custom Container
make check-tf-gpu
# check whether PyTorch can use GPUs in Custom Container
make check-torch-gpu
# check whether DirectRunner can run on GPUs in Custom Container
make check-pipeline

Note these commands will take some time to download your container. You should see outputs similar to these:

Checking Python version on VM...
Python 3.8.10
Checking venv exists on VM...
python3-venv/now 3.8.2-0ubuntu2 amd64 [installed,local]
Checking Beam Version on VM...
2.48.0
Checking Tensorflow on GPU...
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Checking PyTorch on GPU...
True
Tesla T4
...
The DirectRunner run succeeded on GPU!

The last line will display whether the pipeline can run successfully on VM GPUs in Custom Container.

After finishing tests, you can remove this VM,

make delete-vm

Run the Beam pipeline using DataflowRunner on GPUs

This runs a Dataflow job with GPUs,

make run-df-gpu

When using resnet101 to score 50k images, the job took ~1h and cost ~0.5$ with resnet101. For mobilenet_v2, it cost 0.05$ with ~1h. Note the cost and time depends on your job settings and the regions.

FAQ

Permission error when using any GCP command

gcloud auth login
gcloud auth application-default login
# replace it with the appropriate region
gcloud auth configure-docker us-docker.pkg.dev
# or if you use docker-credential-gcr
docker-credential-gcr configure-docker --registries=us-docker.pkg.dev

Make sure you specify the appropriate region for Artifact Registry.

AttributeError: Can't get attribute 'default_tensor_inference_fn'

AttributeError: Can't get attribute 'default_tensor_inference_fn' on <module 'apache_beam.ml.inference.pytorch_inference' from '/usr/local/lib/python3.8/dist-packages/apache_beam/ml/inference/pytorch_inference.py'>

This error indicates your Dataflow job uses the old Beam SDK. If you use --sdk_location container, it means your Docker container has the old Beam SDK.

QUOTA_EXCEEDED

Startup of the worker pool in zone us-central1-a failed to bring up any of the desired 1 workers. Please refer to https://cloud.google.com/dataflow/docs/guides/common-errors#worker-pool-failure for help troubleshooting. QUOTA_EXCEEDED: Instance 'benchmark-tests-pytorch-i-05041052-ufe3-harness-ww4n' creation failed: Quota 'NVIDIA_T4_GPUS' exceeded. Limit: 32.0 in region us-central1.

Please check https://cloud.google.com/compute/docs/regions-zones and select another zone with your desired machine type to relaunch the Dataflow job.

ERROR: failed to solve: failed to fetch anonymous token: unexpected status: 401 Unauthorized

failed to solve with frontend dockerfile.v0: failed to create LLB definition: failed to authorize: rpc error: code = Unknown desc = failed to fetch anonymous token: unexpected status: 401 Unauthorized

Restarting the docker could resolve this issue.

Check the built container

docker run --rm -it --entrypoint=/bin/bash $CUSTOM_CONTAINER_IMAGE

Errors could happen when the custom container is not built correctly

Check Cloud Logs, pay attention to INFO for Worker logs:

INFO 2023-05-06T15:13:01.237562007Z The virtual environment was not created successfully because ensurepip is not
INFO 2023-05-06T15:13:01.237601258Z available. On Debian/Ubuntu systems, you need to install the python3-venv
INFO 2023-05-06T15:13:01.237607714Z package using the following command.

or (might be caused by building the container on Mac OS)

exec /opt/apache/beam/boot: no such file or directory

liferoad/dataflow-ml-starter

Dataflow ML Starter Project

Summary

Prerequisites

Directory structure

User Guide

Step 1: Clone this repo and edit .env

Pipeline Details

`.env` Details

Custom container

Step 2: Initialize a venv for your project

Step 3: Test the Beam pipeline using DirectRunner

Step 4: Run the Beam pipeline using DataflowRunner

Build Custom Container with GPU supports

Test Custom Container using GCE VM

Run the Beam pipeline using DataflowRunner on GPUs

FAQ

Permission error when using any GCP command

AttributeError: Can't get attribute 'default_tensor_inference_fn'

QUOTA_EXCEEDED

ERROR: failed to solve: failed to fetch anonymous token: unexpected status: 401 Unauthorized

Check the built container

Errors could happen when the custom container is not built correctly

Useful Links

liferoad/dataflow-ml-starter

Dataflow ML Starter Project

Summary

Prerequisites

Directory structure

User Guide

Step 1: Clone this repo and edit .env

Pipeline Details

.env Details

Custom container

Step 2: Initialize a venv for your project

Step 3: Test the Beam pipeline using DirectRunner

Step 4: Run the Beam pipeline using DataflowRunner

Build Custom Container with GPU supports

Test Custom Container using GCE VM

Run the Beam pipeline using DataflowRunner on GPUs

FAQ

Permission error when using any GCP command

AttributeError: Can't get attribute 'default_tensor_inference_fn'

QUOTA_EXCEEDED

ERROR: failed to solve: failed to fetch anonymous token: unexpected status: 401 Unauthorized

Check the built container

Errors could happen when the custom container is not built correctly

Useful Links

`.env` Details