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Running on Google Cloud ML Engine

The Tensorflow Object Detection API supports distributed training on Google Cloud ML Engine. This section documents instructions on how to train and evaluate your model using Cloud ML. The reader should complete the following prerequistes:

  1. The reader has created and configured a project on Google Cloud Platform. See the Cloud ML quick start guide.
  2. The reader has installed the Tensorflow Object Detection API as documented in the installation instructions.
  3. The reader has a valid data set and stored it in a Google Cloud Storage bucket. See this page for instructions on how to generate a dataset for the PASCAL VOC challenge or the Oxford-IIIT Pet dataset.
  4. The reader has configured a valid Object Detection pipeline, and stored it in a Google Cloud Storage bucket. See this page for details on how to write a pipeline configuration.

Additionally, it is recommended users test their job by running training and evaluation jobs for a few iterations locally on their own machines.

Packaging

In order to run the Tensorflow Object Detection API on Cloud ML, it must be packaged (along with it's TF-Slim dependency and the pycocotools library). The required packages can be created with the following command

# From tensorflow/models/research/
bash object_detection/dataset_tools/create_pycocotools_package.sh /tmp/pycocotools
python setup.py sdist
(cd slim && python setup.py sdist)

This will create python packages dist/object_detection-0.1.tar.gz, slim/dist/slim-0.1.tar.gz, and /tmp/pycocotools/pycocotools-2.0.tar.gz.

Running a Multiworker (GPU) Training Job on CMLE

Google Cloud ML requires a YAML configuration file for a multiworker training job using GPUs. A sample YAML file is given below:

trainingInput:
  runtimeVersion: "1.9"
  scaleTier: CUSTOM
  masterType: standard_gpu
  workerCount: 9
  workerType: standard_gpu
  parameterServerCount: 3
  parameterServerType: standard


Please keep the following guidelines in mind when writing the YAML configuration:

  • A job with n workers will have n + 1 training machines (n workers + 1 master).
  • The number of parameters servers used should be an odd number to prevent a parameter server from storing only weight variables or only bias variables (due to round robin parameter scheduling).
  • The learning rate in the training config should be decreased when using a larger number of workers. Some experimentation is required to find the optimal learning rate.

The YAML file should be saved on the local machine (not on GCP). Once it has been written, a user can start a training job on Cloud ML Engine using the following command:

# From tensorflow/models/research/
gcloud ml-engine jobs submit training object_detection_`date +%m_%d_%Y_%H_%M_%S` \
    --runtime-version 1.9 \
    --job-dir=gs://${MODEL_DIR} \
    --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
    --module-name object_detection.model_main \
    --region us-central1 \
    --config ${PATH_TO_LOCAL_YAML_FILE} \
    -- \
    --model_dir=gs://${MODEL_DIR} \
    --pipeline_config_path=gs://${PIPELINE_CONFIG_PATH}

Where ${PATH_TO_LOCAL_YAML_FILE} is the local path to the YAML configuration, gs://${MODEL_DIR} specifies the directory on Google Cloud Storage where the training checkpoints and events will be written to and gs://${PIPELINE_CONFIG_PATH} points to the pipeline configuration stored on Google Cloud Storage.

Users can monitor the progress of their training job on the ML Engine Dashboard.

Note: This sample is supported for use with 1.9 runtime version.

Running a TPU Training Job on CMLE

Launching a training job with a TPU compatible pipeline config requires using a similar command:

gcloud ml-engine jobs submit training `whoami`_object_detection_`date +%m_%d_%Y_%H_%M_%S` \
--job-dir=gs://${MODEL_DIR} \
--packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
--module-name object_detection.model_tpu_main \
--runtime-version 1.9 \
--scale-tier BASIC_TPU \
--region us-central1 \
-- \
--tpu_zone us-central1 \
--model_dir=gs://${MODEL_DIR} \
--pipeline_config_path=gs://${PIPELINE_CONFIG_PATH}

In contrast with the GPU training command, there is no need to specify a YAML file and we point to the object_detection.model_tpu_main binary instead of object_detection.model_main. We must also now set scale-tier to be BASIC_TPU and provide a tpu_zone. Finally as before pipeline_config_path points to a points to the pipeline configuration stored on Google Cloud Storage (but is now must be a TPU compatible model).

Running an Evaluation Job on CMLE

Note: You only need to do this when using TPU for training as it does not interleave evaluation during training as in the case of Multiworker GPU training.

Evaluation jobs run on a single machine, so it is not necessary to write a YAML configuration for evaluation. Run the following command to start the evaluation job:

gcloud ml-engine jobs submit training object_detection_eval_`date +%m_%d_%Y_%H_%M_%S` \
    --runtime-version 1.9 \
    --job-dir=gs://${MODEL_DIR} \
    --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
    --module-name object_detection.model_main \
    --region us-central1 \
    --scale-tier BASIC_GPU \
    -- \
    --model_dir=gs://${MODEL_DIR} \
    --pipeline_config_path=gs://${PIPELINE_CONFIG_PATH} \
    --checkpoint_dir=gs://${MODEL_DIR}

Where gs://${MODEL_DIR} points to the directory on Google Cloud Storage where training checkpoints are saved (same as the training job), as well as to where evaluation events will be saved on Google Cloud Storage and gs://${PIPELINE_CONFIG_PATH} points to where the pipeline configuration is stored on Google Cloud Storage.

Typically one starts an evaluation job concurrently with the training job. Note that we do not support running evaluation on TPU, so the above command line for launching evaluation jobs is the same whether you are training on GPU or TPU.

Running Tensorboard

You can run Tensorboard locally on your own machine to view progress of your training and eval jobs on Google Cloud ML. Run the following command to start Tensorboard:

tensorboard --logdir=gs://${YOUR_CLOUD_BUCKET}

Note it may Tensorboard a few minutes to populate with results.