Skip to content

Latest commit

 

History

History
63 lines (49 loc) · 2.1 KB

submit_job.md

File metadata and controls

63 lines (49 loc) · 2.1 KB
Raw

Launch a distributed object detection training job

Requirements

  • Docker
  • Docker Registry
  • Object Detection Training Docker Image

Build the TensorFlow object detection training image, or use the pre-built image lcastell/pets_object_detection in Docker hub.

To build the image:

First copy the Dockerfile file from ./docker directory into your $HOME path

# from your $HOME directory
docker build --pull -t $USER/pets_object_detection -f ./Dockerfile.training .

Push the image to your docker registry

# from your $HOME directory
docker tag  $USER/pets_object_detection  <your_server:your_port>/pets_object_detection
docker push <your_server:your_port>/pets_object_detection

Create training TF-Job deployment and launching it

# from the ks-app directory

PIPELINE_CONFIG_PATH="${MOUNT_PATH}/faster_rcnn_resnet101_pets.config"
TRAINING_DIR="${MOUNT_PATH}/train"

ks param set tf-training-job image ${OBJ_DETECTION_IMAGE}
ks param set tf-training-job mountPath ${MOUNT_PATH}
ks param set tf-training-job pvc ${PVC}
ks param set tf-training-job numPs 1
ks param set tf-training-job numWorkers 1
ks param set tf-training-job pipelineConfigPath ${PIPELINE_CONFIG_PATH}
ks param set tf-training-job trainDir ${TRAINING_DIR}

ks apply ${ENV} -c tf-training-job

For GPU support set the numGpu param like:

# from the ks-app directory
ks param set tf-training-job numGpu 1

Here is a quick description for the tf-training-job component parameters:

  • image string, docker image to use
  • mountPath string, Volume mount path
  • numGpu number, optional param, default to 0
  • numPs number, Number of Parameter servers to use
  • numWorkers number, Number of workers to use
  • pipelineConfigPath string, the path to the pipeline config file in the volume mount
  • pvc string, Persistent Volume Claim name to use
  • trainDir string, Directory where the training outputs will be saved

To see the default values for the tf-training-job component params, please take a look at the params.libsonnet file.

Next

Monitor your job