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template.yaml
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# Copyright 2021 The MLX Contributors
#
# SPDX-License-Identifier: Apache-2.0
# name: (Required) name of this model file
# description: (Optional) description of this model file
# author: (Required for trainable)
# name: (Required for trainable) name of this training job's author
# email: (Required for trainable) email of this training job's author
# framework: (Required)
# name: (Required) ML/DL framework format that the model is stored as.
# version: (Optional) Framework version used for this model
# runtimes: (Required for trainable)
# name: (Required for trainable) programming language for the model runtime
# version: (Required for trainable) programming language version for the model runtime
name: Facial Age estimator Model
model_identifier: facial-age-estimator
description: Sample Model trained to classify the age of the human face.
author:
name: DL Developer
email: "[email protected]"
framework:
name: "tensorflow"
version: "1.13.1"
runtimes:
name: python
version: "3.5"
# labels: (Optional list) labels and tags for this model
# - url: (Optional) Link to the ML/DL model page.
# - pipeline_uuids: (Optional) Linkage with a list of execuable pipelines.
# license: (Optional) License for this model.
# domain: (Optional) Domain metadata for this model.
# website: (Optional) Links that explain this model in more details
# readme_url: (Optional) A Github-flavored markdown file to be rendered in the MLX UI
license: "Apache 2.0"
domain: "Facial Recognition"
website: "https://developer.ibm.com/exchanges/models/all/max-facial-age-estimator"
labels:
- url:
- pipeline_uuids: ["abcd1234"]
readme_url: "https://raw.githubusercontent.com/IBM/MAX-Facial-Age-Estimator/master/README.md"
# train: (optional)
# trainable: (optional) Indicate the model is trainable. Default: False
# credentials_required: (optional) Indicate the credentials is needed for training. Default: False
# tested_platforms(optional list): platform on which this model can trained (current options: watsonml, ffdl, kubeflow)
# model_source: (Required for trainable)
# initial_model: (Required for trainable)
# data_store: (Required) datastore for the model code source
# bucket: (Required) Bucket that has the model code source
# path: (Required) Bucket path that has the model code source
# url: (Optional) Link to the model
# initial_model_local: (Optional)
# path: (Optional) Initial model code in the user local machine
# model_training_results: (Required for trainable)
# trained_model: (Required for trainable)
# data_store: (Required) datastore for the training result source
# bucket: (Required) Bucket that has the training result source
# path: (Required) Bucket path that has the training result source
# url: (Optional) Link to the model
# trained_model_local: (Optional)
# path: (Optional) Path to pull trained model in the user local machine
# data_source: (Optional)
# training_data: (Required for trainable)
# data_store: (Required) datastore for the model data source
# bucket: (Required) Bucket that has the model data source
# path: (Required) Bucket path that has the model data source
# url: (Optional) Link to the model
# training_data_local: (Optional)
# path: (Optional) Initial data files in the user local machine
# mount_type: (Required) object storage mount type
# evaluation_metrics: (optional) Define the metrics for the training job.
# type: (Required) evaluation_metrics type
# in: (Required) Path to store the evaluation_metrics
# training_container_image: (Optional)
# container_image_url: (Optional) Custom training container image url
# container_store: (Optional) container_store for the custom training image
# execution: (Required for trainable)
# command: (Required) Entrypoint commands to execute model code
# name: (Required) T-shirt size for training on Watson Machine Learning
# nodes: (Required) Number of nodes needed for this training job. Default: 1
# training_params: (Optional) list of hyperparameters for the training model
# - (optional) list of key(param name):value(param value)
train:
trainable: true
credentials_required: true
tested_platforms:
- watsonml
- ffdl
model_source:
initial_model:
data_store: age_datastore
bucket: facial-age-estimator
path: 1.0/assets/
url: ""
initial_model_local:
path: /local/1.0/assets/
model_training_results:
trained_model:
data_store: age_datastore
bucket: facial-age-estimator
path: 1.0/assets/
url: ""
trained_model_local:
path: /local/1.0/assets/
data_source:
training_data:
data_store: age_datastore
bucket: facial-age-estimator
path: 1.0/assets/
training_data_url:
training_data_local:
path: /local/1.0/assets/
mount_type: mount_cos
evaluation_metrics:
type: tensorboard
in: "$JOB_STATE_DIR/logs/tb/test"
training_container_image:
container_image_url: tensorflow/tensorflow:latest-gpu-py3
container_store: container_store
execution:
command: python3 convolutional_network.py --trainImagesFile ${DATA_DIR}/train-images-idx3-ubyte.gz
--trainLabelsFile ${DATA_DIR}/train-labels-idx1-ubyte.gz --testImagesFile ${DATA_DIR}/t10k-images-idx3-ubyte.gz
--testLabelsFile ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --learningRate 0.001 --trainingIters 20000
compute_configuration:
name: k80
nodes: 1
training_params:
- learning_rate:
- loss:
- batch_size:
- epoch:
- optimizer:
- xxx
- yyy
- train_op:
# serve: (Optional)
# servable: (Optional) Indicate the model is servable without training. Default: False
# credentials_required: (optional) Indicate the credentials is needed for serving. Default: False
# tested_platforms (optional list): platform on which this model can served (current options: kubernetes, knative, seldon, watsonml, kfserving)
# model_source: (Optional) - (Required if servable is true)
# servable_model: (Required for s3 or url type)
# data_store: (Required for s3 type) datastore for the model source
# bucket: (Required for s3 type) Bucket that has the model source
# path: (Required for s3 type) Source path to the model
# url: (Required for url type) Source URL for the model
# servable_model_local: (Optional)
# path: (Optional) Servable model path in the user local machine
# serving_container_image: (Required for container type)
# container_image_url: (Required for container type) Container image to serve the model.
# container_store: (Optional) container_store name
serve:
servable: true
credentials_required: true
tested_platforms:
- kubernetes
- knative
model_source:
servable_model:
data_store: age_datastore
bucket: facial-age-estimator
path: 2.0/assets/
url: ""
servable_model_local:
path: /local/1.0/assets/
serving_container_image:
container_image_url: "codait/max-facial-age-estimator:latest"
container_store: container_store
# data (Optional) metadata for model training data
# source_id: (Optional) Extension file id regarding the data source.
# domain: (Optional) Metadata about the data domain.
# website: (Optional) Links to the data description
# license: (Optional) Data license
data:
source_id: IMDB-WIKI
domain: "Image"
website: "https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/"
license: "Apache 2.0"
# process: (Optional) - (Required for MAX training models)
# - name: (Required) Script Process name. Can mix any kind of process here
# params: (Optional) Free flowing list of key:value paisrs
# staging_dir: (Optional) Staging directory within the local machine
# trained_model_path: (Optional) trained model path within the object storage bucket
process:
- name: training_post_process
params:
key: value
staging_dir: training_output/
trained_model_path:
# data_stores: (Optional) - (Required for trainable models)
# - name: (Required) name of the data_stores
# connection:
# endpoing: (Required) Object Storage endpoint URL or public Object Storage key link.
# access_key_id: (Required) Object Storage access_key_id
# secret_access_key: (Required) Object secret_access_key
data_stores:
- name: age_datastore
type: s3
connection:
endpoint: https://s3-api.us-geo.objectstorage.softlayer.net
access_key_id: xxxxxxxxxx
secret_access_key: xxxxxxxxxxxxx
# container_stores: (Optional) - (Required for MAX training models)
# - name: (Required) name of the container_store
# connection:
# container_registry: (Required) container registry for this container_store
# container_registry_token: (Required if container registry is private) container registry token
container_stores:
- name: container_store
connection:
container_registry: docker.io
container_registry_token: ""