orchestrate_stanford_cars_tuning_config.yml

# The model.py file is written to run in python3.6, so we specify that language here
language: python3.6

# Select the framework to use for your model
# We want to setup our environment manually so we just choose python
framework: cuda

# Choose the number of GPUs that your model will use
# We don't need any GPUs for this example, so we'll leave this commented out
resources_per_model:
  gpus: 1

# Choose a descriptive name for your model
name:  Stanford Cars Orchestrate CUDA Full Tuning

# Here, we install the requirements
install:
  -  pip install -r stanford_cars_venv_requirements.txt

# Here, we run the model
run:
  -  python orchestrate_experiment_wrapper_stanford_cars_cli.py --path_images ./stanford-car-classification/data/ --path_data ./stanford-car-classification/data/cars_annos.mat --path_labels ./stanford-car-classification/data/cars_meta.mat --model ResNet18 --epochs 35 --validation_frequency 10 --data_subset 1.0  --number_of_classes 196 --no-freeze_weights
# Here, we define optimization details
optimization:
  # Use this name when logging a metric with:
  # orchestrate.io.log_metric()
  # Every experiment needs at least one named metric.
  metrics:
    - name: accuracy

  # Parameter that are defined here are available to
  # orchestrate.io.assignment()
  parameters:
    - name: learning_rate
      type: double
      bounds:
        min: -9.21
        max: 0.0
    - name: learning_rate_scheduler
      type: double
      bounds:
        min: 0.00
        max: 0.99
    - name: batch_size
      type: int
      bounds:
        min: 4
        max: 8
    - name: nesterov
      type: categorical
      categorical_values: ["True", "False"]
    - name: weight_decay
      type: double
      bounds:
        min: -11.3306
        max: 0.00
    - name: momentum
      type: double
      bounds:
        min: 0.001
        max: 0.90
    - name: scheduler_rate
      type: int
      bounds:
        min: 0
        max: 20
  tasks:
    - name: full
      cost: 1.00
    - name: medium
      cost: 0.50
    - name: small
      cost: 0.10

  # Our exaxmple cluster has two machines, so we have enough compute power
  # to execute two models in parallel.
  parallel_bandwidth: 20

  # We want to evaluate our model on sixty different sets of hyperparameters
  observation_budget: 220

# SigOpt Orchestrate creates a container for your model. Since we're using an AWS
# cluster, it's easy to securely store the model in the Amazon Elastic Container Registry.
# Chose a descriptive and unique name for each new experiment configuration file.
repository: orchestrate/stanford-cars-fine-tuning-2