valohai.yaml

---

- step:
    name: Execute python examples/run_glue.py
    image: pytorch/pytorch:nightly-devel-cuda10.0-cudnn7
    command:
      - python /valohai/repository/utils/download_glue_data.py --data_dir=/glue_data
      - pip install -e .
      - pip install -r examples/requirements.txt
      - python examples/run_glue.py --do_train --data_dir=/glue_data/{parameter-value:task_name} {parameters}
    parameters:
      - name: model_type
        pass-as: --model_type={v}
        type: string
        default: bert
      - name: model_name_or_path
        pass-as: --model_name_or_path={v}
        type: string
        default: bert-base-uncased
      - name: task_name
        pass-as: --task_name={v}
        type: string
        default: MRPC
      - name: max_seq_length
        pass-as: --max_seq_length={v}
        description: The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.
        type: integer
        default: 128
      - name: per_gpu_train_batch_size
        pass-as: --per_gpu_train_batch_size={v}
        description: Batch size per GPU/CPU for training.
        type: integer
        default: 8
      - name: per_gpu_eval_batch_size
        pass-as: --per_gpu_eval_batch_size={v}
        description: Batch size per GPU/CPU for evaluation.
        type: integer
        default: 8
      - name: gradient_accumulation_steps
        pass-as: --gradient_accumulation_steps={v}
        description: Number of updates steps to accumulate before performing a backward/update pass.
        type: integer
        default: 1
      - name: learning_rate
        pass-as: --learning_rate={v}
        description: The initial learning rate for Adam.
        type: float
        default: 0.00005
      - name: adam_epsilon
        pass-as: --adam_epsilon={v}
        description: Epsilon for Adam optimizer.
        type: float
        default: 0.00000001
      - name: max_grad_norm
        pass-as: --max_grad_norm={v}
        description: Max gradient norm.
        type: float
        default: 1.0
      - name: num_train_epochs
        pass-as: --num_train_epochs={v}
        description: Total number of training epochs to perform.
        type: integer
        default: 3
      - name: max_steps
        pass-as: --max_steps={v}
        description: If > 0, set total number of training steps to perform. Override num_train_epochs.
        type: integer
        default: -1
      - name: warmup_steps
        pass-as: --warmup_steps={v}
        description: Linear warmup over warmup_steps.
        type: integer
        default: -1
      - name: logging_steps
        pass-as: --logging_steps={v}
        description: Log every X updates steps.
        type: integer
        default: 25
      - name: save_steps
        pass-as: --save_steps={v}
        description: Save checkpoint every X updates steps.
        type: integer
        default: -1
      - name: output_dir
        pass-as: --output_dir={v}
        type: string
        default: /valohai/outputs
      - name: evaluate_during_training
        description: Run evaluation during training at each logging step.
        type: flag
        default: true
      - name: do_lower_case
        description: Set this flag if you are using an uncased model.
        type: flag