To get started with managed spot jobs using SkyPilot, you'll need to follow these steps:

1. Prepare Your Task YAML: Create a task YAML file that describes your job and test it with sky launch.
2. Implement Checkpointing: Optionally, implement checkpointing in your code to save the state of your job periodically.

Here's an example of a task YAML for a BERT fine-tuning task and the command to launch it as a managed spot job:

# bert_qa.yaml
name: bert_qa

resources:
  accelerators: V100:1

workdir: ~/transformers

setup: |
  pip install -e .
  cd examples/pytorch/question-answering/
  pip install -r requirements.txt
  pip install wandb

run: |
  cd ./examples/pytorch/question-answering/
  python run_qa.py \
  --model_name_or_path bert-base-uncased \
  --dataset_name squad \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 50 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --report_to wandb

Launch the job with:

$ sky spot launch -n bert-qa bert_qa.yaml

SkyPilot will manage the launching and monitoring of your spot job, including handling preemptions. For more details, check the Managed Spot Jobs section in the SkyPilot documentation.