Managed Spot Jobs¶

SkyPilot supports managed spot jobs that can automatically recover from preemptions. This feature saves significant cost (e.g., up to 70% for GPU VMs) by making preemptible spot instances practical for long-running jobs.

SkyPilot automatically finds available spot resources across regions and clouds to maximize availability. Here is an example of a BERT training job failing over different regions across AWS and GCP.

To use managed spot jobs, there are two requirements:

Task YAML: Managed Spot requires a YAML to describe the job, tested with sky launch.
Checkpointing (optional): For job recovery due to preemptions, the user application code can checkpoint its progress periodically to a SkyPilot Storage-mounted cloud bucket. The program can reload the latest checkpoint when restarted.

Task YAML¶

To launch a spot job, you can simply reuse your task YAML (recommended to test it with sky launch first). For example, we found the BERT fine-tuning YAML works with sky launch, and want to launch it with SkyPilot managed spot jobs.

We can launch it with the following:

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

# bert_qa.yaml
name: bert_qa

resources:
  accelerators: V100:1

# Assume your working directory is under `~/transformers`.
# To make this example work, please run the following command:
# git clone https://github.com/huggingface/transformers.git ~/transformers -b v4.30.1
workdir: ~/transformers

setup: |
  # Fill in your wandb key: copy from https://wandb.ai/authorize
  # Alternatively, you can use `--env WANDB_API_KEY=$WANDB_API_KEY`
  # to pass the key in the command line, during `sky spot launch`.
  echo export WANDB_API_KEY=[YOUR-WANDB-API-KEY] >> ~/.bashrc

  pip install -e .
  cd examples/pytorch/question-answering/
  pip install -r requirements.txt torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
  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

Note

workdir and file mounts with local files will be automatically uploaded to SkyPilot Storage. Cloud bucket will be created during the job running time, and cleaned up after the job finishes.

SkyPilot will launch and start monitoring the spot job. When a preemption happens, SkyPilot will automatically search for resources across regions and clouds to re-launch the job.

In this example, the job will be restarted from scratch after each preemption recovery. To resume the job from previous states, user’s application needs to implement checkpointing and recovery.

Checkpointing and recovery¶

To allow spot recovery, a cloud bucket is typically needed to store the job’s states (e.g., model checkpoints). Below is an example of mounting a bucket to /checkpoint.

file_mounts:
  /checkpoint:
    name: # NOTE: Fill in your bucket name
    mode: MOUNT

The MOUNT mode in SkyPilot Storage ensures the checkpoints outputted to /checkpoint are automatically synced to a persistent bucket. Note that the application code should save program checkpoints periodically and reload those states when the job is restarted. This is typically achieved by reloading the latest checkpoint at the beginning of your program.

An end-to-end example¶

Below we show an example for fine-tuning a BERT model on a question-answering task with HuggingFace.

# bert_qa.yaml
name: bert_qa

resources:
  accelerators: V100:1

# Assume your working directory is under `~/transformers`.
# To make this example work, please run the following command:
# git clone https://github.com/huggingface/transformers.git ~/transformers -b v4.30.1
workdir: ~/transformers

file_mounts:
  /checkpoint:
    name: # NOTE: Fill in your bucket name
    mode: MOUNT

setup: |
  # Fill in your wandb key: copy from https://wandb.ai/authorize
  # Alternatively, you can use `--env WANDB_API_KEY=$WANDB_API_KEY`
  # to pass the key in the command line, during `sky spot launch`.
  echo export WANDB_API_KEY=[YOUR-WANDB-API-KEY] >> ~/.bashrc

  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 \
  --run_name $SKYPILOT_TASK_ID \
  --output_dir /checkpoint/bert_qa/ \
  --save_total_limit 10 \
  --save_steps 1000

As HuggingFace has built-in support for periodically checkpointing, we only need to pass the highlighted arguments for setting up the output directory and frequency of checkpointing (see more on Huggingface API). You may also refer to another example here for periodically checkpointing with PyTorch.

We also set --run_name to $SKYPILOT_TASK_ID so that the logs for all recoveries of the same job will be saved to the same run in Weights & Biases.

Note

The environment variable $SKYPILOT_TASK_ID (example: “sky-2022-10-06-05-17-09-750781_spot_id-22”) can be used to identify the same job, i.e., it is kept identical across all recoveries of the job. It can be accessed in the task’s run commands or directly in the program itself (e.g., access via os.environ and pass to Weights & Biases for tracking purposes in your training script). It is made available to the task whenever it is invoked.

With the highlighted changes, the managed spot job can now resume training after preemption with sky spot launch! We can enjoy the benefits of cost savings from spot instances without worrying about preemption or losing progress.

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

Useful CLIs¶

Here are some commands for managed spot jobs. Check sky spot --help for more details.

See all spot jobs:

$ sky spot queue

Fetching managed spot job statuses...
Managed spot jobs:
ID NAME     RESOURCES     SUBMITTED   TOT. DURATION   JOB DURATION   #RECOVERIES  STATUS
2  roberta  1x [A100:8]   2 hrs ago   2h 47m 18s      2h 36m 18s     0            RUNNING
1  bert-qa  1x [V100:1]   4 hrs ago   4h 24m 26s      4h 17m 54s     0            RUNNING

Stream the logs of a running spot job:

$ sky spot logs -n bert-qa  # by name
$ sky spot logs 2           # by job ID

Cancel a spot job:

$ sky spot cancel -n bert-qa  # by name
$ sky spot cancel 2           # by job ID

Note

If any failure happens for a spot job, you can check sky spot queue -a for the brief reason of the failure. For more details, it would be helpful to check sky spot logs --controller <job_id>.

Dashboard¶

Use sky spot dashboard to open a dashboard to see all jobs:

$ sky spot dashboard

This automatically opens a browser tab to show the dashboard:

The UI shows the same information as the CLI sky spot queue -a. The UI is especially useful when there are many in-progress jobs to monitor, which the terminal-based CLI may need more than one page to display.

Real-world examples¶

Vicuna LLM chatbot: instructions, YAML
BERT (shown above): YAML
PyTorch DDP, ResNet: YAML
PyTorch Lightning DDP, CIFAR-10: YAML

Spot controller¶

The spot controller is a small on-demand CPU VM running in the cloud that manages all spot jobs of a user. It is automatically launched when the first managed spot job is submitted, and it is autostopped after it has been idle for 10 minutes (i.e., after all spot jobs finish and no new spot job is submitted in that duration). Thus, no user action is needed to manage its lifecycle.

You can see the controller with sky status and refresh its status by using the -r/--refresh flag.

While the cost of the spot controller is negligible (~$0.4/hour when running and less than $0.004/hour when stopped), you can still tear it down manually with sky down <spot-controller-name>, where the <spot-controller-name> can be found in the output of sky status.

Note

Tearing down the spot controller loses all logs and status information for the finished spot jobs. It is only allowed when there are no in-progress spot jobs to ensure no resource leakage.

Customizing spot controller resources¶

You may customize the resources of the spot controller for the following reasons:

Enforcing the spot controller to run on a specific location. (Default: cheapest location)
Changing the maximum number of spot jobs that can be run concurrently. (Default: 16)
Changing the disk_size of the spot controller to store more logs. (Default: 50GB)

To achieve the above, you can specify custom configs in ~/.sky/config.yaml with the following fields:

spot:
  controller:
    resources:
      # All configs below are optional
      # 1. Specify the location of the spot controller.
      cloud: gcp
      region: us-central1
      # 2. Specify the maximum number of spot jobs that can be run concurrently.
      cpus: 4+  # number of vCPUs, max concurrent spot jobs = 2 * cpus
      # 3. Specify the disk_size of the spot controller.
      disk_size: 100

The resources field has the same spec as a normal SkyPilot job; see here.

Managed Spot Jobs

Contents

Managed Spot Jobs¶

Task YAML¶

Checkpointing and recovery¶

An end-to-end example¶

Useful CLIs¶

Dashboard¶

Real-world examples¶

Spot controller¶

Customizing spot controller resources¶