tensorboard_app.py

import subprocess

import sky

# Start the instance.
with sky.Dag() as dag:
    # The working directory contains all code and will be synced to remote.
    workdir = '~/Downloads/tpu'
    subprocess.run(f'cd {workdir} && git checkout 222cc86',
                   shell=True,
                   check=True)
    # The setup command.  Will be run under the working directory.
    setup = 'pip install --upgrade pip && \
        conda init bash && \
        conda activate resnet || \
          (conda create -n resnet python=3.7 -y && \
           conda activate resnet && \
           pip install tensorflow==2.4.0 pyyaml && \
           cd models && pip install -e .)'

    task = sky.Task('setup', workdir=workdir, setup=setup)
    task.set_resources(sky.Resources(sky.AWS(), accelerators={'V100': 1}))
sky.stream_and_get(sky.launch(dag, cluster_name='tb'))

# Run the training task.
# The command to run.  Will be run under the working directory.
run = 'conda activate resnet && mkdir -p resnet-model-dir && \
    export XLA_FLAGS=\'--xla_gpu_cuda_data_dir=/usr/local/cuda/\' && \
    python -u models/official/resnet/resnet_main.py --use_tpu=False \
    --mode=train --train_batch_size=256 --train_steps=250 \
    --iterations_per_loop=125 \
    --data_dir=gs://cloud-tpu-test-datasets/fake_imagenet \
    --model_dir=resnet-model-dir \
    --amp --xla --loss_scale=128'

train = sky.Task(
    'train',
    workdir=workdir,
    run=run,
)

train.set_resources({
    sky.Resources(accelerators='V100'),
})
sky.exec(train, cluster_name='tb')

# Run the tensorboard task.
# Use 'ssh -L 4650:localhost:4650 <cluster_name>' to forward port to local.
# 'ssh -L 4650:localhost:4650 tb'
tensorboard = sky.Task(
    'tensorboard',
    workdir=workdir,
    setup=setup,
    run='conda activate resnet && \
        tensorboard --logdir resnet-model-dir --port 4650',
)
tensorboard.set_resources(sky.Resources())

sky.exec(tensorboard, cluster_name='tb')