diff --git a/examples/hpo/resnet50/README.md b/examples/hpo/resnet50/README.md
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+# Example of Black Box Optimization on ABCI 3.0
+
+This is an example of performing black-box optimization of the learning rate for a ResNet50 model on the MNIST dataset.
+
+## Getting started
+
+In an environment where aiaccel is installed, additionally install pyproject.toml.
+
+```bash
+pip install .
+```
+
+PATH_TO_ENV in job_config.yaml should be changed to the path of the environment prepared above.
+
+```yaml
+    source PATH_TO_ENV
+```
+
+Run the following command to perform black-box optimization.
+
+```bash
+cmd=aiaccel-job pbs --config config_job.yaml
+
+aiaccel-hpo optimize --config config_hpo.yaml -- \
+    $cmd train --n_gpus=1 {config.working_directory}/{job_name}.log -- \
+        aiaccel-torch train config_torch.yaml \
+            working_directory={config.working_directory}/{job_name}/ \
+            task.optimizer_config.optimizer_generator.lr={lr} \
+            out_filename={out_filename}
+```
+
+## Detailed Descriptions
+
+The target function for optimization using aiaccel.hpo.app.optimize is objective_integration.main.
+Within objective_integration.main, aiaccel.torch.app.train is called, and the learning rate is returned.
+
+Detailed descriptions of torch and optimize are available on the [aiaccel document(torch)](https://aistairc.github.io/aiaccel/user_guide/torch.html) [aiaccel document(optimize)](https://aistairc.github.io/aiaccel/user_guide/hpo.html)
diff --git a/examples/hpo/resnet50/config_hpo.yaml b/examples/hpo/resnet50/config_hpo.yaml
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+++ b/examples/hpo/resnet50/config_hpo.yaml
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+params:
+  _convert_: partial
+  _target_: aiaccel.hpo.apps.optimize.HparamsManager
+  lr:
+    _target_: aiaccel.hpo.optuna.suggest_wrapper.SuggestFloat
+    name: lr
+    low: 1.e-6
+    high: 1.e-2
+    log: true
+
+n_trials: 1
+n_max_jobs: 1
diff --git a/examples/hpo/resnet50/config_job.yaml b/examples/hpo/resnet50/config_job.yaml
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+++ b/examples/hpo/resnet50/config_job.yaml
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+walltime: "1:0:0"
+
+script_prologue: |
+    echo Job ID: $PBS_JOBID
+    echo Hostname: $(hostname)
+
+    export NVIDIA_VISIBLE_DEVICES=all
+
+qsub: "qsub -P $JOB_GROUP -l walltime={args.walltime} -v USE_SSH=1"
+
+cpu:
+    qsub_args: "-q rt_HF -l select=1"
+    job: "{command}"
+
+cpu-array:
+    n_tasks_per_proc: 128
+    n_procs: 24
+    qsub_args: "-q rt_HF -l select=1 -J 1-{args.n_tasks}:$(( {args.n_tasks_per_proc} * {args.n_procs} ))"
+    job: "{command}"
+
+gpu:
+    qsub_args: "-q rt_HF -l select=1"
+    job: "{command}"
+
+gpu-array:
+    n_tasks_per_proc: 128
+    n_procs: 8
+    qsub_args: "-q rt_HF -l select=1 -J 1-{args.n_tasks}:$(( {args.n_tasks_per_proc} * {args.n_procs} ))"
+    job: "CUDA_VISIBLE_DEVICES=$(( LOCAL_PROC_INDEX % 8 )) {command}"
+
+mpi:
+    n_nodes: 1
+    qsub_args: >-
+        -q rt_HF
+        -l select={args.n_nodes}:mpiprocs=$(( {args.n_procs} / {args.n_nodes} )):ompthreads=$(( {args.n_nodes} * 96 / {args.n_procs} ))
+    job: |
+        source /etc/profile.d/modules.sh
+        module load hpcx
+
+        mpirun -np {args.n_procs} -bind-to none -map-by slot \
+            -mca pml ob1 -mca btl self,tcp -mca btl_tcp_if_include bond0 \
+            {command}
+
+train:
+    qsub_args: >-
+        -q $( (({args.n_gpus}==1)) && printf rt_HG || printf rt_HF )
+        -l select=$(( ({args.n_gpus} + 7) / 8 )):mpiprocs=$( (({args.n_gpus}==1)) && printf 1 || printf 8 ):ompthreads=$( (({args.n_gpus}==1)) && printf 8 || printf 12 )
+    job: |
+        source /etc/profile.d/modules.sh
+        module load hpcx
+
+        mpirun -np {args.n_gpus} -bind-to none -map-by slot \
+            -mca pml ob1 -mca btl self,tcp -mca btl_tcp_if_include bond0 \
+            -x MAIN_ADDR=$(hostname -i) \
+            -x MAIN_PORT=3000 \
+            -x COLUMNS=120 \
+            -x PYTHONUNBUFFERED=true \
+            {command}
+
diff --git a/examples/hpo/resnet50/config_torch.yaml b/examples/hpo/resnet50/config_torch.yaml
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+++ b/examples/hpo/resnet50/config_torch.yaml
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+_base_:
+  - ${working_directory}/../../../../torch/image_classification/recipes/resnet50.cifar50/config.yaml
+
+trainer:
+  max_epochs: 10
+
+  callbacks:
+    - _target_: aiaccel.torch.lightning.callback.SaveMetricCallback
+      metric_name: "validation/loss"
+      output_path: ${out_filename}
+