# GPU Acceleration 0.0/1.0

**URL:** <https://discuss.ray.io/t/gpu-acceleration-0-0-1-0/15408>\
**Category:** RLlib\
**Created:** [July 31, 2024, 4:34pm UTC](https://discuss.ray.io/t/gpu-acceleration-0-0-1-0/15408 "2024-07-31T16:34:36Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![Brendan\_A](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/brendan_a/32/110_2.png) [@Brendan\_A](https://discuss.ray.io/u/Brendan_A)\
**Post date:** [July 31, 2024, 4:34pm UTC](https://discuss.ray.io/t/gpu-acceleration-0-0-1-0/15408/1 "2024-07-31T16:34:36Z")

</div>

- Medium: It contributes to significant difficulty to complete my task, but I can work around it.

I have recently been training a custom environment locally for testing but have since scaled it up to a VM enabled with an NVIDIA T4 GPU.

My code to setup training looks like this:

```auto
if __name__ == " __main__":

    available_cpus = multiprocessing.cpu_count()
    available_gpus = torch.cuda.device_count()
    
    sample_env = SatelliteSensorEnv()
    
    config = (
        PPOConfig()
        .environment(SatelliteSensorEnv)
        .multi_agent(policies={
                        "satellite": (
                            None,
                            sample_env.observation_space["satellite"],
                            sample_env.action_space["satellite"],
                            PPOConfig.overrides(framework_str="tf")
                        ),
                        "sensor": (
                            None,
                            sample_env.observation_space["sensor"],
                            sample_env.action_space["sensor"],
                            PPOConfig.overrides(framework_str="tf")
                        )},    
                policy_mapping_fn=(lambda agent_id, *args, **kwargs: agent_id))
        .env_runners(batch_mode="complete_episodes",
                      num_env_runners = available_cpus - 1,
                      num_cpus_per_env_runner=1)
        .resources(num_gpus=available_gpus)
        .learners(num_learners=1, num_gpus_per_learner=1)
        .evaluation(evaluation_interval=10, evaluation_duration=10, evaluation_duration_unit='episodes', custom_evaluation_function=custom_eval_function)
    )
    
    tuner = tune.Tuner(
        "PPO",
        param_space=config.to_dict(),
        run_config=train.RunConfig(
            stop={"training_iteration":1000},
            checkpoint_config=train.CheckpointConfig(num_to_keep=5,
                               checkpoint_score_attribute="episode_reward_mean",
                               checkpoint_score_order="max",
                               checkpoint_frequency=10,
                               checkpoint_at_end=True),
            storage_path=f'{os.path.dirname(os.path.abspath( __file__ ))}/Results')
    )
    
    results = tuner.fit()
    
    ray.shutdown()

```

I’ve tried to run this with both the PyTorch and TF as my framework but have run into the same issue below.

```auto
Trial status: 1 RUNNING
Current time: 2024-07-31 16:27:18. Total running time: 3min 0s
Logical resource usage: 16.0/16 CPUs, 1.0/1 GPUs (0.0/1.0 accelerator_type:T4)

```

For whatever reason, I can’t seem to get the GPU acceleration to activate and as such, the training is just about as fast with a CPU as it with GPU. From an `nvidia-smi` call the results show how little of the GPU is actually being used.

```auto
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 529.19 Driver Version: 529.19 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla T4 TCC | 00000001:00:00.0 Off | 0 |
| N/A 48C P0 29W / 70W | 801MiB / 15360MiB | 20% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 17776 C ....conda\envs\rl\python.exe 800MiB |
+-----------------------------------------------------------------------------+

```

I’m hoping someone might have a fix for this so that I can use the GPU effectively. Thanks!

---

<div class="post-metadata">

**Author:** ![rachelgollub](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/rachelgollub/32/6557_2.png) [@rachelgollub](https://discuss.ray.io/u/rachelgollub)\
**Post date:** [January 10, 2025, 9:00pm UTC](https://discuss.ray.io/t/gpu-acceleration-0-0-1-0/15408/2 "2025-01-10T21:00:41Z")

</div>

I’m seeing the same thing – not sure how to get it to use the gpus it says it’s running.

---

<div class="post-metadata">

**Author:** ![ZanhaPeng](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/zanhapeng/32/7699_2.png) [@ZanhaPeng](https://discuss.ray.io/u/ZanhaPeng)\
**Post date:** [March 13, 2025, 1:37pm UTC](https://discuss.ray.io/t/gpu-acceleration-0-0-1-0/15408/3 "2025-03-13T13:37:45Z")

</div>

I’m having the same problem too. Have you managed to solve it?