is alphafold using my GPU

Hey guys,
I get this when i run alphafold

```
python3 docker/run_docker.py --fasta_paths=/home/ahmedhas/Desktop/test.fasta --max_template_date=2021-11-01 --model_preset=multimer --data_dir=/media/ahmedhas/b9f6ec47-bc94-46bb-a83c-d497e70afa67/dataset/ --output_dir=/home/ahmedhas/Desktop/outs --db_preset=reduced_dbs
I0529 16:16:07.631752 140679386469440 run_docker.py:116] Mounting /home/ahmedhas/Desktop -> /mnt/fasta_path_0
I0529 16:16:07.631856 140679386469440 run_docker.py:116] Mounting /media/ahmedhas/b9f6ec47-bc94-46bb-a83c-d497e70afa67/dataset/uniref90 -> /mnt/uniref90_database_path
I0529 16:16:07.631903 140679386469440 run_docker.py:116] Mounting /media/ahmedhas/b9f6ec47-bc94-46bb-a83c-d497e70afa67/dataset/mgnify -> /mnt/mgnify_database_path
I0529 16:16:07.631935 140679386469440 run_docker.py:116] Mounting /media/ahmedhas/b9f6ec47-bc94-46bb-a83c-d497e70afa67/dataset -> /mnt/data_dir
I0529 16:16:07.631963 140679386469440 run_docker.py:116] Mounting /media/ahmedhas/b9f6ec47-bc94-46bb-a83c-d497e70afa67/dataset/pdb_mmcif/mmcif_files -> /mnt/template_mmcif_dir
I0529 16:16:07.631996 140679386469440 run_docker.py:116] Mounting /media/ahmedhas/b9f6ec47-bc94-46bb-a83c-d497e70afa67/dataset/pdb_mmcif -> /mnt/obsolete_pdbs_path
I0529 16:16:07.632029 140679386469440 run_docker.py:116] Mounting /media/ahmedhas/b9f6ec47-bc94-46bb-a83c-d497e70afa67/dataset/uniprot -> /mnt/uniprot_database_path
I0529 16:16:07.632061 140679386469440 run_docker.py:116] Mounting /media/ahmedhas/b9f6ec47-bc94-46bb-a83c-d497e70afa67/dataset/pdb_seqres -> /mnt/pdb_seqres_database_path
I0529 16:16:07.632091 140679386469440 run_docker.py:116] Mounting /media/ahmedhas/b9f6ec47-bc94-46bb-a83c-d497e70afa67/dataset/small_bfd -> /mnt/small_bfd_database_path
I0529 16:16:09.276381 140679386469440 run_docker.py:258] I0529 14:16:09.275249 139670953879360 templates.py:857] Using precomputed obsolete pdbs /mnt/obsolete_pdbs_path/obsolete.dat.
I0529 16:16:18.028860 140679386469440 run_docker.py:258] I0529 14:16:18.028422 139670953879360 xla_bridge.py:353] Unable to initialize backend 'tpu_driver': NOT_FOUND: Unable to find driver in registry given worker:
I0529 16:16:18.099451 140679386469440 run_docker.py:258] I0529 14:16:18.099100 139670953879360 xla_bridge.py:353] Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: "rocm". Available platform names are: CUDA Interpreter Host
I0529 16:16:18.099588 140679386469440 run_docker.py:258] I0529 14:16:18.099411 139670953879360 xla_bridge.py:353] Unable to initialize backend 'tpu': module 'jaxlib.xla_extension' has no attribute 'get_tpu_client'
I0529 16:16:18.099622 140679386469440 run_docker.py:258] I0529 14:16:18.099450 139670953879360 xla_bridge.py:353] Unable to initialize backend 'plugin': xla_extension has no attributes named get_plugin_device_client. Compile TensorFlow with //tensorflow/compiler/xla/python:enable_plugin_device set to true (defaults to false) to enable this.
I0529 16:16:54.207772 140679386469440 run_docker.py:258] I0529 14:16:54.207287 139670953879360 run_alphafold.py:466] Have 25 models: ['model_1_multimer_v3_pred_0', 'model_1_multimer_v3_pred_1', 'model_1_multimer_v3_pred_2', 'model_1_multimer_v3_pred_3', 'model_1_multimer_v3_pred_4', 'model_2_multimer_v3_pred_0', 'model_2_multimer_v3_pred_1', 'model_2_multimer_v3_pred_2', 'model_2_multimer_v3_pred_3', 'model_2_multimer_v3_pred_4', 'model_3_multimer_v3_pred_0', 'model_3_multimer_v3_pred_1', 'model_3_multimer_v3_pred_2', 'model_3_multimer_v3_pred_3', 'model_3_multimer_v3_pred_4', 'model_4_multimer_v3_pred_0', 'model_4_multimer_v3_pred_1', 'model_4_multimer_v3_pred_2', 'model_4_multimer_v3_pred_3', 'model_4_multimer_v3_pred_4', 'model_5_multimer_v3_pred_0', 'model_5_multimer_v3_pred_1', 'model_5_multimer_v3_pred_2', 'model_5_multimer_v3_pred_3', 'model_5_multimer_v3_pred_4']
I0529 16:16:54.207892 140679386469440 run_docker.py:258] I0529 14:16:54.207391 139670953879360 run_alphafold.py:480] Using random seed 364565440744827352 for the data pipeline
I0529 16:16:54.207949 140679386469440 run_docker.py:258] I0529 14:16:54.207519 139670953879360 run_alphafold.py:218] Predicting test
I0529 16:16:54.208099 140679386469440 run_docker.py:258] I0529 14:16:54.207985 139670953879360 pipeline_multimer.py:210] Running monomer pipeline on chain A: sequence_b
I0529 16:16:54.208168 140679386469440 run_docker.py:258] I0529 14:16:54.208112 139670953879360 jackhmmer.py:133] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmp3pinhm7j/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /tmp/tmpz7haf_o8.fasta /mnt/uniref90_database_path/uniref90.fasta"
I0529 16:16:54.229267 140679386469440 run_docker.py:258] I0529 14:16:54.228661 139670953879360 utils.py:36] Started Jackhmmer (uniref90.fasta) query
```
I am not sure if alphafold is running on my GPU or my CPU?

This is what i get when I run `nvidia-smi`
```
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 On | N/A |
| N/A 43C P8 2W / 55W | 1969MiB / 8188MiB | 4% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 5667 G /usr/lib/xorg/Xorg 45MiB |
| 0 N/A N/A 20650 C python 128MiB |
+-----------------------------------------------------------------------------+

```

Thanks alot