Epoch 0: 100%|██████████| 1/1 [00:09<00:00, 9.95s/it, loss=2.3673] Epoch 0 metrics: {'epoch': 0, 'loss': 2.3673205375671387, 'val_loss': 2.359609603881836} Epoch 1: 100%|██████████| 1/1 [00:05<00:00, 5.72s/it, loss=2.3390] Epoch 1 metrics: {'epoch': 1, 'loss': 2.33897066116333, 'val_loss': 2.3596091270446777} Epoch 2: 100%|██████████| 1/1 [00:07<00:00, 7.63s/it, loss=2.3395] Epoch 2 metrics: {'epoch': 2, 'loss': 2.3394534587860107, 'val_loss': 2.359609365463257} Epoch 3: 100%|██████████| 1/1 [00:07<00:00, 7.26s/it, loss=2.3447] Epoch 3 metrics: {'epoch': 3, 'loss': 2.3446695804595947, 'val_loss': 2.359609365463257} Epoch 4: 100%|██████████| 1/1 [00:05<00:00, 5.76s/it, loss=2.3448] Epoch 4 metrics: {'epoch': 4, 'loss': 2.3447532653808594, 'val_loss': 2.359609365463257} Epoch 5: 100%|██████████| 1/1 [00:05<00:00, 5.67s/it, loss=2.3693] Epoch 5 metrics: {'epoch': 5, 'loss': 2.3692800998687744, 'val_loss': 2.3596091270446777} Epoch 6: 100%|██████████| 1/1 [00:06<00:00, 6.38s/it, loss=2.3505] Epoch 6 metrics: {'epoch': 6, 'loss': 2.3504748344421387, 'val_loss': 2.359609603881836} Epoch 7: 100%|██████████| 1/1 [00:06<00:00, 6.72s/it, loss=2.3486] Epoch 7 metrics: {'epoch': 7, 'loss': 2.3485639095306396, 'val_loss': 2.359609603881836} Epoch 8: 100%|██████████| 1/1 [00:05<00:00, 5.68s/it, loss=2.3788] Epoch 8 metrics: {'epoch': 8, 'loss': 2.3788001537323, 'val_loss': 2.359609365463257} Epoch 9: 100%|██████████| 1/1 [00:06<00:00, 6.99s/it, loss=2.3707] Epoch 9 metrics: {'epoch': 9, 'loss': 2.3706822395324707, 'val_loss': 2.359609365463257} Traceback (most recent call last): File "", line 1, in NameError: name 'prepare_model_for_serving' is not defined :31: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. model = torch.load(model_path) :31: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. model = torch.load(model_path) /bin/bash: line 1: torch-model-archiver: command not found Collecting torchserve Downloading torchserve-0.12.0-py3-none-any.whl.metadata (1.4 kB) Collecting torch-model-archiver Downloading torch_model_archiver-0.12.0-py3-none-any.whl.metadata (1.4 kB) Requirement already satisfied: Pillow in /usr/local/lib/python3.11/dist-packages (from torchserve) (11.1.0) Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from torchserve) (5.9.5) Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from torchserve) (24.2) Requirement already satisfied: wheel in /usr/local/lib/python3.11/dist-packages (from torchserve) (0.45.1) Collecting enum-compat (from torch-model-archiver) Downloading enum_compat-0.0.3-py3-none-any.whl.metadata (954 bytes) Downloading torchserve-0.12.0-py3-none-any.whl (42.2 MB) [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m42.2/42.2 MB[0m [31m8.0 MB/s[0m eta [36m0:00:00[0m:00:01[0m00:01[0m [?25hDownloading torch_model_archiver-0.12.0-py3-none-any.whl (16 kB) Downloading enum_compat-0.0.3-py3-none-any.whl (1.3 kB) Installing collected packages: enum-compat, torchserve, torch-model-archiver Successfully installed enum-compat-0.0.3 torch-model-archiver-0.12.0 torchserve-0.12.0 Traceback (most recent call last): File "/usr/local/bin/torch-model-archiver", line 8, in sys.exit(generate_model_archive()) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/model_archiver/model_packaging.py", line 72, in generate_model_archive package_model(config, manifest=manifest) File "/usr/local/lib/python3.11/dist-packages/model_archiver/model_packaging.py", line 45, in package_model model_path = ModelExportUtils.copy_artifacts( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/model_archiver/model_packaging_utils.py", line 215, in copy_artifacts shutil.copy(path, model_path) File "/usr/lib/python3.11/shutil.py", line 431, in copy copyfile(src, dst, follow_symlinks=follow_symlinks) File "/usr/lib/python3.11/shutil.py", line 256, in copyfile with open(src, 'rb') as fsrc: ^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: 'titan_handler.py' :18: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. model = torch.load(model_path) WARNING: sun.reflect.Reflection.getCallerClass is not supported. This will impact performance. 2025-01-18T18:01:30,224 [INFO ] main org.pytorch.serve.servingsdk.impl.PluginsManager - Loading snapshot serializer plugin... nvidia-smi not available or failed: Cannot run program "nvidia-smi": error=2, No such file or directory 2025-01-18T18:01:30,334 [DEBUG] main org.pytorch.serve.util.ConfigManager - xpu-smi not available or failed: Cannot run program "xpu-smi": error=2, No such file or directory 2025-01-18T18:01:30,338 [WARN ] main org.pytorch.serve.util.ConfigManager - Your torchserve instance can access any URL to load models. When deploying to production, make sure to limit the set of allowed_urls in config.properties 2025-01-18T18:01:30,489 [INFO ] main org.pytorch.serve.util.TokenAuthorization - ###### TorchServe now enforces token authorization by default. This requires the correct token to be provided when calling an API. Key file located at /content/key_file.json Check token authorization documenation for information: https://github.com/pytorch/serve/blob/master/docs/token_authorization_api.md ###### 2025-01-18T18:01:30,489 [INFO ] main org.pytorch.serve.servingsdk.impl.PluginsManager - Initializing plugins manager... 2025-01-18T18:01:30,882 [INFO ] main org.pytorch.serve.metrics.configuration.MetricConfiguration - Successfully loaded metrics configuration from /usr/local/lib/python3.11/dist-packages/ts/configs/metrics.yaml 2025-01-18T18:01:31,174 [INFO ] main org.pytorch.serve.ModelServer - Torchserve version: 0.12.0 TS Home: /usr/local/lib/python3.11/dist-packages Current directory: /content Temp directory: /tmp Metrics config path: /usr/local/lib/python3.11/dist-packages/ts/configs/metrics.yaml Number of GPUs: 0 Number of CPUs: 2 Max heap size: 3246 M Python executable: /usr/bin/python3 Config file: N/A Inference address: http://127.0.0.1:8080 Management address: http://127.0.0.1:8081 Metrics address: http://127.0.0.1:8082 Model Store: /content/model_store Initial Models: titan=titan.mar Log dir: /content/logs Metrics dir: /content/logs Netty threads: 0 Netty client threads: 0 Default workers per model: 2 Blacklist Regex: N/A Maximum Response Size: 6553500 Maximum Request Size: 6553500 Limit Maximum Image Pixels: true Prefer direct buffer: false Allowed Urls: [file://.*|http(s)?://.*] Custom python dependency for model allowed: false Enable metrics API: true Metrics mode: LOG Disable system metrics: false Workflow Store: /content/model_store CPP log config: N/A Model config: N/A System metrics command: default Model API enabled: false 2025-01-18T18:01:31,235 [INFO ] main org.pytorch.serve.ModelServer - Loading initial models: titan.mar 2025-01-18T18:02:24,534 [DEBUG] main org.pytorch.serve.wlm.ModelVersionedRefs - Adding new version 1.0 for model titan 2025-01-18T18:02:24,535 [DEBUG] main org.pytorch.serve.wlm.ModelVersionedRefs - Setting default version to 1.0 for model titan 2025-01-18T18:02:24,535 [INFO ] main org.pytorch.serve.wlm.ModelManager - Model titan loaded. 2025-01-18T18:02:24,536 [DEBUG] main org.pytorch.serve.wlm.ModelManager - updateModel: titan, count: 2 2025-01-18T18:02:24,575 [DEBUG] W-9000-titan_1.0 org.pytorch.serve.wlm.WorkerLifeCycle - Worker cmdline: [/usr/bin/python3, /usr/local/lib/python3.11/dist-packages/ts/model_service_worker.py, --sock-type, unix, --sock-name, /tmp/.ts.sock.9000, --metrics-config, /usr/local/lib/python3.11/dist-packages/ts/configs/metrics.yaml] 2025-01-18T18:02:24,581 [DEBUG] W-9001-titan_1.0 org.pytorch.serve.wlm.WorkerLifeCycle - Worker cmdline: [/usr/bin/python3, /usr/local/lib/python3.11/dist-packages/ts/model_service_worker.py, --sock-type, unix, --sock-name, /tmp/.ts.sock.9001, --metrics-config, /usr/local/lib/python3.11/dist-packages/ts/configs/metrics.yaml] 2025-01-18T18:02:24,602 [INFO ] main org.pytorch.serve.ModelServer - Initialize Inference server with: EpollServerSocketChannel. 2025-01-18T18:02:26,923 [INFO ] main org.pytorch.serve.ModelServer - Torchserve stopped. java.io.IOException: Failed to bind to address: http://127.0.0.1:8080 at org.pytorch.serve.ModelServer.initializeServer(ModelServer.java:354) at org.pytorch.serve.ModelServer.startRESTserver(ModelServer.java:415) at org.pytorch.serve.ModelServer.startAndWait(ModelServer.java:124) at org.pytorch.serve.ModelServer.main(ModelServer.java:105) Caused by: io.netty.channel.unix.Errors$NativeIoException: bind(..) failed: Address already in use COMMAND PID USER FD TYPE DEVICE SIZE/OFF NODE NAME node 6 root 21u IPv6 19939 0t0 TCP *:8080 (LISTEN) node 6 root 26u IPv6 1016000 0t0 TCP 17c4989733c5:8080->172.28.0.1:39786 (ESTABLISHED) node 6 root 28u IPv6 1017108 0t0 TCP 17c4989733c5:8080->172.28.0.1:45550 (ESTABLISHED) node 6 root 29u IPv6 25417 0t0 TCP 17c4989733c5:8080->172.28.0.1:55124 (ESTABLISHED) node 6 root 31u IPv6 867221 0t0 TCP 17c4989733c5:8080->172.28.0.1:50646 (ESTABLISHED) usage: torchserve [-h] [-v | --start | --stop] [--ts-config TS_CONFIG] [--model-store MODEL_STORE] [--workflow-store WORKFLOW_STORE] [--models MODEL_PATH1 MODEL_NAME=MODEL_PATH2... [MODEL_PATH1 MODEL_NAME=MODEL_PATH2... ...]] [--log-config LOG_CONFIG] [--cpp-log-config CPP_LOG_CONFIG] [--foreground] [--no-config-snapshots] [--plugins-path PLUGINS_PATH] [--disable-token-auth] [--enable-model-api] torchserve: error: unrecognized arguments: --inference-address http://127.0.0.1:8083 /bin/bash: line 1: nano: command not found /usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'Repository' (from 'huggingface_hub.repository') is deprecated and will be removed from version '1.0'. Please prefer the http-based alternatives instead. Given its large adoption in legacy code, the complete removal is only planned on next major release. For more details, please read https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http. warnings.warn(warning_message, FutureWarning) No files have been modified since last commit. Skipping to prevent empty commit. WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit. No files have been modified since last commit. Skipping to prevent empty commit. WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit. No files have been modified since last commit. Skipping to prevent empty commit. WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit. No files have been modified since last commit. Skipping to prevent empty commit. WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit. No files have been modified since last commit. Skipping to prevent empty commit. WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.