(launcher) xgy@xgy:~$ tao detectnet_v2 train --help 2021-09-08 14:36:21,791 [INFO] root: Registry: [‘nvcr.io’] 2021-09-08 14:36:21,882 [WARNING] tlt.components.docker_handler.docker_handler: Docker will run the commands as root. If you would like to retain your local host permissions, please add the “user”:“UID:GID” in the DockerOptions portion of the “/home/xgy/.tao_mounts.json” file. You can obtain your users UID and GID by using the “id -u” and “id -g” commands on the terminal. 2021-09-08 14:36:23,290 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.
$ tao detectnet_v2 train --help ~/.tao_mounts.json wasn’t found. Falling back to obtain mount points and docker configs from ~/.tlt_mounts.json. Please note that this will be deprecated going forward. 2021-09-08 14:57:15,369 [INFO] root: Registry: [‘nvcr.io’] 2021-09-08 14:57:19,576 [WARNING] tlt.components.docker_handler.docker_handler: Docker will run the commands as root. If you would like to retain your local host permissions, please add the “user”:“UID:GID” in the DockerOptions portion of the “/home/morganh/.tlt_mounts.json” file. You can obtain your users UID and GID by using the “id -u” and “id -g” commands on the terminal. Using TensorFlow backend. usage: detectnet_v2 train [-h] [–num_processes NUM_PROCESSES] [–gpus GPUS] [–gpu_index GPU_INDEX [GPU_INDEX …]] [–use_amp] [–log_file LOG_FILE] [-e EXPERIMENT_SPEC_FILE] [-r RESULTS_DIR] [-n MODEL_NAME] [-v] -k KEY {calibration_tensorfile,dataset_convert,evaluate,export,inference,prune,train} …
optional arguments: -h, --help show this help message and exit –num_processes NUM_PROCESSES, -np NUM_PROCESSES The number of horovod child processes to be spawned. Default is -1(equal to --gpus). –gpus GPUS The number of GPUs to be used for the job. –gpu_index GPU_INDEX [GPU_INDEX …] The indices of the GPU’s to be used. –use_amp Flag to enable Auto Mixed Precision. –log_file LOG_FILE Path to the output log file. -e EXPERIMENT_SPEC_FILE, --experiment_spec_file EXPERIMENT_SPEC_FILE Path to spec file. Absolute path or relative to working directory. If not specified, default spec from spec_loader.py is used. -r RESULTS_DIR, --results_dir RESULTS_DIR Path to a folder where experiment outputs should be written. -n MODEL_NAME, --model_name MODEL_NAME Name of the model file. If not given, then defaults to model.hdf5. -v, --verbose Set verbosity level for the logger. -k KEY, --key KEY The key to load pretrained weights and save intermediate snapshopts and final model.
Seems that your cpu is a bit old. What is the cpu info ? More, please search “Illegal instruction” in Tao forum. Previously some users get the same issues. Unfortunately TAO does not support it.
but it can work well in conversational AI (launcher) xgy@xgy:~$ tao text_classification dataset_convert -h 2021-09-08 15:08:41,843 [INFO] root: Registry: [‘nvcr.io’] 2021-09-08 15:08:41,929 [WARNING] tlt.components.docker_handler.docker_handler: Docker will run the commands as root. If you would like to retain your local host permissions, please add the “user”:“UID:GID” in the DockerOptions portion of the “/home/xgy/.tao_mounts.json” file. You can obtain your users UID and GID by using the “id -u” and “id -g” commands on the terminal. [NeMo W 2021-09-08 07:08:46 experimental:27] Module <class ‘nemo.collections.nlp.modules.common.megatron.megatron_bert.MegatronBertEncoder’> is experimental, not ready for production and is not fully supported. Use at your own risk. INFO: Generating new fontManager, this may take some time… usage: text_classification [-h] -r RESULTS_DIR [-k KEY] [-e EXPERIMENT_SPEC_FILE] [-g GPUS] [-m RESUME_MODEL_WEIGHTS] [-o OUTPUT_SPECS_DIR] {dataset_convert,evaluate,export,finetune,infer,infer_onnx,train,download_specs}
Train Adapt Optimize Toolkit
positional arguments: {dataset_convert,evaluate,export,finetune,infer,infer_onnx,train,download_specs} Subtask for a given task/model.
optional arguments: -h, --help show this help message and exit -r RESULTS_DIR, --results_dir RESULTS_DIR Path to a folder where the experiment outputs should be written. (DEFAULT: ./) -k KEY, --key KEY User specific encoding key to save or load a .tlt model. -e EXPERIMENT_SPEC_FILE, --experiment_spec_file EXPERIMENT_SPEC_FILE Path to the experiment spec file. -g GPUS, --gpus GPUS Number of GPUs to use. The default value is 1. -m RESUME_MODEL_WEIGHTS, --resume_model_weights RESUME_MODEL_WEIGHTS Path to a pre-trained model or model to continue training. -o OUTPUT_SPECS_DIR, --output_specs_dir OUTPUT_SPECS_DIR Path to a target folder where experiment spec files will be downloaded. 2021-09-08 15:08:47,234 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.
My environment is virtual by proxmox,it can use? the cpu info xgy@xgy:/data/xgy/worksapce/tao/cv_samples_v1.2.0$ cat /proc/cpuinfo | grep name | cut -f2 -d: | uniq -c 16 Common KVM processor