nvmidl.apps package

• nvmidl.apps.automl package
  
  • Submodules
  • nvmidl.apps.automl.base_mmar_exec module
  • nvmidl.apps.automl.constants module
  • nvmidl.apps.automl.dummy_controller module
  • nvmidl.apps.automl.dummy_handler module
  • nvmidl.apps.automl.fake_clara_train module
  • nvmidl.apps.automl.mmar_exec module
  • nvmidl.apps.automl.mmar_handler module
  • nvmidl.apps.automl.mmar_trainer module
  • nvmidl.apps.automl.ngc_exec module
  • nvmidl.apps.automl.ssu module
  • nvmidl.apps.automl.test_rec module
  • nvmidl.apps.automl.test_ssu module
  • nvmidl.apps.automl.test_train module
  • nvmidl.apps.automl.train module
  • Module contents
• nvmidl.apps.fed_learn package
  
  • Subpackages
    
    • nvmidl.apps.fed_learn.client package
      
      • Submodules
      • nvmidl.apps.fed_learn.client.admin_local_train module
      • nvmidl.apps.fed_learn.client.admin_process_train module
      • nvmidl.apps.fed_learn.client.fed_local_train module
      • nvmidl.apps.fed_learn.client.fed_local_validate module
      • nvmidl.apps.fed_learn.client.test_client module
      • Module contents
    • nvmidl.apps.fed_learn.components package
      
      • Submodules
      • nvmidl.apps.fed_learn.components.clara_evaluator module
      • Module contents
    • nvmidl.apps.fed_learn.server package
      
      • Submodules
      • nvmidl.apps.fed_learn.server.admin_fed_aggregate module
      • nvmidl.apps.fed_learn.server.fed_aggregate module
      • nvmidl.apps.fed_learn.server.mmar_validator module
      • nvmidl.apps.fed_learn.server.test_server module
      • Module contents
    • nvmidl.apps.fed_learn.trainers package
      
      • Submodules
      • nvmidl.apps.fed_learn.trainers.client_trainer module
      • nvmidl.apps.fed_learn.trainers.server_trainer module
      • Module contents
  • Submodules
  • nvmidl.apps.fed_learn.fed_optimizer module
  • nvmidl.apps.fed_learn.fl_conf module
  • nvmidl.apps.fed_learn.test_connection module
  • Module contents

DLMed script for converting dicom files.

contain_dicom(path)

get_dicom_dir_list(source_dir)

get_image_file_list(source_dir, ext)

resample_image(img, tgt_res)

standardize_ext(ext)

main()

export(model_name, model_file_format, model_file_path, input_node_names, output_node_names, sm_tags='inference', checkpoint_ext='.ckpt', meta_file_ext='.meta', regular_frozen_file_ext='.fzn.pb', trt_file_ext='.trt.pb', max_batch_size=4, is_dynamic_op=False, minimum_segment_size=50, trt_precision_mode='FP32', trtis_export=False, trtis_model_name='tlt_model', trtis_input_shape=None)

Export/convert a specified checkpoint to TRT-optimized frozen graph file

A checkpoint contains 3 files. The names of the 3 files for model ‘foo’ are named like these:

The graph meta file: foo.ckpt.meta The data/weight file: foo.ckpt.data An index file: foo.ckpt.index

The generated regular frozen file name: foo.fzn.pb The generated TRT file name: foo.trt.pb

Parameters

• minimum_segment_size – minimum segment size to be used for TRT optimization

• sm_tags – meta tags for saved model format

• model_file_format – model file format: CKPT (checkpoint) or SM (saved model)

• is_dynamic_op – whether to use dynamic mode when trt-optimizing graph

• max_batch_size – maximum batch size allowed by the TRT graph

• model_name – name of the model

• model_file_path – file directory that contains model files

• input_node_names – comma separated input node names in the graph

• output_node_names – comma separated output node names in the graph

• checkpoint_ext – file extension of the checkpoint files

• meta_file_ext – file extension of the graph meta file

• regular_frozen_file_ext – file extension of the regular frozen graph file

• trt_file_ext – file extension of the TRT-optimized graph file

• trt_precision_mode – precision mode of the TRT optimization

• trtis_export – bool for requesting generate config.pbtxt for TRTIS

• trtis_model_name – Unique model name for TRTIS

• trtis_input_shape – list or tuple to specifiy concrete input shape

Returns

full names of the frozen and TRT-optimized files

main()

normalize_string(item_data, item_name)

trtis_config(meta_file, ckpt_file, trtis_model_name, output_path='.', max_batch_size=1, trtis_input_shape=None)

Generate config.pbtxt for TRTIS

Parameters

• meta_file – Full path to checkpoint metafile

• ckpt_file – Full path to checkpoint datafile

• trtis_model_name – A unique model name inside one TRTIS instance

• output_path – Optional string to store config.pbtxt

• max_batch_size – Optional parameter for TRTIS to allocate necessary workspace

generate_mmar(dest_path, task, include_fl, include_automl)

main()

class TrainEnv(mmar_root, wf_config_file_name, env_config_file_name, log_config_file_name, kv_list)

Bases: object

main()

train(e: nvmidl.apps.mp_train.TrainEnv)

main()