[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-tao-train-reid":3,"mdc-2cfjff-key":34,"related-org-nvidia-tao-train-reid":2295,"related-repo-nvidia-tao-train-reid":2453},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"tao-train-reid","train person re-identification models","Person re-identification (ReID). Learns discriminative embeddings to match the same person across different camera views, based on metric learning. Use when training, evaluating, exporting, or running inference for a TAO person re-identification model. Trigger phrases include \"train ReID\", \"person re-identification\", \"cross-camera person matching\", \"ReID embeddings\", \"person re-id\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,19,20],{"name":13,"slug":14,"type":15},"Deep Learning","deep-learning","tag",{"name":17,"slug":18,"type":15},"Machine Learning","machine-learning",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Computer Vision","computer-vision",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-14T05:30:50.275451","Apache-2.0",281,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"AI agent skills published by NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Ftao-train-reid","---\nname: tao-train-reid\ndescription: Person re-identification (ReID). Learns discriminative embeddings to match the same person across different\n  camera views, based on metric learning. Use when training, evaluating, exporting, or running inference for a TAO person\n  re-identification model. Trigger phrases include \"train ReID\", \"person re-identification\", \"cross-camera person matching\",\n  \"ReID embeddings\", \"person re-id\".\nlicense: Apache-2.0\ncompatibility: Requires docker + nvidia-container-toolkit.\nmetadata:\n  version: \"0.1.0\"\n  author: NVIDIA Corporation\nallowed-tools: Read Bash\ntags:\n- re\n- identification\n---\n\n# Re-Identification\n\nPerson re-identification. Learns discriminative embeddings to match the same person across different camera views. Metric learning based.\n\nSet model.pretrained_model_path for pretrained weights.\n\n## Dataclass Schemas\n\nGenerated TAO Core schemas are packaged in `schemas\u002F\u003Caction>.schema.json`, with `schemas\u002Fmanifest.json` listing available actions. Each generated schema also emits `references\u002Fspec_template_\u003Caction>.yaml` from the schema top-level `default` field. AutoML enablement is declared at the model layer in `references\u002Fskill_info.yaml` via `automl_enabled`. Runnable AutoML still requires `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` to exist and parse. Use the packaged train schema for `automl_default_parameters`, `automl_disabled_parameters`, defaults, min\u002Fmax bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect `~\u002Ftao-core` at runtime; maintainers regenerate schemas\u002Ftemplates before packaging the skill bank.\n\n## Train Action Policy\n\nThis model is AutoML-enabled at the model layer. Before handling any train-stage request, read `references\u002Fskill_info.yaml` and resolve the run override from either an explicit `automl_policy` value or the user's workflow request. Use `automl_policy: on` by default and only expose `on` \u002F `off` in new launch prompts. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as `automl_policy: off` for this run only. When `automl_policy: on`, `automl_enabled: true`, and both `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` are packaged, route the train action through `tao-skill-bank:tao-run-automl` by default with this model's `skill_dir`. Preserve workflow\u002Fapplication overrides for datasets, specs, output directories, GPU\u002Fplatform settings, parent checkpoints, and `automl_policy`. Use direct model training only when `automl_policy: off` or the packaged train schema\u002Ftemplate is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.\n\nNon-train actions such as `evaluate`, `inference`, `export`, and deploy flows stay in this model skill. The per-run `automl_policy` override does not change model metadata.\n\n## Supported Actions\n\nThe packaged Re-Identification PyT CLI supports `train`, `evaluate`, `inference`, `export`, and `default_specs`. This model skill exposes the runnable user actions `train`, `evaluate`, `inference`, and `export`; resume\u002Fretrain is performed through `train` with `train.resume_training_checkpoint_path`.\n\nDo not advertise or synthesize `dataset_convert`, `deploy`, `prune`, `quantize`, `gen_trt_engine`, or standalone `retrain` for this model unless the packaged model skill and real CLI add those actions.\n\n## Training Requirements\n\n- **Dataset type:** re_identification\n- **Formats:** default\n- **Monitoring metric:** cmc_rank_1, maximize\n\n### Per-Action Dataset Requirements\n\n| Action | Spec Key | Source | Files | List? |\n|---|---|---|---|---|\n| evaluate | evaluate.test_dataset | train_datasets | sample_test.tar.gz | No |\n| evaluate | evaluate.query_dataset | train_datasets | sample_query.tar.gz | No |\n| inference | inference.test_dataset | train_datasets | sample_test.tar.gz | No |\n| inference | inference.query_dataset | train_datasets | sample_query.tar.gz | No |\n| train | dataset.train_dataset_dir | train_datasets | sample_train.tar.gz | No |\n| train | dataset.test_dataset_dir | train_datasets | sample_test.tar.gz | No |\n| train | dataset.query_dataset_dir | train_datasets | sample_query.tar.gz | No |\n\n### Typical Spec Overrides\n\nData source overrides are **mandatory for every action** — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in `spec_overrides`.\n\n```python\nS3_TRAIN = \"s3:\u002F\u002Fbucket\u002Fdata\u002Ftrain\"\nCHECKPOINT = \"\u002Fresults\u002F{train_job_id}\u002Fresults_dir\u002Fmodel_epoch_000_step_00099.pth\"\n```\n\n**train (mandatory data sources):**\n```python\n{\n    \"train.num_epochs\": 30,\n    \"train.checkpoint_interval\": 10,\n    \"train.validation_interval\": 10,\n    \"train.num_gpus\": 1,\n    \"dataset.num_classes\": 100,\n    \"dataset.num_workers\": 4,\n    \"dataset.batch_size\": 16,\n    \"dataset.num_instances\": 4,\n    \"dataset.train_dataset_dir\": f\"{S3_TRAIN}\u002Fsample_train.tar.gz\",\n    \"dataset.test_dataset_dir\": f\"{S3_TRAIN}\u002Fsample_test.tar.gz\",\n    \"dataset.query_dataset_dir\": f\"{S3_TRAIN}\u002Fsample_query.tar.gz\",\n}\n```\n\n**resume train (mandatory checkpoint):**\n```python\n{\n    \"train.num_epochs\": 31,\n    \"train.resume_training_checkpoint_path\": CHECKPOINT,\n    \"dataset.num_classes\": 100,\n    \"dataset.batch_size\": 16,\n    \"dataset.num_instances\": 4,\n    \"dataset.train_dataset_dir\": f\"{S3_TRAIN}\u002Fsample_train.tar.gz\",\n    \"dataset.test_dataset_dir\": f\"{S3_TRAIN}\u002Fsample_test.tar.gz\",\n    \"dataset.query_dataset_dir\": f\"{S3_TRAIN}\u002Fsample_query.tar.gz\",\n}\n```\n\n**evaluate (mandatory data sources and checkpoint):**\n```python\n{\n    \"evaluate.test_dataset\": f\"{S3_TRAIN}\u002Fsample_test.tar.gz\",\n    \"evaluate.query_dataset\": f\"{S3_TRAIN}\u002Fsample_query.tar.gz\",\n    \"evaluate.checkpoint\": CHECKPOINT,\n    \"evaluate.output_cmc_curve_plot\": \"\u002Fresults\u002F{evaluate_job_id}\u002Fresults_dir\u002Fcmc_curve.png\",\n    \"evaluate.output_sampled_matches_plot\": \"\u002Fresults\u002F{evaluate_job_id}\u002Fresults_dir\u002Fsampled_matches.png\",\n}\n```\n\n**export (mandatory checkpoint and output):**\n```python\n{\n    \"export.checkpoint\": CHECKPOINT,\n    \"export.onnx_file\": \"\u002Fresults\u002F{export_job_id}\u002Fresults_dir\u002Freid.onnx\",\n}\n```\n\n**inference (mandatory data sources and checkpoint):**\n```python\n{\n    \"inference.test_dataset\": f\"{S3_TRAIN}\u002Fsample_test.tar.gz\",\n    \"inference.query_dataset\": f\"{S3_TRAIN}\u002Fsample_query.tar.gz\",\n    \"inference.checkpoint\": CHECKPOINT,\n    \"inference.output_file\": \"\u002Fresults\u002F{inference_job_id}\u002Fresults_dir\u002Freid_inference.json\",\n}\n```\n\nFor export and inference, provide explicit file paths for `export.onnx_file` and `inference.output_file`. For evaluate, provide explicit file paths for `evaluate.output_cmc_curve_plot` and `evaluate.output_sampled_matches_plot`. Keep these as spec values or `spec_params` mappings; do not declare them as file outputs in `skill_info.yaml` for local Docker until the runner distinguishes files from folders during output pre-creation.\n\n## Eval Dataset\n\nRequired. Evaluation requires test and query datasets for retrieval-based metrics (CMC, mAP).\n\n## Important Parameters\n\n- **dataset.num_classes**: Number of identities. Default 751. Must match the number of unique identities in training data.\n- **model.backbone**: Default resnet_50.\n- **optim.base_lr**: Base learning rate. Default 3.5e-4.\n- **dataset.batch_size**: Per-GPU batch size. Default 64. Re-ID benefits from large batches for better triplet\u002Fcontrastive sampling.\n- **dataset.num_instances**: Number of instances per identity in a batch. Controls sampling strategy for metric learning.\n\n## Multi-GPU \u002F Multi-Node\n\n**Launch method:** Lightning-managed (single `python` process, Lightning spawns workers).\n\n| Spec Key | Description | Default |\n|----------|-------------|---------|\n| `train.num_gpus` | Number of GPUs | 1 |\n| `train.gpu_ids` | GPU device indices | [0] |\n\n- Multi-GPU strategy: `ddp_find_unused_parameters_true`\n- `sync_batchnorm` is always enabled\n- Precision forced to FP16 (`16-mixed`)\n- No explicit `num_nodes` config — single-node oriented\n\n## Hardware\n\nMinimum 1 GPU(s), recommended 2 GPU(s). 16GB+ VRAM per GPU. Re-ID models are relatively lightweight but benefit from large batch sizes for metric learning.\n\n## Error Patterns\n\n**num_classes mismatch**: Ensure dataset.num_classes equals the number of unique identity folders in the training set.\n\n**Invalid triplet batch shape**: `dataset.batch_size` must be compatible with `dataset.num_instances` so each mini-batch can be reshaped for hard-example mining. For local AutoML smoke runs, keep `dataset.batch_size` fixed to a known valid multiple such as 16 with `dataset.num_instances: 4`, and tune `train.optim.base_lr` instead of unconstrained batch size.\n\n**Query\u002Fgallery mismatch**: Query and test (gallery) datasets must share the same identity namespace.\n\n**PyTorch 2.6 checkpoint load failure on checkpoint consumers**: Current Re-ID\ncheckpoints include OmegaConf containers. For checkpoints produced by the same\ntrusted TAO train\u002FAutoML workflow, set\n`TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1` in downstream resume, evaluate, inference,\nand export job env vars so Lightning\u002FPyTorch can load the full checkpoint. Do\nnot use this env var for untrusted checkpoints.\n\n**AutoML metric extraction**: Re-ID train status files report retrieval KPIs such as `cmc_rank_1`, `cmc_rank_5`, `cmc_rank_10`, and `mAP`, plus train loss. Default AutoML train launches must optimize `cmc_rank_1` with `direction: maximize`; do not use `val_loss` as the metric for this model.\n\n**Checkpoint handoff**: Use the checkpoint resolver on the best AutoML child job's `results_dir\u002Ftrain\u002F` folder and select the action-appropriate `model_epoch_*.pth` checkpoint. Re-ID also writes `reid_model_latest.pth`, but that is a latest symlink and should only be used when a caller explicitly requests latest. Preserve the same dataset identity count and query\u002Fgallery archives for downstream actions.\n\n**Default spec generation**: The packaged `default_specs` CLI action does not\nconsume the normal `-e \u003Cspec.yaml>` experiment file for `results_dir`. Invoke it\nwith a Hydra override such as\n`re_identification default_specs results_dir=\u002Fworkspace\u002Frun\u002Fresults\u002Fdefault_specs`.\nPassing only `-e` leaves `cfg.results_dir` unset and fails with\n`MissingMandatoryValue: results_dir`.\n\n## Spec Param \u002F Parent Model Inference\n\nModel-specific inference mappings belong in this MD file, not in `config.json`. Generated runners should read this section and apply the mappings with SDK helpers before `create_job()`. This mirrors the old microservices `infer_params.py` flow.\n\nInference mappings from TAO Core `re_identification.config.json`:\n\n| Action | Spec Field | Inference Function | Meaning |\n|---|---|---|---|\n| evaluate | `encryption_key` | `key` | encryption key |\n| evaluate | `evaluate.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| evaluate | `evaluate.output_cmc_curve_plot` | `create_evaluate_cmc_plot_reid` | ReID CMC plot path |\n| evaluate | `evaluate.output_sampled_matches_plot` | `create_evaluate_matches_plot_reid` | ReID sampled matches plot path |\n| evaluate | `results_dir` | `output_dir` | current job results directory |\n| export | `encryption_key` | `key` | encryption key |\n| export | `export.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| export | `export.onnx_file` | `create_onnx_file` | output ONNX path |\n| export | `results_dir` | `output_dir` | current job results directory |\n| inference | `encryption_key` | `key` | encryption key |\n| inference | `inference.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| inference | `inference.output_file` | `create_inference_result_file_reid` | ReID inference JSON path |\n| inference | `results_dir` | `output_dir` | current job results directory |\n| train | `encryption_key` | `key` | encryption key |\n| train | `model.pretrained_model_path` | `ptm_if_no_resume_model` | PTM when no resume checkpoint exists |\n| train | `results_dir` | `output_dir` | current job results directory |\n| train | `train.resume_training_checkpoint_path` | `resume_model` | model file inferred from the current job results folder |\n\nFor `parent_model` or `parent_model_folder`, pass the upstream train\u002Fexport\u002FAutoML child job id as `parent_job_id`. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to `config.json` and do not patch generated runner scripts to guess checkpoint paths.\n",{"data":35,"body":44},{"name":4,"description":6,"license":26,"compatibility":36,"metadata":37,"allowed-tools":40,"tags":41},"Requires docker + nvidia-container-toolkit.",{"version":38,"author":39},"0.1.0","NVIDIA Corporation","Read Bash",[42,43],"re","identification",{"type":45,"children":46},"root",[47,56,62,67,74,168,174,283,317,323,402,451,457,493,500,716,722,741,772,780,902,910,989,997,1058,1066,1103,1111,1164,1215,1221,1226,1232,1285,1291,1308,1381,1432,1438,1443,1449,1459,1506,1516,1534,1595,1629,1693,1699,1728,1741,2254,2289],{"type":48,"tag":49,"props":50,"children":52},"element","h1",{"id":51},"re-identification",[53],{"type":54,"value":55},"text","Re-Identification",{"type":48,"tag":57,"props":58,"children":59},"p",{},[60],{"type":54,"value":61},"Person re-identification. Learns discriminative embeddings to match the same person across different camera views. Metric learning based.",{"type":48,"tag":57,"props":63,"children":64},{},[65],{"type":54,"value":66},"Set model.pretrained_model_path for pretrained weights.",{"type":48,"tag":68,"props":69,"children":71},"h2",{"id":70},"dataclass-schemas",[72],{"type":54,"value":73},"Dataclass Schemas",{"type":48,"tag":57,"props":75,"children":76},{},[77,79,86,88,94,96,102,104,110,112,118,120,126,128,134,136,142,144,150,152,158,160,166],{"type":54,"value":78},"Generated TAO Core schemas are packaged in ",{"type":48,"tag":80,"props":81,"children":83},"code",{"className":82},[],[84],{"type":54,"value":85},"schemas\u002F\u003Caction>.schema.json",{"type":54,"value":87},", with ",{"type":48,"tag":80,"props":89,"children":91},{"className":90},[],[92],{"type":54,"value":93},"schemas\u002Fmanifest.json",{"type":54,"value":95}," listing available actions. Each generated schema also emits ",{"type":48,"tag":80,"props":97,"children":99},{"className":98},[],[100],{"type":54,"value":101},"references\u002Fspec_template_\u003Caction>.yaml",{"type":54,"value":103}," from the schema top-level ",{"type":48,"tag":80,"props":105,"children":107},{"className":106},[],[108],{"type":54,"value":109},"default",{"type":54,"value":111}," field. AutoML enablement is declared at the model layer in ",{"type":48,"tag":80,"props":113,"children":115},{"className":114},[],[116],{"type":54,"value":117},"references\u002Fskill_info.yaml",{"type":54,"value":119}," via ",{"type":48,"tag":80,"props":121,"children":123},{"className":122},[],[124],{"type":54,"value":125},"automl_enabled",{"type":54,"value":127},". Runnable AutoML still requires ",{"type":48,"tag":80,"props":129,"children":131},{"className":130},[],[132],{"type":54,"value":133},"schemas\u002Ftrain.schema.json",{"type":54,"value":135}," and ",{"type":48,"tag":80,"props":137,"children":139},{"className":138},[],[140],{"type":54,"value":141},"references\u002Fspec_template_train.yaml",{"type":54,"value":143}," to exist and parse. Use the packaged train schema for ",{"type":48,"tag":80,"props":145,"children":147},{"className":146},[],[148],{"type":54,"value":149},"automl_default_parameters",{"type":54,"value":151},", ",{"type":48,"tag":80,"props":153,"children":155},{"className":154},[],[156],{"type":54,"value":157},"automl_disabled_parameters",{"type":54,"value":159},", defaults, min\u002Fmax bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ",{"type":48,"tag":80,"props":161,"children":163},{"className":162},[],[164],{"type":54,"value":165},"~\u002Ftao-core",{"type":54,"value":167}," at runtime; maintainers regenerate schemas\u002Ftemplates before packaging the skill bank.",{"type":48,"tag":68,"props":169,"children":171},{"id":170},"train-action-policy",[172],{"type":54,"value":173},"Train Action Policy",{"type":48,"tag":57,"props":175,"children":176},{},[177,179,184,186,192,194,200,202,208,210,216,218,224,226,231,232,238,240,245,246,251,253,259,261,267,269,274,276,281],{"type":54,"value":178},"This model is AutoML-enabled at the model layer. Before handling any train-stage request, read ",{"type":48,"tag":80,"props":180,"children":182},{"className":181},[],[183],{"type":54,"value":117},{"type":54,"value":185}," and resolve the run override from either an explicit ",{"type":48,"tag":80,"props":187,"children":189},{"className":188},[],[190],{"type":54,"value":191},"automl_policy",{"type":54,"value":193}," value or the user's workflow request. Use ",{"type":48,"tag":80,"props":195,"children":197},{"className":196},[],[198],{"type":54,"value":199},"automl_policy: on",{"type":54,"value":201}," by default and only expose ",{"type":48,"tag":80,"props":203,"children":205},{"className":204},[],[206],{"type":54,"value":207},"on",{"type":54,"value":209}," \u002F ",{"type":48,"tag":80,"props":211,"children":213},{"className":212},[],[214],{"type":54,"value":215},"off",{"type":54,"value":217}," in new launch prompts. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as ",{"type":48,"tag":80,"props":219,"children":221},{"className":220},[],[222],{"type":54,"value":223},"automl_policy: off",{"type":54,"value":225}," for this run only. When ",{"type":48,"tag":80,"props":227,"children":229},{"className":228},[],[230],{"type":54,"value":199},{"type":54,"value":151},{"type":48,"tag":80,"props":233,"children":235},{"className":234},[],[236],{"type":54,"value":237},"automl_enabled: true",{"type":54,"value":239},", and both ",{"type":48,"tag":80,"props":241,"children":243},{"className":242},[],[244],{"type":54,"value":133},{"type":54,"value":135},{"type":48,"tag":80,"props":247,"children":249},{"className":248},[],[250],{"type":54,"value":141},{"type":54,"value":252}," are packaged, route the train action through ",{"type":48,"tag":80,"props":254,"children":256},{"className":255},[],[257],{"type":54,"value":258},"tao-skill-bank:tao-run-automl",{"type":54,"value":260}," by default with this model's ",{"type":48,"tag":80,"props":262,"children":264},{"className":263},[],[265],{"type":54,"value":266},"skill_dir",{"type":54,"value":268},". 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Use direct model training only when ",{"type":48,"tag":80,"props":277,"children":279},{"className":278},[],[280],{"type":54,"value":223},{"type":54,"value":282}," or the packaged train schema\u002Ftemplate is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.",{"type":48,"tag":57,"props":284,"children":285},{},[286,288,294,295,301,302,308,310,315],{"type":54,"value":287},"Non-train actions such as ",{"type":48,"tag":80,"props":289,"children":291},{"className":290},[],[292],{"type":54,"value":293},"evaluate",{"type":54,"value":151},{"type":48,"tag":80,"props":296,"children":298},{"className":297},[],[299],{"type":54,"value":300},"inference",{"type":54,"value":151},{"type":48,"tag":80,"props":303,"children":305},{"className":304},[],[306],{"type":54,"value":307},"export",{"type":54,"value":309},", and deploy flows stay in this model skill. The per-run ",{"type":48,"tag":80,"props":311,"children":313},{"className":312},[],[314],{"type":54,"value":191},{"type":54,"value":316}," override does not change model metadata.",{"type":48,"tag":68,"props":318,"children":320},{"id":319},"supported-actions",[321],{"type":54,"value":322},"Supported Actions",{"type":48,"tag":57,"props":324,"children":325},{},[326,328,334,335,340,341,346,347,352,354,360,362,367,368,373,374,379,380,385,387,392,394,400],{"type":54,"value":327},"The packaged Re-Identification PyT CLI supports ",{"type":48,"tag":80,"props":329,"children":331},{"className":330},[],[332],{"type":54,"value":333},"train",{"type":54,"value":151},{"type":48,"tag":80,"props":336,"children":338},{"className":337},[],[339],{"type":54,"value":293},{"type":54,"value":151},{"type":48,"tag":80,"props":342,"children":344},{"className":343},[],[345],{"type":54,"value":300},{"type":54,"value":151},{"type":48,"tag":80,"props":348,"children":350},{"className":349},[],[351],{"type":54,"value":307},{"type":54,"value":353},", and ",{"type":48,"tag":80,"props":355,"children":357},{"className":356},[],[358],{"type":54,"value":359},"default_specs",{"type":54,"value":361},". This model skill exposes the runnable user actions ",{"type":48,"tag":80,"props":363,"children":365},{"className":364},[],[366],{"type":54,"value":333},{"type":54,"value":151},{"type":48,"tag":80,"props":369,"children":371},{"className":370},[],[372],{"type":54,"value":293},{"type":54,"value":151},{"type":48,"tag":80,"props":375,"children":377},{"className":376},[],[378],{"type":54,"value":300},{"type":54,"value":353},{"type":48,"tag":80,"props":381,"children":383},{"className":382},[],[384],{"type":54,"value":307},{"type":54,"value":386},"; resume\u002Fretrain is performed through ",{"type":48,"tag":80,"props":388,"children":390},{"className":389},[],[391],{"type":54,"value":333},{"type":54,"value":393}," with 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