# t3qai_client Description
a library for t3qai platform client.
The client module provides properties/functions
that links platform and client's learning/Inference algorithm.
- Provide platform path properties
- Provides functions to link learning state, set log, call learning parameter elements, load data, save learning results, and download inference results
## To install with pip
```
pip install t3qai_client
```
## How to Use (Example)
### properties
```
## train
from t3qai_client import T3QAI_TRAIN_OUTPUT_PATH, T3QAI_TRAIN_MODEL_PATH, T3QAI_TRAIN_DATA_PATH, T3QAI_MODULE_PATH
## inference
from t3qai_client import T3QAI_INIT_MODEL_PATH, T3QAI_MODULE_PATH
```
### functions
```
import t3qai_client as tc
## link learning state
tc.train_start()
tc.train_finish(result, result_msg)
## set log
# train
tc.train_set_logger()
# inference
tc.inference_set_logger()
## call learning parameter elements
# train
params = tc.train_load_param()
batch_size= int(params['batch_size'])
# inference
params = tc.inference_load_param()
batch_size= int(params['batch_size'])
## save learning results
# save the learning results inside the platform.
eval_results={}
eval_results['accuracy']= 0.93
eval_results['loss']= 0.003
tc.train_save_result_metrics(eval_results)
## To download inference results at platform (2 options -> file_obj or file_path)
from t3qai_client import DownloadFile
result = DownloadFile(file_obj=resultobj, file_name=filename)
result = DownloadFile(file_path=save_path, file_name=filename)
```
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"description": "# t3qai_client Description\r\na library for t3qai platform client.\r\n\r\nThe client module provides properties/functions\r\nthat links platform and client's learning/Inference algorithm.\r\n\r\n- Provide platform path properties\r\n- Provides functions to link learning state, set log, call learning parameter elements, load data, save learning results, and download inference results\r\n\r\n## To install with pip\r\n```\r\npip install t3qai_client\r\n\r\n```\r\n\r\n## How to Use (Example)\r\n### properties\r\n```\r\n## train\r\nfrom t3qai_client import T3QAI_TRAIN_OUTPUT_PATH, T3QAI_TRAIN_MODEL_PATH, T3QAI_TRAIN_DATA_PATH, T3QAI_MODULE_PATH\r\n\r\n## inference\r\nfrom t3qai_client import T3QAI_INIT_MODEL_PATH, T3QAI_MODULE_PATH\r\n```\r\n\r\n### functions\r\n```\r\nimport t3qai_client as tc\r\n\r\n## link learning state\r\ntc.train_start()\r\ntc.train_finish(result, result_msg)\r\n\r\n## set log\r\n# train\r\ntc.train_set_logger()\r\n# inference\r\ntc.inference_set_logger()\r\n\r\n## call learning parameter elements\r\n# train\r\nparams = tc.train_load_param()\r\nbatch_size= int(params['batch_size'])\r\n# inference\r\nparams = tc.inference_load_param()\r\nbatch_size= int(params['batch_size'])\r\n\r\n## save learning results\r\n# save the learning results inside the platform.\r\neval_results={}\r\neval_results['accuracy']= 0.93\r\neval_results['loss']= 0.003\r\ntc.train_save_result_metrics(eval_results)\r\n\r\n## To download inference results at platform (2 options -> file_obj or file_path)\r\nfrom t3qai_client import DownloadFile\r\nresult = DownloadFile(file_obj=resultobj, file_name=filename)\r\nresult = DownloadFile(file_path=save_path, file_name=filename)\r\n```\r\n",
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