diff --git a/visualdl/component/inference/fastdeploy_client/client_app.py b/visualdl/component/inference/fastdeploy_client/client_app.py index 397b8255ab75984d490f2ab9e0ce426da6054e55..76447abae7c033355c132493eae25db603544e1f 100644 --- a/visualdl/component/inference/fastdeploy_client/client_app.py +++ b/visualdl/component/inference/fastdeploy_client/client_app.py @@ -18,6 +18,7 @@ import numpy as np from .http_client_manager import get_metric_data from .http_client_manager import HttpClientManager from .http_client_manager import metrics_table_head +from .http_client_manager import metrics_table_head_en from .visualizer import visualize_detection from .visualizer import visualize_face_alignment from .visualizer import visualize_face_detection @@ -257,11 +258,400 @@ def create_gradio_client_app(): # noqa:C901 max_lines=1, interactive=False) + lang_text = gr.Textbox( + label="lang", + show_label=False, + value='zh', + max_lines=1, + visible=False + ) # This text box is only used for divide zh and en page + + all_input_output_components = input_accordions + input_name_texts + input_images + \ + input_texts + output_accordions + output_name_texts + output_images + output_texts + + def get_input_output_name(server_ip, server_port, model_name, + model_version, lang_text): + try: + server_addr = server_ip + ':' + server_port + input_metas, output_metas = _http_manager.get_model_meta( + server_addr, model_name, model_version) + except Exception as e: + return {status_text: str(e)} + results = { + component: None + for component in all_input_output_components + } + results[component_format_column] = gr.update(visible=True) + for input_accordio in input_accordions: + results[input_accordio] = gr.update(visible=False) + for output_accordio in output_accordions: + results[output_accordio] = gr.update(visible=False) + results[status_text] = 'Get model inputs and outputs successfully.' + for i, input_meta in enumerate(input_metas): + results[input_accordions[i]] = gr.update(visible=True) + results[input_name_texts[i]] = input_meta['name'] + for i, output_meta in enumerate(output_metas): + results[output_accordions[i]] = gr.update(visible=True) + results[output_name_texts[i]] = output_meta['name'] + return results + + def component_inference(*args): + server_ip = args[0] + http_port = args[1] + metric_port = args[2] + model_name = args[3] + model_version = args[4] + names = args[5:5 + len(input_name_texts)] + images = args[5 + len(input_name_texts):5 + len(input_name_texts) + + len(input_images)] + texts = args[5 + len(input_name_texts) + len(input_images):5 + + len(input_name_texts) + len(input_images) + + len(input_texts)] + task_type = args[-1] + server_addr = server_ip + ':' + http_port + if server_ip and http_port and model_name and model_version: + inputs = {} + for i, input_name in enumerate(names): + if input_name: + if images[i] is not None: + inputs[input_name] = np.array([images[i]]) + if texts[i]: + inputs[input_name] = np.array( + [[texts[i].encode('utf-8')]], dtype=np.object_) + try: + infer_results = _http_manager.infer( + server_addr, model_name, model_version, inputs) + results = {status_text: 'Inference successfully.'} + for i, (output_name, + data) in enumerate(infer_results.items()): + results[output_name_texts[i]] = output_name + results[output_texts[i]] = str(data) + if task_type != 'unspecified': + try: + results[output_images[i]] = supported_tasks[ + task_type](images[0], data) + except Exception: + results[output_images[i]] = None + if metric_port: + html_table = get_metric_data(server_ip, metric_port, + 'zh') + results[output_html_table] = html_table + return results + except Exception as e: + return {status_text: 'Error: {}'.format(e)} + else: + return { + status_text: + 'Please input server addr, model name and model version.' + } + + def raw_inference(*args): + server_ip = args[0] + http_port = args[1] + metric_port = args[2] + model_name = args[3] + model_version = args[4] + payload_text = args[5] + server_addr = server_ip + ':' + http_port + try: + result = _http_manager.raw_infer(server_addr, model_name, + model_version, payload_text) + results = { + status_text: 'Get response from server', + output_raw_text: result + } + if server_ip and metric_port: + html_table = get_metric_data(server_ip, metric_port, 'zh') + results[output_html_table] = html_table + return results + except Exception as e: + return {status_text: 'Error: {}'.format(e)} + + def update_metric(server_ip, metrics_port, lang_text): + if server_ip and metrics_port: + try: + html_table = get_metric_data(server_ip, metrics_port, 'zh') + return { + output_html_table: html_table, + status_text: "Update metrics successfully." + } + except Exception as e: + return {status_text: 'Error: {}'.format(e)} + else: + return { + status_text: 'Please input server ip and metrics_port.' + } + + check_button.click( + fn=get_input_output_name, + inputs=[ + server_addr_text, server_http_port_text, model_name_text, + model_version_text, lang_text + ], + outputs=[ + *all_input_output_components, check_button, + component_format_column, status_text + ]) + component_submit_button.click( + fn=component_inference, + inputs=[ + server_addr_text, server_http_port_text, + server_metric_port_text, model_name_text, model_version_text, + *input_name_texts, *input_images, *input_texts, task_radio + ], + outputs=[ + *output_name_texts, *output_images, *output_texts, status_text, + output_html_table + ]) + raw_submit_button.click( + fn=raw_inference, + inputs=[ + server_addr_text, server_http_port_text, + server_metric_port_text, model_name_text, model_version_text, + raw_payload_text + ], + outputs=[output_raw_text, status_text, output_html_table]) + update_metric_button.click( + fn=update_metric, + inputs=[server_addr_text, server_metric_port_text, lang_text], + outputs=[output_html_table, status_text]) + return block + + +def create_gradio_client_app_en(): # noqa:C901 + css = """ + .gradio-container { + font-family: 'IBM Plex Sans', sans-serif; + } + .gr-button { + color: white; + border-color: black; + background: black; + } + input[type='range'] { + accent-color: black; + } + .dark input[type='range'] { + accent-color: #dfdfdf; + } + #gallery { + min-height: 22rem; + margin-bottom: 15px; + margin-left: auto; + margin-right: auto; + border-bottom-right-radius: .5rem !important; + border-bottom-left-radius: .5rem !important; + } + #gallery>div>.h-full { + min-height: 20rem; + } + .details:hover { + text-decoration: underline; + } + .gr-button { + white-space: nowrap; + } + .gr-button:focus { + border-color: rgb(147 197 253 / var(--tw-border-opacity)); + outline: none; + box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); + --tw-border-opacity: 1; + --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) \ + var(--tw-ring-offset-color); + --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); + --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); + --tw-ring-opacity: .5; + } + .footer { + margin-bottom: 45px; + margin-top: 35px; + text-align: center; + border-bottom: 1px solid #e5e5e5; + } + .footer>p { + font-size: .8rem; + display: inline-block; + padding: 0 10px; + transform: translateY(10px); + background: white; + } + .dark .footer { + border-color: #303030; + } + .dark .footer>p { + background: #0b0f19; + } + .prompt h4{ + margin: 1.25em 0 .25em 0; + font-weight: bold; + font-size: 115%; + } + """ + + block = gr.Blocks(css=css) + + with block: + gr.HTML(""" +
+
+

+ FastDeploy Client +

+
+

+ The client is used for creating requests to fastdeploy server. +

+
+ """) + with gr.Group(): + with gr.Box(): + with gr.Column(): + with gr.Row(): + server_addr_text = gr.Textbox( + label="server ip", + show_label=True, + max_lines=1, + placeholder="localhost", + ) + + server_http_port_text = gr.Textbox( + label="server port", + show_label=True, + max_lines=1, + placeholder="8000", + ) + + server_metric_port_text = gr.Textbox( + label="metrics port", + show_label=True, + max_lines=1, + placeholder="8002", + ) + with gr.Row(): + model_name_text = gr.Textbox( + label="model name", + show_label=True, + max_lines=1, + placeholder="yolov5", + ) + model_version_text = gr.Textbox( + label="model version", + show_label=True, + max_lines=1, + placeholder="1", + ) + + with gr.Box(): + with gr.Tab("Component form"): + check_button = gr.Button("get model input and output") + component_format_column = gr.Column(visible=False) + with component_format_column: + task_radio = gr.Radio( + choices=list(supported_tasks.keys()), + value='unspecified', + label='task type', + visible=True) + gr.Markdown( + "Choose text or image component to input according to data type" + ) + with gr.Row(): + with gr.Column(): + gr.Markdown("Inputs") + input_accordions = [] + input_name_texts = [] + input_images = [] + input_texts = [] + for i in range(6): + accordion = gr.Accordion( + "variable {}".format(i), + open=True, + visible=False) + with accordion: + input_name_text = gr.Textbox( + label="variable name", + interactive=False) + input_image = gr.Image(type='numpy') + input_text = gr.Textbox( + label="text", max_lines=1000) + input_accordions.append(accordion) + input_name_texts.append(input_name_text) + input_images.append(input_image) + input_texts.append(input_text) + + with gr.Column(): + gr.Markdown("Outputs") + output_accordions = [] + output_name_texts = [] + output_images = [] + output_texts = [] + for i in range(6): + accordion = gr.Accordion( + "variable {}".format(i), + open=True, + visible=False) + with accordion: + output_name_text = gr.Textbox( + label="variable name", + interactive=False) + output_text = gr.Textbox( + label="text", + interactive=False, + show_label=True) + output_image = gr.Image( + interactive=False) + output_accordions.append(accordion) + output_name_texts.append(output_name_text) + output_images.append(output_image) + output_texts.append(output_text) + component_submit_button = gr.Button("submit request") + with gr.Tab("Original form"): + gr.Markdown("Request") + raw_payload_text = gr.Textbox( + label="request payload", max_lines=10000) + with gr.Column(): + gr.Markdown("Response") + output_raw_text = gr.Textbox( + label="raw response data", interactive=False) + raw_submit_button = gr.Button("submit request") + + with gr.Box(): + with gr.Column(): + gr.Markdown( + "Metrics（update automatically when submit request，or click update metrics button manually）" + ) + output_html_table = gr.HTML( + label="metrics", + interactive=False, + show_label=False, + value=metrics_table_head_en.format('', '')) + update_metric_button = gr.Button("update metrics") + + status_text = gr.Textbox( + label="status", + show_label=True, + max_lines=1, + interactive=False) + + lang_text = gr.Textbox( + label="lang", + show_label=False, + value='en', + max_lines=1, + visible=False + ) # This text box is only used for divide zh and en page + all_input_output_components = input_accordions + input_name_texts + input_images + \ input_texts + output_accordions + output_name_texts + output_images + output_texts def get_input_output_name(server_ip, server_port, model_name, - model_version): + model_version, lang_text): try: server_addr = server_ip + ':' + server_port input_metas, output_metas = _http_manager.get_model_meta( @@ -273,12 +663,11 @@ def create_gradio_client_app(): # noqa:C901 for component in all_input_output_components } results[component_format_column] = gr.update(visible=True) - # results[check_button] = gr.update(visible=False) for input_accordio in input_accordions: results[input_accordio] = gr.update(visible=False) for output_accordio in output_accordions: results[output_accordio] = gr.update(visible=False) - results[status_text] = 'GetInputOutputName Successful' + results[status_text] = 'Get model inputs and outputs successfully.' for i, input_meta in enumerate(input_metas): results[input_accordions[i]] = gr.update(visible=True) results[input_name_texts[i]] = input_meta['name'] @@ -313,7 +702,7 @@ def create_gradio_client_app(): # noqa:C901 try: infer_results = _http_manager.infer( server_addr, model_name, model_version, inputs) - results = {status_text: 'Inference Successful'} + results = {status_text: 'Inference successfully.'} for i, (output_name, data) in enumerate(infer_results.items()): results[output_name_texts[i]] = output_name @@ -325,7 +714,8 @@ def create_gradio_client_app(): # noqa:C901 except Exception: results[output_images[i]] = None if metric_port: - html_table = get_metric_data(server_ip, metric_port) + html_table = get_metric_data(server_ip, metric_port, + 'en') results[output_html_table] = html_table return results except Exception as e: @@ -352,19 +742,19 @@ def create_gradio_client_app(): # noqa:C901 output_raw_text: result } if server_ip and metric_port: - html_table = get_metric_data(server_ip, metric_port) + html_table = get_metric_data(server_ip, metric_port, 'en') results[output_html_table] = html_table return results except Exception as e: return {status_text: 'Error: {}'.format(e)} - def update_metric(server_ip, metrics_port): + def update_metric(server_ip, metrics_port, lang_text): if server_ip and metrics_port: try: - html_table = get_metric_data(server_ip, metrics_port) + html_table = get_metric_data(server_ip, metrics_port, 'en') return { output_html_table: html_table, - status_text: "Successfully update metrics." + status_text: "Update metrics successfully." } except Exception as e: return {status_text: 'Error: {}'.format(e)} @@ -377,7 +767,7 @@ def create_gradio_client_app(): # noqa:C901 fn=get_input_output_name, inputs=[ server_addr_text, server_http_port_text, model_name_text, - model_version_text + model_version_text, lang_text ], outputs=[ *all_input_output_components, check_button, @@ -404,6 +794,6 @@ def create_gradio_client_app(): # noqa:C901 outputs=[output_raw_text, status_text, output_html_table]) update_metric_button.click( fn=update_metric, - inputs=[server_addr_text, server_metric_port_text], + inputs=[server_addr_text, server_metric_port_text, lang_text], outputs=[output_html_table, status_text]) return block diff --git a/visualdl/component/inference/fastdeploy_client/http_client_manager.py b/visualdl/component/inference/fastdeploy_client/http_client_manager.py index 691594152efd219239e15b4c497052b9b3b8f4ce..e5fe85c0fe6ff40d983ac51e9e0384a9fb1ecaa2 100644 --- a/visualdl/component/inference/fastdeploy_client/http_client_manager.py +++ b/visualdl/component/inference/fastdeploy_client/http_client_manager.py @@ -19,7 +19,6 @@ import numpy as np import requests import tritonclient.http as httpclient from attrdict import AttrDict -from tritonclient.utils import InferenceServerException def convert_http_metadata_config(metadata): @@ -118,8 +117,63 @@ table, th {{ """ +metrics_table_head_en = """ + + +
+ + + + + + + + + + + + + + + + + + + {} +
Model nameExecution metricDelay metric
inference request successinference request failureinference countinference exec countinference request duration(ms)inference queue duration(ms)inference comput input duration(ms)inference compute infer duration +(ms)inference compute output duration(ms)
+
+
+
+
+
+
+
+ + + + + + + + + + + + + + + {} +
GPUPerformance metricMemory
utilization(%)power usage(W)power limit(W)energy consumption(W)total(GB)used(GB)
+
+""" + -def get_metric_data(server_addr, metric_port): # noqa:C901 +def get_metric_data(server_addr, metric_port, lang='zh'): # noqa:C901 ''' Get metrics data from fastdeploy server, and transform it into html table. Args: @@ -235,6 +289,8 @@ def get_metric_data(server_addr, metric_port): # noqa:C901 for item in data]) + "" for data in gpu_data_list ]) + if lang == 'en': + return metrics_table_head_en.format(model_data, gpu_data) return metrics_table_head.format(model_data, gpu_data) @@ -294,7 +350,7 @@ class HttpClientManager: try: model_metadata = fastdeploy_client.get_model_metadata( model_name=model_name, model_version=model_version) - except InferenceServerException as e: + except Exception as e: raise RuntimeError("Failed to retrieve the metadata: " + str(e)) model_metadata = convert_http_metadata_config(model_metadata) diff --git a/visualdl/component/inference/fastdeploy_server.py b/visualdl/component/inference/fastdeploy_server.py index 89b0b13ff443917ea636776274b64defbc857ca7..8d07b1faef6338d36ba714b3556deaf47a309efe 100644 --- a/visualdl/component/inference/fastdeploy_server.py +++ b/visualdl/component/inference/fastdeploy_server.py @@ -25,6 +25,7 @@ from pathlib import Path import requests from .fastdeploy_client.client_app import create_gradio_client_app +from .fastdeploy_client.client_app import create_gradio_client_app_en from .fastdeploy_lib import analyse_config from .fastdeploy_lib import check_process_zombie from .fastdeploy_lib import copy_config_file_to_default_config @@ -53,7 +54,8 @@ class FastDeployServerApi(object): self.root_dir = Path(os.getcwd()) self.opened_servers = { } # Use to store the opened server process pid and process itself - self.client_port = None + self.client_port = None # Chinese version + self.client_en_port = None # English version @result() def get_directory(self, cur_dir): @@ -351,34 +353,43 @@ class FastDeployServerApi(object): version_filenames_dict_for_frontend) return version_info_for_frontend - def create_fastdeploy_client(self): - if self.client_port is None: - - def get_free_tcp_port(): - tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - # tcp.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) - tcp.bind(('localhost', 0)) - addr, port = tcp.getsockname() - tcp.close() - return port - - self.client_port = get_free_tcp_port() - app = create_gradio_client_app() - thread = Process( - target=app.launch, kwargs={'server_port': self.client_port}) - thread.start() - - def check_alive(): - while True: - try: - requests.get('http://localhost:{}/'.format( - self.client_port)) - break - except Exception: - time.sleep(1) - - check_alive() - return self.client_port + def create_fastdeploy_client(self, lang='zh'): + def get_free_tcp_port(): + tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + # tcp.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) + tcp.bind(('localhost', 0)) + addr, port = tcp.getsockname() + tcp.close() + return port + + def check_alive(client_port): + while True: + try: + requests.get('http://localhost:{}/'.format(client_port)) + break + except Exception: + time.sleep(1) + + if lang == 'en': + if self.client_en_port is None: + self.client_en_port = get_free_tcp_port() + app = create_gradio_client_app_en() + thread = Process( + target=app.launch, + kwargs={'server_port': self.client_en_port}) + thread.start() + check_alive(self.client_en_port) + return self.client_en_port + else: + if self.client_port is None: + self.client_port = get_free_tcp_port() + app = create_gradio_client_app() + thread = Process( + target=app.launch, + kwargs={'server_port': self.client_port}) + thread.start() + check_alive(self.client_port) + return self.client_port def _poll_zombie_process(self): # check if there are servers killed by other vdl app instance and become zoombie @@ -410,7 +421,7 @@ def create_fastdeploy_api_call(): 'start_server': (api.start_server, ['config']), 'stop_server': (api.stop_server, ['server_id']), 'get_server_output': (api.get_server_output, ['server_id', 'length']), - 'create_fastdeploy_client': (api.create_fastdeploy_client, []), + 'create_fastdeploy_client': (api.create_fastdeploy_client, ['lang']), 'get_server_list': (api.get_server_list, []), 'get_server_metric': (api.get_server_metric, ['server_id']), 'get_server_config': (api.get_server_config, ['server_id']), diff --git a/visualdl/server/app.py b/visualdl/server/app.py index e451c4e2188b16bce565f3b45ef8de6b4b4030c9..6e96ce02aaaca84e62cb4e2a78aab4c2a3e5e15c 100644 --- a/visualdl/server/app.py +++ b/visualdl/server/app.py @@ -181,6 +181,7 @@ def create_app(args): # noqa: C901 error_msg = '{}'.format(e) return make_response(error_msg) args = urllib.parse.urlencode(request_args) + if args: return redirect( api_path + "/fastdeploy/fastdeploy_client/app?{}".format(args), @@ -201,14 +202,30 @@ def create_app(args): # noqa: C901 Returns: Any thing from gradio server. ''' + lang = 'zh' if request.method == 'POST': + if request.mimetype == 'application/json': + request_args = request.json + else: + request_args = request.form.to_dict() + if 'data' in request_args: + lang = request_args['data'][-1] + request_args['lang'] = lang + elif 'lang' in request_args: + lang = request_args['lang'] + port = fastdeploy_api_call('create_fastdeploy_client', - request.form) - request_args = request.form + request_args) else: + request_args = request.args.to_dict() + if 'data' in request_args: + lang = request_args['data'][-1] + request_args['lang'] = lang + elif 'lang' in request_args: + lang = request_args['lang'] port = fastdeploy_api_call('create_fastdeploy_client', - request.args) - request_args = request.args + request_args) + if path == 'app': proxy_url = request.url.replace( request.host_url.rstrip('/') + api_path + @@ -239,38 +256,82 @@ def create_app(args): # noqa: C901 model_name = start_args.get('default_model_name', '') content = content.decode() try: - default_server_addr = re.search( - '"label": {}.*?"value": "".*?}}'.format( - json.dumps("服务ip", ensure_ascii=True).replace( - '\\', '\\\\')), content).group(0) - cur_server_addr = default_server_addr.replace( - '"value": ""', '"value": "localhost"') - default_http_port = re.search( - '"label": {}.*?"value": "".*?}}'.format( - json.dumps("推理服务端口", ensure_ascii=True).replace( - '\\', '\\\\')), content).group(0) - cur_http_port = default_http_port.replace( - '"value": ""', '"value": "{}"'.format(http_port)) - default_metrics_port = re.search( - '"label": {}.*?"value": "".*?}}'.format( - json.dumps("性能服务端口", ensure_ascii=True).replace( - '\\', '\\\\')), content).group(0) - cur_metrics_port = default_metrics_port.replace( - '"value": ""', '"value": "{}"'.format(metrics_port)) - default_model_name = re.search( - '"label": {}.*?"value": "".*?}}'.format( - json.dumps("模型名称", ensure_ascii=True).replace( - '\\', '\\\\')), content).group(0) - cur_model_name = default_model_name.replace( - '"value": ""', '"value": "{}"'.format(model_name)) - default_model_version = re.search( - '"label": {}.*?"value": "".*?}}'.format( - json.dumps("模型版本", ensure_ascii=True).replace( - '\\', '\\\\')), content).group(0) - cur_model_version = default_model_version.replace( - '"value": ""', '"value": "{}"'.format('1')) - content = content.replace(default_server_addr, - cur_server_addr) + if request_args.get('lang', 'zh') == 'en': + default_server_addr = re.search( + '"label": {}.*?"value": "".*?}}'.format( + json.dumps( + "server ip", ensure_ascii=True).replace( + '\\', '\\\\')), content).group(0) + cur_server_addr = default_server_addr.replace( + '"value": ""', '"value": "localhost"') + default_http_port = re.search( + '"label": {}.*?"value": "".*?}}'.format( + json.dumps( + "server port", ensure_ascii=True).replace( + '\\', '\\\\')), content).group(0) + cur_http_port = default_http_port.replace( + '"value": ""', '"value": "{}"'.format(http_port)) + default_metrics_port = re.search( + '"label": {}.*?"value": "".*?}}'.format( + json.dumps( + "metrics port", ensure_ascii=True).replace( + '\\', '\\\\')), content).group(0) + cur_metrics_port = default_metrics_port.replace( + '"value": ""', + '"value": "{}"'.format(metrics_port)) + default_model_name = re.search( + '"label": {}.*?"value": "".*?}}'.format( + json.dumps( + "model name", ensure_ascii=True).replace( + '\\', '\\\\')), content).group(0) + cur_model_name = default_model_name.replace( + '"value": ""', '"value": "{}"'.format(model_name)) + default_model_version = re.search( + '"label": {}.*?"value": "".*?}}'.format( + json.dumps("model version", + ensure_ascii=True).replace( + '\\', '\\\\')), + content).group(0) + cur_model_version = default_model_version.replace( + '"value": ""', '"value": "{}"'.format('1')) + content = content.replace(default_server_addr, + cur_server_addr) + else: + default_server_addr = re.search( + '"label": {}.*?"value": "".*?}}'.format( + json.dumps("服务ip", ensure_ascii=True).replace( + '\\', '\\\\')), content).group(0) + cur_server_addr = default_server_addr.replace( + '"value": ""', '"value": "localhost"') + default_http_port = re.search( + '"label": {}.*?"value": "".*?}}'.format( + json.dumps( + "推理服务端口", ensure_ascii=True).replace( + '\\', '\\\\')), content).group(0) + cur_http_port = default_http_port.replace( + '"value": ""', '"value": "{}"'.format(http_port)) + default_metrics_port = re.search( + '"label": {}.*?"value": "".*?}}'.format( + json.dumps( + "性能服务端口", ensure_ascii=True).replace( + '\\', '\\\\')), content).group(0) + cur_metrics_port = default_metrics_port.replace( + '"value": ""', + '"value": "{}"'.format(metrics_port)) + default_model_name = re.search( + '"label": {}.*?"value": "".*?}}'.format( + json.dumps("模型名称", ensure_ascii=True).replace( + '\\', '\\\\')), content).group(0) + cur_model_name = default_model_name.replace( + '"value": ""', '"value": "{}"'.format(model_name)) + default_model_version = re.search( + '"label": {}.*?"value": "".*?}}'.format( + json.dumps("模型版本", ensure_ascii=True).replace( + '\\', '\\\\')), content).group(0) + cur_model_version = default_model_version.replace( + '"value": ""', '"value": "{}"'.format('1')) + content = content.replace(default_server_addr, + cur_server_addr) if http_port: content = content.replace(default_http_port, cur_http_port)