Add fastdeploy server and client component (#1169)

* add backend support for fastdeploy server

* fix

* add code

* fix

* fix

* add fastdeploy server component

* add fastdeploy server and client

* add exception description

* fix

* add model repository judgement

* add component tab for fastdeploy client

* update more tasks in fastdeploy client

* sort filenames

* backup config

* noqa for autogenerated file

* add data validation

* add __init__ for package

* add calculating layout for frontend

* add alive server detection and optimize client

* add alive server detection and optimize client

* add alive server detection and optimize client

* add metrics in gradio client

* update presentation

* Change return value to None for frontend performance data when server not ready

* add get_server_config and download_pretrain_model api

* add get_server_config and download_pretrain_model api

* add unit for metric table

* add unit for metric table

* fix a bug

* add judgement pretrained model download

* add judgement pretrained model download

* add version info for frontend

* rename download model

* fix a bug

* add fastdeploy model list

* optimize for choose configuration files

* modify according to frontend need

* fix name in config to model name

* optimize for server list and alive judgement

* keep server name as string type

* optimize process judgement logic

* optimize for deleting resource files

* add rename resource file

* fix

* fix a bug

* optimize code structure

* optimize code structure

* remove chinese tips and remove fastdeploy-python in requirements

requirements.txt

@@ -12,4 +12,8 @@ multiprocess
 packaging
 x2paddle
 rarfile
+gradio
+tritonclient[all]
+attrdict
+psutil
 onnx >= 1.6.0

visualdl/component/inference/fastdeploy_client/__init__.py

+# Copyright (c) 2022 VisualDL Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =======================================================================

visualdl/component/inference/fastdeploy_client/client_app.py

+# Copyright (c) 2022 VisualDL Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =======================================================================
+import gradio as gr
+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 .visualizer import visualize_detection
+from .visualizer import visualize_face_alignment
+from .visualizer import visualize_face_detection
+from .visualizer import visualize_headpose
+from .visualizer import visualize_keypoint_detection
+from .visualizer import visualize_matting
+from .visualizer import visualize_ocr
+from .visualizer import visualize_segmentation
+
+_http_manager = HttpClientManager()
+
+supported_tasks = {
+    'detection': visualize_detection,
+    'facedet': visualize_face_detection,
+    'keypointdetection': visualize_keypoint_detection,
+    'segmentation': visualize_segmentation,
+    'matting': visualize_matting,
+    'ocr': visualize_ocr,
+    'facealignment': visualize_face_alignment,
+    'headpose': visualize_headpose,
+    'unspecified': lambda x: str(x)
+}
+
+
+def create_gradio_client_app():  # 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("""
+              <div style="text-align: center; max-width: 650px; margin: 0 auto;">
+                <div
+                  style="
+                    display: inline-flex;
+                    gap: 0.8rem;
+                    font-size: 1.75rem;
+                    justify-content: center;
+                  "
+                >
+                <h1>
+                FastDeploy Client
+                </h1>
+                </div>
+                <p font-size: 94%">
+                The client is used for creating requests to fastdeploy server.
+                </p>
+              </div>
+          """)
+        with gr.Group():
+            with gr.Box():
+                with gr.Column():
+                    with gr.Row():
+                        server_addr_text = gr.Textbox(
+                            label="服务ip",
+                            show_label=True,
+                            max_lines=1,
+                            placeholder="localhost",
+                        )
+
+                        server_http_port_text = gr.Textbox(
+                            label="推理服务端口",
+                            show_label=True,
+                            max_lines=1,
+                            placeholder="8000",
+                        )
+
+                        server_metric_port_text = gr.Textbox(
+                            label="性能服务端口",
+                            show_label=True,
+                            max_lines=1,
+                            placeholder="8002",
+                        )
+                    with gr.Row():
+                        model_name_text = gr.Textbox(
+                            label="模型名称",
+                            show_label=True,
+                            max_lines=1,
+                            placeholder="yolov5",
+                        )
+                        model_version_text = gr.Textbox(
+                            label="模型版本",
+                            show_label=True,
+                            max_lines=1,
+                            placeholder="1",
+                        )
+
+            with gr.Box():
+                with gr.Tab("组件形式"):
+                    check_button = gr.Button("获取模型输入输出")
+                    component_format_column = gr.Column(visible=False)
+                    with component_format_column:
+                        task_radio = gr.Radio(
+                            choices=list(supported_tasks.keys()),
+                            value='unspecified',
+                            label='任务类型',
+                            visible=True)
+                        gr.Markdown("根据模型需要，挑选文本框或者图像框进行输入")
+                        with gr.Row():
+                            with gr.Column():
+                                gr.Markdown("模型输入")
+                                input_accordions = []
+                                input_name_texts = []
+                                input_images = []
+                                input_texts = []
+                                for i in range(6):
+                                    accordion = gr.Accordion(
+                                        "输入变量 {}".format(i),
+                                        open=True,
+                                        visible=False)
+                                    with accordion:
+                                        input_name_text = gr.Textbox(
+                                            label="变量名", interactive=False)
+                                        input_image = gr.Image(type='numpy')
+                                        input_text = gr.Textbox(
+                                            label="文本框", 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("模型输出")
+                                output_accordions = []
+                                output_name_texts = []
+                                output_images = []
+                                output_texts = []
+                                for i in range(6):
+                                    accordion = gr.Accordion(
+                                        "输出变量 {}".format(i),
+                                        open=True,
+                                        visible=False)
+                                    with accordion:
+                                        output_name_text = gr.Textbox(
+                                            label="变量名", interactive=False)
+                                        output_text = gr.Textbox(
+                                            label="服务返回的原数据",
+                                            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("提交请求")
+                with gr.Tab("原始形式"):
+                    gr.Markdown("模型输入")
+                    raw_payload_text = gr.Textbox(
+                        label="负载数据", max_lines=10000)
+                    with gr.Column():
+                        gr.Markdown("输出")
+                        output_raw_text = gr.Textbox(
+                            label="服务返回的原始数据", interactive=False)
+                    raw_submit_button = gr.Button("提交请求")
+
+            with gr.Box():
+                with gr.Column():
+                    gr.Markdown("服务性能统计（每次提交请求会自动更新数据，您也可以手动点击更新）")
+                    output_html_table = gr.HTML(
+                        label="metrics",
+                        interactive=False,
+                        show_label=False,
+                        value=metrics_table_head.format('', ''))
+                    update_metric_button = gr.Button("更新统计数据")
+
+            status_text = gr.Textbox(
+                label="status",
+                show_label=True,
+                max_lines=1,
+                interactive=False)
+
+        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):
+            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)
+            # 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'
+            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 Successful'}
+                    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)
+                        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)
+                    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):
+            if server_ip and metrics_port:
+                try:
+                    html_table = get_metric_data(server_ip, metrics_port)
+                    return {
+                        output_html_table: html_table,
+                        status_text: "Successfully update metrics."
+                    }
+                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
+            ],
+            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],
+            outputs=[output_html_table, status_text])
+    return block

visualdl/component/inference/fastdeploy_client/http_client_manager.py

+# Copyright (c) 2022 VisualDL Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =======================================================================
+import json
+import re
+
+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):
+    metadata = AttrDict(metadata)
+
+    return metadata
+
+
+def prepare_request(inputs_meta, inputs_data, outputs_meta):
+    '''
+    inputs_meta: inputs meta information from model. name: info
+    inputs_data: users input data. name: data
+    '''
+    # Set the input data
+    inputs = []
+    for input_dict in inputs_meta:
+        input_name = input_dict['name']
+        if input_name not in inputs_data:
+            raise RuntimeError(
+                'Error: input name {} required for model not existed.'.format(
+                    input_name))
+        if input_dict['datatype'] == 'FP32':
+            inputs_data[input_name] = inputs_data[input_name].astype(
+                np.float32
+            ) / 255  # image data returned by gradio is uint8, convert to fp32
+        if len(input_dict['shape']
+               ) == 3 and input_dict['shape'][0] == 3:  # NCHW
+            inputs_data[input_name] = inputs_data[input_name][0].transpose(
+                2, 0, 1)
+        elif len(input_dict['shape']
+                 ) == 4 and input_dict['shape'][1] == 3:  # NCHW
+            inputs_data[input_name] = inputs_data[input_name].transpose(
+                0, 3, 1, 2)
+        infer_input = httpclient.InferInput(
+            input_name, inputs_data[input_name].shape, input_dict['datatype'])
+        infer_input.set_data_from_numpy(inputs_data[input_name])
+        inputs.append(infer_input)
+    outputs = []
+    for output_dict in outputs_meta:
+        infer_output = httpclient.InferRequestedOutput(output_dict.name)
+        outputs.append(infer_output)
+    return inputs, outputs
+
+
+metrics_table_head = """
+<style>
+table, th {{
+  border:0.1px solid black;
+}}
+</style>
+
+<div>
+<table style="width:100%">
+  <tr>
+    <th rowspan="2">模型名称</th>
+    <th colspan="4">执行统计</th>
+    <th colspan="5">延迟统计</th>
+
+  </tr>
+  <tr>
+   <th>请求处理成功数</th>
+  <th>请求处理失败数</th>
+  <th>推理batch数</th>
+  <th>推理样本数</th>
+  <th>请求处理时间(ms)</th>
+  <th>任务队列等待时间(ms)</th>
+  <th>输入处理时间(ms)</th>
+  <th>模型推理时间(ms)</th>
+  <th>输出处理时间(ms)</th>
+  </tr>
+  {}
+</table>
+</div>
+<br>
+<br>
+<br>
+<br>
+<br>
+<div>
+<table style="width:100%">
+  <tr>
+    <th rowspan="2">GPU</th>
+    <th colspan="4">性能指标</th>
+    <th colspan="2">显存</th>
+  </tr>
+  <tr>
+   <th>利用率(%)</th>
+  <th>功率(W)</th>
+  <th>功率限制(W)</th>
+  <th>耗电量(W)</th>
+  <th>总量(GB)</th>
+  <th>已使用(GB)</th>
+  </tr>
+  {}
+</table>
+</div>
+"""
+
+
+def get_metric_data(server_addr, metric_port):  # noqa:C901
+    '''
+    Get metrics data from fastdeploy server, and transform it into html table.
+    Args:
+        server_addr(str): fastdeployserver ip address
+        metric_port(int): fastdeployserver metrics port
+    Returns:
+        htmltable(str): html table to show metrics data
+    '''
+    model_table = {}
+    gpu_table = {}
+    metric_column_name = {
+        "Model": {
+            "nv_inference_request_success", "nv_inference_request_failure",
+            "nv_inference_count", "nv_inference_exec_count",
+            "nv_inference_request_duration_us",
+            "nv_inference_queue_duration_us",
+            "nv_inference_compute_input_duration_us",
+            "nv_inference_compute_infer_duration_us",
+            "nv_inference_compute_output_duration_us"
+        },
+        "GPU": {
+            "nv_gpu_power_usage", "nv_gpu_power_limit",
+            "nv_energy_consumption", "nv_gpu_utilization",
+            "nv_gpu_memory_total_bytes", "nv_gpu_memory_used_bytes"
+        },
+        "CPU": {
+            "nv_cpu_utilization", "nv_cpu_memory_total_bytes",
+            "nv_cpu_memory_used_bytes"
+        }
+    }
+    try:
+        res = requests.get("http://{}:{}/metrics".format(
+            server_addr, metric_port))
+    except Exception:
+        return metrics_table_head.format('', '')
+    metric_content = res.text
+    for content in metric_content.split('\n'):
+        if content.startswith('#'):
+            continue
+        else:
+            res = re.match(r'(\w+){(.*)} (\w+)',
+                           content)  # match output by server metrics interface
+            if not res:
+                continue
+            metric_name = res.group(1)
+            model = res.group(2)
+            value = res.group(3)
+            infos = {}
+            for info in model.split(','):
+                k, v = info.split('=')
+                v = v.strip('"')
+                infos[k] = v
+            if metric_name in [
+                    "nv_inference_request_duration_us",
+                    "nv_inference_queue_duration_us",
+                    "nv_inference_compute_input_duration_us",
+                    "nv_inference_compute_infer_duration_us",
+                    "nv_inference_compute_output_duration_us"
+            ]:
+                value = str(float(value) / 1000)
+            elif metric_name in [
+                    "nv_gpu_memory_total_bytes", "nv_gpu_memory_used_bytes"
+            ]:
+                value = str(float(value) / 1024 / 1024 / 1024)
+            for key, metric_names in metric_column_name.items():
+                if metric_name in metric_names:
+                    if key == 'Model':
+                        model_name = infos['model']
+                        if model_name not in model_table:
+                            model_table[model_name] = {}
+                        model_table[model_name][metric_name] = value
+                    elif key == 'GPU':
+                        gpu_name = infos['gpu_uuid']
+                        if gpu_name not in gpu_table:
+                            gpu_table[gpu_name] = {}
+                        gpu_table[gpu_name][metric_name] = value
+                    elif key == 'CPU':
+                        pass
+    model_data_list = []
+    gpu_data_list = []
+    model_data_metric_names = [
+        "nv_inference_request_success", "nv_inference_request_failure",
+        "nv_inference_exec_count", "nv_inference_count",
+        "nv_inference_request_duration_us", "nv_inference_queue_duration_us",
+        "nv_inference_compute_input_duration_us",
+        "nv_inference_compute_infer_duration_us",
+        "nv_inference_compute_output_duration_us"
+    ]
+    gpu_data_metric_names = [
+        "nv_gpu_utilization", "nv_gpu_power_usage", "nv_gpu_power_limit",
+        "nv_energy_consumption", "nv_gpu_memory_total_bytes",
+        "nv_gpu_memory_used_bytes"
+    ]
+    for k, v in model_table.items():
+        data = []
+        data.append(k)
+        for data_metric in model_data_metric_names:
+            data.append(v[data_metric])
+        model_data_list.append(data)
+    for k, v in gpu_table.items():
+        data = []
+        data.append(k)
+        for data_metric in gpu_data_metric_names:
+            data.append(v[data_metric])
+        gpu_data_list.append(data)
+    model_data = '\n'.join([
+        "<tr>" + '\n'.join(["<td>" + item + "</td>"
+                            for item in data]) + "</tr>"
+        for data in model_data_list
+    ])
+    gpu_data = '\n'.join([
+        "<tr>" + '\n'.join(["<td>" + item + "</td>"
+                            for item in data]) + "</tr>"
+        for data in gpu_data_list
+    ])
+    return metrics_table_head.format(model_data, gpu_data)
+
+
+class HttpClientManager:
+    def __init__(self):
+        self.clients = {}  # server url: httpclient
+
+    def _create_client(self, server_url):
+        if server_url in self.clients:
+            return self.clients[server_url]
+        try:
+            fastdeploy_client = httpclient.InferenceServerClient(server_url)
+            self.clients[server_url] = fastdeploy_client
+            return fastdeploy_client
+        except Exception:
+            raise RuntimeError(
+                'Can not connect to server {}, please check your \
+                    server address'.format(server_url))
+
+    def infer(self, server_url, model_name, model_version, inputs):
+        fastdeploy_client = self._create_client(server_url)
+        input_metadata, output_metadata = self.get_model_meta(
+            server_url, model_name, model_version)
+        inputs, outputs = prepare_request(input_metadata, inputs,
+                                          output_metadata)
+        response = fastdeploy_client.infer(
+            model_name, inputs, model_version=model_version, outputs=outputs)
+
+        results = {}
+        for output in output_metadata:
+            result = response.as_numpy(output.name)  # datatype: numpy
+            if output.datatype == 'BYTES':  # datatype: bytes
+                try:
+                    value = result
+                    if len(result.shape) == 1:
+                        value = result[0]
+                    elif len(result.shape) == 2:
+                        value = result[0][0]
+                    elif len(result.shape) == 3:
+                        value = result[0][0][0]
+                    result = json.loads(value)  # datatype: json
+                except Exception:
+                    pass
+            else:
+                result = result[0]
+            results[output.name] = result
+        return results
+
+    def raw_infer(self, server_url, model_name, model_version, raw_input):
+        url = 'http://{}/v2/models/{}/versions/{}/infer'.format(
+            server_url, model_name, model_version)
+        res = requests.post(url, data=json.dumps(json.loads(raw_input)))
+        return json.dumps(res.json())
+
+    def get_model_meta(self, server_url, model_name, model_version):
+        fastdeploy_client = self._create_client(server_url)
+        try:
+            model_metadata = fastdeploy_client.get_model_metadata(
+                model_name=model_name, model_version=model_version)
+        except InferenceServerException as e:
+            raise RuntimeError("Failed to retrieve the metadata: " + str(e))
+
+        model_metadata = convert_http_metadata_config(model_metadata)
+
+        input_metadata = model_metadata.inputs
+        output_metadata = model_metadata.outputs
+        return input_metadata, output_metadata

visualdl/component/inference/fastdeploy_client/visualizer.py

+# Copyright (c) 2022 VisualDL Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =======================================================================
+import numpy as np
+
+__all__ = [
+    'visualize_detection', 'visualize_keypoint_detection',
+    'visualize_face_detection', 'visualize_face_alignment',
+    'visualize_segmentation', 'visualize_matting', 'visualize_ocr',
+    'visualize_headpose'
+]
+
+
+def visualize_detection(image, data):
+    try:
+        import fastdeploy as fd
+    except Exception:
+        raise RuntimeError(
+            "fastdeploy is required for visualizing results，please refer to \
+        https://github.com/PaddlePaddle/FastDeploy to install fastdeploy")
+    boxes = np.array(data['boxes'])
+    scores = np.array(data['scores'])
+    label_ids = np.array(data['label_ids'])
+    masks = np.array(data['masks'])
+    contain_masks = data['contain_masks']
+    detection_result = fd.C.vision.DetectionResult()
+    detection_result.boxes = boxes
+    detection_result.scores = scores
+    detection_result.label_ids = label_ids
+    detection_result.masks = masks
+    detection_result.contain_masks = contain_masks
+    result = fd.vision.vis_detection(image, detection_result)
+    return result
+
+
+def visualize_keypoint_detection(image, data):
+    try:
+        import fastdeploy as fd
+    except Exception:
+        raise RuntimeError(
+            "fastdeploy is required for visualizing results，please refer to \
+        https://github.com/PaddlePaddle/FastDeploy to install fastdeploy")
+    keypoints = np.array(data['keypoints'])
+    scores = np.array(data['scores'])
+    num_joints = np.array(data['num_joints'])
+
+    detection_result = fd.C.vision.KeyPointDetectionResult()
+    detection_result.keypoints = keypoints
+    detection_result.scores = scores
+    detection_result.num_joints = num_joints
+
+    result = fd.vision.vis_keypoint_detection(image, detection_result)
+    return result
+
+
+def visualize_face_detection(image, data):
+    try:
+        import fastdeploy as fd
+    except Exception:
+        raise RuntimeError(
+            "fastdeploy is required for visualizing results，please refer to \
+        https://github.com/PaddlePaddle/FastDeploy to install fastdeploy")
+    data = np.array(data['data'])
+    scores = np.array(data['scores'])
+    landmarks = np.array(data['landmarks'])
+    landmarks_per_face = data['landmarks_per_face']
+
+    detection_result = fd.C.vision.FaceDetectionResult()
+    detection_result.data = data
+    detection_result.scores = scores
+    detection_result.landmarks = landmarks
+    detection_result.landmarks_per_face = landmarks_per_face
+
+    result = fd.vision.vis_face_detection(image, detection_result)
+    return result
+
+
+def visualize_face_alignment(image, data):
+    try:
+        import fastdeploy as fd
+    except Exception:
+        raise RuntimeError(
+            "fastdeploy is required for visualizing results，please refer to \
+        https://github.com/PaddlePaddle/FastDeploy to install fastdeploy")
+    landmarks = np.array(data['landmarks'])
+
+    facealignment_result = fd.C.vision.FaceAlignmentResult()
+    facealignment_result.landmarks = landmarks
+
+    result = fd.vision.vis_face_alignment(image, facealignment_result)
+    return result
+
+
+def visualize_segmentation(image, data):
+    try:
+        import fastdeploy as fd
+    except Exception:
+        raise RuntimeError(
+            "fastdeploy is required for visualizing results，please refer to \
+        https://github.com/PaddlePaddle/FastDeploy to install fastdeploy")
+    label_ids = np.array(data['label_ids'])
+    score_map = np.array(data['score_map'])
+    shape = np.array(data['shape'])
+
+    segmentation_result = fd.C.vision.SegmentationResult()
+    segmentation_result.shape = shape
+    segmentation_result.score_map = score_map
+    segmentation_result.label_ids = label_ids
+
+    result = fd.vision.vis_segmentation(image, segmentation_result)
+    return result
+
+
+def visualize_matting(image, data):
+    try:
+        import fastdeploy as fd
+    except Exception:
+        raise RuntimeError(
+            "fastdeploy is required for visualizing results，please refer to \
+        https://github.com/PaddlePaddle/FastDeploy to install fastdeploy")
+    alpha = np.array(data['alpha'])
+    foreground = np.array(data['foreground'])
+    contain_foreground = data['contain_foreground']
+    shape = np.array(data['shape'])
+
+    matting_result = fd.C.vision.MattingResult()
+    matting_result.alpha = alpha
+    matting_result.foreground = foreground
+    matting_result.contain_foreground = contain_foreground
+    matting_result.shape = shape
+
+    result = fd.vision.vis_matting(image, matting_result)
+    return result
+
+
+def visualize_ocr(image, data):
+    try:
+        import fastdeploy as fd
+    except Exception:
+        raise RuntimeError(
+            "fastdeploy is required for visualizing results，please refer to \
+        https://github.com/PaddlePaddle/FastDeploy to install fastdeploy")
+    boxes = np.array(data['boxes'])
+    text = np.array(data['text'])
+    rec_scores = np.array(data['rec_scores'])
+    cls_scores = np.array(data['cls_scores'])
+    cls_labels = data['cls_labels']
+
+    ocr_result = fd.C.vision.OCRResult()
+    ocr_result.boxes = boxes
+    ocr_result.text = text
+    ocr_result.rec_scores = rec_scores
+    ocr_result.cls_scores = cls_scores
+    ocr_result.cls_labels = cls_labels
+
+    result = fd.vision.vis_ppocr(image, ocr_result)
+    return result
+
+
+def visualize_headpose(image, data):
+    try:
+        import fastdeploy as fd
+    except Exception:
+        raise RuntimeError(
+            "fastdeploy is required for visualizing results，please refer to \
+        https://github.com/PaddlePaddle/FastDeploy to install fastdeploy")
+    euler_angles = np.array(data['euler_angles'])
+
+    headpose_result = fd.C.vision.HeadPoseResult()
+    headpose_result.euler_angles = euler_angles
+
+    result = fd.vision.vis_headpose(image, headpose_result)
+    return result

visualdl/component/inference/fastdeploy_server.py

+# Copyright (c) 2022 VisualDL Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =======================================================================
+import datetime
+import json
+import os
+import re
+import shutil
+import socket
+import time
+from multiprocessing import Process
+from pathlib import Path
+
+import requests
+
+from .fastdeploy_client.client_app import create_gradio_client_app
+from .fastdeploy_lib import analyse_config
+from .fastdeploy_lib import check_process_zombie
+from .fastdeploy_lib import copy_config_file_to_default_config
+from .fastdeploy_lib import delete_files_for_process
+from .fastdeploy_lib import exchange_format_to_original_format
+from .fastdeploy_lib import generate_metric_table
+from .fastdeploy_lib import get_alive_fastdeploy_servers
+from .fastdeploy_lib import get_config_filenames_for_one_model
+from .fastdeploy_lib import get_config_for_one_model
+from .fastdeploy_lib import get_process_model_configuration
+from .fastdeploy_lib import get_process_output
+from .fastdeploy_lib import get_start_arguments
+from .fastdeploy_lib import json2pbtxt
+from .fastdeploy_lib import kill_process
+from .fastdeploy_lib import launch_process
+from .fastdeploy_lib import mark_pid_for_dead_process
+from .fastdeploy_lib import original_format_to_exchange_format
+from .fastdeploy_lib import validate_data
+from visualdl.server.api import gen_result
+from visualdl.server.api import result
+from visualdl.utils.dir import FASTDEPLOYSERVER_PATH
+
+
+class FastDeployServerApi(object):
+    def __init__(self):
+        self.root_dir = Path(os.getcwd())
+        self.opened_servers = {
+        }  # Use to store the opened server process pid and process itself
+        self.client_port = None
+
+    @result()
+    def get_directory(self, cur_dir):
+        if self.root_dir not in Path(os.path.abspath(cur_dir)).parents:
+            cur_dir = '.'
+        cur_dir, sub_dirs, filenames = os.walk(cur_dir).send(None)
+        if Path(self.root_dir) != Path(os.path.abspath(cur_dir)):
+            sub_dirs.append('..')
+        sub_dirs = sorted(sub_dirs)
+        directorys = {
+            'parent_dir':
+            os.path.relpath(Path(os.path.abspath(cur_dir)), self.root_dir),
+            'sub_dir':
+            sub_dirs
+        }
+        return directorys
+
+    @result()
+    def get_config(self, cur_dir):
+        all_model_configs, all_model_versions = analyse_config(cur_dir)
+        return original_format_to_exchange_format(all_model_configs,
+                                                  all_model_versions)
+
+    @result()
+    def config_update(self, cur_dir, model_name, config, config_filename):
+        config = json.loads(config)
+        all_models = exchange_format_to_original_format(config)
+        model_dir = os.path.join(os.path.abspath(cur_dir), model_name)
+        filtered_config = validate_data(all_models[model_name])
+        text_proto = json2pbtxt(json.dumps(filtered_config))
+        # backup user's config data first, when data corrupted by front-end, we still can recovery data
+        # backup config filename: {original_name}_vdlbackup_{datetime}.pbtxt
+        # backup config can only used to restore config.pbtxt
+        if 'vdlbackup' in config_filename:
+            raise RuntimeError(
+                "Backup config file is not permitted to update.")
+        basename = os.path.splitext(config_filename)[0]
+        shutil.copy(
+            os.path.join(model_dir, config_filename),
+            os.path.join(
+                model_dir, '{}_vdlbackup_{}.pbtxt'.format(
+                    basename,
+                    datetime.datetime.now().isoformat())))
+        with open(os.path.join(model_dir, config_filename), 'w') as f:
+            f.write(text_proto)
+        return
+
+    @result()
+    def start_server(self, configs):
+        configs = json.loads(configs)
+        process = launch_process(configs)
+        if process.poll() is not None:
+            raise RuntimeError(
+                "Failed to launch fastdeployserver，please check fastdeployserver is installed in environment."
+            )
+        server_name = configs['server-name'] if configs[
+            'server-name'] else str(process.pid)
+        self.opened_servers[server_name] = process
+        return server_name
+
+    @result()
+    def stop_server(self, server_id):
+        if server_id in self.opened_servers:  # check if server_id in self.opened_servers
+            kill_process(self.opened_servers[server_id])
+            del self.opened_servers[server_id]
+        elif server_id in set(
+                os.listdir(FASTDEPLOYSERVER_PATH)):  # check if server_id in
+            # FASTDEPLOYSERVER_PATH(may be launched by other vdl app instance by gunicorn)
+            kill_process(server_id)
+        delete_files_for_process(server_id)
+        self._poll_zombie_process()
+
+    @result('text/plain')
+    def get_server_output(self, server_id, length):
+        length = int(length)
+        if server_id in self.opened_servers:  # check if server_id in self.opened_servers
+            return get_process_output(server_id, length)
+        elif str(server_id) in set(
+                os.listdir(FASTDEPLOYSERVER_PATH)):  # check if server_id in
+            # FASTDEPLOYSERVER_PATH(may be launched by other vdl app instance by gunicorn)
+            return get_process_output(server_id, length)
+        else:
+            return
+
+    @result()
+    def get_server_metric(self, server_id):
+        args = get_start_arguments(server_id)
+        host = 'localhost'
+        port = args.get('metrics-port', 8002)
+        return generate_metric_table(host, port)
+
+    @result()
+    def get_server_list(self):
+        return get_alive_fastdeploy_servers()
+
+    @result()
+    def check_server_alive(self, server_id):
+        self._poll_zombie_process()
+        if check_process_zombie(server_id) is True:
+            raise RuntimeError(
+                "Server {} is down due to exception or killed，please check the reason according to the log, \
+                then close this server.".format(server_id))
+        return
+
+    @result()
+    def get_server_config(self, server_id):
+        return get_process_model_configuration(server_id)
+
+    @result()
+    def get_pretrain_model_list(self):
+        '''
+        Get all available fastdeploy models from hub server.
+        '''
+        res = requests.get(
+            'http://paddlepaddle.org.cn/paddlehub/fastdeploy_listmodels')
+        result = res.json()
+        if result['status'] != 0:
+            raise RuntimeError(
+                "Failed to get pre-trained model list from hub server.")
+        else:
+            data = result['data']
+            model_list = {}
+            for category, models in data.items():
+                if category not in model_list:
+                    model_list[category] = set()
+                for model in models:
+                    model_list[category].add(model['name'])
+            # adapt data format for frontend
+            models_info = []
+            for category, model_names in model_list.items():
+                models_info.append({
+                    "value": category,
+                    "label": category,
+                    "children": []
+                })
+                for model_name in sorted(model_names):
+                    models_info[-1]["children"].append({
+                        "value": model_name,
+                        "label": model_name
+                    })
+            return models_info
+
+    @result()
+    def download_pretrain_model(self, cur_dir, model_name, version,
+                                pretrain_model_name):
+        version_resource_dir = os.path.join(
+            os.path.abspath(cur_dir), model_name, version)
+        try:
+            import fastdeploy as fd
+        except Exception:
+            raise RuntimeError(
+                "fastdeploy is required for visualizing results，please refer to \
+            https://github.com/PaddlePaddle/FastDeploy to install fastdeploy")
+        model_path = fd.download_model(
+            name=pretrain_model_name, path=version_resource_dir)
+        if model_path:
+            if '.onnx' in model_path:
+                shutil.move(
+                    model_path,
+                    os.path.join(os.path.dirname(model_path), 'model.onnx'))
+            else:
+                for filename in os.listdir(model_path):
+                    if '.pdmodel' in filename or '.pdiparams' in filename:
+                        shutil.move(
+                            os.path.join(model_path, filename),
+                            os.path.join(
+                                os.path.dirname(model_path), 'model{}'.format(
+                                    os.path.splitext(filename)[1])))
+                    else:
+                        shutil.move(
+                            os.path.join(model_path, filename),
+                            os.path.join(
+                                os.path.dirname(model_path), filename))
+                shutil.rmtree(model_path)
+            version_info_for_frontend = []
+            for version_name in os.listdir(os.path.join(cur_dir, model_name)):
+                if re.match(
+                        r'\d+',
+                        version_name):  # version directory consists of numbers
+                    version_filenames_dict_for_frontend = {}
+                    version_filenames_dict_for_frontend['title'] = version_name
+                    version_filenames_dict_for_frontend['key'] = version_name
+                    version_filenames_dict_for_frontend['children'] = []
+                    for filename in os.listdir(
+                            os.path.join(cur_dir, model_name, version_name)):
+                        version_filenames_dict_for_frontend['children'].append(
+                            {
+                                'title': filename,
+                                'key': filename
+                            })
+                    version_info_for_frontend.append(
+                        version_filenames_dict_for_frontend)
+            return version_info_for_frontend
+        else:
+            raise RuntimeError(
+                "Failed to download pre-trained model {}.".format(
+                    pretrain_model_name))
+
+    @result()
+    def get_config_for_model(self, cur_dir, name, config_filename):
+        return get_config_for_one_model(cur_dir, name, config_filename)
+
+    @result()
+    def get_config_filenames_for_model(self, cur_dir, name):
+        return get_config_filenames_for_one_model(cur_dir, name)
+
+    @result()
+    def delete_config_for_model(self, cur_dir, name, config_filename):
+        if self.root_dir not in Path(
+                os.path.abspath(cur_dir)
+        ).parents:  # should prevent user remove files outside model-repository
+            raise RuntimeError(
+                'Failed to delete config file, please check filepath.')
+        if os.path.exists(os.path.join(cur_dir, name, config_filename)):
+            os.remove(os.path.join(cur_dir, name, config_filename))
+        return get_config_filenames_for_one_model(cur_dir, name)
+
+    @result()
+    def set_default_config_for_model(self, cur_dir, name, config_filename):
+        model_dir = os.path.join(os.path.abspath(cur_dir), name)
+        # backup config.pbtxt to config_vdlbackup_{datetime}.pbtxt
+        if os.path.exists(os.path.join(model_dir, 'config.pbtxt')):
+            shutil.copy(
+                os.path.join(model_dir, 'config.pbtxt'),
+                os.path.join(
+                    model_dir, 'config_vdlbackup_{}.pbtxt'.format(
+                        datetime.datetime.now().isoformat())))
+        if config_filename != 'config.pbtxt':
+            copy_config_file_to_default_config(model_dir, config_filename)
+        return
+
+    @result()
+    def delete_resource_for_model(self, cur_dir, model_name, version,
+                                  resource_filename):
+        if self.root_dir not in Path(
+                os.path.abspath(cur_dir)
+        ).parents:  # should prevent user remove files outside model-repository
+            raise RuntimeError(
+                'Failed to delete resource file, please check filepath.')
+        resource_path = os.path.join(
+            os.path.abspath(cur_dir), model_name, version, resource_filename)
+        if os.path.exists(resource_path):
+            os.remove(resource_path)
+        version_info_for_frontend = []
+        for version_name in os.listdir(os.path.join(cur_dir, model_name)):
+            if re.match(r'\d+',
+                        version_name):  # version directory consists of numbers
+                version_filenames_dict_for_frontend = {}
+                version_filenames_dict_for_frontend['title'] = version_name
+                version_filenames_dict_for_frontend['key'] = version_name
+                version_filenames_dict_for_frontend['children'] = []
+                for filename in os.listdir(
+                        os.path.join(cur_dir, model_name, version_name)):
+                    version_filenames_dict_for_frontend['children'].append({
+                        'title':
+                        filename,
+                        'key':
+                        filename
+                    })
+                version_info_for_frontend.append(
+                    version_filenames_dict_for_frontend)
+        return version_info_for_frontend
+
+    @result()
+    def rename_resource_for_model(self, cur_dir, model_name, version,
+                                  resource_filename, new_filename):
+        if self.root_dir not in Path(
+                os.path.abspath(cur_dir)
+        ).parents:  # should prevent user remove files outside model-repository
+            raise RuntimeError(
+                'Failed to rename resource file, please check filepath.')
+        resource_path = os.path.join(
+            os.path.abspath(cur_dir), model_name, version, resource_filename)
+        new_file_path = os.path.join(
+            os.path.abspath(cur_dir), model_name, version, new_filename)
+        if os.path.exists(resource_path):
+            shutil.move(resource_path, new_file_path)
+        version_info_for_frontend = []
+        for version_name in os.listdir(os.path.join(cur_dir, model_name)):
+            if re.match(r'\d+',
+                        version_name):  # version directory consists of numbers
+                version_filenames_dict_for_frontend = {}
+                version_filenames_dict_for_frontend['title'] = version_name
+                version_filenames_dict_for_frontend['key'] = version_name
+                version_filenames_dict_for_frontend['children'] = []
+                for filename in os.listdir(
+                        os.path.join(cur_dir, model_name, version_name)):
+                    version_filenames_dict_for_frontend['children'].append({
+                        'title':
+                        filename,
+                        'key':
+                        filename
+                    })
+                version_info_for_frontend.append(
+                    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 _poll_zombie_process(self):
+        # check if there are servers killed by other vdl app instance and become zoombie
+        should_delete = []
+        for server_id, process in self.opened_servers.items():
+            if process.poll() is not None:
+                mark_pid_for_dead_process(server_id)
+                should_delete.append(server_id)
+
+        for server_id in should_delete:
+            del self.opened_servers[server_id]
+
+
+def create_fastdeploy_api_call():
+    api = FastDeployServerApi()
+    routes = {
+        'get_directory': (api.get_directory, ['dir']),
+        'config_update': (api.config_update,
+                          ['dir', 'name', 'config', 'config_filename']),
+        'get_config': (api.get_config, ['dir']),
+        'get_config_filenames_for_model': (api.get_config_filenames_for_model,
+                                           ['dir', 'name']),
+        'get_config_for_model': (api.get_config_for_model,
+                                 ['dir', 'name', 'config_filename']),
+        'set_default_config_for_model': (api.set_default_config_for_model,
+                                         ['dir', 'name', 'config_filename']),
+        'delete_config_for_model': (api.delete_config_for_model,
+                                    ['dir', 'name', 'config_filename']),
+        '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, []),
+        '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']),
+        'get_pretrain_model_list': (api.get_pretrain_model_list, []),
+        'check_server_alive': (api.check_server_alive, ['server_id']),
+        'download_pretrain_model':
+        (api.download_pretrain_model,
+         ['dir', 'name', 'version', 'pretrain_model_name']),
+        'delete_resource_for_model':
+        (api.delete_resource_for_model,
+         ['dir', 'name', 'version', 'resource_filename']),
+        'rename_resource_for_model': (api.rename_resource_for_model, [
+            'dir', 'name', 'version', 'resource_filename', 'new_filename'
+        ])
+    }
+
+    def call(path: str, args):
+        route = routes.get(path)
+        if not route:
+            return json.dumps(gen_result(
+                status=1, msg='api not found')), 'application/json', None
+        method, call_arg_names = route
+        call_args = [args.get(name) for name in call_arg_names]
+        return method(*call_args)
+
+    return call

visualdl/component/inference/proto/__init__.py

+# Copyright (c) 2022 VisualDL Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =======================================================================