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Unverified Commit 701ad2ab authored by Yuge Zhang's avatar Yuge Zhang Committed by GitHub
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Add rules for ipynb in Chinese link (#4460)

parent f1e70073
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Showing with 16 additions and 1613 deletions
docs/tools/chineselink.py
View file @ 701ad2ab
...@@ -40,6 +40,13 @@ whitelist = [ ...@@ -40,6 +40,13 @@ whitelist = [
'Compression/QuickStart.rst', 'Compression/QuickStart.rst',
] ]
suffix_list = [
'.html',
'.md',
'.rst',
'.ipynb',
]
for path in whitelist: for path in whitelist:
assert (Path('zh_CN') / path).exists(), path assert (Path('zh_CN') / path).exists(), path
...@@ -90,7 +97,7 @@ for path in walk(Path('en_US')): ...@@ -90,7 +97,7 @@ for path in walk(Path('en_US')):
relative_path = path.relative_to('en_US') relative_path = path.relative_to('en_US')
if relative_path.as_posix().startswith('_build'): if relative_path.as_posix().startswith('_build'):
continue continue
if path.suffix in ('.html', '.md', '.rst'): if path.suffix in suffix_list:
target_path = (Path('zh_CN') / relative_path) target_path = (Path('zh_CN') / relative_path)
if relative_path.as_posix() in whitelist: if relative_path.as_posix() in whitelist:
# whitelist files. should be translated # whitelist files. should be translated
...@@ -109,7 +116,7 @@ for path in walk(Path('en_US')): ...@@ -109,7 +116,7 @@ for path in walk(Path('en_US')):
# delete redundant files # delete redundant files
for path in walk(Path('zh_CN')): for path in walk(Path('zh_CN')):
if path.suffix in ('.html', '.md', '.rst'): if path.suffix in suffix_list:
relative_path = path.relative_to('zh_CN') relative_path = path.relative_to('zh_CN')
if not (Path('en_US') / relative_path).exists(): if not (Path('en_US') / relative_path).exists():
failed_files.append('(redundant) ' + path.as_posix()) failed_files.append('(redundant) ' + path.as_posix())
......
docs/zh_CN/Compression/compression_pipeline_example.ipynb
View file @ 701ad2ab
./en_US/Compression/compression_pipeline_example.ipynb ../../en_US/Compression/compression_pipeline_example.ipynb
\ No newline at end of file \ No newline at end of file
docs/zh_CN/NAS/BenchmarksExample.ipynb deleted 100644 → 0
View file @ f1e70073
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NAS 基准测试示例"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pprint\n",
"import time\n",
"\n",
"from nni.nas.benchmarks.nasbench101 import query_nb101_trial_stats\n",
"from nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats\n",
"from nni.nas.benchmarks.nds import query_nds_trial_stats\n",
"\n",
"ti = time.time()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NAS-Bench-101"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"使用以下架构为例：\n",
"\n",
"![nas-101](../../img/nas-bench-101-example.png)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"arch = {\n",
" 'op1': 'conv3x3-bn-relu',\n",
" 'op2': 'maxpool3x3',\n",
" 'op3': 'conv3x3-bn-relu',\n",
" 'op4': 'conv3x3-bn-relu',\n",
" 'op5': 'conv1x1-bn-relu',\n",
" 'input1': [0],\n",
" 'input2': [1],\n",
" 'input3': [2],\n",
" 'input4': [0],\n",
" 'input5': [0, 3, 4],\n",
" 'input6': [2, 5]\n",
"}\n",
"for t in query_nb101_trial_stats(arch, 108, include_intermediates=True):\n",
" pprint.pprint(t)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"一个 NAS-Bench-101 的网络结构可以被训练多次。 生成器返回的每一个元素是一个字典，包含了该 Trial 设置（网络结构+超参数）中其中一个训练结果，如训练集/验证集/测试集准确率，训练时间，Epoch数等等。 NAS-Bench-201 和 NDS 的结果遵循了相似的格式。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NAS-Bench-201"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"使用以下架构为例：\n",
"\n",
"![nas-201](../../img/nas-bench-201-example.png)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"arch = {\n",
" '0_1': 'avg_pool_3x3',\n",
" '0_2': 'conv_1x1',\n",
" '1_2': 'skip_connect',\n",
" '0_3': 'conv_1x1',\n",
" '1_3': 'skip_connect',\n",
" '2_3': 'skip_connect'\n",
"}\n",
"for t in query_nb201_trial_stats(arch, 200, 'cifar100'):\n",
" pprint.pprint(t)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"中间结果也可得到。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"for t in query_nb201_trial_stats(arch, None, 'imagenet16-120', include_intermediates=True):\n",
" print(t['config'])\n",
" print('Intermediates:', len(t['intermediates']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NDS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"使用以下架构为例：<br>\n",
"![nds](../../img/nas-bench-nds-example.png)\n",
"\n",
"这里，`bot_muls`, `ds`, `num_gs`, `ss` 和 `ws` 分别表示 \"bottleneck multipliers\", \"depths\", \"number of groups\", \"strides\" 和 \"widths\" 。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"model_spec = {\n",
" 'bot_muls': [0.0, 0.25, 0.25, 0.25],\n",
" 'ds': [1, 16, 1, 4],\n",
" 'num_gs': [1, 2, 1, 2],\n",
" 'ss': [1, 1, 2, 2],\n",
" 'ws': [16, 64, 128, 16]\n",
"}\n",
"# Use none as a wildcard\n",
"for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):\n",
" pprint.pprint(t)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"model_spec = {\n",
" 'bot_muls': [0.0, 0.25, 0.25, 0.25],\n",
" 'ds': [1, 16, 1, 4],\n",
" 'num_gs': [1, 2, 1, 2],\n",
" 'ss': [1, 1, 2, 2],\n",
" 'ws': [16, 64, 128, 16]\n",
"}\n",
"for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10', include_intermediates=True):\n",
" pprint.pprint(t['intermediates'][:10])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}\n",
"for t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):\n",
" pprint.pprint(t)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# get the first one\n",
"pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# count number\n",
"model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}\n",
"cell_spec = {\n",
" 'normal_0_op_x': 'avg_pool_3x3',\n",
" 'normal_0_input_x': 0,\n",
" 'normal_0_op_y': 'conv_7x1_1x7',\n",
" 'normal_0_input_y': 1,\n",
" 'normal_1_op_x': 'sep_conv_3x3',\n",
" 'normal_1_input_x': 2,\n",
" 'normal_1_op_y': 'sep_conv_5x5',\n",
" 'normal_1_input_y': 0,\n",
" 'normal_2_op_x': 'dil_sep_conv_3x3',\n",
" 'normal_2_input_x': 2,\n",
" 'normal_2_op_y': 'dil_sep_conv_3x3',\n",
" 'normal_2_input_y': 2,\n",
" 'normal_3_op_x': 'skip_connect',\n",
" 'normal_3_input_x': 4,\n",
" 'normal_3_op_y': 'dil_sep_conv_3x3',\n",
" 'normal_3_input_y': 4,\n",
" 'normal_4_op_x': 'conv_7x1_1x7',\n",
" 'normal_4_input_x': 2,\n",
" 'normal_4_op_y': 'sep_conv_3x3',\n",
" 'normal_4_input_y': 4,\n",
" 'normal_concat': [3, 5, 6],\n",
" 'reduce_0_op_x': 'avg_pool_3x3',\n",
" 'reduce_0_input_x': 0,\n",
" 'reduce_0_op_y': 'dil_sep_conv_3x3',\n",
" 'reduce_0_input_y': 1,\n",
" 'reduce_1_op_x': 'sep_conv_3x3',\n",
" 'reduce_1_input_x': 0,\n",
" 'reduce_1_op_y': 'sep_conv_3x3',\n",
" 'reduce_1_input_y': 0,\n",
" 'reduce_2_op_x': 'skip_connect',\n",
" 'reduce_2_input_x': 2,\n",
" 'reduce_2_op_y': 'sep_conv_7x7',\n",
" 'reduce_2_input_y': 0,\n",
" 'reduce_3_op_x': 'conv_7x1_1x7',\n",
" 'reduce_3_input_x': 4,\n",
" 'reduce_3_op_y': 'skip_connect',\n",
" 'reduce_3_input_y': 4,\n",
" 'reduce_4_op_x': 'conv_7x1_1x7',\n",
" 'reduce_4_input_x': 0,\n",
" 'reduce_4_op_y': 'conv_7x1_1x7',\n",
" 'reduce_4_input_y': 5,\n",
" 'reduce_concat': [3, 6]\n",
"}\n",
"\n",
"for t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):\n",
" assert t['config']['model_spec'] == model_spec\n",
" assert t['config']['cell_spec'] == cell_spec\n",
" pprint.pprint(t)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# count number\n",
"print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NLP"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"metadata": false
}
},
"source": [
"使用以下两种结构作为示例。 \n",
"论文中的 arch 被称为嵌套变量的 “receipe”，目前还没有在 NNI 的基准测试中使用。\n",
"一个架构有多个节点，节点输入和节点操作，您可以参考文档了解更多详细信息。\n",
"\n",
"arch1 : <img src=\"../../img/nas-bench-nlp-example1.jpeg\" width=400 height=300 /> \n",
"\n",
"\n",
"arch2 : <img src=\"../../img/nas-bench-nlp-example2.jpeg\" width=400 height=300 /> \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'config': {'arch': {'h_new_0_input_0': 'node_3',\n 'h_new_0_input_1': 'node_2',\n 'h_new_0_input_2': 'node_1',\n 'h_new_0_op': 'blend',\n 'node_0_input_0': 'x',\n 'node_0_input_1': 'h_prev_0',\n 'node_0_op': 'linear',\n 'node_1_input_0': 'node_0',\n 'node_1_op': 'activation_tanh',\n 'node_2_input_0': 'h_prev_0',\n 'node_2_input_1': 'node_1',\n 'node_2_input_2': 'x',\n 'node_2_op': 'linear',\n 'node_3_input_0': 'node_2',\n 'node_3_op': 'activation_leaky_relu'},\n 'dataset': 'ptb',\n 'id': 20003},\n 'id': 16291,\n 'test_loss': 4.680262297102549,\n 'train_loss': 4.132040537087838,\n 'training_time': 177.05208373069763,\n 'val_loss': 4.707944253177966}\n"
]
}
],
"source": [
"import pprint\n",
"from nni.nas.benchmarks.nlp import query_nlp_trial_stats\n",
"\n",
"arch1 = {'h_new_0_input_0': 'node_3', 'h_new_0_input_1': 'node_2', 'h_new_0_input_2': 'node_1', 'h_new_0_op': 'blend', 'node_0_input_0': 'x', 'node_0_input_1': 'h_prev_0', 'node_0_op': 'linear','node_1_input_0': 'node_0', 'node_1_op': 'activation_tanh', 'node_2_input_0': 'h_prev_0', 'node_2_input_1': 'node_1', 'node_2_input_2': 'x', 'node_2_op': 'linear', 'node_3_input_0': 'node_2', 'node_3_op': 'activation_leaky_relu'}\n",
"for i in query_nlp_trial_stats(arch=arch1, dataset=\"ptb\"):\n",
" pprint.pprint(i)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[{'current_epoch': 46,\n 'id': 1796,\n 'test_loss': 6.233430054978619,\n 'train_loss': 6.4866799231542664,\n 'training_time': 146.5680329799652,\n 'val_loss': 6.326836978687959},\n {'current_epoch': 47,\n 'id': 1797,\n 'test_loss': 6.2402057403023825,\n 'train_loss': 6.485401405247535,\n 'training_time': 146.05511450767517,\n 'val_loss': 6.3239741605870865},\n {'current_epoch': 48,\n 'id': 1798,\n 'test_loss': 6.351145308363877,\n 'train_loss': 6.611281181173992,\n 'training_time': 145.8849437236786,\n 'val_loss': 6.436160816865809},\n {'current_epoch': 49,\n 'id': 1799,\n 'test_loss': 6.227155079159031,\n 'train_loss': 6.473414458249545,\n 'training_time': 145.51414465904236,\n 'val_loss': 6.313294354607077}]\n"
]
}
],
"source": [
"arch2 = {\"h_new_0_input_0\":\"node_0\",\"h_new_0_input_1\":\"node_1\",\"h_new_0_op\":\"elementwise_sum\",\"node_0_input_0\":\"x\",\"node_0_input_1\":\"h_prev_0\",\"node_0_op\":\"linear\",\"node_1_input_0\":\"node_0\",\"node_1_op\":\"activation_tanh\"}\n",
"for i in query_nlp_trial_stats(arch=arch2, dataset='wikitext-2', include_intermediates=True):\n",
" pprint.pprint(i['intermediates'][45:49])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"pycharm": {},
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Elapsed time: 5.60982608795166 seconds\n"
]
}
],
"source": [
"print('Elapsed time: ', time.time() - ti, 'seconds')"
]
}
],
"metadata": {
"file_extension": ".py",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"name": "python",
"version": "3.8.5-final"
},
"mimetype": "text/x-python",
"name": "python",
"npconvert_exporter": "python",
"orig_nbformat": 2,
"pygments_lexer": "ipython3",
"version": 3
},
"nbformat": 4,
"nbformat_minor": 2
}
\ No newline at end of file
docs/zh_CN/NAS/BenchmarksExample.ipynb 0 → 120000
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../../en_US/NAS/BenchmarksExample.ipynb
\ No newline at end of file
docs/zh_CN/Tutorial/python_api_connect.ipynb deleted 100644 → 0
View file @ f1e70073
{
"cells": [
{
"cell_type": "markdown",
"id": "white-electron",
"metadata": {},
"source": [
"## 连接并管理已存在的 Experiment"
]
},
{
"cell_type": "markdown",
"id": "recent-italic",
"metadata": {},
"source": [
"### 1. 连接 Experiment"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "statistical-repair",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-02-25 07:50:38] Connect to port 8080 success, experiment id is IF0JnfLE, status is RUNNING.\n"
]
}
],
"source": [
"from nni.experiment import Experiment\n",
"experiment = Experiment.connect(8080)"
]
},
{
"cell_type": "markdown",
"id": "defensive-scratch",
"metadata": {},
"source": [
"### 2. Experiment 查看和管理"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "independent-touch",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 'IF0JnfLE',\n",
" 'revision': 6,\n",
" 'maxTrialNum': 10,\n",
" 'logDir': '/home/ningshang/nni-experiments/IF0JnfLE',\n",
" 'nextSequenceId': 2,\n",
" 'params': {'authorName': 'default',\n",
" 'experimentName': 'example_sklearn-classification',\n",
" 'trialConcurrency': 1,\n",
" 'maxExecDuration': 3600,\n",
" 'maxTrialNum': 5,\n",
" 'searchSpace': '{\"C\": {\"_type\": \"uniform\", \"_value\": [0.1, 1]}, \"kernel\": {\"_type\": \"choice\", \"_value\": [\"linear\", \"rbf\", \"poly\", \"sigmoid\"]}, \"degree\": {\"_type\": \"choice\", \"_value\": [1, 2, 3, 4]}, \"gamma\": {\"_type\": \"uniform\", \"_value\": [0.01, 0.1]}, \"coef0\": {\"_type\": \"uniform\", \"_value\": [0.01, 0.1]}}',\n",
" 'trainingServicePlatform': 'local',\n",
" 'tuner': {'builtinTunerName': 'TPE',\n",
" 'classArgs': {'optimize_mode': 'maximize'},\n",
" 'checkpointDir': '/home/ningshang/nni-experiments/IF0JnfLE/checkpoint'},\n",
" 'versionCheck': True,\n",
" 'clusterMetaData': [{'key': 'trial_config',\n",
" 'value': {'command': 'python3 main.py',\n",
" 'codeDir': '/home/ningshang/nni/examples/trials/sklearn/classification/.',\n",
" 'gpuNum': 0}}]},\n",
" 'startTime': 1614239412494}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"experiment.get_experiment_profile()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "printable-bookmark",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-03-05 12:18:32] (root) Successfully update maxTrialNum.\n"
]
}
],
"source": [
"experiment.update_max_trial_number(10)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "marine-serial",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 'IF0JnfLE',\n",
" 'revision': 8,\n",
" 'execDuration': 28,\n",
" 'logDir': '/home/ningshang/nni-experiments/IF0JnfLE',\n",
" 'nextSequenceId': 2,\n",
" 'params': {'authorName': 'default',\n",
" 'experimentName': 'example_sklearn-classification',\n",
" 'trialConcurrency': 1,\n",
" 'maxExecDuration': 3600,\n",
" 'maxTrialNum': 200,\n",
" 'searchSpace': '{\"C\": {\"_type\": \"uniform\", \"_value\": [0.1, 1]}, \"kernel\": {\"_type\": \"choice\", \"_value\": [\"linear\", \"rbf\", \"poly\", \"sigmoid\"]}, \"degree\": {\"_type\": \"choice\", \"_value\": [1, 2, 3, 4]}, \"gamma\": {\"_type\": \"uniform\", \"_value\": [0.01, 0.1]}, \"coef0\": {\"_type\": \"uniform\", \"_value\": [0.01, 0.1]}}',\n",
" 'trainingServicePlatform': 'local',\n",
" 'tuner': {'builtinTunerName': 'TPE',\n",
" 'classArgs': {'optimize_mode': 'maximize'},\n",
" 'checkpointDir': '/home/ningshang/nni-experiments/IF0JnfLE/checkpoint'},\n",
" 'versionCheck': True,\n",
" 'clusterMetaData': [{'key': 'trial_config',\n",
" 'value': {'command': 'python3 main.py',\n",
" 'codeDir': '/home/ningshang/nni/examples/trials/sklearn/classification/.',\n",
" 'gpuNum': 0}}]},\n",
" 'startTime': 1614239412494}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"experiment.get_experiment_profile()"
]
},
{
"cell_type": "markdown",
"id": "opened-lounge",
"metadata": {},
"source": [
"### 3. 停止 Experiment"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "emotional-machinery",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-02-25 07:50:49] Stopping experiment, please wait...\n",
"[2021-02-25 07:50:49] Experiment stopped\n"
]
}
],
"source": [
"experiment.stop()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "nni-dev",
"language": "python",
"name": "nni-dev"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/zh_CN/Tutorial/python_api_connect.ipynb 0 → 120000
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../../en_US/Tutorial/python_api_connect.ipynb
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docs/zh_CN/Tutorial/python_api_start.ipynb deleted 100644 → 0
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{
"cells": [
{
"cell_type": "markdown",
"id": "technological-script",
"metadata": {},
"source": [
"## 启动并管理一个新的 Experiment"
]
},
{
"cell_type": "markdown",
"id": "reported-somerset",
"metadata": {},
"source": [
"### 1. 定义搜索空间"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "potential-williams",
"metadata": {},
"outputs": [],
"source": [
"search_space = {\n",
" \"C\": {\"_type\":\"quniform\",\"_value\":[0.1, 1, 0.1]},\n",
" \"kernel\": {\"_type\":\"choice\",\"_value\":[\"linear\", \"rbf\", \"poly\", \"sigmoid\"]},\n",
" \"degree\": {\"_type\":\"choice\",\"_value\":[1, 2, 3, 4]},\n",
" \"gamma\": {\"_type\":\"quniform\",\"_value\":[0.01, 0.1, 0.01]},\n",
" \"coef0\": {\"_type\":\"quniform\",\"_value\":[0.01, 0.1, 0.01]}\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "greek-archive",
"metadata": {},
"source": [
"### 2. 配置 Experiment "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fiscal-expansion",
"metadata": {},
"outputs": [],
"source": [
"from nni.experiment import Experiment\n",
"experiment = Experiment('local')\n",
"experiment.config.experiment_name = 'Example'\n",
"experiment.config.trial_concurrency = 2\n",
"experiment.config.max_trial_number = 10\n",
"experiment.config.search_space = search_space\n",
"experiment.config.trial_command = 'python3 main.py'\n",
"experiment.config.trial_code_directory = './'\n",
"experiment.config.tuner.name = 'TPE'\n",
"experiment.config.tuner.class_args['optimize_mode'] = 'maximize'\n",
"experiment.config.training_service.use_active_gpu = True"
]
},
{
"cell_type": "markdown",
"id": "received-tattoo",
"metadata": {},
"source": [
"### 3. 启动 Experiment"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "pleasant-patent",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-03-05 12:12:19] Creating experiment, Experiment ID: wdt0le3v\n",
"[2021-03-05 12:12:19] Starting web server...\n",
"[2021-03-05 12:12:20] Setting up...\n",
"[2021-03-05 12:12:20] Web UI URLs: http://127.0.0.1:8080 http://10.0.1.5:8080 http://172.17.0.1:8080\n"
]
}
],
"source": [
"experiment.start(8080)"
]
},
{
"cell_type": "markdown",
"id": "miniature-prison",
"metadata": {},
"source": [
"### 4. Experiment 查看和管理"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "animated-english",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'RUNNING'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"experiment.get_status()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "alpha-ottawa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[TrialResult(parameter={'C': 0.30000000000000004, 'kernel': 'linear', 'degree': 3, 'gamma': 0.03, 'coef0': 0.07}, value=0.9888888888888889, trialJobId='VLqU9'),\n",
" TrialResult(parameter={'C': 0.5, 'kernel': 'sigmoid', 'degree': 1, 'gamma': 0.03, 'coef0': 0.07}, value=0.8888888888888888, trialJobId='DLo6r')]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"experiment.export_data()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "unique-rendering",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'DLo6r': [TrialMetricData(timestamp=1614946351592, trialJobId='DLo6r', parameterId='1', type='FINAL', sequence=0, data=0.8888888888888888)],\n",
" 'VLqU9': [TrialMetricData(timestamp=1614946351607, trialJobId='VLqU9', parameterId='0', type='FINAL', sequence=0, data=0.9888888888888889)]}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"experiment.get_job_metrics()"
]
},
{
"cell_type": "markdown",
"id": "welsh-difference",
"metadata": {},
"source": [
"### 5. 停止 Experiment"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "technological-cleanup",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-03-05 12:12:40] Stopping experiment, please wait...\n",
"[2021-03-05 12:12:42] Experiment stopped\n"
]
}
],
"source": [
"experiment.stop()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "nni-dev",
"language": "python",
"name": "nni-dev"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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docs/zh_CN/Tutorial/python_api_start.ipynb 0 → 120000
View file @ 701ad2ab
../../en_US/Tutorial/python_api_start.ipynb
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