codejedi commited on
Commit
f2f82a7
·
1 Parent(s): 0b98932

update the model

Browse files
Hack49_Training.ipynb ADDED
@@ -0,0 +1,480 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metadata": {
3
+ "kernelspec": {
4
+ "language": "python",
5
+ "display_name": "Python 3",
6
+ "name": "python3"
7
+ },
8
+ "language_info": {
9
+ "name": "python",
10
+ "version": "3.7.12",
11
+ "mimetype": "text/x-python",
12
+ "codemirror_mode": {
13
+ "name": "ipython",
14
+ "version": 3
15
+ },
16
+ "pygments_lexer": "ipython3",
17
+ "nbconvert_exporter": "python",
18
+ "file_extension": ".py"
19
+ },
20
+ "kaggle": {
21
+ "accelerator": "gpu",
22
+ "dataSources": [
23
+ {
24
+ "sourceId": 9679350,
25
+ "sourceType": "datasetVersion",
26
+ "datasetId": 5916065
27
+ }
28
+ ],
29
+ "dockerImageVersionId": 30302,
30
+ "isInternetEnabled": true,
31
+ "language": "python",
32
+ "sourceType": "notebook",
33
+ "isGpuEnabled": true
34
+ },
35
+ "colab": {
36
+ "name": "Hack49-Training",
37
+ "provenance": []
38
+ }
39
+ },
40
+ "nbformat_minor": 0,
41
+ "nbformat": 4,
42
+ "cells": [
43
+ {
44
+ "source": [
45
+ "# IMPORTANT: SOME KAGGLE DATA SOURCES ARE PRIVATE\n",
46
+ "# RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES.\n",
47
+ "import kagglehub\n",
48
+ "kagglehub.login()\n"
49
+ ],
50
+ "metadata": {
51
+ "id": "qWlTgQVSmgbJ"
52
+ },
53
+ "cell_type": "code",
54
+ "outputs": [],
55
+ "execution_count": null
56
+ },
57
+ {
58
+ "source": [
59
+ "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
60
+ "# THEN FEEL FREE TO DELETE THIS CELL.\n",
61
+ "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
62
+ "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
63
+ "# NOTEBOOK.\n",
64
+ "\n",
65
+ "datajediai_hack49_alzheimer_dataset_path = kagglehub.dataset_download('datajediai/hack49-alzheimer-dataset')\n",
66
+ "\n",
67
+ "print('Data source import complete.')\n"
68
+ ],
69
+ "metadata": {
70
+ "id": "1uar05ngmgbJ"
71
+ },
72
+ "cell_type": "code",
73
+ "outputs": [],
74
+ "execution_count": null
75
+ },
76
+ {
77
+ "cell_type": "code",
78
+ "source": [
79
+ "pip install boto3"
80
+ ],
81
+ "metadata": {
82
+ "execution": {
83
+ "iopub.status.busy": "2024-10-21T05:31:24.709096Z",
84
+ "iopub.execute_input": "2024-10-21T05:31:24.709572Z",
85
+ "iopub.status.idle": "2024-10-21T05:31:37.120182Z",
86
+ "shell.execute_reply.started": "2024-10-21T05:31:24.709539Z",
87
+ "shell.execute_reply": "2024-10-21T05:31:37.118905Z"
88
+ },
89
+ "trusted": true,
90
+ "id": "Mm8ZSAxkmgbK",
91
+ "outputId": "e4930bdb-3291-4ae3-ffb1-2cee2f31c0d8"
92
+ },
93
+ "execution_count": null,
94
+ "outputs": [
95
+ {
96
+ "name": "stdout",
97
+ "text": "Requirement already satisfied: boto3 in /opt/conda/lib/python3.7/site-packages (1.24.93)\nRequirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /opt/conda/lib/python3.7/site-packages (from boto3) (0.6.0)\nRequirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.7/site-packages (from boto3) (1.0.1)\nRequirement already satisfied: botocore<1.28.0,>=1.27.93 in /opt/conda/lib/python3.7/site-packages (from boto3) (1.27.93)\nRequirement already satisfied: urllib3<1.27,>=1.25.4 in /opt/conda/lib/python3.7/site-packages (from botocore<1.28.0,>=1.27.93->boto3) (1.26.12)\nRequirement already satisfied: python-dateutil<3.0.0,>=2.1 in /opt/conda/lib/python3.7/site-packages (from botocore<1.28.0,>=1.27.93->boto3) (2.8.2)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.28.0,>=1.27.93->boto3) (1.15.0)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
98
+ "output_type": "stream"
99
+ }
100
+ ]
101
+ },
102
+ {
103
+ "cell_type": "code",
104
+ "source": [
105
+ "import os\n",
106
+ "import pandas as pd\n",
107
+ "import torch\n",
108
+ "import torchaudio\n",
109
+ "import torch.nn as nn\n",
110
+ "import torch.optim as optim\n",
111
+ "# Custom collate function for DataLoader\n",
112
+ "import torch.nn.functional as F\n",
113
+ "from torch.utils.data import Dataset, DataLoader, random_split\n",
114
+ "dataset_dir = '/kaggle/input/hack49-alzheimer-dataset/Hack49-Alzheimer-Dataset'\n",
115
+ "# Custom Dataset class\n",
116
+ "class HealthAudioDataset(Dataset):\n",
117
+ " def __init__(self, root_dir, device='cpu'):\n",
118
+ " self.root_dir = root_dir\n",
119
+ " self.file_list = []\n",
120
+ " self.labels = []\n",
121
+ " self.dataframe = []\n",
122
+ " self.device = device\n",
123
+ " for label, subdir in enumerate(['Healthy', 'NotHealthy']):\n",
124
+ " subdir_path = os.path.join(root_dir, subdir)\n",
125
+ " for wav_file in os.listdir(subdir_path):\n",
126
+ " if wav_file.endswith('.wav'):\n",
127
+ " self.file_list.append(os.path.join(subdir_path, wav_file))\n",
128
+ " self.labels.append(label)\n",
129
+ " self.dataframe = pd.DataFrame({'file_path': self.file_list, 'label': self.labels})\n",
130
+ "\n",
131
+ " def __len__(self):\n",
132
+ " return len(self.file_list)\n",
133
+ "\n",
134
+ " def __getitem__(self, idx):\n",
135
+ " wav_path = self.file_list[idx]\n",
136
+ " label = self.labels[idx]\n",
137
+ " waveform, sample_rate = torchaudio.load(wav_path)\n",
138
+ " waveform = waveform.to(self.device)\n",
139
+ " if sample_rate != bundle.sample_rate:\n",
140
+ " waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)\n",
141
+ " return waveform, label\n",
142
+ "\n",
143
+ " def get_dataframe(self):\n",
144
+ " return self.dataframe\n",
145
+ "\n",
146
+ "\n",
147
+ "class SelfAttention(nn.Module):\n",
148
+ " def __init__(self, input_dim, num_heads):\n",
149
+ " super(SelfAttention, self).__init__()\n",
150
+ " self.multihead_attn = nn.MultiheadAttention(embed_dim=input_dim, num_heads=num_heads)\n",
151
+ " self.layer_norm = nn.LayerNorm(input_dim)\n",
152
+ "\n",
153
+ " def forward(self, x):\n",
154
+ " attn_output, _ = self.multihead_attn(x, x, x)\n",
155
+ " x = x + attn_output # Add & Normalize\n",
156
+ " x = self.layer_norm(x)\n",
157
+ " return x\n",
158
+ "\n",
159
+ "class TimeSeriesClassifier(nn.Module):\n",
160
+ " def __init__(self, input_dim, num_heads, hidden_dim, output_dim):\n",
161
+ " super(TimeSeriesClassifier, self).__init__()\n",
162
+ " self.self_attention = SelfAttention(input_dim, num_heads)\n",
163
+ " self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
164
+ " self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
165
+ "\n",
166
+ " def forward(self, x):\n",
167
+ " # x: [batch_size, seq_len, input_dim]\n",
168
+ " x = x.permute(1, 0, 2) # Change to [seq_len, batch_size, input_dim]\n",
169
+ " x = self.self_attention(x)\n",
170
+ " x = x.permute(1, 0, 2) # Change back to [batch_size, seq_len, input_dim]\n",
171
+ " x = torch.mean(x, dim=1) # Global Average Pooling over the time dimension\n",
172
+ " x = torch.relu(self.fc1(x))\n",
173
+ " x = torch.sigmoid(self.fc2(x)) # Sigmoid for binary classification\n",
174
+ " return x\n",
175
+ "\n",
176
+ "\n",
177
+ "# Define the combined Encoder-Decoder model\n",
178
+ "class EncoderDecoder(nn.Module):\n",
179
+ " def __init__(self, encoder, decoder):\n",
180
+ " super(EncoderDecoder, self).__init__()\n",
181
+ " self.encoder = encoder\n",
182
+ " self.decoder = decoder\n",
183
+ " def forward(self, x):\n",
184
+ " emission, _ = self.encoder(x)\n",
185
+ " x = self.decoder(emission)\n",
186
+ " return x\n",
187
+ "\n",
188
+ "\n",
189
+ "\n",
190
+ "\n",
191
+ "def collate_fn(batch):\n",
192
+ " waveforms = [item[0] for item in batch]\n",
193
+ " labels = [item[1] for item in batch]\n",
194
+ "\n",
195
+ " # Find the length of the longest waveform in the batch\n",
196
+ " max_length = max(waveform.size(1) for waveform in waveforms)\n",
197
+ "\n",
198
+ " # Pad all waveforms to the length of the longest waveform\n",
199
+ " padded_waveforms = [F.pad(waveform, (0, max_length - waveform.size(1))) for waveform in waveforms]\n",
200
+ " padded_waveforms = torch.stack(padded_waveforms)\n",
201
+ "\n",
202
+ " labels = torch.tensor(labels)\n",
203
+ " return padded_waveforms, labels\n",
204
+ "\n",
205
+ "\n",
206
+ "\n",
207
+ "\n"
208
+ ],
209
+ "metadata": {
210
+ "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
211
+ "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
212
+ "execution": {
213
+ "iopub.status.busy": "2024-10-21T05:31:37.122433Z",
214
+ "iopub.execute_input": "2024-10-21T05:31:37.122772Z",
215
+ "iopub.status.idle": "2024-10-21T05:31:37.149038Z",
216
+ "shell.execute_reply.started": "2024-10-21T05:31:37.122737Z",
217
+ "shell.execute_reply": "2024-10-21T05:31:37.148219Z"
218
+ },
219
+ "trusted": true,
220
+ "id": "slo_6EJUmgbK"
221
+ },
222
+ "execution_count": null,
223
+ "outputs": []
224
+ },
225
+ {
226
+ "cell_type": "code",
227
+ "source": [
228
+ "# Initialize everything\n",
229
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
230
+ "bundle = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H\n",
231
+ "encoder = bundle.get_model().to(device)\n",
232
+ "\n",
233
+ "# Example usage\n",
234
+ "batch_size = 32\n",
235
+ "seq_len = 10 # Number of time steps\n",
236
+ "input_dim = 29\n",
237
+ "hidden_dim = 15\n",
238
+ "# Creating a dummy input tensor with shape [batch_size, seq_len, input_dim]\n",
239
+ "dummy_input = torch.randn(batch_size, seq_len, input_dim)\n",
240
+ "\n",
241
+ "decoder = TimeSeriesClassifier(input_dim = input_dim,\n",
242
+ " num_heads = input_dim,\n",
243
+ " hidden_dim = hidden_dim,\n",
244
+ " output_dim = 1)\n",
245
+ "output = decoder(dummy_input)\n",
246
+ "print(output)\n",
247
+ "decoder = decoder.to(device)\n",
248
+ "model = EncoderDecoder(encoder, decoder).to(device)\n",
249
+ "\n",
250
+ "# Freeze the encoder parameters\n",
251
+ "for param in model.encoder.parameters():\n",
252
+ " param.requires_grad = False\n",
253
+ "\n",
254
+ "# Dataset and DataLoaders\n",
255
+ "\n",
256
+ "dataset = HealthAudioDataset(dataset_dir, device)\n",
257
+ "train_size = int(0.8 * len(dataset))\n",
258
+ "test_size = len(dataset) - train_size\n",
259
+ "train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
260
+ "\n",
261
+ "train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True, collate_fn=collate_fn)\n",
262
+ "test_loader = DataLoader(test_dataset, batch_size=2, shuffle=True, collate_fn=collate_fn)\n",
263
+ "\n",
264
+ "# Print the contents of the train_loader"
265
+ ],
266
+ "metadata": {
267
+ "execution": {
268
+ "iopub.status.busy": "2024-10-21T05:31:37.150151Z",
269
+ "iopub.execute_input": "2024-10-21T05:31:37.15093Z",
270
+ "iopub.status.idle": "2024-10-21T05:31:38.413704Z",
271
+ "shell.execute_reply.started": "2024-10-21T05:31:37.150901Z",
272
+ "shell.execute_reply": "2024-10-21T05:31:38.412655Z"
273
+ },
274
+ "trusted": true,
275
+ "id": "__Tb1u1wmgbK",
276
+ "outputId": "d6b97111-e800-40db-8873-fff376726885"
277
+ },
278
+ "execution_count": null,
279
+ "outputs": [
280
+ {
281
+ "name": "stdout",
282
+ "text": "tensor([[0.5726],\n [0.5791],\n [0.5756],\n [0.5718],\n [0.6160],\n [0.5886],\n [0.5634],\n [0.5896],\n [0.5754],\n [0.6070],\n [0.5597],\n [0.5673],\n [0.5942],\n [0.5967],\n [0.5815],\n [0.5582],\n [0.5873],\n [0.6225],\n [0.5830],\n [0.5879],\n [0.5958],\n [0.5540],\n [0.5812],\n [0.6039],\n [0.5825],\n [0.5635],\n [0.6145],\n [0.5483],\n [0.5770],\n [0.5952],\n [0.6086],\n [0.5806]], grad_fn=<SigmoidBackward0>)\n",
283
+ "output_type": "stream"
284
+ }
285
+ ]
286
+ },
287
+ {
288
+ "cell_type": "code",
289
+ "source": [
290
+ "# Loss and optimizer\n",
291
+ "criterion = nn.BCELoss()\n",
292
+ "optimizer = optim.Adam(model.parameters(), lr=1e-4)\n",
293
+ "\n",
294
+ "def verification(model):\n",
295
+ " # Testing script\n",
296
+ " test_loss = 0.0\n",
297
+ " with torch.no_grad():\n",
298
+ " for i, (inputs_batch, labels_batch) in enumerate(test_loader):\n",
299
+ " for batch_idx in range(inputs_batch.size(0)):\n",
300
+ " inputs, labels = inputs_batch[batch_idx], labels_batch[batch_idx]\n",
301
+ " inputs = inputs.to(device)\n",
302
+ " labels = labels.to(device).float().unsqueeze(0) # Convert to float and add batch dimension\n",
303
+ " outputs = model(inputs)\n",
304
+ " labels = labels.view(outputs.shape) # Ensure labels match the shape of outputs\n",
305
+ " loss = criterion(outputs, labels)\n",
306
+ " test_loss += loss.item()\n",
307
+ " ret = test_loss / len(test_loader)\n",
308
+ " print(f'Test Loss: {ret:.4f}')\n",
309
+ " return ret\n",
310
+ "verification(model)\n",
311
+ "# save the model\n"
312
+ ],
313
+ "metadata": {
314
+ "execution": {
315
+ "iopub.status.busy": "2024-10-21T05:31:38.416523Z",
316
+ "iopub.execute_input": "2024-10-21T05:31:38.417174Z",
317
+ "iopub.status.idle": "2024-10-21T05:31:39.180667Z",
318
+ "shell.execute_reply.started": "2024-10-21T05:31:38.417142Z",
319
+ "shell.execute_reply": "2024-10-21T05:31:39.179716Z"
320
+ },
321
+ "trusted": true,
322
+ "id": "fXi2w6_dmgbL",
323
+ "outputId": "de44529f-7406-42b4-ec62-c01f85b4c675"
324
+ },
325
+ "execution_count": null,
326
+ "outputs": [
327
+ {
328
+ "name": "stdout",
329
+ "text": "Test Loss: 1.6243\n",
330
+ "output_type": "stream"
331
+ },
332
+ {
333
+ "execution_count": 99,
334
+ "output_type": "execute_result",
335
+ "data": {
336
+ "text/plain": "1.6242794593175252"
337
+ },
338
+ "metadata": {}
339
+ }
340
+ ]
341
+ },
342
+ {
343
+ "cell_type": "code",
344
+ "source": [
345
+ "print(f\"Training started... Using {device}\")\n",
346
+ "num_epochs = 0\n",
347
+ "while verification(model) > 0.10:\n",
348
+ " model.train()\n",
349
+ " running_loss = 0.0\n",
350
+ " for i, (inputs_batch, labels_batch) in enumerate(train_loader):\n",
351
+ "# print(f'Batch {i + 1}:')\n",
352
+ " for batch_idx in range(inputs_batch.size(0)):\n",
353
+ " inputs, labels = inputs_batch[batch_idx], labels_batch[batch_idx]\n",
354
+ " inputs = inputs.to(device)\n",
355
+ " labels = labels.to(device).float().unsqueeze(0) # Convert to float and add batch dimension\n",
356
+ "\n",
357
+ " # print(f' Input {batch_idx + 1}: {inputs.size()}, Label: {labels.size()}')\n",
358
+ " # print(f' Input data type: {inputs.dtype}, Label data type: {labels.dtype}')\n",
359
+ "\n",
360
+ " optimizer.zero_grad()\n",
361
+ " outputs = model(inputs)\n",
362
+ " # print(f' Output: {outputs.size()}')\n",
363
+ " labels = labels.view(outputs.shape) # Ensure labels match the shape of outputs\n",
364
+ " loss = criterion(outputs, labels)\n",
365
+ " loss.backward()\n",
366
+ " optimizer.step()\n",
367
+ " running_loss += loss.item()\n",
368
+ "\n",
369
+ "\n",
370
+ " print(f'Epoch [{epoch + 1}/{num_epochs}], Batch [{i + 1}], Loss: {running_loss / 100:.4f}')\n",
371
+ " epoch = epoch + 1\n",
372
+ "\n",
373
+ "print(\"Training completed.\")\n",
374
+ "# now generate the testing script\n",
375
+ "\n"
376
+ ],
377
+ "metadata": {
378
+ "execution": {
379
+ "iopub.status.busy": "2024-10-21T05:39:17.441427Z",
380
+ "iopub.execute_input": "2024-10-21T05:39:17.441812Z",
381
+ "iopub.status.idle": "2024-10-21T05:39:59.551312Z",
382
+ "shell.execute_reply.started": "2024-10-21T05:39:17.44178Z",
383
+ "shell.execute_reply": "2024-10-21T05:39:59.550324Z"
384
+ },
385
+ "trusted": true,
386
+ "id": "BRtV6wDfmgbL",
387
+ "outputId": "93af8a83-48c1-41b2-e1f3-b27afa75c285"
388
+ },
389
+ "execution_count": null,
390
+ "outputs": [
391
+ {
392
+ "name": "stdout",
393
+ "text": "Training started... Using cuda\nTest Loss: 0.1040\nEpoch [283/0], Batch [12], Loss: 0.0445\nTest Loss: 0.1708\nEpoch [284/0], Batch [12], Loss: 0.0302\nTest Loss: 0.1152\nEpoch [285/0], Batch [12], Loss: 0.0298\nTest Loss: 0.1269\nEpoch [286/0], Batch [12], Loss: 0.0319\nTest Loss: 0.1675\nEpoch [287/0], Batch [12], Loss: 0.0276\nTest Loss: 0.1064\nEpoch [288/0], Batch [12], Loss: 0.0250\nTest Loss: 0.1328\nEpoch [289/0], Batch [12], Loss: 0.0220\nTest Loss: 0.1303\nEpoch [290/0], Batch [12], Loss: 0.0219\nTest Loss: 0.1164\nEpoch [291/0], Batch [12], Loss: 0.0200\nTest Loss: 0.1139\nEpoch [292/0], Batch [12], Loss: 0.0342\nTest Loss: 0.1391\nEpoch [293/0], Batch [12], Loss: 0.0213\nTest Loss: 0.1166\nEpoch [294/0], Batch [12], Loss: 0.0271\nTest Loss: 0.0904\nTraining completed.\n",
394
+ "output_type": "stream"
395
+ }
396
+ ]
397
+ },
398
+ {
399
+ "cell_type": "code",
400
+ "source": [],
401
+ "metadata": {
402
+ "trusted": true,
403
+ "id": "FU8OK0LumgbL"
404
+ },
405
+ "execution_count": null,
406
+ "outputs": []
407
+ },
408
+ {
409
+ "cell_type": "code",
410
+ "source": [
411
+ "model"
412
+ ],
413
+ "metadata": {
414
+ "execution": {
415
+ "iopub.status.busy": "2024-10-21T05:39:12.138603Z",
416
+ "iopub.status.idle": "2024-10-21T05:39:12.138955Z",
417
+ "shell.execute_reply.started": "2024-10-21T05:39:12.138776Z",
418
+ "shell.execute_reply": "2024-10-21T05:39:12.138793Z"
419
+ },
420
+ "trusted": true,
421
+ "id": "jIbFxLW-mgbM"
422
+ },
423
+ "execution_count": null,
424
+ "outputs": []
425
+ },
426
+ {
427
+ "cell_type": "code",
428
+ "source": [
429
+ "torch.save(model.state_dict(), 'hack49_encoder_decoder_model.pth')\n",
430
+ "import boto3\n",
431
+ "\n",
432
+ "def upload_to_s3(file_path, bucket_name, object_name, access_key, secret_key):\n",
433
+ " # Initialize a session using your AWS credentials\n",
434
+ " s3_client = boto3.client('s3',\n",
435
+ " region_name='us-east-2',\n",
436
+ " aws_access_key_id=access_key,\n",
437
+ " aws_secret_access_key=secret_key)\n",
438
+ "\n",
439
+ " try:\n",
440
+ " # Uploads the given file using a managed uploader\n",
441
+ " s3_client.upload_file(file_path, bucket_name, object_name)\n",
442
+ " print(f'Successfully uploaded {file_path} to {bucket_name}/{object_name}')\n",
443
+ " except Exception as e:\n",
444
+ " print(f'Error uploading file: {e}')\n",
445
+ "\n",
446
+ "# Example usage\n",
447
+ "file_path = 'hack49_encoder_decoder_model.pth'\n",
448
+ "bucket_name = 'my-ai-models-darcy'\n",
449
+ "# get time\n",
450
+ "import datetime\n",
451
+ "now = datetime.datetime.now()\n",
452
+ "object_name = f'hack49_encoder_decoder_model_{now.strftime(\"%Y-%m-%d_%H-%M-%S\")}.pth'\n",
453
+ "access_key = 'XXXX'\n",
454
+ "secret_key = 'XXXX'\n",
455
+ "\n",
456
+ "upload_to_s3(file_path, bucket_name, object_name, access_key, secret_key)\n"
457
+ ],
458
+ "metadata": {
459
+ "execution": {
460
+ "iopub.status.busy": "2024-10-21T05:40:04.454771Z",
461
+ "iopub.execute_input": "2024-10-21T05:40:04.455706Z",
462
+ "iopub.status.idle": "2024-10-21T05:40:09.278691Z",
463
+ "shell.execute_reply.started": "2024-10-21T05:40:04.455668Z",
464
+ "shell.execute_reply": "2024-10-21T05:40:09.277704Z"
465
+ },
466
+ "trusted": true,
467
+ "id": "zDZBAM2omgbM",
468
+ "outputId": "ec41c422-578a-4a27-bfbb-c5a3d569744a"
469
+ },
470
+ "execution_count": null,
471
+ "outputs": [
472
+ {
473
+ "name": "stdout",
474
+ "text": "Successfully uploaded hack49_encoder_decoder_model.pth to my-ai-models-darcy/hack49_encoder_decoder_model_2024-10-21_05-40-05.pth\n",
475
+ "output_type": "stream"
476
+ }
477
+ ]
478
+ }
479
+ ]
480
+ }
README.md ADDED
File without changes
hack49_encoder_decoder_model.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:3d2f7f3d08c6a1fca65b976c89206b9f350823584659ddf1112af7d1a9440bda
3
- size 377671796
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a5846121d9ffd5981799137784baaa21ac6913e7de5cab7aa38162de2504c5e6
3
+ size 377681541