Elgato_dark/chronos-forecasting
1
0
Fork 0
mirror of https://github.com/amazon-science/chronos-forecasting synced 2026-05-24 01:58:27 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Add logging steps in fine-tuning example (#334)

Browse source 
*Issue #, if available:*

*Description of changes:*


By submitting this pull request, I confirm that you can use, modify,
copy, and redistribute this contribution, under the terms of your
choice.
This commit is contained in:
Abdul Fatir 2025-10-21 19:58:26 +02:00 committed by GitHub
parent 6c4f999922
commit 0c51188db7
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
1 changed files with 22 additions and 1 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

23
notebooks/chronos-2-quickstart.ipynb
View file

@ -1450,7 +1450,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_2205321/1561245919.py:2: FutureWarning: Fine-tuning support is experimental and may be changed in future versions.\n",
"/tmp/ipykernel_2466579/3520825307.py:2: FutureWarning: Fine-tuning support is experimental and may be changed in future versions.\n",
" finetuned_pipeline = pipeline.fit(\n",
"/fsx/ansarnd/repos/chronos-forecasting/.venv/lib/python3.11/site-packages/torch/backends/cuda/__init__.py:131: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)\n",
" return torch._C._get_cublas_allow_tf32()\n",
@ -1474,6 +1474,26 @@
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>0.387400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.345000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.362900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>0.346400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>0.343400</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
],
@ -1493,6 +1513,7 @@
" num_steps=50, # few fine-tuning steps for a quick demo\n",
" learning_rate=1e-5,\n",
" batch_size=32,\n",
" logging_steps=10,\n",
")"
]
},