{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"ds-1000","formal_name":"DS-1000","introduction":"DS-1000 builds 1,000 data-science problems from real StackOverflow questions across seven libraries including NumPy, Pandas and Matplotlib. The problems are perturbed so that recalling the original answer does not solve them.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://ds1000-code-gen.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"3edbc8b4-282c-59fd-85e1-217ec29f288e","task_key":"default--test--934","task_revision_id":"1","upstream_id":"934","short_description":"Is it possible in PyTorch to change the learning rate of the optimizer in the…","config":"default","split":"test","body":"{\"library\":\"Pytorch\",\"prompt\":\"Problem:\\n\\nIs it possible in PyTorch to change the learning rate of the optimizer in the middle of training dynamically (I don't want to define a learning rate schedule beforehand)?\\n\\nSo let's say I have an optimizer:\\n\\noptim = torch.optim.SGD(..., lr=0.005)\\nNow due to some tests which I perform during training, I realize my learning rate is too high so I want to change it to say 0.0005. There doesn't seem to be a method optim.set_lr(0.0005) but is there some way to do this?\\n\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nimport torch\\noptim = load_data()\\n</code>\\nBEGIN SOLUTION\\n<code>\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://ds1000-code-gen.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}