wehub-resource-sync
521 lines
18 KiB
Plaintext
521 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "df28ea8a",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"from fastai.imports import *\n",
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"from fastai.data.all import *\n",
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"from fastai.optimizer import *\n",
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"from fastai.learner import *\n",
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"from fastai.callback.core import *\n",
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"from torch.utils.data import TensorDataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7ac716b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| default_exp test_utils"
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]
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},
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{
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"cell_type": "markdown",
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"id": "820003d6",
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"metadata": {},
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"source": [
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"# Synthetic Learner\n",
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"\n",
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"> For quick testing of the training loop and Callbacks"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dc714875",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"from torch.utils.data import TensorDataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8e7a5089",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"def synth_dbunch(a=2, b=3, bs=16, n_train=10, n_valid=2, cuda=False):\n",
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" def get_data(n):\n",
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" x = torch.randn(bs*n, 1)\n",
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" return TensorDataset(x, a*x + b + 0.1*torch.randn(bs*n, 1))\n",
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" train_ds = get_data(n_train)\n",
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" valid_ds = get_data(n_valid)\n",
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" device = default_device() if cuda else None\n",
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" train_dl = TfmdDL(train_ds, bs=bs, shuffle=True, num_workers=0)\n",
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" valid_dl = TfmdDL(valid_ds, bs=bs, num_workers=0)\n",
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" return DataLoaders(train_dl, valid_dl, device=device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e8873a3b",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"class RegModel(Module):\n",
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" def __init__(self): self.a,self.b = nn.Parameter(torch.randn(1)),nn.Parameter(torch.randn(1))\n",
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" def forward(self, x): return x*self.a + self.b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "225217e5",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"@delegates(Learner.__init__)\n",
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"def synth_learner(n_trn=10, n_val=2, cuda=False, lr=1e-3, data=None, model=None, **kwargs):\n",
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" if data is None: data=synth_dbunch(n_train=n_trn,n_valid=n_val, cuda=cuda)\n",
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" if model is None: model=RegModel()\n",
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" return Learner(data, model, lr=lr, loss_func=MSELossFlat(),\n",
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" opt_func=partial(SGD, mom=0.9), **kwargs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cd6c1092",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"class VerboseCallback(Callback):\n",
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" \"Callback that prints the name of each event called\"\n",
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" def __call__(self, event_name):\n",
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" print(event_name)\n",
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" super().__call__(event_name)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b524bba1",
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"metadata": {},
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"source": [
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"## Install Utils"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9f6e55cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"def get_env(name):\n",
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" \"Return env var value if it's defined and not an empty string, or return Unknown\"\n",
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" res = os.environ.get(name,'')\n",
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" return res if len(res) else \"Unknown\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9154380b",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"def try_import(module):\n",
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" \"Try to import `module`. Returns module's object on success, None on failure\"\n",
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" try: return importlib.import_module(module)\n",
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" except: return None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "22f1d4cb",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"def nvidia_smi(cmd = \"nvidia-smi\"):\n",
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" try: res = run(cmd)\n",
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" except OSError as e: return None\n",
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" return res"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f895dd6f",
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"metadata": {},
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"outputs": [],
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"source": [
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"res = nvidia_smi()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "19c54b0e",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"def nvidia_mem():\n",
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" try: mem = run(\"nvidia-smi --query-gpu=memory.total --format=csv,nounits,noheader\")\n",
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" except: return None\n",
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" return mem.strip().split('\\n')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f2410ca7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['48600', '7982']"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"nvidia_mem()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d28e2673",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"def show_install(show_nvidia_smi:bool=False):\n",
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" \"Print user's setup information\"\n",
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"\n",
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" import fastai, platform, fastprogress, fastcore\n",
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"\n",
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" rep = []\n",
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" opt_mods = []\n",
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"\n",
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" rep.append([\"=== Software ===\", None])\n",
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" rep.append([\"python\", platform.python_version()])\n",
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" rep.append([\"fastai\", fastai.__version__])\n",
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" rep.append([\"fastcore\", fastcore.__version__])\n",
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" rep.append([\"fastprogress\", fastprogress.__version__])\n",
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" rep.append([\"torch\", torch.__version__])\n",
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"\n",
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" # nvidia-smi\n",
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" smi = nvidia_smi()\n",
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" if smi:\n",
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" match = re.findall(r'Driver Version: +(\\d+\\.\\d+)', smi)\n",
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" if match: rep.append([\"nvidia driver\", match[0]])\n",
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"\n",
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" available = \"available\" if torch.cuda.is_available() else \"**Not available** \"\n",
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" rep.append([\"torch cuda\", f\"{torch.version.cuda} / is {available}\"])\n",
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"\n",
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" # no point reporting on cudnn if cuda is not available, as it\n",
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" # seems to be enabled at times even on cpu-only setups\n",
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" if torch.cuda.is_available():\n",
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" enabled = \"enabled\" if torch.backends.cudnn.enabled else \"**Not enabled** \"\n",
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" rep.append([\"torch cudnn\", f\"{torch.backends.cudnn.version()} / is {enabled}\"])\n",
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"\n",
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" rep.append([\"\\n=== Hardware ===\", None])\n",
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"\n",
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" gpu_total_mem = []\n",
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" nvidia_gpu_cnt = 0\n",
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" if smi:\n",
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" mem = nvidia_mem()\n",
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" nvidia_gpu_cnt = len(ifnone(mem, []))\n",
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"\n",
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" if nvidia_gpu_cnt: rep.append([\"nvidia gpus\", nvidia_gpu_cnt])\n",
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"\n",
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" torch_gpu_cnt = torch.cuda.device_count()\n",
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" if torch_gpu_cnt:\n",
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" rep.append([\"torch devices\", torch_gpu_cnt])\n",
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" # information for each gpu\n",
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" for i in range(torch_gpu_cnt):\n",
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" rep.append([f\" - gpu{i}\", (f\"{gpu_total_mem[i]}MB | \" if gpu_total_mem else \"\") + torch.cuda.get_device_name(i)])\n",
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" else:\n",
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" if nvidia_gpu_cnt:\n",
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" rep.append([f\"Have {nvidia_gpu_cnt} GPU(s), but torch can't use them (check nvidia driver)\", None])\n",
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" else:\n",
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" rep.append([f\"No GPUs available\", None])\n",
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"\n",
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"\n",
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" rep.append([\"\\n=== Environment ===\", None])\n",
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"\n",
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" rep.append([\"platform\", platform.platform()])\n",
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"\n",
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" if platform.system() == 'Linux':\n",
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" distro = try_import('distro')\n",
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" if distro:\n",
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" # full distro info\n",
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" rep.append([\"distro\", ' '.join(distro.linux_distribution())])\n",
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" else:\n",
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" opt_mods.append('distro');\n",
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" # partial distro info\n",
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" rep.append([\"distro\", platform.uname().version])\n",
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"\n",
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" rep.append([\"conda env\", get_env('CONDA_DEFAULT_ENV')])\n",
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" rep.append([\"python\", sys.executable])\n",
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" rep.append([\"sys.path\", \"\\n\".join(sys.path)])\n",
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"\n",
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" print(\"\\n\\n```text\")\n",
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"\n",
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" keylen = max([len(e[0]) for e in rep if e[1] is not None])\n",
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" for e in rep:\n",
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" print(f\"{e[0]:{keylen}}\", (f\": {e[1]}\" if e[1] is not None else \"\"))\n",
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"\n",
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" if smi:\n",
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" if show_nvidia_smi: print(f\"\\n{smi}\")\n",
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" else:\n",
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" if torch_gpu_cnt: print(\"no nvidia-smi is found\")\n",
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" else: print(\"no supported gpus found on this system\")\n",
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"\n",
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" print(\"```\\n\")\n",
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"\n",
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" print(\"Please make sure to include opening/closing ``` when you paste into forums/github to make the reports appear formatted as code sections.\\n\")\n",
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"\n",
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" if opt_mods:\n",
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" print(\"Optional package(s) to enhance the diagnostics can be installed with:\")\n",
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" print(f\"pip install {' '.join(opt_mods)}\")\n",
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" print(\"Once installed, re-run this utility to get the additional information\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "48fc017c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"```text\n",
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"=== Software === \n",
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"python : 3.8.5\n",
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"fastai : 2.2.4\n",
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"fastcore : 1.3.16\n",
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"fastprogress : 0.2.7\n",
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"torch : 1.7.0\n",
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"nvidia driver : 460.32\n",
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"torch cuda : 11.0 / is available\n",
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"torch cudnn : 8003 / is enabled\n",
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"\n",
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"=== Hardware === \n",
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"nvidia gpus : 2\n",
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"torch devices : 2\n",
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" - gpu0 : Quadro RTX 8000\n",
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" - gpu1 : GeForce RTX 2070 SUPER\n",
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"\n",
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"=== Environment === \n",
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"platform : Linux-5.8.0-36-generic-x86_64-with-glibc2.10\n",
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"distro : #40~20.04.1-Ubuntu SMP Wed Jan 6 10:15:55 UTC 2021\n",
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"conda env : fastai\n",
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"python : /home/tcapelle/miniconda3/envs/fastai/bin/python\n",
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"sys.path : /home/tcapelle/Apps/fastai/nbs\n",
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"/home/tcapelle/miniconda3/envs/fastai/lib/python38.zip\n",
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"/home/tcapelle/miniconda3/envs/fastai/lib/python3.8\n",
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"/home/tcapelle/miniconda3/envs/fastai/lib/python3.8/lib-dynload\n",
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"\n",
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"/home/tcapelle/miniconda3/envs/fastai/lib/python3.8/site-packages\n",
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"/home/tcapelle/Apps/fastai\n",
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"/home/tcapelle/Apps/nbdev\n",
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"/home/tcapelle/Apps/fastcore\n",
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"/home/tcapelle/SteadySun/app-suneye\n",
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"/home/tcapelle/miniconda3/envs/fastai/lib/python3.8/site-packages/IPython/extensions\n",
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"/home/tcapelle/.ipython\n",
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"\n",
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"Mon Jan 18 21:35:23 2021 \n",
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"+-----------------------------------------------------------------------------+\n",
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"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
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"|-------------------------------+----------------------+----------------------+\n",
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"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
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"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
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"| | | MIG M. |\n",
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"|===============================+======================+======================|\n",
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"| 0 Quadro RTX 8000 Off | 00000000:08:00.0 Off | Off |\n",
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"| 33% 38C P8 11W / 260W | 12857MiB / 48600MiB | 0% Default |\n",
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"| | | N/A |\n",
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"+-------------------------------+----------------------+----------------------+\n",
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"| 1 GeForce RTX 207... Off | 00000000:09:00.0 Off | N/A |\n",
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"| 0% 45C P8 29W / 215W | 7466MiB / 7982MiB | 0% Default |\n",
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"| | | N/A |\n",
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"+-------------------------------+----------------------+----------------------+\n",
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" \n",
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"+-----------------------------------------------------------------------------+\n",
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"| Processes: |\n",
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"| GPU GI CI PID Type Process name GPU Memory |\n",
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"| ID ID Usage |\n",
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"|=============================================================================|\n",
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"| 0 N/A N/A 1152 G /usr/lib/xorg/Xorg 10MiB |\n",
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"| 0 N/A N/A 1345 G /usr/bin/gnome-shell 4MiB |\n",
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"| 0 N/A N/A 51371 C ...a3/envs/fastai/bin/python 827MiB |\n",
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"| 0 N/A N/A 52150 C ...a3/envs/fastai/bin/python 11011MiB |\n",
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"| 0 N/A N/A 52914 C ...a3/envs/fastai/bin/python 1001MiB |\n",
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"| 1 N/A N/A 1152 G /usr/lib/xorg/Xorg 4MiB |\n",
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"| 1 N/A N/A 51371 C ...a3/envs/fastai/bin/python 6601MiB |\n",
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"| 1 N/A N/A 52914 C ...a3/envs/fastai/bin/python 857MiB |\n",
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"+-----------------------------------------------------------------------------+\n",
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"\n",
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"```\n",
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"\n",
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"Please make sure to include opening/closing ``` when you paste into forums/github to make the reports appear formatted as code sections.\n",
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"\n",
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"Optional package(s) to enhance the diagnostics can be installed with:\n",
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"pip install distro\n",
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"Once installed, re-run this utility to get the additional information\n"
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]
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}
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],
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"source": [
|
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"#| hide\n",
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"show_install(True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ff778d66",
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"metadata": {},
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"source": [
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"## - Export"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9d153d1e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Converted 00_torch_core.ipynb.\n",
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"Converted 01_layers.ipynb.\n",
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"Converted 01a_losses.ipynb.\n",
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"Converted 02_data.load.ipynb.\n",
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"Converted 03_data.core.ipynb.\n",
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"Converted 04_data.external.ipynb.\n",
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"Converted 05_data.transforms.ipynb.\n",
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"Converted 06_data.block.ipynb.\n",
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"Converted 07_vision.core.ipynb.\n",
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"Converted 08_vision.data.ipynb.\n",
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"Converted 09_vision.augment.ipynb.\n",
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"Converted 09b_vision.utils.ipynb.\n",
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"Converted 09c_vision.widgets.ipynb.\n",
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"Converted 10_tutorial.pets.ipynb.\n",
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"Converted 10b_tutorial.albumentations.ipynb.\n",
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"Converted 11_vision.models.xresnet.ipynb.\n",
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"Converted 12_optimizer.ipynb.\n",
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"Converted 13_callback.core.ipynb.\n",
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"Converted 13a_learner.ipynb.\n",
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"Converted 13b_metrics.ipynb.\n",
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"Converted 14_callback.schedule.ipynb.\n",
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"Converted 14a_callback.data.ipynb.\n",
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"Converted 15_callback.hook.ipynb.\n",
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"Converted 15a_vision.models.unet.ipynb.\n",
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"Converted 16_callback.progress.ipynb.\n",
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"Converted 17_callback.tracker.ipynb.\n",
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"Converted 18_callback.fp16.ipynb.\n",
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"Converted 18a_callback.training.ipynb.\n",
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"Converted 18b_callback.preds.ipynb.\n",
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"Converted 19_callback.mixup.ipynb.\n",
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"Converted 20_interpret.ipynb.\n",
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"Converted 20a_distributed.ipynb.\n",
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"Converted 21_vision.learner.ipynb.\n",
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"Converted 22_tutorial.imagenette.ipynb.\n",
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"Converted 23_tutorial.vision.ipynb.\n",
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"Converted 24_tutorial.siamese.ipynb.\n",
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"Converted 24_vision.gan.ipynb.\n",
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"Converted 30_text.core.ipynb.\n",
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"Converted 31_text.data.ipynb.\n",
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"Converted 32_text.models.awdlstm.ipynb.\n",
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"Converted 33_text.models.core.ipynb.\n",
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"Converted 34_callback.rnn.ipynb.\n",
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"Converted 35_tutorial.wikitext.ipynb.\n",
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"Converted 36_text.models.qrnn.ipynb.\n",
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"Converted 37_text.learner.ipynb.\n",
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"Converted 38_tutorial.text.ipynb.\n",
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"Converted 39_tutorial.transformers.ipynb.\n",
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"Converted 40_tabular.core.ipynb.\n",
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"Converted 41_tabular.data.ipynb.\n",
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"Converted 42_tabular.model.ipynb.\n",
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"Converted 43_tabular.learner.ipynb.\n",
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"Converted 44_tutorial.tabular.ipynb.\n",
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"Converted 45_collab.ipynb.\n",
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"Converted 46_tutorial.collab.ipynb.\n",
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"Converted 50_tutorial.datablock.ipynb.\n",
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"Converted 60_medical.imaging.ipynb.\n",
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"Converted 61_tutorial.medical_imaging.ipynb.\n",
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"Converted 65_medical.text.ipynb.\n",
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"Converted 70_callback.wandb.ipynb.\n",
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"Converted 71_callback.tensorboard.ipynb.\n",
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"Converted 72_callback.neptune.ipynb.\n",
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"Converted 73_callback.captum.ipynb.\n",
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"Converted 97_test_utils.ipynb.\n",
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"Converted 99_pytorch_doc.ipynb.\n",
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"Converted dev-setup.ipynb.\n",
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"Converted index.ipynb.\n",
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"Converted quick_start.ipynb.\n",
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"Converted tutorial.ipynb.\n"
|
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"#| hide\n",
|
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"from nbdev import *\n",
|
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"nbdev_export()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c7280387",
|
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"metadata": {},
|
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"outputs": [],
|
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"source": []
|
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}
|
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],
|
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"metadata": {
|
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"jupytext": {
|
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"split_at_heading": true
|
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},
|
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"kernelspec": {
|
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"display_name": "python3",
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"language": "python",
|
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"name": "python3"
|
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}
|
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},
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"nbformat": 4,
|
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"nbformat_minor": 5
|
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}
|