{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Google_Colab_for_Fastai_General_Template4.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"[View in Colaboratory](https://colab.research.google.com/gist/ELC/35db433bec8401e886e227d50aa448e3/google_colab_for_fastai_general_template4.ipynb)"
]
},
{
"metadata": {
"id": "PjOMeCoHHlzQ",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Google Colab for Fast.ai Course Template\n",
"\n",
"Remember to enable the GPU! ***Edit > Notebook settings > set \"Hardware Accelerator\" to GPU.***\n",
"\n",
"Check [the source]() of this template for updates\n"
]
},
{
"metadata": {
"id": "ArPdbxB-vl9Y",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Installing dependencies ##\n",
"We need to manually install fastai and pytorch. And maybe other things that fastai depends on (see [here](https://github.com/fastai/fastai/blob/master/requirements.txt)).\n",
"\n",
"I will be referring to [this fastai forum thread](http://forums.fast.ai/t/colaboratory-and-fastai/10122/6) and [this blogpost](https://towardsdatascience.com/fast-ai-lesson-1-on-google-colab-free-gpu-d2af89f53604) if I get stuck. This is also a handy resource for using pytorch in colab: https://jovianlin.io/pytorch-with-gpu-in-google-colab/ (and his [example notebook](https://colab.research.google.com/drive/1jxUPzMsAkBboHMQtGyfv5M5c7hU8Ss2c#scrollTo=ed-8FUn2GqQ4)!). And this [post](https://medium.com/@chsafouane/getting-started-with-pytorch-on-google-colab-811c59a656b6). **Be careful with python and python3 being the same in this notebook, also there is no difference between pip and pip3**"
]
},
{
"metadata": {
"id": "SY72s-PAwUio",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 84
},
"outputId": "ae0b6d52-8aa0-4a9d-baf5-78614a7aeb1f"
},
"cell_type": "code",
"source": [
"!python3 -V\n",
"!python -V\n",
"!pip -V\n",
"!pip3 -V"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Python 3.6.6\n",
"Python 3.6.6\n",
"pip 18.0 from /usr/local/lib/python3.6/dist-packages/pip (python 3.6)\n",
"pip 18.0 from /usr/local/lib/python3.6/dist-packages/pip (python 3.6)\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "HJoT6vSgGdAe",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"**Installing fastai (1.x) from PyPI and installing PyTorch 1.x with CUDA 9.2** \n"
]
},
{
"metadata": {
"id": "av1b-3YWBbT2",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"!pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu92/torch_nightly.html\n",
"!pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ torchvision==0.2.1.post1\n",
"!pip install fastai"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "TBT_tbpj-7hZ",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"**Installing LEGACY fastai (0.7) from source and installing PyTorch 0.3.1 with CUDA 9.1** \n",
"\n",
"Installing from pypi is not recommended as mentioned in [fastai-github-readme](https://github.com/fastai/fastai) (due to it's rapid changes and lack of tests) and you don't want to use conda on Google Colab. So here are few steps to install the library from source."
]
},
{
"metadata": {
"id": "qECKi529HtXm",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 84
},
"outputId": "7a406fa5-05ba-45b9-cba3-13ccdc9bf203"
},
"cell_type": "code",
"source": [
"%%bash\n",
"\n",
"if ! [ -d fastai ]\n",
"then\n",
" git clone https://github.com/fastai/fastai.git\n",
"fi\n",
"\n",
"cd fastai\n",
"\n",
"git pull\n",
"\n",
"cd old\n",
"\n",
"pip -q install . && echo Successfully Installed Fastai 0.7\n",
"\n",
"pip -q install http://download.pytorch.org/whl/cu91/torch-0.3.1-cp36-cp36m-linux_x86_64.whl && echo Successfully Installed PyTorch\n",
"\n",
"pip -q install torchvision && echo Successfully Installed TorchVision"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"Already up to date.\n",
"Successfully Installed Fastai 0.7\n",
"Successfully Installed PyTorch\n",
"Successfully Installed TorchVision\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "sIIDTp5G1Hs2",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"**Import all the libraries**"
]
},
{
"metadata": {
"id": "XB3543WIHN0h",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Imports for FastAI 1.x"
]
},
{
"metadata": {
"id": "x2kfLCuPHM4b",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from fastai.imports import *"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ja8LBm3DZ6vZ",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Imports for FastAI Legacy"
]
},
{
"metadata": {
"id": "akD5dZfY1Fx8",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# This file contains all the main external libs we'll use\n",
"from fastai.imports import *\n",
"from fastai.transforms import *\n",
"from fastai.conv_learner import *\n",
"from fastai.model import *\n",
"from fastai.dataset import *\n",
"from fastai.sgdr import *\n",
"from fastai.plots import *"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "MgvJGuuJs_tL",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## GPU Check ##\n",
"\n",
"Check whether the GPU is enabled"
]
},
{
"metadata": {
"id": "zt_ux_PqxL2N",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "f207fa8c-4fa9-4f99-de97-af00e6a02a6e"
},
"cell_type": "code",
"source": [
"f'Is CUDA and CUDNN enabled: {torch.cuda.is_available()} and {torch.backends.cudnn.enabled}'"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Is CUDA and CUDNN enabled: True and True'"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"metadata": {
"id": "NrbLtmTPHyl0",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"**Check how much of the GPU is available**\n",
"\n",
"I'm using the following code from [a stackoverflow thread](https://stackoverflow.com/questions/48750199/google-colaboratory-misleading-information-about-its-gpu-only-5-ram-available\n",
") to check what % of the GPU is being utilized right now. 100% is bad; 0% is good (all free for me to use!)."
]
},
{
"metadata": {
"id": "tCHMN-qZs5NJ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 67
},
"outputId": "e8ac7284-4039-43b2-fd59-0d43ee129998"
},
"cell_type": "code",
"source": [
"# memory footprint support libraries/code\n",
"\n",
"!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n",
"!pip -q install gputil\n",
"!pip -q install psutil\n",
"!pip -q install humanize\n",
"\n",
"import psutil\n",
"import humanize\n",
"import os\n",
"import GPUtil as GPU\n",
"\n",
"GPUs = GPU.getGPUs()\n",
"gpu = GPUs[0]\n",
"process = psutil.Process(os.getpid())\n",
"\n",
"print(f\"Number of GPUs: {len(GPUs)}\")\n",
"print(f\"Gen RAM Free: {humanize.naturalsize( psutil.virtual_memory().available )} | Proc size: {humanize.naturalsize( process.memory_info().rss)}\")\n",
"print(\"GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB\".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))"
],
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": [
"Number of GPUs: 1\n",
"Gen RAM Free: 12.8 GB | Proc size: 260.7 MB\n",
"GPU RAM Free: 11430MB | Used: 11MB | Util 0% | Total 11441MB\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "q0WZ3Smd3P6w",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Ready to Go!"
]
}
]
}