{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "Julia_1.6.0.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"display_name": "Julia 1.6",
"language": "julia",
"name": "julia-1.6"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.6.0"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "PMGwZ7aFJL8Y",
"outputId": "59417b40-28ad-480b-80e6-240809f2c89b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 86
}
},
"source": [
"# Installation cell\n",
"%%capture\n",
"%%shell\n",
"if ! command -v julia 3>&1 > /dev/null\n",
"then\n",
" wget -q 'https://julialang-s3.julialang.org/bin/linux/x64/1.6/julia-1.6.1-linux-x86_64.tar.gz' \\\n",
" -O /tmp/julia.tar.gz\n",
" tar -x -f /tmp/julia.tar.gz -C /usr/local --strip-components 1\n",
" rm /tmp/julia.tar.gz\n",
"fi\n",
"julia -e 'using Pkg; pkg\"add IJulia; precompile;\"'\n",
"echo 'Done'"
],
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/latex": "Unrecognized magic \\texttt{\\%\\%capture}.\n\nJulia does not use the IPython \\texttt{\\%magic} syntax. To interact with the IJulia kernel, use \\texttt{IJulia.somefunction(...)}, for example. Julia macros, string macros, and functions can be used to accomplish most of the other functionalities of IPython magics.\n\n",
"text/markdown": "Unrecognized magic `%%capture`.\n\nJulia does not use the IPython `%magic` syntax. To interact with the IJulia kernel, use `IJulia.somefunction(...)`, for example. Julia macros, string macros, and functions can be used to accomplish most of the other functionalities of IPython magics.\n",
"text/plain": [
" Unrecognized magic \u001b[36m%%capture\u001b[39m.\n",
"\n",
" Julia does not use the IPython \u001b[36m%magic\u001b[39m syntax. To interact with the IJulia\n",
" kernel, use \u001b[36mIJulia.somefunction(...)\u001b[39m, for example. Julia macros, string\n",
" macros, and functions can be used to accomplish most of the other\n",
" functionalities of IPython magics."
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XdMpcQduyaQc"
},
"source": [
"After you run the first cell (the the cell directly above this text), go to Colab's menu bar and select **Edit** and select **Notebook settings** from the drop down. Select *Julia 1.6* in Runtime type. You can also select your prefered harwdware acceleration (defaults to GPU). \n",
"\n",
"
You should see something like this:\n",
"\n",
"> \n",
"\n",
"
Click on SAVE\n",
"
**We are ready to get going**\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iIxu4TjlJnBG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8684ec42-0472-4d2f-9f4a-32fa08d02a74"
},
"source": [
"VERSION"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"v\"1.6.1\""
]
},
"metadata": {
"tags": []
},
"execution_count": 1
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rR7Ox6Ax0ypi"
},
"source": [
"**The next three cells are for GPU benchmarking. If you are using this notebook for the first time and have GPU enabled, you can give it a try.** "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0y5TUI-Ll4cY"
},
"source": [
"### Optional GPU Experiments"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vHKANz2J0GDW",
"outputId": "e6850b7b-c397-4696-cd7a-06b78d9820bf",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"using Pkg\n",
"Pkg.add([\"BenchmarkTools\", \"CUDA\"])\n",
"using BenchmarkTools, CUDA\n",
"\n",
"if has_cuda_gpu()\n",
" print(\"The GPU device is:\", CUDA.device())\n",
"end"
],
"execution_count": 3,
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `~/.julia/registries/General`\n",
"\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m OpenSpecFun_jll ───── v0.5.5+0\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Adapt ─────────────── v3.3.1\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m LLVMExtra_jll ─────── v0.0.10+0\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m TimerOutputs ──────── v0.5.13\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ExprTools ─────────── v0.1.6\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m BenchmarkTools ────── v1.2.0\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m DocStringExtensions ─ v0.8.5\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Compat ────────────── v3.38.0\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Random123 ─────────── v1.4.2\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Requires ──────────── v1.1.3\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m LogExpFunctions ───── v0.3.3\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ChainRulesCore ────── v1.6.0\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m RandomNumbers ─────── v1.5.3\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m LLVM ──────────────── v4.5.3\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m CUDA ──────────────── v3.4.2\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m CEnum ─────────────── v0.4.1\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m BFloat16s ─────────── v0.1.0\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Reexport ──────────── v1.2.2\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m IrrationalConstants ─ v0.1.0\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m SpecialFunctions ──── v1.6.2\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m AbstractFFTs ──────── v1.0.1\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m GPUCompiler ───────── v0.12.9\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m GPUArrays ─────────── v8.1.1\n",
"\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.6/Project.toml`\n",
" \u001b[90m [6e4b80f9] \u001b[39m\u001b[92m+ BenchmarkTools v1.2.0\u001b[39m\n",
" \u001b[90m [052768ef] \u001b[39m\u001b[92m+ CUDA v3.4.2\u001b[39m\n",
"\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.6/Manifest.toml`\n",
" \u001b[90m [621f4979] \u001b[39m\u001b[92m+ AbstractFFTs v1.0.1\u001b[39m\n",
" \u001b[90m [79e6a3ab] \u001b[39m\u001b[92m+ Adapt v3.3.1\u001b[39m\n",
" \u001b[90m [ab4f0b2a] \u001b[39m\u001b[92m+ BFloat16s v0.1.0\u001b[39m\n",
" \u001b[90m [6e4b80f9] \u001b[39m\u001b[92m+ BenchmarkTools v1.2.0\u001b[39m\n",
" \u001b[90m [fa961155] \u001b[39m\u001b[92m+ CEnum v0.4.1\u001b[39m\n",
" \u001b[90m [052768ef] \u001b[39m\u001b[92m+ CUDA v3.4.2\u001b[39m\n",
" \u001b[90m [d360d2e6] \u001b[39m\u001b[92m+ ChainRulesCore v1.6.0\u001b[39m\n",
" \u001b[90m [34da2185] \u001b[39m\u001b[92m+ Compat v3.38.0\u001b[39m\n",
" \u001b[90m [ffbed154] \u001b[39m\u001b[92m+ DocStringExtensions v0.8.5\u001b[39m\n",
" \u001b[90m [e2ba6199] \u001b[39m\u001b[92m+ ExprTools v0.1.6\u001b[39m\n",
" \u001b[90m [0c68f7d7] \u001b[39m\u001b[92m+ GPUArrays v8.1.1\u001b[39m\n",
" \u001b[90m [61eb1bfa] \u001b[39m\u001b[92m+ GPUCompiler v0.12.9\u001b[39m\n",
" \u001b[90m [92d709cd] \u001b[39m\u001b[92m+ IrrationalConstants v0.1.0\u001b[39m\n",
" \u001b[90m [929cbde3] \u001b[39m\u001b[92m+ LLVM v4.5.3\u001b[39m\n",
" \u001b[90m [2ab3a3ac] \u001b[39m\u001b[92m+ LogExpFunctions v0.3.3\u001b[39m\n",
" \u001b[90m [74087812] \u001b[39m\u001b[92m+ Random123 v1.4.2\u001b[39m\n",
" \u001b[90m [e6cf234a] \u001b[39m\u001b[92m+ RandomNumbers v1.5.3\u001b[39m\n",
" \u001b[90m [189a3867] \u001b[39m\u001b[92m+ Reexport v1.2.2\u001b[39m\n",
" \u001b[90m [ae029012] \u001b[39m\u001b[92m+ Requires v1.1.3\u001b[39m\n",
" \u001b[90m [276daf66] \u001b[39m\u001b[92m+ SpecialFunctions v1.6.2\u001b[39m\n",
" \u001b[90m [a759f4b9] \u001b[39m\u001b[92m+ TimerOutputs v0.5.13\u001b[39m\n",
" \u001b[90m [dad2f222] \u001b[39m\u001b[92m+ LLVMExtra_jll v0.0.10+0\u001b[39m\n",
" \u001b[90m [efe28fd5] \u001b[39m\u001b[92m+ OpenSpecFun_jll v0.5.5+0\u001b[39m\n",
" \u001b[90m [8bb1440f] \u001b[39m\u001b[92m+ DelimitedFiles\u001b[39m\n",
" \u001b[90m [8ba89e20] \u001b[39m\u001b[92m+ Distributed\u001b[39m\n",
" \u001b[90m [4af54fe1] \u001b[39m\u001b[92m+ LazyArtifacts\u001b[39m\n",
" \u001b[90m [37e2e46d] \u001b[39m\u001b[92m+ LinearAlgebra\u001b[39m\n",
" \u001b[90m [9abbd945] \u001b[39m\u001b[92m+ Profile\u001b[39m\n",
" \u001b[90m [1a1011a3] \u001b[39m\u001b[92m+ SharedArrays\u001b[39m\n",
" \u001b[90m [2f01184e] \u001b[39m\u001b[92m+ SparseArrays\u001b[39m\n",
" \u001b[90m [10745b16] \u001b[39m\u001b[92m+ Statistics\u001b[39m\n",
" \u001b[90m [e66e0078] \u001b[39m\u001b[92m+ CompilerSupportLibraries_jll\u001b[39m\n",
" \u001b[90m [05823500] \u001b[39m\u001b[92m+ OpenLibm_jll\u001b[39m\n",
"\u001b[32m\u001b[1m Building\u001b[22m\u001b[39m Random123 → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/0e8b146557ad1c6deb1367655e052276690e71a3/build.log`\n",
"\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mOpenLibm_jll\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mAbstractFFTs\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mDocStringExtensions\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mRequires\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mCEnum\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mExprTools\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mReexport\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mAdapt\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mCompat\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mBFloat16s\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mCompilerSupportLibraries_jll\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mIrrationalConstants\u001b[39m\n",
"\u001b[32m ✓ \u001b[39mBenchmarkTools\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mRandomNumbers\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mTimerOutputs\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mLLVMExtra_jll\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mOpenSpecFun_jll\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mChainRulesCore\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mRandom123\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mGPUArrays\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mLogExpFunctions\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mLLVM\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mSpecialFunctions\u001b[39m\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mGPUCompiler\u001b[39m\n",
"\u001b[32m ✓ \u001b[39mCUDA\n",
" 25 dependencies successfully precompiled in 62 seconds (15 already precompiled)\n",
"\u001b[32m\u001b[1m Downloading\u001b[22m\u001b[39m artifact: CUDA\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"The GPU device is:CuDevice(0)"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "q7Mbcm00lnxO",
"outputId": "bc6a9587-5be2-4b05-fad6-b8e9226b2b38",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"mcpu = rand(2^10, 2^10)\n",
"@benchmark mcpu*mcpu"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"BenchmarkTools.Trial: 82 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m56.400 ms\u001b[22m\u001b[39m … \u001b[35m74.484 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 3.67%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m60.914 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m61.581 ms\u001b[22m\u001b[39m ± \u001b[32m 3.354 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m0.68% ± 1.54%\n",
"\n",
" \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▆\u001b[39m▃\u001b[39m▄\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[34m█\u001b[39m\u001b[39m \u001b[32m▃\u001b[39m\u001b[39m \u001b[39m▃\u001b[39m▃\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▆\u001b[39m▄\u001b[39m▄\u001b[39m▁\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m▆\u001b[32m█\u001b[39m\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▁\u001b[39m▆\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m \u001b[39m▁\n",
" 56.4 ms\u001b[90m Histogram: frequency by time\u001b[39m 74.1 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m8.00 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m2\u001b[39m."
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RMHRbF5J-vmW",
"outputId": "7420e4e1-aa40-4150-f17b-883ca668f3dc",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"println(\"The CuArrray operation should take around 0.5 ms(excluding CUDA downloading time which is a one time process), and should be much faster. If so, the GPU is working.\")\n",
"mgpu = cu(mcpu)\n",
"@benchmark CUDA.@sync mgpu*mgpu"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The CuArrray operation should take around 0.5 ms(excluding CUDA downloading time which is a one time process), and should be much faster. If so, the GPU is working.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"BenchmarkTools.Trial: 3470 samples with 1 evaluation.\n",
" Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m962.769 μs\u001b[22m\u001b[39m … \u001b[35m383.623 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 1.51%\n",
" Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m 1.105 ms \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n",
" Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m 1.431 ms\u001b[22m\u001b[39m ± \u001b[32m 11.102 ms\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m0.32% ± 0.04%\n",
"\n",
" \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\u001b[39m▅\u001b[39m▇\u001b[34m█\u001b[39m\u001b[39m▆\u001b[39m▅\u001b[39m▃\u001b[39m▃\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
" \u001b[39m▁\u001b[39m▃\u001b[39m▅\u001b[39m▇\u001b[39m▆\u001b[39m█\u001b[39m▇\u001b[39m▆\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▃\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▆\u001b[39m▇\u001b[39m█\u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▆\u001b[39m▆\u001b[39m▆\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m▃\n",
" 963 μs\u001b[90m Histogram: frequency by time\u001b[39m 1.26 ms \u001b[0m\u001b[1m<\u001b[22m\n",
"\n",
" Memory estimate\u001b[90m: \u001b[39m\u001b[33m21.23 KiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m681\u001b[39m."
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vla4-2F7bvZO",
"outputId": "bc018352-8329-4cc8-b66f-e121f2f94915",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"] add GMT.jl"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n",
"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m GMT ─ v0.37.0\n",
"\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.6/Project.toml`\n",
" \u001b[90m [5752ebe1] \u001b[39m\u001b[92m+ GMT v0.37.0\u001b[39m\n",
"\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.6/Manifest.toml`\n",
" \u001b[90m [5752ebe1] \u001b[39m\u001b[92m+ GMT v0.37.0\u001b[39m\n",
"\u001b[32m\u001b[1m Building\u001b[22m\u001b[39m GMT → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/dd5213e842cd5266f691c6efbcb71df450d7dec2/build.log`\n",
"\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n",
"\u001b[32m ✓ \u001b[39mGMT\n",
" 1 dependency successfully precompiled in 312 seconds (40 already precompiled)\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ySGDN61n-0Sh"
},
"source": [
"!sudo apt update\n",
"!sudo apt upgrade -y\n",
"!sudo apt install -y build-essential cmake libcurl4-gnutls-dev libnetcdf-dev gdal-bin libgdal-dev libfftw3-dev libpcre3-dev liblapack-dev libblas-dev libglib2.0-dev ghostscript ghostscript-x graphicsmagick ffmpeg xdg-utils"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "FQUdsF2YDDZ1"
},
"source": [
"!git clone --depth 50 https://github.com/GenericMappingTools/gmt"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "XCRcav3EDWUC"
},
"source": [
"!cmake /content/gmt"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ChzivzSvDekS"
},
"source": [
"!cmake --build . --target install"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VgHFd17JdTKI"
},
"source": [
"using GMT\n",
"bar([0. 1 2 3; 1 2 3 4], stack=true, hbar=true, fill=[\"red\", \"green\", \"blue\"], show=true)"
],
"execution_count": null,
"outputs": []
}
]
}