nbs/CUDA_GPU_Management.ipynb

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    "# CUDA GPU Management\n",
    "> A handler on GPU management"
   ]
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   "source": [
    "## Handler on a single CUDA device"
   ]
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   "source": [
    "This tool will find the CUDA info from the system, the most current info about\n",
    "* How much is the total CUDA memory\n",
    "* How much will the CUDA memory is **in use**\n",
    "* How much CUDA memory is **free**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import torch\n",
    "from forgebox.ftorch.cuda import CudaDevice, CudaHandler, Cudas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial"
   ]
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   "source": [
    "These handler is to simplify the multi-cuda situation, where you can allocate the most idle GPU\n",
    "\n",
    "Substantiate the handler"
   ]
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  {
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   "execution_count": null,
   "metadata": {},
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   "source": [
    "ch = CudaHandler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Found the most idle device, by measure of CUDA memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dev = ch.idle()()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How we use the device in pytorch coding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_torch_tensor.to(dev)"
   ]
  }
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