mirror of
https://github.com/qdrant/fastembed.git
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2827 lines
83 KiB
Plaintext
2827 lines
83 KiB
Plaintext
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"metadata": {
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"id": "ntGNDuSCeAR2"
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},
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"source": [
|
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"# FastEmbed on GPU\n",
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"\n",
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||
"As of version 0.2.7 FastEmbed supports GPU acceleration.\n",
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"\n",
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"This notebook covers the installation process and usage of fastembed on GPU.\n",
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"\n",
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"## Installation\n",
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"\n",
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"Fastembed depends on `onnxruntime` and inherits its scheme of GPU support.\n",
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"\n",
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"In order to use GPU with onnx models, you would need to have `onnxruntime-gpu` package, which substitutes all the `onnxruntime` functionality.\n",
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"Fastembed mimics this behavior and requires `fastembed-gpu` package to be installed."
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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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"metadata": {
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"id": "GK2XADwUeEK7"
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},
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"outputs": [],
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"source": [
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"!pip install fastembed-gpu"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "3aiGPqjCeGzo"
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"source": [
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"**NOTE**: `onnxruntime-gpu` and `onnxruntime` can't be installed in the same environment. If you have `onnxruntime` installed, you would need to uninstall it before installing `onnxruntime-gpu`. Same is true for `fastembed` and `fastembed-gpu`.\n",
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"\n",
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"### CUDA 12.x support\n",
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"\n",
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"By default `onnxruntime-gpu` is shipped with CUDA 11.8 support.\n",
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"CUDA 12.x support requires installation of `onnxruntime-gpu` with providing of a direct url:"
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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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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "OoSfWFFZeJ5t",
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"outputId": "417b9332-6a7b-4000-c74b-4ed2b5b76590"
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},
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"outputs": [],
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"source": [
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"!pip install onnxruntime-gpu -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/ -qq\n",
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"!pip install fastembed-gpu -qqq"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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"metadata": {
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"id": "3xx3r-9jgAMi"
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||
},
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||
"source": [
|
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"You can check your CUDA version using such commands as `nvidia-smi` or `nvcc --version`\n",
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"\n",
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"Google Colab notebooks have CUDA 12.x."
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||
]
|
||
},
|
||
{
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||
"cell_type": "markdown",
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"metadata": {
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"id": "Igv5RXhSeO68"
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},
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"source": [
|
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"### CUDA drivers\n",
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"\n",
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"FastEmbed does not include CUDA drivers and CuDNN libraries.\n",
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"You would need to take care of the environment setup on your own.\n",
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"Dependencies required for the chosen onnxruntime version can be found [here](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements)"
|
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"## Usage"
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]
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},
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"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
|
||
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
||
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
||
"You will be able to reuse this secret in all of your notebooks.\n",
|
||
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
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" warnings.warn(\n"
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"metadata": {},
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"['CUDAExecutionProvider', 'CPUExecutionProvider']"
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]
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},
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"execution_count": 2,
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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": [
|
||
"from typing import List\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"from fastembed import TextEmbedding\n",
|
||
"\n",
|
||
"embedding_model_gpu = TextEmbedding(\n",
|
||
" model_name=\"BAAI/bge-small-en-v1.5\", providers=[\"CUDAExecutionProvider\"]\n",
|
||
")\n",
|
||
"embedding_model_gpu.model.model.get_providers()"
|
||
]
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||
},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "iPtoHf7GeV-i"
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||
},
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||
"outputs": [],
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||
"source": [
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||
"documents: List[str] = list(np.repeat(\"Demonstrating GPU acceleration in fastembed\", 500))"
|
||
]
|
||
},
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{
|
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"cell_type": "code",
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"execution_count": 4,
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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": [
|
||
"43.4 ms ± 2.06 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%%timeit\n",
|
||
"list(embedding_model_gpu.embed(documents))"
|
||
]
|
||
},
|
||
{
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"cell_type": "code",
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"embedding_model_cpu = TextEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
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"embedding_model_cpu.model.model.get_providers()"
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"text": [
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"4.33 s ± 591 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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]
|
||
}
|
||
],
|
||
"source": [
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||
"%%timeit\n",
|
||
"list(embedding_model_cpu.embed(documents))"
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]
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}
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