Post by joitarani99 on Mar 14, 2024 9:03:38 GMT
Linux ageing headers Install Nvidia drivers for all kernels for kernel in Linux version list do apt install y linuxheaderskernel done apt install y NVidia driver hop python pip git pip install nvitop.. jupyter.. accelerate.. peft.. bitsandbytes.. transformers nvidiadriversinstall.sh. The result is an env like this... Now we have a virtual server with Nvidia drivers a set of tools a Python environment with the libraries necessary to run LLM. Running jupyternotebook For testing we used the training part of the dataset with texts ranging from to characters.
The distribution of text sizes is shown in the graph Test results Of course we tested both video cards using the Buy Email List same script. We summarized the training results of each model and the results of text generation in tables. The Out of Memory CUDA OOM error indicates that the video card did not have enough memory to complete the task. Here we will not go into detail about RAM loading since the purpose of the test is to compare the capabilities of video cards and not to find the optimal way to train LLM. If youre interested in learning more about how GPU memory is consumed when training large language models.
We recommend this article. When reading all the tables below it is worth considering that memory percentages and GPU core usage are taken from the nvitop utility. Training As we can see when working with small batches both video cards successfully cope with the load. By the way the comparison clearly shows why you cant rely only on technical characteristics when choosing a video card the A which has less memory and the number of cores coped with one of the tests much better than the more powerful A Ada although it used . of its computing resource.
The distribution of text sizes is shown in the graph Test results Of course we tested both video cards using the Buy Email List same script. We summarized the training results of each model and the results of text generation in tables. The Out of Memory CUDA OOM error indicates that the video card did not have enough memory to complete the task. Here we will not go into detail about RAM loading since the purpose of the test is to compare the capabilities of video cards and not to find the optimal way to train LLM. If youre interested in learning more about how GPU memory is consumed when training large language models.
We recommend this article. When reading all the tables below it is worth considering that memory percentages and GPU core usage are taken from the nvitop utility. Training As we can see when working with small batches both video cards successfully cope with the load. By the way the comparison clearly shows why you cant rely only on technical characteristics when choosing a video card the A which has less memory and the number of cores coped with one of the tests much better than the more powerful A Ada although it used . of its computing resource.