{"id":4731,"date":"2026-03-11T21:35:38","date_gmt":"2026-03-11T16:05:38","guid":{"rendered":"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success\/"},"modified":"2026-03-11T21:35:38","modified_gmt":"2026-03-11T16:05:38","slug":"top-artificial-intelligence-best-practices-for-success","status":"publish","type":"post","link":"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success\/","title":{"rendered":"Top Artificial Intelligence Best Practices for Success"},"content":{"rendered":"<p>text<br \/>\n[2023-10-27T14:22:01.442Z] kernel: [12409.552101] python3[14201]: segfault at 0 ip 00007f8e12a34b12 sp 00007ffc8e12a340 error 4 in libtorch_cuda.so[7f8e10000000+12a34000]<br \/>\n[2023-10-27T14:22:01.443Z] torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 12.50 GiB (GPU 0; 23.65 GiB total capacity; 18.21 GiB already allocated; 4.12 GiB free; 19.00 GiB reserved in total by PyTorch) If reserved memory is &gt;&gt; allocated memory try setting max_split_size_mb to avoid fragmentation.<br \/>\n[2023-10-27T14:22:01.445Z] Terminated: 15 (SIGTERM)<br \/>\n[2023-10-27T14:22:01.446Z] Environment: Ubuntu 22.04.3 LTS, Python 3.10.12, PyTorch 2.0.1+cu118, NVIDIA-SMI 535.104.05, Driver 535.104.05, CUDA 12.1, Hardware: 1x RTX 3090 (24GB), 64GB DDR4 RAM, i9-12900K.<\/p>\n<hr \/>\n<p>Listen close, kid. I saw you staring at that stack trace like it was written in Linear A. You think you\u2019re doing &#8220;artificial intelligence&#8221; because you imported a library that\u2019s larger than the entire operating system I used to run a bank on in 1992. You\u2019re not. You\u2019re just piling abstractions on top of a leaking basement. You sent that job to the GPU without checking the memory map, didn&#8217;t you? You trusted the &#8220;magic&#8221; of the caching allocator. Now the kernel is screaming, the OOM killer is sharpening its knife, and you\u2019re wondering why your &#8220;best practices&#8221; didn&#8217;t save you.<\/p>\n<p>Sit down. I\u2019m going to walk you through the wreckage of this migration. Maybe if I document these grievances, you\u2019ll stop treating the hardware like an infinite resource and start treating it like the finicky, silicon-etched beast it actually is.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_80 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69d4e01ae4d47\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69d4e01ae4d47\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success\/#Log_Entry_1_The_Dependency_Hellscape_and_the_Myth_of_Reproducibility\" >Log Entry 1: The Dependency Hellscape and the Myth of Reproducibility<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success\/#Log_Entry_2_Data_Ingestion_and_the_Silent_Failure_of_Sanitization\" >Log Entry 2: Data Ingestion and the Silent Failure of Sanitization<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success\/#Log_Entry_3_The_VRAM_Mirage_and_the_Caching_Allocators_Lies\" >Log Entry 3: The VRAM Mirage and the Caching Allocator&#8217;s Lies<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success\/#Log_Entry_4_The_Latency_Lie_and_the_Python_Tax\" >Log Entry 4: The Latency Lie and the Python Tax<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success\/#Log_Entry_5_Determinism_Seeds_and_the_Ghost_in_the_Machine\" >Log Entry 5: Determinism, Seeds, and the Ghost in the Machine<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success\/#Log_Entry_6_The_Cost_of_Abstraction_and_the_Final_Grievance\" >Log Entry 6: The Cost of Abstraction and the Final Grievance<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Log_Entry_1_The_Dependency_Hellscape_and_the_Myth_of_Reproducibility\"><\/span>Log Entry 1: The Dependency Hellscape and the Myth of Reproducibility<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>[2023-10-27T15:04:12+00:00]<br \/>\nState: <code>pip freeze &gt; requirements.txt<\/code> (A document of lies)<br \/>\nEnvironment: <code>venv<\/code> isolated, yet bleeding.<\/p>\n<p>The first thing you did was run <code>pip install torch torchvision torchaudio<\/code>. You thought that was enough. You didn&#8217;t check the <code>glibc<\/code> version on the host. You didn&#8217;t check if <code>libstdc++.so.6<\/code> was pointing to a version that actually supports the symbols required by the pre-compiled binaries you just shoved into your <code>\/site-packages<\/code>. <\/p>\n<p>In 1984, we wrote Makefiles. We knew where every header lived. Today, you have &#8220;artificial intelligence&#8221; frameworks that pull in 4GB of dependencies just to multiply two matrices. Your first &#8220;best practice&#8221; is this: <strong>If you cannot reproduce the environment down to the specific shared object hash, you do not have a model; you have a coincidence.<\/strong><\/p>\n<p>Look at your <code>pip list<\/code>. You have <code>numpy==1.24.3<\/code> and <code>pandas==2.0.3<\/code>. But wait, another sub-dependency pulled in a different version of <code>six<\/code> or <code>requests<\/code>, and now your runtime is a minefield of <code>ImportError: cannot import name '...' from '...'<\/code>. <\/p>\n<p>You need to use a lockfile. Not a suggestion, a lockfile. <code>poetry.lock<\/code> or <code>conda-lock<\/code>. And even then, you\u2019re at the mercy of the Python package index. I\u2019ve seen &#8220;artificial intelligence&#8221; projects die because a developer deleted a repository on GitHub that a setup script was curling in the background. <\/p>\n<p>Stop using <code>pip install<\/code>. Use a container, but don&#8217;t use a &#8220;vibrant&#8221; base image that updates every night. Use a specific SHA256 hash of a Debian Slim or Alpine image. If you don&#8217;t control the bytes, the bytes will eventually control you.<\/p>\n<pre class=\"codehilite\"><code class=\"language-bash\"># Example of what you SHOULD have run to see the rot:\nldd \/home\/user\/venv\/lib\/python3.10\/site-packages\/torch\/lib\/libtorch_cpu.so\n# Check for &quot;not found&quot; or version mismatches in the output.\n# If you see a mismatch in GLIBC_2.34, your &quot;modern&quot; OS is too old for your &quot;modern&quot; AI.\n<\/code><\/pre>\n<h2><span class=\"ez-toc-section\" id=\"Log_Entry_2_Data_Ingestion_and_the_Silent_Failure_of_Sanitization\"><\/span>Log Entry 2: Data Ingestion and the Silent Failure of Sanitization<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>[2023-10-27T16:45:33+00:00]<br \/>\nState: <code>DataLoader<\/code> hung at <code>__next__()<\/code><br \/>\nEnvironment: <code>num_workers=16<\/code>, <code>pin_memory=True<\/code><\/p>\n<p>You tried to feed the beast. You pointed your <code>DataLoader<\/code> at a directory of 10 million JPEGs and wondered why the CPU usage hit 100% while the GPU sat at 0% utilization. You\u2019re starving the silicon, kid. <\/p>\n<p>&#8220;Artificial intelligence&#8221; is 90% IO and 10% math, but you spent all your time on the math. You didn&#8217;t check for corrupted headers. You didn&#8217;t check for zero-byte files. You didn&#8217;t check for NaNs in your CSVs. One single <code>NaN<\/code> in a weight initialization or a training sample, and your loss function becomes <code>inf<\/code>. You\u2019ve spent $400 of the company\u2019s cloud credits training a model to output &#8220;Nothing.&#8221;<\/p>\n<p>The &#8220;best practice&#8221; here is <strong>defensive data engineering<\/strong>. You don&#8217;t trust the data. You <code>grep<\/code> it, you <code>awk<\/code> it, you validate the checksums. <\/p>\n<pre class=\"codehilite\"><code class=\"language-python\"># Your junior-level mistake:\ndataset = MyDataset(root_dir=&quot;.\/data&quot;)\nloader = DataLoader(dataset, batch_size=64, num_workers=16)\n\n# What you should have done:\n# 1. Check for file integrity before the loop.\n# 2. Use mmap (memory mapping) for large datasets to avoid copying buffers.\n# 3. Profile the bottleneck using 'iostat -x 1'.\n<\/code><\/pre>\n<p>If your disk latency is high, your &#8220;artificial intelligence&#8221; is just a very expensive way to wait for a spinning platter or a saturated NVMe bus. And for the love of Ken Thompson, stop using <code>pandas<\/code> for datasets larger than your RAM. Use <code>vaex<\/code> or <code>polars<\/code>, or better yet, write a binary format that matches the memory layout of your input tensors. Every time you convert a string to a float in a training loop, a kernel architect loses their wings.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Log_Entry_3_The_VRAM_Mirage_and_the_Caching_Allocators_Lies\"><\/span>Log Entry 3: The VRAM Mirage and the Caching Allocator&#8217;s Lies<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>[2023-10-27T18:12:09+00:00]<br \/>\nState: <code>nvidia-smi<\/code> showing 23.5GB\/24GB used.<br \/>\nEnvironment: PyTorch 2.0.1, <code>max_split_size_mb<\/code> unset.<\/p>\n<p>This is where you hit the wall today. <code>CUDA_ERROR_OUT_OF_MEMORY<\/code>. You looked at <code>nvidia-smi<\/code> and saw you had 4GB free, so you tried to allocate a 2GB tensor. It failed. Why? Fragmentation.<\/p>\n<p>The PyTorch caching allocator is a black box that tries to be smarter than the driver. It holds onto memory blocks because <code>cudaMalloc<\/code> is an expensive syscall. But if your model has varying sequence lengths or you\u2019re doing dynamic graph construction, you end up with a &#8220;Swiss cheese&#8221; memory map. You have 4GB total, but the largest contiguous block is 512MB. <\/p>\n<p>You didn&#8217;t profile your memory. You didn&#8217;t use <code>torch.cuda.memory_summary()<\/code>. You just kept bumping the batch size because some <a href=\"https:\/\/itsupportwale.com\/blog\/\" title=\"Read more about blog\">blog<\/a> post told you it would &#8220;improve convergence.&#8221; <\/p>\n<p><strong>Best Practice: Deterministic Memory Budgeting.<\/strong><br \/>\nYou calculate the memory footprint of your weights, your gradients, and your optimizer states (Adam takes 2x the weight memory, kid, learn it). Then you leave a 15% buffer for the kernel and the context. <\/p>\n<pre class=\"codehilite\"><code class=\"language-bash\"># Run this while your script is dying:\nnvidia-smi --query-gpu=memory.used,memory.free,utilization.gpu --format=csv -l 1\n<\/code><\/pre>\n<p>If you see <code>utilization.gpu<\/code> dropping to 0% while <code>memory.used<\/code> stays high, you\u2019re either in a deadlock or you\u2019re thrashing the swap because you forgot that <code>pin_memory=True<\/code> consumes host RAM. You\u2019re trying to run a marathon while breathing through a straw.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Log_Entry_4_The_Latency_Lie_and_the_Python_Tax\"><\/span>Log Entry 4: The Latency Lie and the Python Tax<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>[2023-10-27T20:30:00+00:00]<br \/>\nState: <code>strace -c<\/code> output showing excessive <code>futex<\/code> calls.<br \/>\nEnvironment: FastAPI wrapper around a Transformer model.<\/p>\n<p>You finally got the model trained. Now you want to &#8220;deploy&#8221; it. You wrapped it in a FastAPI web server because that\u2019s what the &#8220;artificial intelligence&#8221; tutorials told you to do. Now your p99 latency is 450ms for a task that takes the GPU 15ms to compute. <\/p>\n<p>Where is the time going? It\u2019s going to the Global Interpreter Lock (GIL). It\u2019s going to JSON serialization. It\u2019s going to the context switch between the user-space Python process and the kernel-space network stack. <\/p>\n<p>You\u2019re using <code>uvicorn<\/code> with 4 workers, each loading a 10GB model into VRAM. You just ran out of memory again, didn&#8217;t you? Because you didn&#8217;t realize that each worker is a separate process with its own copy of the weights unless you\u2019re using a shared memory segment or a model server that actually understands how to manage resources.<\/p>\n<p><strong>Best Practice: Move the Inference out of the Interpreter.<\/strong><br \/>\nIf you care about performance, you export to ONNX or TensorRT. You write a C++ or Rust wrapper. You use <code>gRPC<\/code> with <code>protobuf<\/code> instead of bloated JSON. <\/p>\n<pre class=\"codehilite\"><code class=\"language-bash\"># See how much time you're wasting in syscalls:\nstrace -p &lt;python_pid&gt; -c\n<\/code><\/pre>\n<p>If I see <code>select()<\/code> or <code>poll()<\/code> taking up 40% of your execution time, I\u2019m pulling the plug. &#8220;Artificial intelligence&#8221; doesn&#8217;t excuse sloppy systems engineering. You\u2019re building a bridge out of toothpicks and wondering why it sways in the wind.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Log_Entry_5_Determinism_Seeds_and_the_Ghost_in_the_Machine\"><\/span>Log Entry 5: Determinism, Seeds, and the Ghost in the Machine<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>[2023-10-27T22:15:45+00:00]<br \/>\nState: <code>seed=42<\/code> set, yet results differ across runs.<br \/>\nEnvironment: Multi-GPU training via <code>DistributedDataParallel<\/code>.<\/p>\n<p>You came to me crying that your model isn&#8217;t reproducible. &#8220;But Jenkins, I set <code>random.seed(42)<\/code> and <code>np.random.seed(42)<\/code>!&#8221; <\/p>\n<p>Did you set <code>torch.backends.cudnn.deterministic = True<\/code>? Did you set <code>torch.backends.cudnn.benchmark = False<\/code>? No, because you wanted that extra 5% throughput. Well, the cuDNN autotuner picked a different convolution algorithm on run #2 because the GPU temperature was 5 degrees higher and the clock speed throttled. <\/p>\n<p>In &#8220;artificial intelligence,&#8221; non-determinism is a cancer. If you can&#8217;t reproduce a bug, you can&#8217;t fix the bug. If your weights drift because of floating-point accumulation errors in a non-deterministic atomic addition on the GPU, you\u2019re not doing science; you\u2019re doing alchemy.<\/p>\n<p><strong>Best Practice: Lock the State.<\/strong><br \/>\nYou lock the seeds, you lock the algorithms, and you document the hardware. If you move from an A100 to an H100, your results <em>will<\/em> change. If you change your version of CUDA from 11.8 to 12.1, the underlying PTX instructions change. <\/p>\n<pre class=\"codehilite\"><code class=\"language-python\"># The bare minimum for sanity:\nimport torch\nimport numpy as np\nimport random\n\ndef seed_everything(seed):\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n    import os\n    os.environ['PYTHONHASHSEED'] = str(seed)\n<\/code><\/pre>\n<p>And even then, you\u2019re still at the mercy of the <code>DataLoader<\/code>&#8216;s multi-processing. If the OS schedules worker #3 before worker #1, your data order changes, and your gradient descent takes a different path down the manifold. You need to use a sampler that is tied to the global seed.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Log_Entry_6_The_Cost_of_Abstraction_and_the_Final_Grievance\"><\/span>Log Entry 6: The Cost of Abstraction and the Final Grievance<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>[2023-10-28T01:00:00+00:00]<br \/>\nState: Cloud bill exceeded monthly budget in 4 days.<br \/>\nEnvironment: Kubernetes cluster with &#8220;Auto-scaling&#8221; enabled.<\/p>\n<p>The final insult. You put your &#8220;artificial intelligence&#8221; pipeline on a Kubernetes cluster with auto-scaling. You thought the &#8220;cloud&#8221; would handle the load. But your liveness probes were failing because the model took 60 seconds to load into memory, so Kubernetes kept killing the pod and restarting it in a death loop. Each restart pulled 10GB of container images over the network. Your egress costs are now higher than your rent.<\/p>\n<p>You\u2019ve ignored the &#8220;unsexy&#8221; parts: the cold-start latency, the health-check timeouts, the resource limits in your YAML files. <\/p>\n<pre class=\"codehilite\"><code class=\"language-yaml\"># Your broken deployment.yaml\nresources:\n  limits:\n    nvidia.com\/gpu: 1\n    memory: &quot;16Gi&quot; # Model is 15.5Gi. You forgot the overhead.\n  requests:\n    nvidia.com\/gpu: 1\n    memory: &quot;16Gi&quot;\n<\/code><\/pre>\n<p>When the model tries to allocate a scratch buffer for the attention mechanism, the pod hits the 16Gi limit and the kernel sends a <code>SIGKILL<\/code>. You don&#8217;t even get a stack trace. Just a <code>OOMKilled<\/code> status and a confused junior developer.<\/p>\n<p><strong>Best Practice: Profile the Baseline.<\/strong><br \/>\nBefore you ever touch a cloud provider, you run your workload on a local machine with a profiler. You find the &#8220;steady state&#8221; memory usage. You find the &#8220;peak&#8221; memory usage. You set your limits at 1.2x the peak. <\/p>\n<p>&#8220;Artificial intelligence&#8221; is not a get-out-of-jail-free card for basic systems architecture. It is a high-performance computing (HPC) workload. If you treat it like a CRUD app, it will break your heart and your bank account.<\/p>\n<p>Stop looking for &#8220;magic&#8221; solutions. Stop reading hype-filled blogs about &#8220;shaping the future.&#8221; Go back to the basics. Check your pointers. Watch your memory alignment. Profile your IO. If you can&#8217;t explain what every byte in your VRAM is doing, you haven&#8217;t finished your job.<\/p>\n<p>Now, clear that <code>\/tmp<\/code> directory, reset the XID error on the GPU with <code>nvidia-smi -r<\/code>, and start over. And this time, use a debugger, not a &#8220;vibe.&#8221;<\/p>\n<hr \/>\n<p><strong>Grievance Log Closed.<\/strong><br \/>\n<strong>Status: Kernel Tainted.<\/strong><br \/>\n<strong>Author: Jenkins, Senior Kernel Architect (Ret.)<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>text [2023-10-27T14:22:01.442Z] kernel: [12409.552101] python3[14201]: segfault at 0 ip 00007f8e12a34b12 sp 00007ffc8e12a340 error 4 in libtorch_cuda.so[7f8e10000000+12a34000] [2023-10-27T14:22:01.443Z] torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 12.50 GiB (GPU 0; 23.65 GiB total capacity; 18.21 GiB already allocated; 4.12 GiB free; 19.00 GiB reserved in total by PyTorch) If reserved memory is &gt;&gt; allocated memory try &#8230; <a title=\"Top Artificial Intelligence Best Practices for Success\" class=\"read-more\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success\/\" aria-label=\"Read more  on Top Artificial Intelligence Best Practices for Success\">Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4731","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Top Artificial Intelligence Best Practices for Success - ITSupportWale<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top Artificial Intelligence Best Practices for Success - ITSupportWale\" \/>\n<meta property=\"og:description\" content=\"text [2023-10-27T14:22:01.442Z] kernel: [12409.552101] python3[14201]: segfault at 0 ip 00007f8e12a34b12 sp 00007ffc8e12a340 error 4 in libtorch_cuda.so[7f8e10000000+12a34000] [2023-10-27T14:22:01.443Z] torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 12.50 GiB (GPU 0; 23.65 GiB total capacity; 18.21 GiB already allocated; 4.12 GiB free; 19.00 GiB reserved in total by PyTorch) If reserved memory is &gt;&gt; allocated memory try ... 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