Canonicalはセキュリティ関連の修正で半月リリースが遅れたものの、4月中に無事Ubuntu Linux 24.04 LTSを公開しました。Linuxがインストールゲームだった時代はとうの昔に終わり、枯れたPCでは22.04時代のノウハウそのままでトラブルもなく24.04を導入できています。うーん、書くことがない。
一方、面白みはないもののUbuntuを取り巻く環境は良くなっています。Windows 11に非対応とされサポート打ち切りが近づくIntel Core i 7xxx(第七世代)以前の中古ノートPCはフルHDモデルですら1万円台でゴミのように叩き売られ、Core i8xxx(第八世代)以降のPCも軒並み値崩れしました。自慢みたいになるので詳しくは書きませんが、私はWindows11が動くフルHD/NVMe SSDのノートPCを2万円台で入手したり、最新に近いCore i7-11700搭載のミニタワーを5万円台で入手したりしています。
Windows11ではインテルCore i 第7世代以前のCPUはサポート外となりました(7820HQなど例外あり)。結果、以前の中古PCやリース落ちPCが値崩れし、とてもお買い得になっています。一昨年は5万円を滅多に切らなかったフルHD&SSDノートも今では5万円を切るものがチラホラ出てきました。端末として割り切るなら個人的なイチオシは『ELITEBOOK Folio G1』です。このようなお古のノートにUbuntuを入れて再生させるわけです。
$ python
Python 3.9.12 (main, Apr 5 2022, 06:56:58)
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from tensorflow.python.client import device_lib
2022-05-12 18:53:47.784386: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
>>> device_lib.list_local_devices()
2022-05-12 18:53:51.799093: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-05-12 18:53:51.802081: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2022-05-12 18:53:51.831519: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-12 18:53:51.832063: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1650 computeCapability: 7.5
coreClock: 1.515GHz coreCount: 14 deviceMemorySize: 3.82GiB deviceMemoryBandwidth: 178.84GiB/s
2022-05-12 18:53:51.832111: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
2022-05-12 18:53:51.849984: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10
2022-05-12 18:53:51.850051: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10
2022-05-12 18:53:51.860952: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2022-05-12 18:53:51.864006: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2022-05-12 18:53:51.882123: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2022-05-12 18:53:51.885419: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10
2022-05-12 18:53:51.918256: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7
2022-05-12 18:53:51.918459: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-12 18:53:51.918860: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-12 18:53:51.919152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2022-05-12 18:53:51.919358: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
2022-05-12 18:53:52.570930: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-05-12 18:53:52.570970: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0
2022-05-12 18:53:52.570977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N
2022-05-12 18:53:52.571411: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-12 18:53:52.571620: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-12 18:53:52.571762: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-12 18:53:52.571880: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 3410 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
2022-05-12 18:53:52.573073: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 14858614513891176676
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 3575906304
locality {
bus_id: 1
links {
}
}
incarnation: 13770775266605951589
physical_device_desc: "device: 0, name: NVIDIA GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5"
]
The script xrdp-installer-1.4.sh can be run against all supported Ubuntu Operating System. Version 1.4 has been updated and it will detect Ubuntu 22.04 as a supported Operating system. The script has been mainly tested against Ubuntu Gnome desktop. However, the script tries to detect the Desktop interface in use and might be able to work against some other desktop interface such as Budgie, Plasma, Linux Mint, Gnome…(Best effort !)
SSDを選ぶ際にもうひとつ重要なのは「予算が許す限り大容量のものにする」ことです。というのも、SSDなどの不揮発性メモリは書き込み回数に限界があり、残り容量が少なくなると同じ領域に繰り返し書き込むため劣化しやすいと言われているからです。そういう意味でも、2022年現在では2TBの2.5インチ SATA SSDはコスパ的に悪くない選択肢と言えます。なお、今回は『SanDisk SDSSDH3 2T00』を選びました。
Ubuntuは長期サポート版(LTS = Long Time Support)と無印版があり、LTSは2年ごとのリリースとなっています。サーバーとして真面目に使うならLTSが前提となるため、2022年4月21日にリリース予定のUbuntu Linux 22.04 LTSが初の最新Ryzen APU対応LTSバージョンとなります。
データ活用の観点からも、従来kibanaやPythonなどに限られていた分析ツールがJDBC/ODBC対応の各種BIツールに拡がることが期待されます。無償で多機能なPower BI Desktopなどが使えるはずですし、PostgreSQL自体が持っているマテリアライズドビューなどの機能を用いてRDB側でクエリを高速化することも(スキル次第では)出来るでしょう。
