google_cloud_platform
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| - | ===== Google Cloud Engine ===== | + | ## Google Cloud Engine |
| + | ### GCE是什麼？ | ||
| + | 那么GCE很像传统上大家所熟悉的伺服器，但它假設在Google自己的數據中心，由專人維護，並且通過Google超快速的全球私有光線網絡互相連接。 | ||
| + | 字幕：GCE可以做什么？ | ||
| + | GCE擁有豐富且強大的配套功能。 | ||
| + | 首先，在GCE上，我們首先可以客制化虛擬機。我們可以選擇從微型实例到配备多达 160 个 vCPU 和高达 3.75 TB 系统内存。 | ||
| + | 我們還可以為虛擬機添加永久性磁盘，如果虚拟机终止运行，其永久性磁盘会保留数据，且可立刻掛載到其他虛擬機上。 | ||
| + | 另外，如果使用GCE的全球负载平衡器功能，還可以實現最優化的網絡分流。 | ||
| - | ===== BigQuery ===== | + | 字幕：為甚麼我們選擇GCE？ |
| + | 客戶選擇GCE的一大理由是它的穩定和來自于Google的自動維護的支持。 | ||
| - | === Design === | + | 例如，我們的客戶MANTAN公司是一家1500万PV/月的新聞網站，通過導入GCE讓MANTAN公司的網站能輕鬆應對流量高峰，保證了訪客的流暢閱覽體驗。 |
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| + | MANTAN公司之所以選擇GCE，是因為Google的伺服器和负载平衡器的能力就足夠應對全球規模的訪問。其次GCE還提供自動維護功能，即使因為虚拟机负载量极高導致进入维护，系统也会自动将您运行中的虚拟机移至附近的主机系统。甚至出現一些硬件故障時，您都不需要重新启动虚拟机。在穩定的GCE上您不會錯失任何一次商業機會。 | ||
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| + | |||
| + | ## BigQuery | ||
| + | |||
| + | ### Design | ||
| BigQuery provides external access to the [[Dremel (software)|Dremel]] technology, a scalable, interactive ''ad hoc'' query system for analysis of read-only nested data. To use the data in BigQuery, it first must be uploaded to [[Google Storage]] and in a second step imported using the BigQuery HTTP API. BigQuery requires all requests to be authenticated, supporting a number of Google-proprietary mechanisms as well as [[OAuth]]. | BigQuery provides external access to the [[Dremel (software)|Dremel]] technology, a scalable, interactive ''ad hoc'' query system for analysis of read-only nested data. To use the data in BigQuery, it first must be uploaded to [[Google Storage]] and in a second step imported using the BigQuery HTTP API. BigQuery requires all requests to be authenticated, supporting a number of Google-proprietary mechanisms as well as [[OAuth]]. | ||
| - | === Features === | + | ### Features |
| * **Managing data** - create and delete tables based on a JSON-encoded schema, import data encoded as CSV or JSON from Google Storage. | * **Managing data** - create and delete tables based on a JSON-encoded schema, import data encoded as CSV or JSON from Google Storage. | ||
| * **Query** - the queries are expressed in a standard SQL dialect[6] and the results are returned in JSON with a maximum reply length of approximately 128 MB, or an unlimited size when large query results are enabled.[7] | * **Query** - the queries are expressed in a standard SQL dialect[6] and the results are returned in JSON with a maximum reply length of approximately 128 MB, or an unlimited size when large query results are enabled.[7] | ||
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| * **Access control** - is possible to share datasets with arbitrary individuals, groups, or the world. | * **Access control** - is possible to share datasets with arbitrary individuals, groups, or the world. | ||
| + | |||
| + | ## Cloud ML | ||
| + | |||
| + | 1. Cloud Machine Learning Engine：用户自己提供算法。在本地機器上進行的機器學習訓練时data的整理和机器的scaling是很麻烦的，GCP可以自动帮忙做这些。 | ||
| + | 2. Cloud AutoML、Natural Language和Vision等：用户提供Data。 | ||
| + | 3. Cloud Speech-to-Text API、Cloud Video Intelligence等：Google训练完成的。用户不需要提供Data。 | ||
| + | |||
| + | ### Note | ||
| + | |||
| + | - Unordered List Item Google Cloud ML使用的 ML frameworks: TensorFlow, scikit-learn, XGBoost, Keras. | ||
| + | - 谷歌自己研发的ML专用芯片 —— TPU：the TPU is on average about 15X-30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU’s GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU. [[https://arxiv.org/pdf/1704.04760.pdf]] | ||
google_cloud_platform.1544781125.txt.gz · Last modified: 2018/12/14 17:52 by admin