Linux

Kubernets Wiki Recently read [Large-scale cluster management at Google with Borg](https://static.googleusercontent.com/media/research.google.com/en//pubs/ archive/43438.pdf), Borg is Google’s own internal development of a set of cluster management system, and then the open source community slowly began to incubate and launch the Kubernets cluster management system, k8s and Borg has a very deep relationship of origin. Starting with Kubernets, let’s look at the components of K8s: Kubelet Kubelet runs on every node and manages the containers on the node. ...

Why The Mathematica + Python web framework is used to provide an external service platform, which, due to the fact that it is running on the machine, can be used as an external service. ARM Mathematica currently only supports 32-bit v6 v7, but a runtime library for armhf v6 v7 needs to be added to aarch64. Small TIPS To be able to run Mathematica ARM 32Bit on Ubuntu 20.04 aarch64 systems, a review of the A lot of information about it, and finally the experiment worked, and you can run 32-bit Mathematica on an aarch64 system in the following way. ...

Today, in the course of work, I need to parse a JSON file to get a keyword, and I’m going to use Mathematica to practice. ``` `` { “apiVersion”: “v1”, “data”: { “voting-keystore.json”: “{ "version": 4, "pubkey": " a94300da6d73a11d8ba72c99dd385b7a9d5043c9ee83ffd4190d89b7677e68d384fbceb556d2a0652e81250ad4b8477d", ""uuid”: "09e5c322-bf6b-401a -b90d-1aef6d57996a", "crypto": {"checksum": {"message": "704e947d244bc8020ef6d6f0844c4aea08af18ecf345bb98ab999df6441377d7\ "function": "sha256", "params": {}}, "kdf": {"message": """, "function": "scrypt", "params": {"p": 1, "n": 32768, "salt": "b891518a06f1d3f67a6d485bba8627f5320497f98c08cb52e4391e50071ac97d", "dklen": 32, "r": 8}}, "cipher": {\" message": "bcd85ce03e82e645e135e13501f717d907c03a23ee1f8dc6bd045c53001719ddc7a5\", "function": "aes-128-ctr", "paragraphs": {"iv\ “: "7f05ffce95bcd0157e520c6b949b90b1"}}}, "path": """}”" }, “kind”: “ConfigMap”, “metadata”: { “creationTimestamp”: “2020-07-25T11:35:24Z”, “managedFields”: [ { “apiVersion”: “v1”, “fieldsType”: “FieldsV1”, “ffieldsV1”: { “f:data”: { “.” : {}, “f:voting-keystore.json”: {} } }, “manager”: “Swagger-Codegen”, “operation”: “Update”, “time”: “2020-07-25T11:35:24Z” } ], “name”: “lighthouse-validator-a94300da6d73”, “namespace”: “eth”, “resourceVersion”: “1200502”, “selfLink”: “/api/v1/namespaces/eth/configmaps/lighthouse-validator-a94300da6d73”, “uid”: “05b37640-be81-4944-9c9f-868431b6107f” } } ...

Horovod I have written an article about how to build a distributed training cluster on Raspberry Pie 4 using the distributed training system that comes with TF2.0. However, there is a drawback: we need to start the training program at each node, and the distributed training will only work after all the nodes are started. MPI is mainly used in the field of supercomputing. Building MPI cluster on Raspberry, firstly, it can be used to learn distributed computing on supercomputing, and secondly, it can observe the performance of TF> distributed training on ARMv8 in practice. ...