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Watchme ure1/25/2024 ![]() Watchme watchme-sklearn ADD results decorator-psutils-plot_embeddingĬommit 9aa96290f4bd1b45e33f54a050899f1adaf308f1 Watchme watchme-sklearn ADD results decorator-psutils-tsne_embeddingĬommit b53b4ab7896b1668fa3562334db633431170bb6f Each function getsĪ decorator folder, and within each folder is a result.json file and a TIMESTAMP.Ĭommit 6f10453a429ab3e2ad835520443bf127c466ac40 (HEAD -> master ) Here is a glimpse at what was created in my watchme home. The provided labels, contrary to other methods.Ĭomputing Linear Discriminant Analysis projection Module, are supervised dimensionality reduction method, i.e. Module, and Neighborhood Components Analysis, from the :mod: `sklearn.neighbors ` Linear Discriminant Analysis, from the :mod: `sklearn.discriminant_analysis ` Of the embedding, i.e., the embedding does not depend on random This example, which is not the default setting. T-SNE will be initialized with the embedding that is generated by PCA in Which the classes are linearly-separable. However, it is often useful to cast a dataset into a representation in Representation on which we apply a dimensionality reduction method. Technically a manifold embedding method, as it learn a high-dimensional The RandomTreesEmbedding, from the :mod: `sklearn.ensemble ` module, is not Manifold learning on handwritten digits: Locally Linear Embedding, Isomap.Īn illustration of various embeddings on the digits dataset. Generating watcher config /home/vanessa/.watchme/watchme-sklearn/watchme.cfg Sudo singularity build watchme-sklearn.sif SingularityĪdding watcher /home/vanessa/.watchme/watchme-sklearn. See that the function above is called mds_embedding? Then in this simple script, I could basically run all of the various plotting functions This is just one of the functions - you can see all of the functions here. Since these functions are really fast, I chose every quarter second. The second keyword argument, seconds, indicates how often I want to collect metrics. This watcher doesn’t have to exist on my computer. The first argument “watchme-sklearn” is the watcher name. stress_ ) plot_embedding ( X_mds, "MDS embedding of the digits (time %.2fs)" % ( time () - t0 )) MDS ( n_components = 2, n_init = 1, max_iter = 100 ) t0 = time () X_mds = clf. # MDS embedding of the digits monitor_resources ( 'watchme-sklearn', seconds = 0.25 ) def mds_embedding (): print ( "Computing MDS embedding" ) clf = manifold. Solve this issue by way of creating a decorator. Id in advance, but what if you want to run something on the fly? I decided to The task is pretty cool if you want to schedule monitoring for a process name or The command used to start up slack is ridiculous. By the way, if you do run this task for slack, These, but instead of for your entire computer, for a Python function or specific It might also have to do with whatever I was running, hereīut I digress! With the monitor process task, you can create similar plots to What are the spikes? It could be that the spikes (more free memory) indicateĪ restart of my computer. It’s only a month of data, but there are still That’s the virtual memory that is free on my computer, in bytes, for the span of just overĪ month that the task has been running. Yep, I created it, scheduled it, and forgot about it. Of a plot for one of the tasks from a a system watcher. What kind of result can you get? To give you a sense, here is an example The schedule will use cron to run the watcher at theįrequency you desired. $ watchme add-task system task-monitor-slack -type psutils then I would use the watchme schedule command to specify how often I want toĬollect metrics.
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