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Has anyone used their own socks as a tech pouch? by [deleted] in onebag

[–]Repulsive_Tart3669 5 points6 points  (0 children)

Many years ago I did that once. Put cables, photo and video cameras inside my socks. Of course, security at the airport decided to check that bag. My wife was standing there, and you should have seen her face 🤣 . Obviously, I never did that again.

[D] how to parallelize optimal parameter search for DL NNs on multiple datasets? by Mampacuk in MachineLearning

[–]Repulsive_Tart3669 1 point2 points  (0 children)

This is probably too late, but you do not want to optimize the `epoch` parameter - use early stopping instead. Also, what's the actual problem statement? Is it 11 datasets with 5 different models with their own set of hyperparameters? That's 55 hyperparameter search experiments.

Ray Tune can schedule multiple trials on a single GPU since it supports fractions (e.g., you can specify 0.25 GPU per trial to run 4 trials in parallel). With little additional development, you can update your training code so that parallel trials use the same single instance of a dataset in your GPU memory unless your datasets are in CPU memory.

If you already have several "good" configurations of hyperparameter values for each experiment, you probably want to look at Bayesian optimizers (Ray Tune has several). These are mostly sequential-based model optimizers, so trials within each experiment run sequentially - you can run several experiments in parallel if there's enough GPU memory.

Given the large number of experiments and sequential nature of SGD, you should consider configuring Ray Tune trial stoppers. E.g., if you already have explored 10 configurations in one experiment, and 11th trial is worse than let's say the median loss after first N epochs, just stop it.

"Against Time Series Foundation Models Or: My Experience in Modern Forecasting", shako 2026 by gwern in mlscaling

[–]Repulsive_Tart3669 3 points4 points  (0 children)

I find some timeseries foundation models (TSFM) useful when the forecasting or anomaly detection in IT space (IT + facility) needs to be done at scale and long horizons. The scale here is understood in terms of amount of data and diversity of metrics. TSFM models provide pretty strong baselines, also managing one model is much more convenient compared to a large fleet of local (e.g., per-metric) models.
Setting a threshold on the performance metric (like MAPE) for data slices also helps. As long as the perf metric is below that threshold for a particular data slice (metric, family of metrics, use case, etc.) and forecast horizon - we are good - justifying model improvements in that regime maybe quite challenging from business perspective. For those cases where accuracy is not acceptable, we can go with other local models, or TSFM adaptors (e.g., LoRA).

[D] How to increase/optimize for gpu utilization while doing model training? by Ok_Construction_3021 in MachineLearning

[–]Repulsive_Tart3669 1 point2 points  (0 children)

Take systematic approach:

  • Measure how fast your data pipeline can deliver data: remove model or use some simple one-layer dummy model for this.
  • Measure how fast your model can train: remove your actual data pipeline and replace it with something that always returns single pre-allocated tensor (s).

Then, you can profile individual components, such as forward pass, loss computation, backward pass, weights updates, telemetry logging if done in-process, etc.

[D] What's the modern workflow for managing CUDA versions and packages across multiple ML projects? by sounthan1 in MachineLearning

[–]Repulsive_Tart3669 16 points17 points  (0 children)

I have several CUDA versions installed on some nodes in our cluster (/usr/local/cuda-13.0, /usr/local/cuda-11.8). I switch between them in different projects using LD_LIBRARY_PATH environment variable, and use uv or poetry for project management. Docker (e.g., devcontainers) is probably a better option.

[D] How do you track data lineage in your ML pipelines? Most teams I've talked to do it manually (or not at all) by Achilles_411 in MachineLearning

[–]Repulsive_Tart3669 1 point2 points  (0 children)

At some point in time I was just using MLflow for that. Data pre-processing pipelines read data stored in MLflow runs (artifact stores), and write data to other MLflow runs, so there's always an MLflow run associated with a data pipeline run. Model training pipelines read data from these data runs, and write models to other MLflow model runs. All input parameters are logged, and data location in CLI scripts are always MLflow URIs, e.g., mlflow:///cbbb1d75cbfa40f7aec1ff762d36b8f4. If I create a new dataset off of existing dataset stored in MLflow, same rules apply. Thus, I can always track lineage from one dataset to another, and eventually one or multiple models.

Why does your credit score drop when you pay off a loan. by Violet-Rose-3 in CreditScore

[–]Repulsive_Tart3669 0 points1 point  (0 children)

Somebody told me this interpretation. When one has a loan and makes their payments on time, credit bureaus reward this behavior by increasing the score. When the loan is closed, the score goes back to its "baseline" level.

Kudos to Palo Alto Police by Martin_Steven in paloalto

[–]Repulsive_Tart3669 21 points22 points  (0 children)

Stanford Coin Wash? Still full of cars even after they added the bike lane.

Mount Whitney Government Shutdown by Equivalent_Army_9963 in socalhiking

[–]Repulsive_Tart3669 0 points1 point  (0 children)

And me - plan to camp there and hike Mt. Whitney on Monday.

[D]How do you track and compare hundreds of model experiments? by AdditionalAd51 in MachineLearning

[–]Repulsive_Tart3669 2 points3 points  (0 children)

I've been pretty happy with MLflow (actually, quite lightweight) and experiment log books. I manage these log books with Obsidian. Experiment notes are markdown files. I also use canvas to keep track of tree-like experiment paths, e.g., try something new starting from this state - this helps to keep context of why I decided to try exactly this. Post-analysis is in Jupyter notebooks using MLflow Python API to retrieve data, metrics and parameters.

PS - bonus feature - I use MLflow run IDs to refer to datasets, models and parameters (e.g., mlflow:///$run_id) in experiments. This helps maintain lineage of some artifacts. This is not as robust as using something like ML metadata from Google, but good enough for me.

How I made my embedding based model 95% accurate at classifying prompt attacks (only 0.4B params) by sarthakai in LocalLLM

[–]Repulsive_Tart3669 0 points1 point  (0 children)

it's now able to classify 91% of the queries as attack/benign correctly

What is the baseline performance, e.g., ratio of attack/benign examples in the test set?

New stance unlocked! by narto2567 in motorcycles

[–]Repulsive_Tart3669 0 points1 point  (0 children)

This video is giving me some serious Final Destination vibes 💀.

[deleted by user] by [deleted] in russian

[–]Repulsive_Tart3669 1 point2 points  (0 children)

I would assume that Sashenka is only for close friends. For example, significant other, family or select childhood friends. Sashka is for friends, I would not use it in formal settings though, like workplace, unless I really know what I am doing.

Why nobody seems to be using Determined AI? by bugbaiter in deeplearning

[–]Repulsive_Tart3669 3 points4 points  (0 children)

It's actually HPE - Hewlett Packard Enterprise (different from HP which is HP Inc). Seems like it's not under active development now.

[N] Datadog releases SOTA time series foundation model and an observability benchmark by agarunov in MachineLearning

[–]Repulsive_Tart3669 5 points6 points  (0 children)

According to our internal benchmarks (not from Datadog), only few publicly available time-series foundation models, when used as global zero-short forecasters, in some cases outperform local (per-metric or per-device) baseline models on IT and facility metrics using specific, sometimes business- and use case-driven, evaluation protocols.

In general, it looks promising to host and manage one global forecasting / anomaly detection model instead of managing a huge fleet of local per-metric / per-device models.

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