Request regarding the DARTS and NB101 spaces about eagle HOT 4 CLOSED

Hi, we've update the code, the Google Drive and the README with things needed to train accuracy predictors on NB101, DARTS (very basic support) and NB-ASR (as extra, see: https://github.com/SamsungLabs/nb-asr) search spaces. We also added support for zero-cost metrics (https://github.com/SamsungLabs/zero-cost-nas).

Feel free to reopen the issue if something is unclear/missing/failing! :)

from eagle.

Hi Adithya,

Sorry for the delay in releasing the code - we have everything ready in our internal repo but unfortunately it takes some time to make those things public. I'll let you know through this issue when this happens.

However, just to make it clear, the support for DARTS and NB1 search spaces is limited to accuracy prediction only. The reason behind it is that we simply did not have enough resources to run extensive benchmarking (like for NB2) for larger search spaces, so there's no latency dataset for the other two. If you are interested in measuring and predicting latency, e.g. for NB1, you'd need to have access to the relevant HW and implement necessary functions to create models from model identifiers (like arch vectors). I'm here to help if you decide to do something like that and run into any problems.

Thanks for you interest in our work! I hope you find it useful.

from eagle.

Thanks for the response - I really appreciate it! I completely understand the issues with simulating the whole NASBench-101/DARTS type spaces - I was particularly curious since I did see that you had simulated NASBench 101 for the Best of Both Worlds paper.

As for HW metrics for such spaces, what are your thoughts on performance predictions trained on a limited number of points? I did see NB-301 take one such approach (albeit, for accuracy) and just wanted to see your thoughts for the same

from eagle.

Hi Adithya,

Thank you for following our papers! Regarding performance prediction, the BRP-NAS paper (https://arxiv.org/pdf/2007.08668.pdf) has some relavent numbers in the NB-201 search space: Table 1 shows the latency predictor trained by 900 points and tested on 14k points; Table 2 shows the accuracy predictor trained by [50/100/200] points.

It will be more challenging in larger search spaces such as DARTS. That's why we proposed the binary relation prediction approach supplemented by iterative data selection. In this case, the predictor focuses on ranking of models rather than the absolute values of performance.

In the Best of both world paper (https://arxiv.org/pdf/2002.05022.pdf), we have a latency model (a latency lookup table of operations and a scheduler). It works well if you know the specific details of the target hardware and can develop an hardware-dependent latency model. It is a slight different approach from the GCN predictor which is purely trained by measured latency.

from eagle.