You: i am trying to deduplicate a large dataset with minhash LSH, and unfortunately my machine JUST runs out of ram at the end parts (when clustering. what parameters can I try changing to reduce memory use? ``` !python -m text_dedup.minhash \ --path $ds_name \ --split $data_split \ --cache_dir "./cache" \ --output $out_dir \ --column $text_column \ --ngram 5 --threshold 0.6 \ --hash_func xxh3 --hash_bits 16 --num_perm 256 \ --batch_size 5000 print(f"output dir is:\n\t{out_dir}") !ls $out_dir ``` console ``` (fingerprinting loaded from cache) Iterating MinHashes...: 100% 600/600 [06:51<00:00, 1.46it/s] Clustering...: 100% 32/32 [00:30<00:00, 1.04it/s] ^C output dir is: output/minhash/refinedweb-3m/train ``` ChatGPT: In your case, since you're running out of RAM specifically during the clustering step, it might not be as easy to directly tweak MinHash LSH parameters to alleviate this. However, there are still a few strategies you can consider to lower memory usage: ### Direct LSH Parameters 1. Threshold: By increasing the threshold, you'll likely get fewer, larger clusters. This may reduce the overhead for managing many small clusters. However, you may lose some of the fine-grained deduplication. 2. Number of Permutations (`num_perm`): A lower number of permutations will reduce the size of each MinHash, thus decreasing memory usage. However, accuracy may be compromised. 3. Hash Bits (`hash_bits`): Lowering the hash bit size will also reduce the size of each MinHash. But, again, this comes at the cost of accuracy. ### Indirect Options 1. Batch Size (`batch_size`): A smaller batch size during the clustering process might help in your case. However, it's worth noting that it might also slow down the clustering step. 2. External Sort/Merge: If possible, consider using a disk-based sort or merge algorithm for clustering, although this might require a custom implementation rather than using the library function directly. ### Code Optimizations 1. In-place Operations: Ensure that the clustering algorithm (or the library you're using) performs in-place operations where possible to minimize memory usage. You might need to delve into the source code or write a custom clustering algorithm for this. ### Architecture 1. Distributed Systems: If your data is really large, sometimes the only option is to distribute the task over multiple machines. ### Swap Space 1. Disk Swap: As a last resort, you could increase the swap space. This would significantly slow down your program but may prevent it from running out of RAM. Here's how you might modify your script to try some of these options: ```python !python -m text_dedup.minhash \ --path $ds_name \ --split $data_split \ --cache_dir "./cache" \ --output $out_dir \ --column $text_column \ --ngram 5 --threshold 0.7 \ # Increased threshold --hash_func xxh3 --hash_bits 12 --num_perm 128 \ # Reduced hash_bits and num_perm --batch_size 2000 # Reduced batch size ``` If I had to choose one thing to try first, I would probably reduce `batch_size`. It's the least likely to compromise the quality of your clustering while potentially giving you a notable reduction in memory usage. You: thanks. does the number of ngrams impact anything ChatGPT: Yes, the number of n-grams can impact both computational cost and memory usage, although the specific effect can be somewhat dependent on the underlying implementation. Here's how it generally works: ### Computational Cost 1. Higher Computational Load: More n-grams mean that each document is represented as a larger set, which can increase the time complexity for hashing and comparison. ### Memory Usage 1. More Memory: The more n-grams you have, the larger the set representation of each document. This could increase the memory footprint of each MinHash object. 2. Temporary Structures: The process of generating n-grams could itself take up additional memory, depending on how it's implemented. 3. Clustering Impact: More n-grams might lead to a more fragmented set of clusters, potentially increasing memory usage during the clustering step. ### Trade-offs 1. Accuracy vs. Efficiency: More n-grams could make the hashing process more sensitive to subtle differences between documents, which might be what you want for deduplication. On the other hand, fewer n-grams could make the process faster and less memory-intensive but potentially less accurate. To experiment with reducing n-grams, you could adjust the `--ngram` option: ```python !python -m text_dedup.minhash \ --path $ds_name \ --split $data_split \ --cache_dir "./cache" \ --output $out_dir \ --column $text_column \ --ngram 4 \ # Reduced from 5 to 4 ... # Rest of the options remain the same ``` If I had to choose one modification to make, and assuming you're not too concerned about a small potential loss in accuracy, I'd recommend reducing the number of n-grams slightly (e.g., from 5 to 4). This is because it could result in a win-win of less computational work and less memory usage without a drastic impact on the deduplication quality. You: ok and just to check, reducing ngrams is 'better' than reducing the hash bits in terms of accuracy penalty? assume random web documents ChatGPT: The trade-offs between reducing the number of n-grams and reducing the hash bits are a bit nuanced. Here's how they generally play out: ### Reducing N-grams 1. Effect on Similarity: Reducing the n-grams can make your MinHashing less sensitive to smaller changes. You might end up with a less detailed but faster and more memory-efficient representation. This could mean that somewhat dissimilar documents could be clustered together. 2. Semantic Importance: Reducing n-grams usually loses more of the semantic aspects of the text, which can be significant especially if your texts are short. ### Reducing Hash Bits 1. Hash Collisions: Reducing hash bits increases the probability of hash collisions. This would mean that very dissimilar items could be erroneously considered similar, although MinHash is somewhat resilient to this. 2. Effect on Similarity: Reducing hash bits will make your representation less granular, but it would not generally lose the 'semantic' aspect as reducing n-grams might. ### Random Web Documents For random web documents, which can vary greatly in length and content, you may find that reducing n-grams could lead to a significant loss of accuracy, especially if your texts are generally short or very diverse. If I had to choose between reducing n-grams and hash bits, considering random web documents, I'd lean towards reducing hash bits. This is because hash bits would mostly affect the granularity of the MinHash but are less likely to compromise the semantic aspects of the text, which can be crucial in a deduplication task across diverse types of documents. You: what is the impact (both in memory and accuracy) of decreasing the num_perm? ChatGPT: The `num_perm` parameter, which stands for the number of permutations, is an important configuration in MinHash. Changing this value can have several consequences: ### Memory Usage 1. Reduced MinHash Size: Decreasing `num_perm` directly reduces the size of each MinHash signature. Since each MinHash is a compact representation created by taking the minimum hash value across the `num_perm` permutations, fewer permutations means fewer hash values to store. This can lead to a substantial reduction in memory usage. ### Computational Cost 1. Faster Computation: Fewer permutations also mean faster hashing and comparison operations. However, this is usually less of a concern compared to memory usage. ### Accuracy 1. Reduced Sensitivity to Differences: Lowering `num_perm` will make the MinHash less sensitive to small differences between sets. As a result, the deduplication process may not be as precise. 2. Increased False Positives/Negatives: A less sensitive MinHash can increase both false positive and false negative rates. False positives occur when two different sets are incorrectly identified as similar. False negatives occur when two similar sets are not identified as such. 3. Jaccard Similarity Estimate: The `num_perm` also affects the quality of the Jaccard Similarity estimate between two sets. A higher `num_perm` will provide a more accurate estimate, which is crucial for deduplication tasks. ### Recommended Action If you want to experiment with changing `num_perm`, you could try halving it as a start: ```python !python -m text_dedup.minhash \ --path $ds_name \ --split $data_split \ --cache_dir "./cache" \ --output $out_dir \ --column $text_column \ --num_perm 128 \ # Reduced from 256 to 128 ... # Rest of the options remain the same ``` If I had to choose a single option, I would reduce `num_perm` only if you can afford some loss in accuracy for a significant gain in memory efficiency. Reducing `num_perm` offers a relatively straightforward way to reduce both computational and memory costs, but it can compromise the quality of your deduplication. You: in terms of going forward from the original case, if you had to decrease ngram/hash_bits/num_perm which would you try first and what order ChatGPT: Reducing any of these parameters—`ngram`, `hash_bits`, or `num_perm`—will have implications on both computational cost and the accuracy of your deduplication process. Given that your concern is primarily with memory usage, here's the order I would recommend: 1. `num_perm` (Number of Permutations): Reducing this is likely to give you the most straightforward memory savings without a very significant impact on semantic granularity. MinHash is generally resilient to changes in the number of permutations, especially when the original number is relatively high (like your 256). Try halving it to 128 and observe the impact. 2. `hash_bits` (Hash Bits): Reducing the number of hash bits will also reduce the size of each MinHash, but the risk of hash collisions increases. However, MinHash is designed to be somewhat resistant to hash collisions, so you might find this to be a good trade-off. I'd try reducing it to 12 or 14 bits and see how it performs. 3. `ngram` (N-grams): This would be my last resort among the three. Reducing the n-gram size will compromise the semantic granularity of your text representation, potentially having the most severe impact on deduplication accuracy. The smaller the n-gram, the less context you're taking into account, which might not be ideal especially for random web documents that can be quite diverse. Here's how you could modify your command to experiment with these parameters one at a time: ```bash # Reduce num_perm !python -m text_dedup.minhash \ --num_perm 128 \ ... # Other parameters remain the same # Reduce hash_bits !python -m text_dedup.minhash \ --hash_bits 12 \ ... # Other parameters remain the same # Reduce ngram !python -m text_dedup.minhash \ --ngram 4 \ ... # Other parameters remain the same ``` By trying these adjustments one at a time, you can observe their individual impacts on memory usage and deduplication quality. This will give you a better understanding of which parameter is most effective for your specific use case. You: can I do num_perm 192 or is that pointless ChatGPT: You can certainly set `num_perm` to 192; it's not pointless. The number of permutations (`num_perm`) doesn't have to be halved or follow any specific pattern. It's a matter of finding the right trade-off between memory usage and accuracy for your specific application. Reducing `num_perm` from 256 to 192 would likely still offer you some memory savings without as drastic an impact on accuracy as going down to 128. It's a more moderate adjustment that sits between the original setting and a more aggressive reduction, giving you the chance to evaluate how sensitive your application is to changes in this parameter. Here's how to adjust `num_perm` in your script: ```bash # Reduce num_perm to 192 !python -m text_dedup.minhash \ --num_perm 192 \ ... # Other parameters remain the same ``` By doing this, you'll be able to observe if the slightly lower number of permutations significantly impacts the quality of your deduplication, and whether the memory savings are sufficient for your machine's capabilities. You: outside of the memory thing, what would typical values for --threshold be here? ChatGPT: The `--threshold` parameter in MinHash LSH usually refers to the Jaccard similarity threshold that dictates whether two documents are considered "similar" and thus candidates for clustering together. The choice of a suitable value for this threshold depends on the specific needs of your deduplication task and the nature of the documents you're dealing with. Here are some general guidelines: ### Typical Values 1. High Similarity (\~0.8 - 0.95): If your documents are mostly unique and you're looking to catch near-duplicates or very similar documents, you might want a high threshold. This ensures that only documents that are very similar get clustered together. 2. Moderate Similarity (\~0.5 - 0.8): If your dataset contains variations of documents that should still be considered duplicates, a moderate threshold would be more appropriate. This is often a good starting point for many applications. 3. Low Similarity (\~0.2 - 0.5): This setting would be used in cases where even loosely related documents should be clustered together. Use this cautiously, as it can lead to over-clustering and might group together documents that are not actually duplicates. ### Considerations - False Positives vs False Negatives: A higher threshold may reduce false positives but increase false negatives, whereas a lower threshold may do the opposite. - Performance: Lower thresholds may require more memory and computational power since more pairs of documents will be considered as potential duplicates. - Data Nature: In the context of random web documents, which can vary greatly in content and length, it might be beneficial to start with a moderate threshold and adjust according to the results. If I had to choose, I would likely start with a moderate threshold value around 0.6 to 0.7. This is often a good compromise between reducing false positives and false negatives for a wide range of document types. You can then fine-tune this value based on the results you observe in your specific dataset.