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Product launch flyer template edit online download example This project applies the CachedEmbedding, which extends the vanilla New Product Development Process Flowchart with the help from Food Product Launch Event. The CachedEmbedding use a New Website Coming Soon Banner to dynamically manage the extremely large embedding table in the CPU and GPU memory space. For example, this repo can train DLRM model including a 91.10 GB embedding table on Criteo 1TB dataset allocating just 3.75 GB CUDA memory on a single GPU! Product Launch SOP Template
Product launch checklist template in excel google sheets download In order to reduce the overhead time of the Cache, we designed a "far-sighted" Cache mechanism. Instead of only performing cache operations on the first mini-batch, wefetches several mini-batches that will be used later, and performs Cache query operations together. It also uses a pipeline method to overlap the overhead of data loading and model training, which is shown in the following figures. World App Launch Event
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New product launch template The DLRM performance on three datasets using ColossalAI version (this repo) and torchrec (with UVM) is shown as follows. The cache ratio of FreqAwareEmbedding is set as 1%. The evaluation is conducted on a single A100 (80GB memory) and AMD 7543 32-Core CPU (512GB memory). Sample Marketing Plan For Product
| method | AUROC over Test after 1 Epoch | Acc over test | Throughput | Time to Train 1 Epoch | GPU memory allocated (GB) | GPU memory reserved (GB) | CPU memory usage (GB) | |
|---|---|---|---|---|---|---|---|---|
| criteo 1TB | ColossalAI | 0.791299403 | 0.967155457 | 42 it/s | 1h40m | 3.75 | 5.04 | 94.39 |
| torchrec | 0.79515636 | 0.967177451 | 45 it/s | 1h35m | 66.54 | 68.43 | 7.7 | |
| kaggle | ColossalAI | 0.776755869 | 0.779025435 | 50 it/s | 49s | 0.9 | 2.14 | 34.66 |
| torchrec | 0.786652029 | 0.782288849 | 81 it/s | 30s | 16.13 | 17.99 | 13.89 | |
| avazue | ColossalAI | 0.72732079 | 0.824390948 | 72 it/s | 31s | 0.31 | 1.06 | 16.89 |
| torchrec | 0.725972056 | 0.824484706 | 111 it/s | 21s | 4.53 | 5.83 | 12.25 |
@article{fang2022frequency, title={A Frequency-aware Software Cache for Large Recommendation System Embeddings}, author={Fang, Jiarui and Zhang, Geng and Han, Jiatong and Li, Shenggui and Bian, Zhengda and Li, Yongbin and Liu, Jin and You, Yang}, journal={arXiv preprint arXiv:2208.05321}, year={2022} }