Install RecBole¶
RecBole can be installed from conda, pip and source files.
System requirements¶
RecBole is compatible with the following operating systems:
Linux
Windows 10
macOS X
Python 3.7 (or later), torch 1.7.0 (or later) are required to install our library. If you want to use RecBole with GPU, please ensure that CUDA or CUDAToolkit version is 9.2 or later. This requires NVIDIA driver version >= 396.26 (for Linux) or >= 397.44 (for Windows10).
Install from conda¶
Conda can be installed from miniconda or
the full anaconda.
If you are in China, Tsinghua Mirrors is recommended.
After installing conda,
run conda create -n recbole python=3.7 to create the Python 3.7 conda environment.
Then the environment can be activated by conda activate recbole.
At last, run the following command to install RecBole:
conda install -c aibox recbole
Install from pip¶
To install RecBole from pip, only the following command is needed:
pip install recbole
Install from source¶
Download the source files from GitHub.
git clone https://github.com/RUCAIBox/RecBole.git && cd RecBole
Run the following command to install:
pip install -e . --verbose
Try to run：¶
To check if you have successfully installed the RecBole, you can create a new python file (e.g., run.py), and write the following code:
from recbole.quick_start import run_recbole
run_recbole(model='BPR', dataset='ml-100k')
Then run the following command:
python run.py
This will perform the training and test of the BPR model on the ml-100k dataset, and you will obtain some output like:
05 Aug 02:16 INFO ml-100k
The number of users: 944
Average actions of users: 106.04453870625663
The number of items: 1683
Average actions of items: 59.45303210463734
The number of inters: 100000
The sparsity of the dataset: 93.70575143257098%
Remain Fields: ['user_id', 'item_id', 'rating', 'timestamp']
05 Aug 02:16 INFO [Training]: train_batch_size = [2048] negative sampling: [{'uniform': 1}]
05 Aug 02:16 INFO [Evaluation]: eval_batch_size = [4096] eval_args: [{'split': {'RS': [0.8, 0.1, 0.1]}, 'group_by': 'user', 'order': 'RO', 'mode': 'full'}]
05 Aug 02:16 INFO BPR(
(user_embedding): Embedding(944, 64)
(item_embedding): Embedding(1683, 64)
(loss): BPRLoss()
)
Trainable parameters: 168128
Train 0: 100%|████████████████████████| 40/40 [00:00<00:00, 219.54it/s, GPU RAM: 0.01 G/11.91 G]
05 Aug 02:16 INFO epoch 0 training [time: 0.19s, train loss: 27.7228]
Evaluate : 100%|██████████████████████| 472/472 [00:00<00:00, 506.11it/s, GPU RAM: 0.01 G/11.91 G]
05 Aug 02:16 INFO epoch 0 evaluating [time: 0.94s, valid_score: 0.020500]
05 Aug 02:16 INFO valid result:
recall@10 : 0.0067 mrr@10 : 0.0205 ndcg@10 : 0.0086 hit@10 : 0.0732 precision@10 : 0.0081
...
Train 96: 100%|████████████████████████| 40/40 [00:00<00:00, 230.65it/s, GPU RAM: 0.01 G/11.91 G]
05 Aug 02:19 INFO epoch 96 training [time: 0.18s, train loss: 3.7170]
Evaluate : 100%|██████████████████████| 472/472 [00:00<00:00, 800.46it/s, GPU RAM: 0.01 G/11.91 G]
05 Aug 02:19 INFO epoch 96 evaluating [time: 0.60s, valid_score: 0.375200]
05 Aug 02:19 INFO valid result:
recall@10 : 0.2162 mrr@10 : 0.3752 ndcg@10 : 0.2284 hit@10 : 0.7508 precision@10 : 0.1602
05 Aug 02:19 INFO Finished training, best eval result in epoch 85
05 Aug 02:19 INFO Loading model structure and parameters from saved/BPR-Aug-05-2021_02-17-51.pth
Evaluate : 100%|██████████████████████| 472/472 [00:00<00:00, 832.85it/s, GPU RAM: 0.01 G/11.91 G]
05 Aug 02:19 INFO best valid : {'recall@10': 0.2195, 'mrr@10': 0.3871, 'ndcg@10': 0.2344, 'hit@10': 0.7582, 'precision@10': 0.1627}
05 Aug 02:19 INFO test result: {'recall@10': 0.2523, 'mrr@10': 0.4855, 'ndcg@10': 0.292, 'hit@10': 0.7953, 'precision@10': 0.1962}