对,使用更多计算时间来弥补模型训练中的算力不足是可行的。主要有以下几点建议:
-
适当减小batch size,这样每次迭代的计算量会减少,可以用更长的时间去迭代达到收敛。但是batch size不要设置过小,太小会使训练效率降低。
-
适当减小模型参数量,例如减少隐层单元数、降低嵌入维度等,可以减小每次迭代的计算量。需要权衡模型性能损失。
-
使用梯度积累(gradient accumulation),每次不完全更新权重,积累几步梯度后再更新,相当于用多个小batch模拟一个大batch,既可以减小每次计算量,又不至于batch size太小。
-
降低训练精度,例如使用半精度浮点数(FP16)训练,可以减少算力需求。但精度降低会影响模型效果。
-
扩充训练周期,允许更长时间去训练模型。可以在计算资源允许的情况下,训练更多epochs。
-
利用云计算资源,扩充计算能力。如果条件允许,可以租用云GPU服务器来加速训练。
所以综合优化batch size、模型大小、训练精度、训练周期等 hyperparameters,充分利用计算资源,适当延长训练时间,可以弥补部分算力不足带来的影响。需要根据实际情况,权衡效率和效果之间的平衡。
Using extended training time can help compensate for inadequate computational power when training large models to some extent, but it may not be a complete solution. Here are a few points to consider:
-
Diminishing Returns: While increasing training time can lead to incremental improvements in model performance, there's a point where the returns diminish. After a certain duration, the marginal gains achieved by further training may become negligible.
-
Resource Constraints: Training large models requires substantial computational resources, including processing power, memory, and storage. If your hardware or infrastructure is insufficient, extending training time alone might not be enough to overcome these limitations.
-
Practical Considerations: Longer training times can be impractical due to factors such as cost, energy consumption, and project deadlines. Additionally, longer training times may lead to slower experimentation cycles, hindering the iterative development process.
-
Algorithmic Efficiency: Efficient model architectures, appropriate hyperparameter tuning, and optimization techniques can significantly impact training time. Exploring these avenues can help achieve better results within limited computational resources.
To address computational limitations more effectively, you could consider alternative approaches such as distributed training across multiple machines, utilizing cloud computing resources, or exploring pretraining and transfer learning techniques to leverage pre-trained models.
Ultimately, finding the right balance between training time and computational resources is crucial to achieving desired results while considering practical constraints.