From 82e5ca37a8bd80f56c99f9d790a03b458ced716b Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 25 二月 2025 14:28:34 +0800
Subject: [PATCH] Large-Scale Data Training
---
docs/tutorial/README.md | 30 ++++++++++++++++++++++++++----
1 files changed, 26 insertions(+), 4 deletions(-)
diff --git a/docs/tutorial/README.md b/docs/tutorial/README.md
index 74febcd..933b611 100644
--- a/docs/tutorial/README.md
+++ b/docs/tutorial/README.md
@@ -211,7 +211,7 @@
### Detailed Parameter Description:
```shell
-funasr/bin/train.py \
+funasr/bin/train_ds.py \
++model="${model_name_or_model_dir}" \
++train_data_set_list="${train_data}" \
++valid_data_set_list="${val_data}" \
@@ -252,7 +252,7 @@
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
-../../../funasr/bin/train.py ${train_args}
+../../../funasr/bin/train_ds.py ${train_args}
```
--nnodes represents the total number of participating nodes, while --nproc_per_node indicates the number of processes running on each node.
@@ -264,7 +264,7 @@
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
-../../../funasr/bin/train.py ${train_args}
+../../../funasr/bin/train_ds.py ${train_args}
```
On the worker node (assuming the IP is 192.168.1.2), you need to ensure that the MASTER_ADDR and MASTER_PORT environment variables are set to match those of the master node, and then run the same command:
@@ -273,7 +273,7 @@
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
-../../../funasr/bin/train.py ${train_args}
+../../../funasr/bin/train_ds.py ${train_args}
```
--nnodes indicates the total number of nodes participating in the training, --node_rank represents the ID of the current node, and --nproc_per_node specifies the number of processes running on each node (usually corresponds to the number of GPUs).
@@ -321,6 +321,28 @@
++jsonl_file_in="../../../data/list/train.jsonl"
```
+
+#### Large-Scale Data Training
+When dealing with large datasets (e.g., 50,000 hours or more), memory issues may arise, especially in multi-GPU experiments. To address this, split the JSONL files into slices, write them into a TXT file (one slice per line), and set `data_split_num`. For example:
+```shell
+train_data="/root/data/list/data.list"
+
+funasr/bin/train_ds.py \
+++train_data_set_list="${train_data}" \
+++dataset_conf.data_split_num=256
+```
+**Details:**
+- `data.list`: A plain text file listing the split JSONL files. For example, the content of `data.list` might be:
+ ```bash
+ data/list/train.0.jsonl
+ data/list/train.1.jsonl
+ ...
+ ```
+- `data_split_num`: Specifies the number of slice groups. For instance, if `data.list` contains 512 lines and `data_split_num=256`, the data will be divided into 256 groups, each containing 2 JSONL files. This ensures that only 2 JSONL files are loaded for training at a time, reducing memory usage during training. Note: Groups are created sequentially.
+
+**Recommendation:**
+If the dataset is extremely large and contains heterogeneous data types, perform **data balancing** during splitting to ensure uniformity across groups.
+
#### Training log
##### log.txt
--
Gitblit v1.9.1