modify getting_started.md

open-mmlab · Mar 26, 2022 · c7c0a09 · c7c0a09
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diff --git a/docs/en/getting_started.md b/docs/en/getting_started.md
@@ -31,19 +31,19 @@ python tools/train.py ${CONFIG_FILE}
 ### Train with single/multiple GPUs
 
 ```shell
-sh tools/dist_train.sh ${CONFIG_FILE} ${GPUS} --work_dir ${YOUR_WORK_DIR} [optional arguments]
+sh tools/dist_train.sh ${CONFIG_FILE} ${GPUS} --work-dir ${YOUR_WORK_DIR} [optional arguments]
 ```
 
 Optional arguments are:
 
-- `--resume_from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
+- `--resume-from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
 - `--deterministic`: Switch on "deterministic" mode which slows down training but the results are reproducible.
 
 An example:
 
 ```shell
 # checkpoints and logs saved in WORK_DIR=work_dirs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k/
-sh tools/dist_train.sh configs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k.py 8 --work_dir work_dirs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k/
+sh tools/dist_train.sh configs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k.py 8 --work-dir work_dirs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k/
 ```
 
 **Note**: During training, checkpoints and logs are saved in the same folder structure as the config file under `work_dirs/`. Custom work directory is not recommended since evaluation scripts infer work directories from the config file name. If you want to save your weights somewhere else, please use symlink, for example:
@@ -61,7 +61,7 @@ GPUS_PER_NODE=${GPUS_PER_NODE} GPUS=${GPUS} SRUN_ARGS=${SRUN_ARGS} sh tools/slur
 An example:
 
 ```shell
-GPUS_PER_NODE=8 GPUS=8 sh tools/srun_train.sh Dummy Test_job configs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k.py work_dirs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k/
+GPUS_PER_NODE=8 GPUS=8 sh tools/slurm_train.sh Dummy Test_job configs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k.py work_dirs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k/
 ```
 
 ### Train with multiple machines
@@ -91,8 +91,8 @@ If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training
 If you use `dist_train.sh` to launch training jobs:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 sh tools/dist_train.sh ${CONFIG_FILE} 4 --work_dir tmp_work_dir_1
-CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 sh tools/dist_train.sh ${CONFIG_FILE} 4 --work_dir tmp_work_dir_2
+CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 sh tools/dist_train.sh ${CONFIG_FILE} 4 --work-dir tmp_work_dir_1
+CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 sh tools/dist_train.sh ${CONFIG_FILE} 4 --work-dir tmp_work_dir_2
 ```
 
 If you use launch training jobs with slurm, you have two options to set different communication ports:

diff --git a/docs/zh_cn/getting_started.md b/docs/zh_cn/getting_started.md
@@ -31,12 +31,12 @@ python tools/train.py ${CONFIG_FILE}
 ### 使用 单张/多张 显卡训练
 
 ```shell
-sh tools/dist_train.sh ${CONFIG_FILE} ${GPUS} --work_dir ${YOUR_WORK_DIR} [optional arguments]
+sh tools/dist_train.sh ${CONFIG_FILE} ${GPUS} --work-dir ${YOUR_WORK_DIR} [optional arguments]
 ```
 
 可选参数:
 
-- `--resume_from ${CHECKPOINT_FILE}`: 从某个 checkpoint 处继续训练.
+- `--resume-from ${CHECKPOINT_FILE}`: 从某个 checkpoint 处继续训练.
 - `--deterministic`: 开启 "deterministic" 模式, 虽然开启会使得训练速度降低，但是会保证结果可复现。
 
 例如:
@@ -57,7 +57,7 @@ GPUS_PER_NODE=${GPUS_PER_NODE} GPUS=${GPUS} SRUN_ARGS=${SRUN_ARGS} sh tools/slur
 例如:
 
 ```shell
-GPUS_PER_NODE=8 GPUS=8 sh tools/srun_train.sh Dummy Test_job configs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k.py work_dirs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k/
+GPUS_PER_NODE=8 GPUS=8 sh tools/slurm_train.sh Dummy Test_job configs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k.py work_dirs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k/
 ```
 
 ### 使用多台机器训练
@@ -87,8 +87,8 @@ NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_tr
 如果您使用  `dist_train.sh`  来启动训练任务:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 sh tools/dist_train.sh ${CONFIG_FILE} 4 --work_dir tmp_work_dir_1
-CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 sh tools/dist_train.sh ${CONFIG_FILE} 4 --work_dir tmp_work_dir_2
+CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 sh tools/dist_train.sh ${CONFIG_FILE} 4 --work-dir tmp_work_dir_1
+CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 sh tools/dist_train.sh ${CONFIG_FILE} 4 --work-dir tmp_work_dir_2
 ```
 
 如果您使用 slurm 来启动训练任务，你有两种方式来为每个任务设置不同的端口: