@@ -278,13 +278,13 @@ Here is an example of using 8 GPUs to load TSN checkpoint.
If you can run MMAction on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
Here is an example of using 16 GPUs to train TSN on the dev partition in a slurm cluster. (use `GPUS_PER_NODE=8` to specify a single slurm cluster node with 8 GPUs.)
```shell
GPUS_PER_NODE=8 ./tools/slurm_train.sh dev tsn_r50_k400 configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb 16
GPUS=16 ./tools/slurm_train.sh dev tsn_r50_k400 configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb
```
You can check [slurm_train.sh](../tools/slurm_train.sh) for full arguments and environment variables.
@@ -136,16 +136,10 @@ ln -s $KINETICS400_ROOT data
### Using multiple MMAction versions
If there are more than one mmaction on your machine, and you want to use them alternatively, the recommended way is to create multiple conda environments and use different environments for different versions.
The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMAction in the current directory.
Another way is to insert the following code to the main scripts (`train.py`, `test.py` or any other scripts you run)