forked from tangger/lerobot
9d002032d1
WIP Aloha env tests pass Rendering works (fps look fast tho? TODO action bounding is too wide [-1,1]) Update README Copy past from act repo Remove download.py add a WIP for Simxarm Remove download.py add a WIP for Simxarm Add act yaml (TODO: try train.py) Training can runs (TODO: eval) Add tasks without end_effector that are compatible with dataset, Eval can run (TODO: training and pretrained model) Add AbstractEnv, Refactor AlohaEnv, Add rendering_hook in env, Minor modifications, (TODO: Refactor Pusht and Simxarm) poetry lock fix bug in compute_stats for action normalization fix more bugs in normalization fix training fix import PushtEnv inheriates AbstractEnv, Improve factory Normalization Add _make_env to EnvAbstract Add call_rendering_hooks to pusht env SimxarmEnv inherites from AbstractEnv (NOT TESTED) Add aloha tests artifacts + update pusht stats fix image normalization: before env was in [0,1] but dataset in [0,255], and now both in [0,255] Small fix on simxarm Add next to obs Add top camera to Aloha env (TODO: make it compatible with set of cameras) Add top camera to Aloha env (TODO: make it compatible with set of cameras)
24 lines
737 B
Bash
24 lines
737 B
Bash
#!/bin/bash
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#SBATCH --nodes=1 # total number of nodes (N to be defined)
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#SBATCH --ntasks-per-node=1 # number of tasks per node (here 8 tasks, or 1 task per GPU)
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#SBATCH --gres=gpu:1 # number of GPUs reserved per node (here 8, or all the GPUs)
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#SBATCH --cpus-per-task=8 # number of cores per task (8x8 = 64 cores, or all the cores)
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#SBATCH --time=2-00:00:00
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#SBATCH --output=/home/rcadene/slurm/%j.out
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#SBATCH --error=/home/rcadene/slurm/%j.err
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#SBATCH --qos=medium
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#SBATCH --mail-user=re.cadene@gmail.com
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#SBATCH --mail-type=ALL
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CMD=$@
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echo "command: $CMD"
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apptainer exec --nv \
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~/apptainer/nvidia_cuda:12.2.2-devel-ubuntu22.04.sif $SHELL
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source ~/.bashrc
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#conda activate fowm
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conda activate lerobot
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srun $CMD
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