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# Train DeepSpeech2 on PaddleCloud
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>Note:
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>Please make sure [PaddleCloud Client](https://github.com/PaddlePaddle/cloud/blob/develop/doc/usage_cn.md#%E4%B8%8B%E8%BD%BD%E5%B9%B6%E9%85%8D%E7%BD%AEpaddlecloud) has be installed and current directory is `deep_speech_2/cloud/`
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## Step 1: Upload Data
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Provided with several input manifests, `pcloud_upload_data.sh` will pack and upload all the containing audio files to PaddleCloud filesystem, and also generate some corresponding manifest files with updated cloud paths.
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Please modify the following arguments in `pcloud_upload_data.sh`:
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- `IN_MANIFESTS`： Paths (in local filesystem) of manifest files containing the audio files to be uploaded. Multiple paths can be concatenated with a whitespace delimeter.
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- `OUT_MANIFESTS`: Paths (in local filesystem) to write the updated output manifest files to. Multiple paths can be concatenated with a whitespace delimeter. The values of `audio_filepath` in the output manifests are updated with cloud filesystem paths.
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- `CLOUD_DATA_DIR`: Directory (in PaddleCloud filesystem) to upload the data to. Don't forget to replace `USERNAME` in the default directory and make sure that you have the permission to write it.
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- `NUM_SHARDS`: Number of data shards / parts (in tar files) to be generated when packing and uploading data. Smaller `num_shards` requires larger temoporal local disk space for packing data.
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By running:
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```
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sh pcloud_upload_data.sh
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```
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all the audio files will be uploaded to PaddleCloud filesystem, and you will get modified manifests files in `OUT_MANIFESTS`.
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You have to take this step only once, in the very first time you do the cloud training. Later on, the data is persisitent on the cloud filesystem and reusable for further job submissions.
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## Step 2: Configure Training
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Configure cloud training arguments in `pcloud_submit.sh`, with the following arguments:
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- `TRAIN_MANIFEST`: Manifest filepath (in local filesystem) for training. Notice that the`audio_filepath` should be in cloud filesystem, like those generated by `pcloud_upload_data.sh`.
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- `DEV_MANIFEST`: Manifest filepath (in local filesystem) for validation.
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- `CLOUD_MODEL_DIR`: Directory (in PaddleCloud filesystem) to save the model parameters (checkpoints). Don't forget to replace `USERNAME` in the default directory and make sure that you have the permission to write it.
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- `BATCH_SIZE`: Training batch size for a single node.
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- `NUM_GPU`: Number of GPUs allocated for a single node.
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- `NUM_NODE`: Number of nodes (machines) allocated for this job.
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- `IS_LOCAL`: Set to False to enable parameter server, if using multiple nodes.
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Configure other training hyper-parameters in `pcloud_train.sh` as you wish, just as what you can do in local training.
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By running:
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```
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sh pcloud_submit.sh
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```
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you submit a training job to PaddleCloud. And you will see the job name when the submission is done.
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## Step 3 Get Job Logs
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Run this to list all the jobs you have submitted, as well as their running status:
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```
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paddlecloud get jobs
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```
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Run this, the corresponding job's logs will be printed.
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```
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paddlecloud logs -n 10000 $REPLACED_WITH_YOUR_ACTUAL_JOB_NAME
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```
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## More Help
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For more information about the usage of PaddleCloud, please refer to [PaddleCloud Usage](https://github.com/PaddlePaddle/cloud/blob/develop/doc/usage_cn.md#提交任务).
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