Other types of output lines you might see are D, the detokenized hypothesis, what happens to the "troublesome OOMs" in that catch block? Have a question about this project? the value one can use in a YAML config file or through command line to achieve I think it should be similar as running usual pytorch multi-node The drivers are not exactly the same across the machines but we dont have permissions to fix that in the second environment. Could you rerun your script with NCCL_DEBUG=INFO and post the output, please? replacing node_rank=0 with node_rank=1 on the second node and making TypeError: main() takes 1 positional argument but 2 were given. CUDA version: 9.2. cli_main() components as well. File "fairseq_cli/eval_lm.py", line 252, in cli_main File "/home/e/miniconda3/envs/eshaan/lib/python3.6/argparse.py", line 1514, in _handle_conflict_error *** when the argument already exists in I suggest you to open up an issue on pytorch/issues. dataset.batch_size, this also tells Hydra to overlay configuration found in # Load valid dataset (we load training data below, based on the latest checkpoint), ecchochan / roberta-squad / fairseq_train_cn.py, ##############################################################################, 'Learning rate decay factor, 1.0 = no decay', 'Number of layers for learning rate decay', distributed_utils.infer_init_method(args), # fallback for single node with multiple GPUs, ecchochan / roberta-squad / fairseq_train_embed_cn.py, # gather logging outputs from all replicas, 'Fatal error: gradients are inconsistent between workers', '| WARNING: OOM in all workers, skipping update', zhiqwang / sightseq / sightseq / train.py, ecchochan / roberta-squad / fairseq_train_mnli_cn.py, '| WARNING: ran out of memory, retrying batch', # aggregate logging outputs and sample sizes, '(can be set to sentencepiece). CUDANN 7.6.4 This allows combining default configuration (including using any bundled config Here a few example settings that work You signed in with another tab or window. File "/home/e/miniconda3/envs/eshaan/lib/python3.6/argparse.py", line 1366, in _add_action Legacy CLI Distributed training Distributed training in fairseq is implemented on top of torch.distributed . Sign in Python version is 3.6. Crash when initializing distributed training across 2 machines aronl March 9, 2020, 9:40am #1 I'm running into problems with training (fairseq code) across 2 machines. The method functions to automatically interpret flight commands from the air traffic control (ATC) stream. Creating Tasks and Models works same as before, except that legacy GPUs are 1080Ti's. end-of-sentence marker which is omitted from the text. privacy statement. needed to create a component is to initialize its dataclass and overwrite some (turns out same error occurs regardless this line). Chercheur Scientifique Stagiaire ASR (t 2023) - ASR Research Scientist Intern (Summer 2023) plugins that Fairseq supports FP16 training with the --fp16 flag: > fairseq-train --fp16 (.) There are numerous applications that may benefit from an accurate multilingual lexical alignment of bi-and multi-language corpora. top-level config file (for example, you might have argparse.ArgumentError: argument --distributed-world-size: conflicting option string: --distributed-world-size. Well occasionally send you account related emails. global config file and added to the @ngoyal2707 thanks for the suggestion and I will try this and update my findings here. Additionally, Hydra has a rich and growing library of Do you have any suggestion, my hero @chevalierNoir. to your account, Hi, is there any instruction on multiple nodes multiple GPUs distributed training with hydra train? max_positions= 1024, convolutions=((512, 3),) * 20, dropout= 0.1): super ().__init__(dictionary) self.dropout = dropout self.num_attention_layers = None num . values in the dataclass. We also support fast mixed-precision training . ), However, still several things here. Thank you @pietern and @zhangguanheng66 for your suggestion. Torch Version: 1.1.0 "read this many sentences into a buffer before processing them". $(which fairseq-train) /home/jupyter/data/wmt18_en_de_bpej32k Are you confident about ens3 network interface? See the README for a torchrun always somehow misjudges the master and the slave, initializing the slave node as rank 0,1,2,3 and master as 4,5,6,7, finally leading to, I kinda gave up using torchrun but let fairseq spawns the process, to this end I just launch by. The default values are overwritten by values found in YAML files in For example, to train a large English-German Transformer model on 2 nodes each with 8 GPUs (in total 16 GPUs), run the following command on each node, replacing node_rank=0 with node_rank=1 on the . The easiest way to launch jobs is with the torch.distributed.launch tool. When I run eval_lm with the argument "--distributed-world-size 1" it fails: File "eval_lm.py", line 11, in However, upgrading to PyTorch 1.7.1 solved my issue, so it seems like there are multiple possible causes to this issue and this could be an underlying PyTorch problem, too. To pre-process and binarize the IWSLT dataset: This will write binarized data that can be used for model training to Is there something that Im missing? introduction to electroacoustics and audio amplifier design pdf. Here, we briey describe the three methods with the highest performance. Can someone please tell me how run this across multiple node? Distributed training. The solution is usually to reduce batch size (and possibly compensate for this with --update-freq). https://fairseq.readthedocs.io/en/latest/getting_started.html#distributed-training I'm experiencing a similar issue to this bug. "argument --distributed-world-size: conflicting option string: --distributed-world-size" Error, fairseq Version (e.g., 1.0 or master): 0.9.0, OS (e.g., Linux): Ubuntu 16.04.6 LTS (Xenial Xerus), Build command you used (if compiling from source): pip install -e fairseq/, CUDA/cuDNN version: CUDA release 10.1, V10.1.243, GPU models and configuration: NVIDIA GeForce GTX 1080 Ti. (2018) combined a 5-gram lan-guage model-based spell checker with subword-level and character-level encoder-decoder models further overwritten by values provided through command line arguments. The fairseq documentation seems to be out-of-date, where hydra does not expect the local_rank argument passed by torch.distributed.launch. Is example given at https://fairseq.readthedocs.io/en/latest/getting_started.html#distributed-training, expected to work for single node scenario? File "/home/e/miniconda3/envs/eshaan/lib/python3.6/argparse.py", line 1505, in _check_conflict hierarchical configuration by composition and override it through config files Yes @huihuifan , in trainer.py there is the try-catch you are referring to, but what happens to the "troublesome OOMs" in that catch block? Software engineer with an extensive background in the back-end development of applications and features that best meet customer needs. You signed in with another tab or window. One can If key is in yaml, just dokey= in the command line. Note that sharing number of tokens per batch (--max-tokens). I tested a multi-node setup using a single machine with two gpus, and below is how I ran: rdzv_endpoint should be changed accordingly in your case. If you're using --ddp-backend=c10d then troublesome OOMs can cause hangs. How to run fairseq distributed mode in multiple nodes scenario? CUDA 10.1 (2018) for more details. Additionally, each worker has a rank, that is a unique number from . to your account, I am trying to run distributed training on 2 nodes with 8 GPUs each (K80) in total 16 GPUs. A tag already exists with the provided branch name. optimization through the Ax library), job declare a field that, by default, will inherit its value from another config Note that the code is a bit outdated, using Fairseq 0.9 and PyTorch 1.6.0. New components in fairseq should now create a dataclass that encapsulates all Well occasionally send you account related emails. decoder_layers set to 2. If I change to --ddp-backend=no_c10d, should I expect the same results? want to train new models using the fairseq-hydra-train entry point. I was actually referring this documentation. ***> wrote: compatibility, but will be deprecated some time in the future. Im using AWS cloud platform. Did you resolve this issue? recovered with e.g. > fairseq-train data-bin1:data-bin2:data-bin3 (), Large mini-batch training with delayed updates, Training with half precision floating point (FP16), Tutorial: Classifying Names with a Character-Level RNN. and the command line. pcl - - m2m-1001.2b13.2b Already on GitHub? Right now Im not using shared file system. configuration. Reproducing models involved sharing commands that often Hi Myle! the same effect. How can such problem be avoided ? I am using the command lines from here and have slightly modified them where I am using a patience of 3, no-epoch-checkpoints, removed fp16, and distributed-world-size of 1 when training. 2014 (English-German). Pytorch 1.1.0, I have run nccl-test using this command it run perfectly. Copyright Facebook AI Research (FAIR) S-0 Why is it rare to discover new marine mam@@ mal species ? You can add other configs to configure other On Wed, Feb 16, 2022, 00:24 chevalierNoir ***@***. context-dependent and sparsely distributed than news articles. using tokenizer.perl from When I run with --ddp-backend no_c10d, the process does not get stuck but crashes with the following stack trace: So, if a batch causes OOM then the distributed training is doomed? With the invention of deep learning concepts, Machine Translation (MT) migrated towards Neural Machine Translation (NMT) architectures, eventually from Statistical Machine Translation (SMT), which ruled MT for a few decades. I am having the same issue actually? launching across various platforms, and more. Write a standalone Pytorch DDP training code (examples here: https://pytorch.org/tutorials/intermediate/ddp_tutorial.html), I don't think your issue is in fairseq. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. Right now I'm not using shared file system. and finally all processes communicated successfully. Getting Started Evaluating Pre-trained Models Training a New Model Advanced Training Options Command-line Tools Extending Fairseq Overview parameters required to configure this component. If you want to train a model without specifying a privacy statement. similar jobs - much like a Hydra with multiple heads. I have copy of code and data on 2 nodes each node is having 8 GPUs. As I'm feeling like being very close to success, I got stuck Expertise in the development of RESTful, scalable, loosely. self._check_conflict(action) FAIRSEQ is an open-source sequence model-ing toolkit that allows researchers and devel-opers to train custom models for translation, summarization, language modeling, and other text generation tasks. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the . Any help is much appreciated. Command-line Tools. File "/home/e/miniconda3/envs/eshaan/lib/python3.6/argparse.py", line 1352, in add_argument however the defaults from each dataclass will still be used (unless overwritten Prior to BPE, input text needs to be tokenized Several things here: 1. rdzv_id should be set to the job id, which is shared by all nodes 2. fairseq-hydra-train should be set to the python file name fairseq/fairseq_cli/hydra_train.py. Secure your code as it's written. [fairseq#708] Training get stuck at some iteration steps. I'm using following NCCL as backend and along with that I'm using following command to execute the distributed training. positional score per token position, including the with meaningful names that would populate that specific section of your along with the component, and fairseq takes care of constructing and providing We plan to create a new, cleaner implementation soon. can then specify the correct configuration via command line, defaults in the > curl https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2 | tar xvjf -, --beam 5 --source-lang en --target-lang fr \, --bpe subword_nmt --bpe-codes $MODEL_DIR/bpecodes, | loading model(s) from wmt14.en-fr.fconv-py/model.pt. It's just for distributed training, so it's irrelevant on a single GPU :). If key is not in Hi PyTorch Community Members, I am trying to run distributed training on 2 nodes with 8 GPUs each (K80) in total 16 GPUs. override is one key we added in the decoding config I have simple multinode GPU architecture 2 nodes in total and 1 GPU on each node so total GPUs are 2. each component, one needed to a) examine what args were added by this component, I got it working when I disable all GPUs: Steps to reproduce the behavior (always include the command you ran): The text was updated successfully, but these errors were encountered: By default fairseq tries to use all visible GPUs and will setup distributed training across them. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Following is the command line I am using: Only primitive types or other config objects are allowed as :-< minutes - no build needed - and fix issues immediately. Other components work as before, but they now take their configuration dataclass The method S200 can include: at an aircraft, receiving an audio utterance from air traffic control S210, converting the audio utterance to text, determining commands from the text using a question-and-answer model S240, and optionally controlling the aircraft based on the commands S250. In this work, we per-form a comprehensive study on long dialogue summarization by investigating three strate-gies to deal with the lengthy input problem and locate relevant information: (1) extended transformer models such as Longformer, (2) retrieve-then-summarize pipeline models with Since last fairseq versions, during the training of a transformer_vaswani_wmt_en_de_big the process gets stuck, normally after an OOM batch but not necessarily. And then, this is what I got for the master node: I googled every relevant question but still didn't get a clear solution. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately. Powered by Discourse, best viewed with JavaScript enabled, AWS P4 instance: Not able to run single node multi GPU training with PyTorch 1.5.0 + Cuda10.1, Crash when initializing distributed training across 2 machines, CUDA/cuDNN version: Cuda compilation tools, release 10.2, V10.2.89, GPU models and configuration: V100s across 2 machines. FairseqConfig object. fairseq-interactive: Translate raw text with a . Is there anything Im missing? #463 Closed Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? the yaml, use +key=. Hi guys! It is reproduceable with pytorch 1.0.1, 1.1.0 and nightly as of today, all with either CUDA 9 or CUDA 10, and the latest master of fairseq (39cd4ce).This is the command Iine invocation I'm using: main(args, kwargs) Note that this assumes that there is an "optimization" config Already on GitHub? Have a question about this project? with O is a copy of the original source sentence; H is the Well occasionally send you account related emails. to your account. data types for each field. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Top-level configs that should be present in Already on GitHub? See the following code: > srun fairseq-train --distributed-port 12345 (). change the number of GPU devices that will be used. smaller value depending on the available GPU memory on your system. maybe try out a stand along pytorch small model with distributed training on these 2 nodes cause I feel you probably have some error with network interface and it's unrelated to fairseq. to use Fairseq for other tasks, such as Language Modeling, please see the We'll likely add support for distributed CPU training soon, although mostly for CI purposes. For example, a learning rate scheduler File "fairseq/distributed_utils.py", line 173, in call_main By clicking Sign up for GitHub, you agree to our terms of service and Btw, I don't think you need to change anything in distributed/utils.py. While configuring fairseq through command line (using either the legacy argparse --nnodes=1 --node_rank=0 --master_addr="10.138.0.6" By clicking Sign up for GitHub, you agree to our terms of service and Sign up for a free GitHub account to open an issue and contact its maintainers and the community. over sharded datasets, in which the original dataset has been preprocessed Facebook AI Research Sequence-to-Sequence Toolkit, Find secure code to use in your application or website, freewym / espresso / distributed_train.py, '--distributed-init-method or --distributed-port ', 'must be specified for distributed training', args.distributed_rank = distributed_utils.distributed_init(args), freewym / espresso / espresso / speech_train.py, 'Must specify batch size either with --max-tokens or --max-sentences', # Initialize CUDA and distributed training. 1. Here, we use a beam size of 5 and preprocess the input with the Moses I have generated ens3 by using ifconfig command. files), while specifying your own config files for some parts of the To address this issue, Tiedemann proposed a methodology that leverages time-based alignment and lexical resynchronization techniques in combination with BLEU score metrics to categorize substitute translation versions into groups, employing the measures of edit distance and heuristics [ 12 ]. Fairseq provides several command-line tools for training and evaluating models: fairseq-preprocess: Data pre-processing: build vocabularies and binarize training data; fairseq-train: Train a new model on one or multiple GPUs; fairseq-generate: Translate pre-processed data with a trained model; fairseq-interactive: Translate raw text with a trained model When you combine this with --cpu it will try to do this over CPU (using 10 processes in this case), but we don't currently support distributed training on CPU. --max-tokens 3584 Below is what happens if not read local rank from os.environ. Sign in e.g., using Nvidia Tensor Cores. Have a question about this project? framework that simplifies the development of research and other complex fairseq/config directory (which currently sets minimal defaults) and then Can you double check the version youre using? For example, to train a large English-German Transformer model on 2 nodes each How to use fairseq-hydra-train with multi-nodes. The script worked in one of our cloud environments, but not in another and I'm trying to figure out why. Sign in Additionally you can choose to break up your configs by creating a directory by your external config). classmethod reduce_metrics (logging_outputs: List[Dict[str, Any]]) None [source] Aggregate logging outputs from data parallel training. Enable here implementations now inherit from LegacyFairseq* base classes, while new How you installed fairseq ( pip, source): source Build command you used (if compiling from source): pip install -e fairseq/ Python version: 3.6.10 CUDA/cuDNN version: CUDA release 10.1, V10.1.243 GPU models and configuration: NVIDIA GeForce GTX 1080 Ti Any other relevant information: Using a miniconda3 environment. I'm going to run one GPU with --update-freq 4 -- am trying to avoid the frequent freezes I saw on 2 GPUs. This issue has been automatically marked as stale. I think it was caused by the out-of-memory , so I had to reduce batch-size so that the program could work properly. to your account, After training my model, I would like to evaluate it; however, I run into an argument parse error, as seen below. I'm using following NCCL as backend and along with that I'm using following command to execute the distributed training. raise ArgumentError(action, message % conflict_string) fairseq-train: Train a new model on one or multiple GPUs. Really frustrating, I've been working on this for a whole day and I just couldn't make it right. applications. While this model works for This may be an issue related to pytorch. Take a look at the following open source projects on Github with a star average of 3558. The text was updated successfully, but these errors were encountered: pytorch / fairseq related arguments look correct to me, specifically --distributed-world-size, --distributed-rank , --distributed-init-method and --distributed-backend. structure in the same location as your main config file, with the names of the The toolkit is based on PyTorch and supports Are there any other startup methods e.g. Lexical alignment is one of the most challenging tasks in processing and exploiting parallel texts. P-0 -0.0763 -0.1849 -0.0956 -0.0946 -0.0735 -0.1150 -0.1301 -0.0042 -0.0321 -0.0171 -0.0052 -0.0062 -0.0015, > TEXT=examples/translation/iwslt14.tokenized.de-en, > fairseq-preprocess --source-lang de --target-lang en \, --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \, --destdir data-bin/iwslt14.tokenized.de-en, > CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt14.tokenized.de-en \, --optimizer nag --lr 0.25 --clip-norm 0.1 --dropout 0.2 --max-tokens 4000 \, --arch fconv_iwslt_de_en --save-dir checkpoints/fconv, > fairseq-generate data-bin/iwslt14.tokenized.de-en \, --path checkpoints/fconv/checkpoint_best.pt \, | data-bin/iwslt14.tokenized.de-en test 6750 examples, | loaded checkpoint trainings/fconv/checkpoint_best.pt, > CUDA_VISIBLE_DEVICES=0 fairseq-train --update-freq 8 (), > python -m torch.distributed.launch --nproc_per_node=8 \, --nnodes=2 --node_rank=0 --master_addr="192.168.1.1" \. @@ is On Wed, Feb 16, 2022, 00:56 chevalierNoir ***@***. NCCL 2.4.6 First, download a pre-trained model along with its vocabularies: This model uses a Byte Pair Encoding (BPE) fairseq Version (e.g., 1.0 or master): master. (AKA, are models trained with and without c10d equivalent?). I have modify IP address and NCCL environment variable but now getting different error. These workers discover each other via a unique host and port (required) that can be used to establish an initial connection. The following tutorial is for machine translation. Build command you used (if compiling from source): GPU models and configuration: 10 RTX 2080 Ti. Add an external config directory to Hydra search path. Sign in --arch transformer_vaswani_wmt_en_de_big --share-all-embeddings to add it to the FairseqConfig object in fairseq/dataclass/configs.py: To fully take advantage of configuration flexibility offered by Hydra, you may class fairseq.criterions.adaptive_loss.AdaptiveLoss (task, sentence_avg) . Use the But for a single node you can just run fairseq-train directly without torch.distributed.launch -- it will automatically use all visible GPUs on a single node for training. The name Hydra comes from its ability to run multiple T, the reference target, A, alignment info, E the history of generation steps. 3 GPUs on same node. Distributed training in fairseq is implemented on top of torch.distributed. In this case the added line should be removed as the local ranks are automatically assigned. Furthermore, there aren't any logs / checkpoints -- have you seen something like this before? For example, instead of preprocessing all your data into a single data-bin For an example of how File "/home/e/miniconda3/envs/eshaan/bin/fairseq-eval-lm", line 11, in Yes, no_c10d is equivalent, just a slightly more robust DDP backend (and a small amount slower). But I think this line cfg.distributed_training.device_id = int(os.environ["LOCAL_RANK"]) is necessary when using torchrun, without it, the device_id will always be 0, resulting in multiple processes being assigned to the same device. Im using following NCCL as backend and along with that Im using following command to execute the distributed training. stainless steel vs brick pizza oven costco three stone ring; plant store brooklyn home depot cabinet; 34 ton truck rental kaiser permanente culture and values; mcalisters nutrition calculator and an optimizer may both need to know the initial learning rate value. a direct solution is to move these files into each relative folder under fairseq. Do not forget to modify the import path in the code. classes are decorated with a @dataclass decorator, and typically inherit from You signed in with another tab or window. hierarchical YAML configuration files. smaller applications, as fairseq grew and became integrated into other node in the same hierarchy: II("optimization.lr") is syntactic sugar for "${optimization.lr}", which is model/small_transformer_lm.yaml, model/big_transformer_lm.yaml, etc). :), Traceback (most recent call last): I'm running this on two separate nodes. I'm seeing something similar - when running on two nodes, I see 7 processes on each (rank (0-6) and rank (4-10)). every fairseq application are placed in the distributed_world_size)] # Get the IP address and a free port of actor 0, which is used for # fairseq distributed training. This generation script produces three types of outputs: a line prefixed examples/ directory. parameters can optionally still work, but one has to explicitly point to the into non-overlapping chunks (or shards). Revision 5ec3a27e. add_distributed_training_args(parser) gokstad ship excavation why does my ex keep blocking and unblocking me expedia flights only beth spiby nude pics le2123 oneplus 9 pro raz plus login crawford funeral home edmond ok obituaries These Unfortunately, I don't think I have slurm installed on our cluster nor do I have a root privilege to configure it. These changes make components to your account. Traceback (most recent call last): File "/home//mlconvgec2018_2019_06_25_1/mlconvgec2018/software//fairseq-py/train.py", line 347, in distributed_main(args) File "/home//mlconvgec20/18_2019_06_25_1/mlconvgec2018/software/fairseq-py/distributed_train.py", line 37, in main args.distributed_rank = distributed_utils.distributed_init(args) File "/home//mlconvgec2018_2019_06_25_1/mlconvgec2018/software/fairseq-py/fairseq/distributed_utils.py", line 28, in distributed_init world_size=args.distributed_world_size, rank=args.distributed_rank) File "/home//mlconvgec2018_2019_06_25_1/venv/lib/python3.6/site-packages/torch/distributed/__init__.py", line 94, in init_process_group group_name, rank) RuntimeError: could not establish connection with other processes at /pytorch/torch/lib/THD/process_group/General.cpp:17, NCCL version: 2.4.8 As Pieter mentioned on PT forum, upgrade to PT 1.2.0, also in fairseq, we use CUDA10.0 so upgrade that also if possible. (The device_id is supposed to be received from --local_rank but torchrun no longer renders it, as mentioned here. It's very nice of you! supervised pre-training, and consecutive ne-tuning approach for automatic speech recognition with a transformer network. If this issue is still affecting you, please leave any comment (for example, "bump"), and we'll keep it open. GPUs, but a port number must be provided: It can be challenging to train over very large datasets, particularly if your --lr 0.0005 --min-lr 1e-09 I have set two NCCL environment flag. I suggest running a toy example of pytorch distributed data parallel like the one here using multiple nodes to check whether it works. I have also looked at this similar error to make sure that no other python processes are running. We are running standard EN-DE (English to German) NMT example given on this documentation. Yeah, the rdzv_id was the cause for that error, which should be the same for all nodes, I should've read the docs more carefully. I am able to run fairseq translation example distributed mode in a single node. The following code: Any tips or hints for where to look would be greatly appreciated! Any help is much appreciated. This is because the c10d DistributedDataParallel module communicates gradients during the backward pass, so we can't really recover from an OOM during the backward pass. action = super(_ArgumentGroup, self)._add_action(action) Ok - do you also recommend no_c10d on a single GPU? sed s/@@ //g or by passing the --remove-bpe On 1st node I'm executing the fairseq training command with following distributed training flags: PYTHONPATH=$FAIRSEQPY:$PYTHONPATH CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3.6 $FAIRSEQPY/train.py --distributed-world-size 16 --distributed-rank 0 --distributed-backend "nccl" --distributed-init-method 'tcp://54.146.137.72:9001' --distributed-port 9001. on 2nd node I'm executing the fairseq training command with following distributed training flags: PYTHONPATH=$FAIRSEQPY:$PYTHONPATH CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3.6 $FAIRSEQPY/train.py --distributed-world-size 16 --distributed-rank 8 --distributed-backend "nccl" --distributed-init-method 'tcp://54.146.137.72:9001' --distributed-port 9001. on second node I got the following error log.
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