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found **Cannot get amd_mem_obj** error #11
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need more details. How can I reproduce? |
Thanks for your reply, I added some system information, hope it helps os version: root@x570-wifi:/home/user# cat /etc/os-release
PRETTY_NAME="Ubuntu 22.04.2 LTS"
NAME="Ubuntu"
VERSION_ID="22.04"
VERSION="22.04.2 LTS (Jammy Jellyfish)"
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
UBUNTU_CODENAME=jammy
rocm-smi
package version
torch and other package version requests==2.31.0
torch==2.2.1
accelerate==0.27.0
transformers==4.38.1
datasets==2.17.1
numpy==1.26.4
gitpython==3.1.42 some train code print_in_color("Starting training...", "\033[34m") # Blue for start
tokenizer = AutoTokenizer.from_pretrained(task_args["model_name"])
def tokenize_function(examples):
return tokenizer(
examples["text"], padding="max_length", truncation=True
)
model = AutoModelForSequenceClassification.from_pretrained(
task_args["model_name"], num_labels=task_args["num_labels"]
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"[debug] cuda status: {torch.cuda.is_available()}")
model.to(device)
print("[debug] load model done")
dataset = load_dataset(task_args["dataset_name"])
print("[debug] load dataset")
tokenized_datasets = dataset.map(tokenize_function, batched=True)
small_train_dataset = (
tokenized_datasets["train"].shuffle(seed=task_args["seed"]).select(range(task_args["num_rows"]))
)
small_eval_dataset = (
tokenized_datasets["train"].shuffle(seed=task_args["seed"]).select(range(task_args["num_rows"]))
)
training_args = TrainingArguments(
output_dir="my_model", evaluation_strategy="epoch", save_strategy='epoch',
)
print("[debug] start train")
trainer = Trainer(
model=model,
args=training_args,
train_dataset=small_train_dataset,
eval_dataset=small_eval_dataset,
compute_metrics=compute_metrics,
)
trainer.train()
trainer.save_model("my_model")
the error on
|
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