OSError：[Errno 30]只读文件系统：'2024-04-05-06-15-30.176066'文件“/var/task/lambda_function.py”，在lambda_handler model_load = StarDist2D（conf）

Question

在运行 aws lambda 函数并通过 lambda 中运行的容器对 DL 模型进行推理时，当我从 s3 下载模型时，它正在运行，但是当我实例化 StarDist2D(conf) 对象时，它会给出此错误。不确定是否是由于 Stardist 或某些实例对象或其他原因造成的，因为错误前面有时间戳，正如您在问题标题中看到的那样。

lambda_handler 的代码

        def download_model_from_s3(bucket_name, model_key, local_model_path):
    try:
        s3_client.download_file(bucket_name, model_key, local_model_path)
        return True
    except Exception as e:
        print(f"Error downloading model file from S3: {e}")
        return False

local_model_path = '/tmp/' + model_file_name
def download_model_from_s3(bucket_name, model_key, local_model_path):
    try:
        s3_client.download_file(bucket_name, model_key, local_model_path)
        return True
    except Exception as e:
        print(f"Error downloading model file from S3: {e}")
        return False
 def lambda_handler(event, context):
    # Check if the event is an S3 event
    print("this is event",event)
    if 'Records' in event and len(event['Records']) > 0 and 's3' in event['Records'][0]:
        bucket = event['Records'][0]['s3']['bucket']['name']
        key = urllib.parse.unquote_plus(event['Records'][0]['s3']['object']['key'], encoding='utf-8')
        print("this is key",key)
        image_name = key.split("/")[-1]
        print("this is image_name",image_name)
        
        # Check if the uploaded object is in the validationimages folder
        if key.startswith('ValidationImages/'):
            # Process the uploaded image here
            print("line no 67")
            np.random.seed(42)
            image_path = '/tmp/'+image_name
            s3_client.download_file(bucket_name, key, image_path)
            
            def imreadReshape(key):
                if ".tif" in image_name:
                    imageRead = imread(image_path)
                    if np.ndim(imageRead) == 2:
                        return imageRead
                    imageRead = np.array(imageRead)
                    imageRead = cv2.resize(imageRead,(768,768))
                    return imageRead[:,:,0]
                else:
                    print("line no 80")
                    imageRead = cv2.imread(image_path)
                    print("line no 82")
                    if np.ndim(imageRead) == 2:
                        return imageRead
                    imageRead = cv2.resize(imageRead,(768,768))
                    return imageRead[:,:,0]
            X_val = [image_name]
            X_val = list(map(imreadReshape,X_val))
            n_channel = 1 if X_val[0].ndim == 2 else X_val[0].shape[-1]  #If no third dim. then number of channels = 1. Otherwise get the num channels from the last dim.
            axis_norm = (0,1)  
            if n_channel > 1:
                print("Normalizing image channels %s." % ('jointly' if axis_norm is None or 2 in axis_norm else 'independently'))
                sys.stdout.flush()
            
            X_val = [x/255 for x in X_val]
            rng = np.random.RandomState(42)
            
            print(Config2D.__doc__)
            gputools_available()
            
            n_rays = 32 #ok
            use_gpu = True and gputools_available() #ok
            
            grid = (2,2) # ok
            
            conf = Config2D (
                n_rays       = n_rays,
                grid         = grid,
                use_gpu      = use_gpu,
                n_channel_in = n_channel,
                train_patch_size = (768,768)
            )
            
            if download_model_from_s3(bucket_name, model_key, local_model_path):
            ## Load the model
                new_model = tf.keras.models.load_model(local_model_path)
            print("Load Model Complete")
            model_load = StarDist2D(conf)

Answer 1

我解决了。实际上我们需要将代码开头的lambda环境的默认目录更改为/tmp/，以便将所有内容写入tmp。因此，只需添加一条语句即可解决问题。

   os.chdir("/tmp/")